diff --git a/dot-env.template b/dot-env.template
index f0b5bdc0f..8033bdfba 100644
--- a/dot-env.template
+++ b/dot-env.template
@@ -92,4 +92,12 @@ ASSEMBLYAI_API_KEY=...
OPENROUTER_API_KEY=...
# Piper
-PIPER_BASE_URL=...
\ No newline at end of file
+PIPER_BASE_URL=...
+
+# Smart turn
+LOCAL_SMART_TURN_MODEL_PATH=
+FAL_SMART_TURN_API_KEY=...
+
+# Twilio
+TWILIO_ACCOUNT_SID=
+TWILIO_AUTH_TOKEN=
\ No newline at end of file
diff --git a/examples/canonical-metrics/bot.py b/examples/canonical-metrics/bot.py
index 513de9b14..871d0542d 100644
--- a/examples/canonical-metrics/bot.py
+++ b/examples/canonical-metrics/bot.py
@@ -43,9 +43,7 @@ async def main():
DailyParams(
audio_out_enabled=True,
audio_in_enabled=True,
- camera_out_enabled=False,
- vad_enabled=True,
- vad_audio_passthrough=True,
+ video_out_enabled=False,
vad_analyzer=SileroVADAnalyzer(),
transcription_enabled=True,
#
@@ -72,7 +70,7 @@ async def main():
# voice_id="gD1IexrzCvsXPHUuT0s3",
)
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
messages = [
{
diff --git a/examples/chatbot-audio-recording/bot.py b/examples/chatbot-audio-recording/bot.py
index 92644301e..5428a0d2f 100644
--- a/examples/chatbot-audio-recording/bot.py
+++ b/examples/chatbot-audio-recording/bot.py
@@ -66,9 +66,7 @@ async def main():
DailyParams(
audio_out_enabled=True,
audio_in_enabled=True,
- camera_out_enabled=False,
- vad_enabled=True,
- vad_audio_passthrough=True,
+ video_out_enabled=False,
vad_analyzer=SileroVADAnalyzer(),
transcription_enabled=True,
#
@@ -95,7 +93,7 @@ async def main():
# voice_id="gD1IexrzCvsXPHUuT0s3",
)
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
messages = [
{
diff --git a/examples/deployment/flyio-example/bot.py b/examples/deployment/flyio-example/bot.py
index 2e610cb23..45d94a9e3 100644
--- a/examples/deployment/flyio-example/bot.py
+++ b/examples/deployment/flyio-example/bot.py
@@ -41,8 +41,7 @@ async def main(room_url: str, token: str):
api_key=daily_api_key,
audio_in_enabled=True,
audio_out_enabled=True,
- camera_out_enabled=False,
- vad_enabled=True,
+ video_out_enabled=False,
vad_analyzer=SileroVADAnalyzer(),
transcription_enabled=True,
),
@@ -53,7 +52,7 @@ async def main(room_url: str, token: str):
voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
)
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
messages = [
{
diff --git a/examples/deployment/modal-example/bot.py b/examples/deployment/modal-example/bot.py
index 49e1acc0a..1d8949ca0 100644
--- a/examples/deployment/modal-example/bot.py
+++ b/examples/deployment/modal-example/bot.py
@@ -32,9 +32,9 @@ async def main(room_url: str, token: str):
token,
"bot",
DailyParams(
+ audio_in_enabled=True,
audio_out_enabled=True,
transcription_enabled=True,
- vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
),
)
@@ -43,7 +43,7 @@ async def main(room_url: str, token: str):
api_key=os.getenv("CARTESIA_API_KEY", ""), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121"
)
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
messages = [
{
diff --git a/examples/deployment/pipecat-cloud-daily-pstn-server/README.md b/examples/deployment/pipecat-cloud-daily-pstn-server/README.md
new file mode 100644
index 000000000..075b69399
--- /dev/null
+++ b/examples/deployment/pipecat-cloud-daily-pstn-server/README.md
@@ -0,0 +1,178 @@
+# Handling PSTN/SIP Dial-in on Pipecat Cloud
+
+This repository contains two server implementations for handling
+the pinless dial-in workflow in Pipecat Cloud. This is the companion to the
+Pipecat Cloud [pstn_sip starter image](https://github.com/daily-co/pipecat-cloud-images/tree/main/pipecat-starters/pstn_sip).
+In addition you can use `/api/dial` to trigger dial-out, and
+eventually, call-transfers.
+
+1. [FastAPI Server](fastapi-webhook-server/README.md) -
+ A FastAPI implementation that handles PSTN (Public Switched Telephone
+ Network) and SIP (Session Initiation Protocol) calls using the Daily API.
+
+2. [Next.js Serverless](nextjs-webhook-server/README.md) -
+ A Next.js API implementation designed for deployment on Vercel's
+ serverless platform.
+
+Both implementations provide:
+
+- HMAC signature validation for pinless webhook
+- Structured logging
+- Support for dial-in and dial-out settings
+- Voicemail detection and call transfer functionality (coming soon)
+- Test request handling
+
+## Choosing an Implementation
+
+- Use the **FastAPI Server** if you:
+
+ - Need a standalone server
+ - Prefer Python and FastAPI
+ - Want to deploy to traditional hosting platforms
+
+- Use the **Next.js Serverless** implementation if you:
+ - Want serverless deployment
+ - Prefer JavaScript/TypeScript
+ - Already use Next.js and Vercel for other projects
+ - Need quick scaling and zero maintenance
+
+## Prerequisites
+
+### Environment Variables
+
+Both implementations require similar environment variables:
+
+- `PIPECAT_CLOUD_API_KEY`: Pipecat Cloud API Key, begins with pk\_\*
+- `AGENT_NAME`: Your Daily agent name
+- `PINLESS_HMAC_SECRET`: Your HMAC secret for request verification
+- `LOG_LEVEL`: (Optional) Logging level (defaults to 'info')
+
+See the individual README files in each implementation directory for
+specific setup instructions.
+
+### Phone number setup
+
+You can buy a phone number through the Pipecat Cloud Dashboard:
+
+1. Go to `Settings` > `Telephony`
+2. Follow the UI to purchase a phone number
+3. Configure the webhook URL to receive incoming calls (e.g. `https://my-webhook-url.com/api/dial`)
+
+Or purchase the number using Daily's
+[PhoneNumbers API](https://docs.daily.co/reference/rest-api/phone-numbers).
+
+```bash
+curl --request POST \
+--url https://api.daily.co/v1/domain-dialin-config \
+--header 'Authorization: Bearer $TOKEN' \
+--header 'Content-Type: application/json' \
+--data-raw '{
+ "type": "pinless_dialin",
+ "name_prefix": "Customer1",
+ "phone_number": "+1PURCHASED_NUM",
+ "room_creation_api": "https://example.com/api/dial",
+ "hold_music_url": "https://example.com/static/ringtone.mp3",
+ "timeout_config": {
+ "message": "No agent is available right now"
+ }
+}'
+```
+
+The API will return a static SIP URI (`sip_uri`) that can be called
+from other SIP services.
+
+### `room_creation_api`
+
+To make and receive calls currently you have to host a server that
+handles incoming calls. In the coming weeks, incoming calls will be
+directly handled within Daily and we will expose an endpoint similar
+to `{service}/start` that will manage this for you.
+
+In the meantime, the server described below serves as the webhook
+handler for the `room_creation_api`. Configure your pinless phone
+number or SIP interconnect to the `ngrok` tunnel or
+the actual server URL, append `/api/dial` to the webhook URL.
+
+## Example curl commands
+
+Note: Replace `http://localhost:3000` with your actual server URL and
+phone numbers with valid values for your use case.
+
+### Dialin Request
+
+The server will receive a request when a call is received from Daily.
+
+### Dialout Request
+
+Dial a number, will use any purchased number
+
+```bash
+curl -X POST http://localhost:3000/api/dial \
+ -H "Content-Type: application/json" \
+ -d '{
+ "dialout_settings": [
+ {
+ "phoneNumber": "+1234567890",
+ }
+ ]
+ }'
+```
+
+Dial a number with callerId, which is the UUID of a purchased number.
+
+```bash
+curl -X POST http://localhost:3000/api/dial \
+ -H "Content-Type: application/json" \
+ -d '{
+ "dialout_settings": [
+ {
+ "phoneNumber": "+1234567890",
+ "callerId": "purchased_phone_uuid"
+ }
+ ]
+ }'
+```
+
+Dial a number
+
+```bash
+curl -X POST http://localhost:3000/api/dial \
+ -H "Content-Type: application/json" \
+ -d '{
+ "dialout_settings": [
+ {
+ "phoneNumber": "+1234567890",
+ "callerId": "purchased_phone_uuid"
+ }
+ ]
+ }'
+```
+
+### Advanced Request with Voicemail Detection
+
+```bash
+curl -X POST http://localhost:3000/api/dial \
+ -H "Content-Type: application/json" \
+ -d '{
+ "To": "+1234567890",
+ "From": "+1987654321",
+ "callId": "call-uuid-123",
+ "callDomain": "domain-uuid-456",
+ "dialout_settings": [
+ {
+ "phoneNumber": "+1234567890",
+ "callerId": "purchased_phone_uuid"
+ }
+ ],
+ "voicemail_detection": {
+ "testInPrebuilt": true
+ },
+ "call_transfer": {
+ "mode": "dialout",
+ "speakSummary": true,
+ "storeSummary": true,
+ "operatorNumber": "+1234567890",
+ "testInPrebuilt": true
+ }
+ }'
+```
diff --git a/examples/deployment/pipecat-cloud-daily-pstn-server/fastapi-webhook-server/README.md b/examples/deployment/pipecat-cloud-daily-pstn-server/fastapi-webhook-server/README.md
new file mode 100644
index 000000000..77ee54232
--- /dev/null
+++ b/examples/deployment/pipecat-cloud-daily-pstn-server/fastapi-webhook-server/README.md
@@ -0,0 +1,98 @@
+# FastAPI server for handling Daily PSTN/SIP Webhook
+
+A FastAPI server that handles PSTN (Public Switched Telephone Network) and SIP (Session Initiation Protocol) calls using the Daily API.
+
+## Setup
+
+1. Clone the repository
+
+2. Navigate to the `fastapi-webhook-server` directory:
+
+ ```bash
+ cd fastapi-webhook-server
+ ```
+
+3. Install dependencies:
+
+ ```bash
+ pip install -r requirements.txt
+ ```
+
+4. Copy `env.example` to `.env`:
+
+ ```bash
+ cp env.example .env
+ ```
+
+5. Update `.env` with your credentials:
+
+ - `AGENT_NAME`: Your Daily agent name
+ - `PIPECAT_CLOUD_API_KEY`: Your Daily API key
+ - `PINLESS_HMAC_SECRET`: Your HMAC secret for request verification
+
+## Running the Server
+
+Start the server:
+
+```bash
+python server.py
+```
+
+The server will run on `http://localhost:7860` and you can expose it via ngrok for testing:
+
+```bash
+`ngrok http 7860`
+```
+
+> Tip: Use a subdomain for a consistent URL (e.g. `ngrok http -subdomain=mydomain http://localhost:7860`)
+
+## API Endpoints
+
+### GET /
+
+Health check endpoint that returns a "Hello, World!" message.
+
+### POST /api/dial
+
+Initiates a PSTN/SIP call with the following request body format:
+
+```json
+{
+ "To": "+14152251493",
+ "From": "+14158483432",
+ "callId": "string-contains-uuid",
+ "callDomain": "string-contains-uuid",
+ "dialout_settings": [
+ {
+ "phoneNumber": "+14158483432",
+ "callerId": "+14152251493"
+ }
+ ],
+ "voicemail_detection": {
+ "testInPrebuilt": true
+ },
+ "call_transfer": {
+ "mode": "dialout",
+ "speakSummary": true,
+ "storeSummary": true,
+ "operatorNumber": "+14152250006",
+ "testInPrebuilt": true
+ }
+}
+```
+
+#### Response
+
+Returns a JSON object containing:
+
+- `status`: Success/failure status
+- `data`: Response from Daily API
+- `room_properties`: Properties of the created Daily room
+
+## Error Handling
+
+- 401: Invalid signature
+- 400: Invalid authorization header (e.g. missing Daily API key in bot.py)
+- 405: Method not allowed (e.g. incorrect route on the webhook URL)
+- 500: Server errors (missing API key, network issues)
+- Other status codes are passed through from the Daily API
diff --git a/examples/deployment/pipecat-cloud-daily-pstn-server/fastapi-webhook-server/env.example b/examples/deployment/pipecat-cloud-daily-pstn-server/fastapi-webhook-server/env.example
new file mode 100644
index 000000000..295042651
--- /dev/null
+++ b/examples/deployment/pipecat-cloud-daily-pstn-server/fastapi-webhook-server/env.example
@@ -0,0 +1,3 @@
+AGENT_NAME="your-agent-name"
+PIPECAT_CLOUD_API_KEY="your-daily-api-key"
+PINLESS_HMAC_SECRET="hmac-secret-pinless-dialin"
\ No newline at end of file
diff --git a/examples/deployment/pipecat-cloud-daily-pstn-server/fastapi-webhook-server/requirements.txt b/examples/deployment/pipecat-cloud-daily-pstn-server/fastapi-webhook-server/requirements.txt
new file mode 100644
index 000000000..fcce91a24
--- /dev/null
+++ b/examples/deployment/pipecat-cloud-daily-pstn-server/fastapi-webhook-server/requirements.txt
@@ -0,0 +1,6 @@
+fastapi
+uvicorn
+python-dotenv
+requests
+pydantic
+loguru
\ No newline at end of file
diff --git a/examples/deployment/pipecat-cloud-daily-pstn-server/fastapi-webhook-server/server.py b/examples/deployment/pipecat-cloud-daily-pstn-server/fastapi-webhook-server/server.py
new file mode 100644
index 000000000..26dd59283
--- /dev/null
+++ b/examples/deployment/pipecat-cloud-daily-pstn-server/fastapi-webhook-server/server.py
@@ -0,0 +1,202 @@
+#
+# Copyright (c) 2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+# server.py
+
+
+import base64 # for calculating hmac signature
+import hmac
+import os # for accessing environment variables
+import time # for setting expiration time
+from typing import Any, Dict, List, Optional
+
+import requests
+from dotenv import load_dotenv
+from fastapi import FastAPI, HTTPException, Request
+from loguru import logger
+from pydantic import BaseModel, Field
+
+load_dotenv(override=True)
+
+app = FastAPI()
+
+
+class RoomRequest(BaseModel):
+ test: Optional[str] = Field(None, alias="Test", description="Test field")
+ To: Optional[str] = Field(None, alias="to", description="Destination phone number")
+ From: Optional[str] = Field(None, alias="from", description="Source phone number")
+ callId: Optional[str] = Field(None, alias="call_id", description="Unique call identifier")
+ callDomain: Optional[str] = Field(
+ None, alias="call_domain", description="Call domain identifier"
+ )
+ dialout_settings: Optional[List[Dict[str, Any]]] = Field(
+ None, description="An array of phone numbers or SIP URIs to dialout to"
+ )
+ voicemail_detection: Optional[Dict[str, Any]] = Field(
+ None, description="A flag to perform voicemail or answeing-machine detection"
+ )
+ call_transfer: Optional[Dict[str, Any]] = Field(None, description="to initiate a call transfer")
+
+ class Config:
+ populate_by_name = True
+ alias_generator = None
+
+
+"""
+ body can contain any fields, but for handling PSTN/SIP,
+ we recommend sending the following custom values:
+ dialin, dialout, voicemail detection, and call transfer
+
+
+ "To": "+14152251493",
+ "From": "+14158483432",
+ "callId": "string-contains-uuid",
+ "callDomain": "string-contains-uuid"
+ These need to be remapped to dialin_settings
+
+ "dialout_settings": [
+ {"phoneNumber": "+14158483432", "callerId": "+14152251493"},
+ {"sipUri": "sip:username@sip.hostname"}
+ ],
+ },
+
+ voicemail_detection:{
+ testInPrebuilt: true
+ },
+
+ "call_transfer": {
+ "mode": "dialout",
+ "speakSummary": true,
+ "storeSummary": true,
+ "operatorNumber": "+14152250006",
+ "testInPrebuilt": true
+ }
+"""
+
+
+@app.get("/")
+async def read_root():
+ return {"message": "Hello, World!"}
+
+
+@app.post("/api/dial")
+async def dial(request: RoomRequest, raw_request: Request):
+ logger.info("Incoming request to /dial:")
+ logger.info(f"Headers: {dict(raw_request.headers)}")
+ raw_body = await raw_request.body()
+ raw_body_str = raw_body.decode()
+ logger.info(f"Raw body: {raw_body_str}")
+ logger.info(f"Parsed body: {request.dict()}")
+
+ # calculate signature and compare/verify
+ hmac_secret = os.getenv("PINLESS_HMAC_SECRET")
+ timestamp = raw_request.headers.get("x-pinless-timestamp")
+ signature = raw_request.headers.get("x-pinless-signature")
+
+ if not hmac_secret:
+ logger.debug("Skipping HMAC validation - PINLESS_HMAC_SECRET not set")
+ elif timestamp and signature:
+ message = timestamp + "." + raw_body_str
+
+ base64_decoded_secret = base64.b64decode(hmac_secret)
+ computed_signature = base64.b64encode(
+ hmac.new(base64_decoded_secret, message.encode(), "sha256").digest()
+ ).decode()
+
+ if computed_signature != signature:
+ logger.error(f"Invalid signature. Expected {signature}, got {computed_signature}")
+ raise HTTPException(status_code=401, detail="Invalid signature")
+ else:
+ logger.debug("Skipping HMAC validation - no signature headers present")
+
+ if request.test == "test":
+ logger.debug("Test request received")
+ return {"status": "success", "message": "Test request received"}
+
+ dialin_settings = None
+ # these fields are camelCase in the request
+ required_fields = ["To", "From", "callId", "callDomain"]
+ if all(
+ field in request.dict() and request.dict()[field] is not None for field in required_fields
+ ):
+ # transform from camelCase to snake_case because daily-python expects snake_case
+ dialin_settings = {
+ "From": request.From,
+ "To": request.To,
+ "call_id": request.callId,
+ "call_domain": request.callDomain,
+ # transform from camelCase to snake_case
+ }
+ logger.debug(f"Populated dialin_settings from request: {dialin_settings}")
+
+ daily_room_properties = {
+ "enable_dialout": request.dialout_settings is not None,
+ }
+
+ if dialin_settings is not None:
+ sip_config = {
+ "display_name": request.From,
+ "sip_mode": "dial-in",
+ "num_endpoints": 2 if request.call_transfer is not None else 1,
+ "codecs": {"audio": ["OPUS"]},
+ }
+ daily_room_properties["sip"] = sip_config
+
+ # Setting default expiry to 5 minutes from now
+ daily_room_properties["exp"] = int(time.time()) + (5 * 60)
+
+ logger.debug(f"Daily room properties: {daily_room_properties}")
+ payload = {
+ "createDailyRoom": True,
+ "dailyRoomProperties": daily_room_properties,
+ "body": {
+ "dialin_settings": dialin_settings,
+ "dialout_settings": request.dialout_settings,
+ "voicemail_detection": request.voicemail_detection,
+ "call_transfer": request.call_transfer,
+ },
+ }
+
+ pcc_api_key = os.getenv("PIPECAT_CLOUD_API_KEY")
+ agent_name = os.getenv("AGENT_NAME", "my-first-agent")
+
+ if not pcc_api_key:
+ raise HTTPException(status_code=500, detail="DAILY_API_KEY environment variable is not set")
+
+ headers = {"Authorization": f"Bearer {pcc_api_key}", "Content-Type": "application/json"}
+
+ url = f"https://api.pipecat.daily.co/v1/public/{agent_name}/start"
+
+ logger.debug(f"Making API call to Daily: {url} {headers} {payload}")
+
+ try:
+ response = requests.post(url, json=payload, headers=headers)
+ response.raise_for_status()
+ response_data = response.json()
+ logger.debug(f"Response: {response_data}")
+ return {
+ "status": "success",
+ "data": response_data,
+ "room_properties": daily_room_properties,
+ }
+ except requests.exceptions.HTTPError as e:
+ # Pass through the status code and error details from the Daily API
+ status_code = e.response.status_code
+ error_detail = e.response.json() if e.response.content else str(e)
+ logger.error(f"HTTP error: {error_detail}")
+ raise HTTPException(status_code=status_code, detail=error_detail)
+ except requests.exceptions.RequestException as e:
+ logger.error(f"Request error: {str(e)}")
+ raise HTTPException(status_code=500, detail=str(e))
+
+
+if __name__ == "__main__":
+ try:
+ import uvicorn
+
+ uvicorn.run(app, host="0.0.0.0", port=7860)
+ except KeyboardInterrupt:
+ logger.info("Server stopped manually")
diff --git a/examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/.gitignore b/examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/.gitignore
new file mode 100644
index 000000000..400a7e48d
--- /dev/null
+++ b/examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/.gitignore
@@ -0,0 +1,53 @@
+# dependencies
+/node_modules
+/.pnp
+.pnp.js
+
+# testing
+/coverage
+
+# next.js
+/.next/
+/out/
+
+# production
+/build
+
+# misc
+.DS_Store
+*.pem
+
+# debug
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+.pnpm-debug.log*
+
+# local env files
+.env*.local
+
+# vercel
+.vercel
+
+# typescript
+*.tsbuildinfo
+next-env.d.ts
+
+# IDE specific files
+.idea/
+.vscode/
+*.swp
+*.swo
+
+# Logs
+logs
+*.log
+
+# OS generated files
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+Thumbs.db
\ No newline at end of file
diff --git a/examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/README.md b/examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/README.md
new file mode 100644
index 000000000..c56e4f7dd
--- /dev/null
+++ b/examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/README.md
@@ -0,0 +1,115 @@
+# Next.js server for handling Daily PSTN/SIP Webhook
+
+Next.js API routes for handling Daily PSTN/SIP Pipecat requests.
+
+## Features
+
+- API endpoint for handling Daily PSTN/SIP Pipecat requests
+- HMAC signature validation
+- Structured logging with Pino
+- Support for dial-in and dial-out settings
+- Voicemail detection and call transfer functionality
+- Test request handling
+
+## Setup
+
+1. Clone the repository
+
+2. Navigate to the `nextjs-webhook-server` directory:
+
+ ```bash
+ cd nextjs-webhook-server
+ ```
+
+3. Install dependencies:
+
+ ```bash
+ npm install
+ ```
+
+4. Create `.env.local` file with your credentials:
+
+ ```bash
+ cp env.local.example .env.local
+ ```
+
+5. Update your `.env` with your secrets:
+
+ ```bash
+ PIPECAT_CLOUD_API_KEY=pk_*
+ AGENT_NAME=my-first-agent
+ PINLESS_HMAC_SECRET=your_hmac_secret
+ LOG_LEVEL=info
+ ```
+
+### Running the server
+
+Run the development server:
+
+```bash
+npm run dev
+```
+
+The server will run on `http://localhost:7860` and you can expose it via ngrok for testing:
+
+```bash
+`ngrok http 7860`
+```
+
+> Tip: Use a subdomain for a consistent URL (e.g. `ngrok http -subdomain=mydomain http://localhost:7860`)
+
+## API Endpoints
+
+### GET /api
+
+Returns a simple "Hello, World!" message with a cute cat emoji to verify the server is running.
+
+### POST /api/dial
+
+Handles dial-in and dial-out requests for Pipecat Cloud.
+
+#### Test Requests
+
+The endpoint handles test requests when a webhook is configured. Send a request with `"Test": "test"` to verify your setup:
+
+```json
+{
+ "Test": "test"
+}
+```
+
+#### Production Request Format
+
+```json
+{
+ // for dial-in from webhook
+ "To": "+14152251493",
+ "From": "+14158483432",
+ "callId": "string-contains-uuid",
+ "callDomain": "string-contains-uuid",
+ // for making a dial out to a phone or SIP
+ "dialout_settings": [
+ { "phoneNumber": "+14158483432", "callerId": "purchased_phone_uuid" },
+ { "sipUri": "sip:username@sip.hostname.com" }
+ ]
+}
+```
+
+## Deployment
+
+The application is configured for Vercel deployment:
+
+1. Push your code to a Git repository
+2. Import your project in Vercel dashboard
+3. Configure environment variables:
+ - `PIPECAT_CLOUD_API_KEY`
+ - `AGENT_NAME`
+ - `PINLESS_HMAC_SECRET`
+ - `LOG_LEVEL` (optional, defaults to 'info')
+4. Deploy!
+
+## Security
+
+- HMAC signature validation for request authentication
+- Environment variables for sensitive credentials
+- Method validation (POST only for /dial)
diff --git a/examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/env.local.example b/examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/env.local.example
new file mode 100644
index 000000000..916aab13a
--- /dev/null
+++ b/examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/env.local.example
@@ -0,0 +1,4 @@
+AGENT_NAME=my-first-agent
+PIPECAT_CLOUD_API_KEY=your_daily_api_key
+PINLESS_HMAC_SECRET=your_hmac_secret
+LOG_LEVEL="info"
\ No newline at end of file
diff --git a/examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/package-lock.json b/examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/package-lock.json
new file mode 100644
index 000000000..2710cd886
--- /dev/null
+++ b/examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/package-lock.json
@@ -0,0 +1,5447 @@
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+ "has-tostringtag": "^1.0.2"
+ },
+ "engines": {
+ "node": ">= 0.4"
+ },
+ "funding": {
+ "url": "https://github.com/sponsors/ljharb"
+ }
+ },
+ "node_modules/word-wrap": {
+ "version": "1.2.5",
+ "resolved": "https://registry.npmjs.org/word-wrap/-/word-wrap-1.2.5.tgz",
+ "integrity": "sha512-BN22B5eaMMI9UMtjrGd5g5eCYPpCPDUy0FJXbYsaT5zYxjFOckS53SQDE3pWkVoWpHXVb3BrYcEN4Twa55B5cA==",
+ "dev": true,
+ "license": "MIT",
+ "engines": {
+ "node": ">=0.10.0"
+ }
+ },
+ "node_modules/wrap-ansi": {
+ "version": "8.1.0",
+ "resolved": "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-8.1.0.tgz",
+ "integrity": "sha512-si7QWI6zUMq56bESFvagtmzMdGOtoxfR+Sez11Mobfc7tm+VkUckk9bW2UeffTGVUbOksxmSw0AA2gs8g71NCQ==",
+ "dev": true,
+ "license": "MIT",
+ "dependencies": {
+ "ansi-styles": "^6.1.0",
+ "string-width": "^5.0.1",
+ "strip-ansi": "^7.0.1"
+ },
+ "engines": {
+ "node": ">=12"
+ },
+ "funding": {
+ "url": "https://github.com/chalk/wrap-ansi?sponsor=1"
+ }
+ },
+ "node_modules/wrap-ansi-cjs": {
+ "name": "wrap-ansi",
+ "version": "7.0.0",
+ "resolved": "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-7.0.0.tgz",
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+ "license": "MIT",
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+ },
+ "engines": {
+ "node": ">=10"
+ },
+ "funding": {
+ "url": "https://github.com/chalk/wrap-ansi?sponsor=1"
+ }
+ },
+ "node_modules/wrap-ansi-cjs/node_modules/emoji-regex": {
+ "version": "8.0.0",
+ "resolved": "https://registry.npmjs.org/emoji-regex/-/emoji-regex-8.0.0.tgz",
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+ "dev": true,
+ "license": "MIT"
+ },
+ "node_modules/wrap-ansi-cjs/node_modules/string-width": {
+ "version": "4.2.3",
+ "resolved": "https://registry.npmjs.org/string-width/-/string-width-4.2.3.tgz",
+ "integrity": "sha512-wKyQRQpjJ0sIp62ErSZdGsjMJWsap5oRNihHhu6G7JVO/9jIB6UyevL+tXuOqrng8j/cxKTWyWUwvSTriiZz/g==",
+ "dev": true,
+ "license": "MIT",
+ "dependencies": {
+ "emoji-regex": "^8.0.0",
+ "is-fullwidth-code-point": "^3.0.0",
+ "strip-ansi": "^6.0.1"
+ },
+ "engines": {
+ "node": ">=8"
+ }
+ },
+ "node_modules/wrap-ansi/node_modules/ansi-regex": {
+ "version": "6.1.0",
+ "resolved": "https://registry.npmjs.org/ansi-regex/-/ansi-regex-6.1.0.tgz",
+ "integrity": "sha512-7HSX4QQb4CspciLpVFwyRe79O3xsIZDDLER21kERQ71oaPodF8jL725AgJMFAYbooIqolJoRLuM81SpeUkpkvA==",
+ "dev": true,
+ "license": "MIT",
+ "engines": {
+ "node": ">=12"
+ },
+ "funding": {
+ "url": "https://github.com/chalk/ansi-regex?sponsor=1"
+ }
+ },
+ "node_modules/wrap-ansi/node_modules/ansi-styles": {
+ "version": "6.2.1",
+ "resolved": "https://registry.npmjs.org/ansi-styles/-/ansi-styles-6.2.1.tgz",
+ "integrity": "sha512-bN798gFfQX+viw3R7yrGWRqnrN2oRkEkUjjl4JNn4E8GxxbjtG3FbrEIIY3l8/hrwUwIeCZvi4QuOTP4MErVug==",
+ "dev": true,
+ "license": "MIT",
+ "engines": {
+ "node": ">=12"
+ },
+ "funding": {
+ "url": "https://github.com/chalk/ansi-styles?sponsor=1"
+ }
+ },
+ "node_modules/wrap-ansi/node_modules/strip-ansi": {
+ "version": "7.1.0",
+ "resolved": "https://registry.npmjs.org/strip-ansi/-/strip-ansi-7.1.0.tgz",
+ "integrity": "sha512-iq6eVVI64nQQTRYq2KtEg2d2uU7LElhTJwsH4YzIHZshxlgZms/wIc4VoDQTlG/IvVIrBKG06CrZnp0qv7hkcQ==",
+ "dev": true,
+ "license": "MIT",
+ "dependencies": {
+ "ansi-regex": "^6.0.1"
+ },
+ "engines": {
+ "node": ">=12"
+ },
+ "funding": {
+ "url": "https://github.com/chalk/strip-ansi?sponsor=1"
+ }
+ },
+ "node_modules/wrappy": {
+ "version": "1.0.2",
+ "resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz",
+ "integrity": "sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ==",
+ "dev": true,
+ "license": "ISC"
+ },
+ "node_modules/yocto-queue": {
+ "version": "0.1.0",
+ "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz",
+ "integrity": "sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==",
+ "dev": true,
+ "license": "MIT",
+ "engines": {
+ "node": ">=10"
+ },
+ "funding": {
+ "url": "https://github.com/sponsors/sindresorhus"
+ }
+ }
+ }
+}
diff --git a/examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/package.json b/examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/package.json
new file mode 100644
index 000000000..e2b012aa9
--- /dev/null
+++ b/examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/package.json
@@ -0,0 +1,22 @@
+{
+ "name": "my-daily-app",
+ "version": "0.1.0",
+ "private": true,
+ "scripts": {
+ "dev": "next dev -p 7860",
+ "build": "next build",
+ "start": "next start -p 7860",
+ "lint": "next lint"
+ },
+ "dependencies": {
+ "axios": "^1.6.0",
+ "next": "^14.0.0",
+ "pino": "^8.15.0",
+ "react": "^18.2.0",
+ "react-dom": "^18.2.0"
+ },
+ "devDependencies": {
+ "eslint": "^8.46.0",
+ "eslint-config-next": "^14.0.0"
+ }
+}
diff --git a/examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/pages/api/dial.js b/examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/pages/api/dial.js
new file mode 100644
index 000000000..a1905bd06
--- /dev/null
+++ b/examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/pages/api/dial.js
@@ -0,0 +1,176 @@
+import { logger } from '../../lib/utils';
+import axios from 'axios';
+import crypto from 'crypto';
+
+const validateSignature = (body, signature, timestamp, secret) => {
+ // Skip if any required fields are missing
+ if (!signature || !timestamp || !secret) {
+ logger.warn('Missing required fields for HMAC validation');
+ return true;
+ }
+
+ try {
+ const decodedSecret = Buffer.from(secret, 'base64');
+ const hmac = crypto.createHmac('sha256', decodedSecret);
+ const signatureData = `${timestamp}.${body}`;
+ const computedSignature = hmac.update(signatureData).digest('base64');
+
+ logger.debug('Signature validation:', {
+ timestamp,
+ signatureData: signatureData.substring(0, 50) + '...',
+ computedSignature,
+ receivedSignature: signature
+ });
+
+ return computedSignature === signature;
+ } catch (error) {
+ logger.error('Error validating signature:', error);
+ return true; // Allow request to proceed on error
+ }
+};
+
+export default async function handler(req, res) {
+ // Only allow POST requests
+ if (req.method !== 'POST') {
+ return res.status(405).json({ error: 'Method not allowed' });
+ }
+
+ try {
+ logger.info('Incoming request to /api/dial:');
+ logger.info(`Headers: ${JSON.stringify(req.headers)}`);
+
+ const rawBody = JSON.stringify(req.body);
+ logger.info(`Raw body: ${rawBody}`);
+
+ const signature = req.headers['x-pinless-signature'];
+ const timestamp = req.headers['x-pinless-timestamp'];
+
+ if (signature && timestamp) {
+ logger.info('Validating HMAC signature');
+ if (!validateSignature(rawBody, signature, timestamp, process.env.PINLESS_HMAC_SECRET)) {
+ logger.error('Invalid HMAC signature', { signature, timestamp });
+ return res.status(401).json({
+ error: 'Invalid signature',
+ message: 'Invalid HMAC signature'
+ });
+ }
+ } else {
+ logger.info('Skipping HMAC validation - no signature headers present');
+ }
+
+ // Extract request data
+ const {
+ Test: test,
+ To,
+ From,
+ callId,
+ callDomain,
+ dialout_settings,
+ voicemail_detection,
+ call_transfer
+ } = req.body;
+
+ // Handle test requests when a webhook is configured
+ if (test === 'test') {
+ logger.debug('Test request received');
+ return res.status(200).json({ status: 'success', message: 'Test request received' });
+ }
+
+ // Process dialin settings
+ let dialin_settings = null;
+ const requiredFields = ['To', 'From', 'callId', 'callDomain'];
+
+ if (requiredFields.every(field => req.body[field] !== undefined && req.body[field] !== null)) {
+ dialin_settings = {
+ // snake_case because pipecat expects this format
+ From,
+ To,
+ call_id: callId,
+ call_domain: callDomain,
+ };
+ logger.debug(`Populated dialin_settings from request: ${JSON.stringify(dialin_settings)}`);
+ }
+
+ // Set up Daily room properties
+ const daily_room_properties = {
+ enable_dialout: dialout_settings !== undefined && dialout_settings !== null,
+ exp: Math.floor(Date.now() / 1000) + (5 * 60), // 5 minutes from now
+ };
+
+ // Configure SIP if dialin settings are provided
+ if (dialin_settings !== null) {
+ const sip_config = {
+ display_name: From,
+ sip_mode: 'dial-in',
+ num_endpoints: call_transfer !== null ? 2 : 1,
+ codecs: {"audio": ["OPUS"]},
+ };
+ daily_room_properties.sip = sip_config;
+ }
+
+ // Prepare payload for {service}/start API call
+ const payload = {
+ createDailyRoom: true,
+ dailyRoomProperties: daily_room_properties,
+ body: {
+ dialin_settings,
+ dialout_settings,
+ voicemail_detection,
+ call_transfer,
+ },
+ };
+
+ logger.debug(`Daily room properties: ${JSON.stringify(daily_room_properties)}`);
+
+ // Get Daily API key and agent name from environment variables
+ const pccApiKey = process.env.PIPECAT_CLOUD_API_KEY;
+ const agentName = process.env.AGENT_NAME || 'my-first-agent';
+
+ if (!pccApiKey) {
+ throw new Error('PIPECAT_CLOUD_API_KEY environment variable is not set');
+ }
+
+ // Set up headers for Daily API call
+ const headers = {
+ 'Authorization': `Bearer ${pccApiKey}`,
+ 'Content-Type': 'application/json',
+ };
+
+ const url = `https://api.pipecat.daily.co/v1/public/${agentName}/start`;
+ logger.debug(`Making API call to Daily: ${url} ${JSON.stringify(headers)} ${JSON.stringify(payload)}`);
+
+ try {
+ const response = await axios.post(url, payload, { headers });
+ logger.debug(`Response: ${JSON.stringify(response.data)}`);
+
+ return res.status(200).json({
+ status: 'success',
+ data: response.data,
+ room_properties: daily_room_properties,
+ });
+ } catch (error) {
+ if (error.response) {
+ // Pass through status code and error details from the Daily API
+ const statusCode = error.response.status;
+ const errorDetail = error.response.data || error.message;
+ logger.error(`HTTP error: ${JSON.stringify(errorDetail)}`);
+ return res.status(statusCode).json(errorDetail);
+ } else {
+ logger.error(`Request error: ${error.message}`);
+ return res.status(500).json({ error: error.message });
+ }
+ }
+ } catch (error) {
+ logger.error(`Unexpected error: ${error.message}`);
+ return res.status(500).json({ error: 'Internal server error', message: error.message });
+ }
+}
+
+// Configure body parser to preserve raw body text
+export const config = {
+ api: {
+ bodyParser: {
+ sizeLimit: '1mb',
+ },
+ },
+};
diff --git a/examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/pages/api/index.js b/examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/pages/api/index.js
new file mode 100644
index 000000000..3da638d25
--- /dev/null
+++ b/examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/pages/api/index.js
@@ -0,0 +1,6 @@
+import { logger } from '../../lib/utils';
+
+export default function handler(req, res) {
+ logger.info('Received request to /api');
+ res.status(200).json({ message: 'Hello, World! from ααα’' });
+}
diff --git a/examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/vercel.js b/examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/vercel.js
new file mode 100644
index 000000000..7966370a8
--- /dev/null
+++ b/examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/vercel.js
@@ -0,0 +1,6 @@
+module.exports = {
+ version: 2,
+ buildCommand: "next build",
+ outputDirectory: ".next",
+ cleanUrls: true
+};
\ No newline at end of file
diff --git a/examples/deployment/pipecat-cloud-example/bot.py b/examples/deployment/pipecat-cloud-example/bot.py
index a0e3f5cfe..75b1b2b99 100644
--- a/examples/deployment/pipecat-cloud-example/bot.py
+++ b/examples/deployment/pipecat-cloud-example/bot.py
@@ -50,9 +50,9 @@ async def main(room_url: str, token: str):
token,
"bot",
DailyParams(
+ audio_in_enabled=True,
audio_out_enabled=True,
transcription_enabled=True,
- vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
),
)
@@ -61,7 +61,7 @@ async def main(room_url: str, token: str):
api_key=os.getenv("CARTESIA_API_KEY"), voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22"
)
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
messages = [
{
diff --git a/examples/fal-smart-turn/.gitignore b/examples/fal-smart-turn/.gitignore
new file mode 100644
index 000000000..0d298d282
--- /dev/null
+++ b/examples/fal-smart-turn/.gitignore
@@ -0,0 +1,51 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+dist/
+*.egg-info/
+.installed.cfg
+*.egg
+.pytest_cache/
+.coverage
+.coverage.*
+.env
+.venv
+env/
+venv/
+ENV/
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# JavaScript/Node.js
+node_modules/
+dist/
+dist-ssr/
+*.local
+.env.local
+.env.development.local
+.env.test.local
+.env.production.local
+
+# Logs
+logs/
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+
+# Editor/IDE
+.vscode/*
+!.vscode/extensions.json
+.idea/
+*.swp
+*.swo
+.DS_Store
+
+# Project specific
+runpod.toml
\ No newline at end of file
diff --git a/examples/fal-smart-turn/README.md b/examples/fal-smart-turn/README.md
new file mode 100644
index 000000000..40f32bf18
--- /dev/null
+++ b/examples/fal-smart-turn/README.md
@@ -0,0 +1,148 @@
+# Smart Turn Detection Demo
+
+This demo showcases Pipecat's Smart Turn Detection feature - an advanced conversational turn detection system that uses machine learning to identify when a speaker has finished their turn in a conversation. Unlike basic Voice Activity Detection (VAD) which only detects speech vs. silence, Smart Turn detects natural conversational cues like intonation patterns, pacing, and linguistic signals.
+
+This demo uses the [pipecat-ai/smart-turn](https://huggingface.co/pipecat-ai/smart-turn) model - an open-source, community-driven conversational turn detection model designed to provide more natural turn-taking in voice interactions. The model is being hosted on Fal's infrastructure for GPU acceleration, offering inference times between 50-70ms.
+
+In the client UI, you can see the transcription messages along with the smart-turn model's prediction results in real-time.
+
+## Run the demo locally
+
+### Run the Server
+
+1. Set up and activate your virtual environment:
+
+ ```bash
+ python3 -m venv venv
+ source venv/bin/activate # On Windows: venv\Scripts\activate
+ ```
+
+2. Install dependencies:
+
+ ```bash
+ pip install -r requirements.txt
+ ```
+
+3. Create your .env file and set your env vars:
+
+ ```bash
+ cp env.example .env
+ ```
+
+ Keys to provide:
+
+ - GOOGLE_API_KEY
+ - CARTESIA_API_KEY
+ - DEEPGRAM_API_KEY
+ - DAILY_API_KEY
+ - FAL_SMART_TURN_API_KEY
+
+4. Run the server:
+
+ ```bash
+ LOCAL=1 python server.py
+ ```
+
+### Run the client
+
+1. Open a new terminal and navigate to the client directory:
+
+ ```bash
+ cd client
+ ```
+
+2. Install dependencies:
+
+ ```bash
+ npm install
+ ```
+
+3. Create your .env.local file:
+
+ ```bash
+ cp env.local.example .env.local
+ ```
+
+ > Note: No keys need to be modified. `NEXT_PUBLIC_API_BASE_URL` is already configured for local use.
+
+4. Start the development server:
+
+ ```bash
+ npm run dev
+ ```
+
+5. Open [http://localhost:3000](http://localhost:3000) in your browser.
+
+## Deploy the app
+
+### Deploy the server to Pipecat Cloud
+
+1. Navigate to server
+
+ ```bash
+ cd server
+ ```
+
+2. You should already have a .env set up from running locally. If not, do that now.
+
+3. Update your build and deploy scripts.
+
+ - In build.sh, set `DOCKER_USERNAME` and `AGENT_NAME`.
+ - In pcc-deploy.toml, set `image`, which specifies where your Docker image is stored.
+
+4. Build your Docker image by running the build script:
+
+ ```bash
+ ./build.sh
+ ```
+
+ > Note: This builds, tags and pushes your docker image and assumes Docker Hub is the container registry.
+
+5. Make sure you have the Pipecat Cloud CLI installed:
+
+ ```bash
+ pip install pipecatcloud
+ ```
+
+6. Login via the Pipecat Cloud CLI:
+
+ ```bash
+ pcc auth login
+ ```
+
+ > Note: If you don't have an account, sign up at https://pipecat.daily.co.
+
+7. Add a secrets set:
+
+ ```bash
+ pcc secrets set pcc-smart-turn-secrets --file .env
+ ```
+
+8. Deploy your agent:
+
+ ```bash
+ pcc deploy
+ ```
+
+ > Note: This uses your pcc-deploy.toml settings. Modify as needed.
+
+### Deploy the client to Vercel
+
+This project uses TypeScript, React, and Next.js, making it a perfect fit for [Vercel](https://vercel.com/).
+
+- In your client directory, install Vercel's CLI tool: `npm install -g vercel`
+- Verify it's installed using `vercel --version`
+- Log in your Vercel account using `vercel login`
+- Deploy your client to Vercel using `vercel`
+
+Follow the vercel prompts to deploy your project.
+
+### Test your deployed app
+
+Now with the client and server deployed, you can join the call using your Vercel URL.
+
+See the debug information for the Smart Turn data. It prints a log line for each smart-turn inference:
+
+```
+Smart Turn: COMPLETE, Probability: 95.3%, Model inference: 65.23ms, Server processing: 82.09ms, End-to-end: 245.43ms
+```
diff --git a/examples/fal-smart-turn/client/.gitignore b/examples/fal-smart-turn/client/.gitignore
new file mode 100644
index 000000000..5ef6a5207
--- /dev/null
+++ b/examples/fal-smart-turn/client/.gitignore
@@ -0,0 +1,41 @@
+# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
+
+# dependencies
+/node_modules
+/.pnp
+.pnp.*
+.yarn/*
+!.yarn/patches
+!.yarn/plugins
+!.yarn/releases
+!.yarn/versions
+
+# testing
+/coverage
+
+# next.js
+/.next/
+/out/
+
+# production
+/build
+
+# misc
+.DS_Store
+*.pem
+
+# debug
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+.pnpm-debug.log*
+
+# env files (can opt-in for committing if needed)
+.env*
+
+# vercel
+.vercel
+
+# typescript
+*.tsbuildinfo
+next-env.d.ts
diff --git a/examples/fal-smart-turn/client/env.local.example b/examples/fal-smart-turn/client/env.local.example
new file mode 100644
index 000000000..f99567196
--- /dev/null
+++ b/examples/fal-smart-turn/client/env.local.example
@@ -0,0 +1,3 @@
+NEXT_PUBLIC_API_BASE_URL=http://localhost:7860
+PIPECAT_CLOUD_API_KEY=
+AGENT_NAME=pcc-smart-turn
\ No newline at end of file
diff --git a/examples/fal-smart-turn/client/eslint.config.mjs b/examples/fal-smart-turn/client/eslint.config.mjs
new file mode 100644
index 000000000..c85fb67c4
--- /dev/null
+++ b/examples/fal-smart-turn/client/eslint.config.mjs
@@ -0,0 +1,16 @@
+import { dirname } from "path";
+import { fileURLToPath } from "url";
+import { FlatCompat } from "@eslint/eslintrc";
+
+const __filename = fileURLToPath(import.meta.url);
+const __dirname = dirname(__filename);
+
+const compat = new FlatCompat({
+ baseDirectory: __dirname,
+});
+
+const eslintConfig = [
+ ...compat.extends("next/core-web-vitals", "next/typescript"),
+];
+
+export default eslintConfig;
diff --git a/examples/fal-smart-turn/client/next.config.ts b/examples/fal-smart-turn/client/next.config.ts
new file mode 100644
index 000000000..e9ffa3083
--- /dev/null
+++ b/examples/fal-smart-turn/client/next.config.ts
@@ -0,0 +1,7 @@
+import type { NextConfig } from "next";
+
+const nextConfig: NextConfig = {
+ /* config options here */
+};
+
+export default nextConfig;
diff --git a/examples/fal-smart-turn/client/package-lock.json b/examples/fal-smart-turn/client/package-lock.json
new file mode 100644
index 000000000..e8ea9f21b
--- /dev/null
+++ b/examples/fal-smart-turn/client/package-lock.json
@@ -0,0 +1,5419 @@
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+ "license": "MIT",
+ "dependencies": {
+ "regenerator-runtime": "^0.14.0"
+ },
+ "engines": {
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+ }
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+ "node_modules/word-wrap": {
+ "version": "1.2.5",
+ "resolved": "https://registry.npmjs.org/word-wrap/-/word-wrap-1.2.5.tgz",
+ "integrity": "sha512-BN22B5eaMMI9UMtjrGd5g5eCYPpCPDUy0FJXbYsaT5zYxjFOckS53SQDE3pWkVoWpHXVb3BrYcEN4Twa55B5cA==",
+ "dev": true,
+ "license": "MIT",
+ "engines": {
+ "node": ">=0.10.0"
+ }
+ },
+ "node_modules/yocto-queue": {
+ "version": "0.1.0",
+ "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz",
+ "integrity": "sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==",
+ "dev": true,
+ "license": "MIT",
+ "engines": {
+ "node": ">=10"
+ },
+ "funding": {
+ "url": "https://github.com/sponsors/sindresorhus"
+ }
+ }
+ }
+}
diff --git a/examples/fal-smart-turn/client/package.json b/examples/fal-smart-turn/client/package.json
new file mode 100644
index 000000000..a5fb0d586
--- /dev/null
+++ b/examples/fal-smart-turn/client/package.json
@@ -0,0 +1,28 @@
+{
+ "name": "my-nextjs-app",
+ "version": "0.1.0",
+ "private": true,
+ "scripts": {
+ "dev": "next dev",
+ "build": "next build",
+ "start": "next start",
+ "lint": "next lint"
+ },
+ "dependencies": {
+ "@pipecat-ai/client-js": "^0.3.5",
+ "@pipecat-ai/client-react": "^0.3.5",
+ "@pipecat-ai/daily-transport": "^0.3.7",
+ "next": "15.2.3",
+ "react": "^19.0.0",
+ "react-dom": "^19.0.0"
+ },
+ "devDependencies": {
+ "@eslint/eslintrc": "^3",
+ "@types/node": "^20",
+ "@types/react": "^19",
+ "@types/react-dom": "^19",
+ "eslint": "^9",
+ "eslint-config-next": "15.2.3",
+ "typescript": "^5"
+ }
+}
diff --git a/examples/fal-smart-turn/client/public/favicon.svg b/examples/fal-smart-turn/client/public/favicon.svg
new file mode 100644
index 000000000..a0260a809
--- /dev/null
+++ b/examples/fal-smart-turn/client/public/favicon.svg
@@ -0,0 +1,7 @@
+
+
diff --git a/examples/fal-smart-turn/client/src/app/api/connect/route.ts b/examples/fal-smart-turn/client/src/app/api/connect/route.ts
new file mode 100644
index 000000000..18b4f535a
--- /dev/null
+++ b/examples/fal-smart-turn/client/src/app/api/connect/route.ts
@@ -0,0 +1,44 @@
+import { NextResponse, NextRequest } from 'next/server';
+
+export async function POST(request: NextRequest) {
+ const { MY_CUSTOM_DATA } = await request.json();
+
+ try {
+ const response = await fetch(
+ `https://api.pipecat.daily.co/v1/public/${process.env.AGENT_NAME}/start`,
+ {
+ method: 'POST',
+ headers: {
+ Authorization: `Bearer ${process.env.PIPECAT_CLOUD_API_KEY}`,
+ 'Content-Type': 'application/json',
+ },
+ body: JSON.stringify({
+ // Create Daily room
+ createDailyRoom: true,
+ // Optionally set Daily room properties
+ dailyRoomProperties: { start_video_off: true },
+ // Optionally pass custom data to the bot
+ body: { MY_CUSTOM_DATA },
+ }),
+ }
+ );
+
+ if (!response.ok) {
+ throw new Error(`API responded with status: ${response.status}`);
+ }
+
+ const data = await response.json();
+
+ // Transform the response to match what RTVI client expects
+ return NextResponse.json({
+ room_url: data.dailyRoom,
+ token: data.dailyToken,
+ });
+ } catch (error) {
+ console.error('API error:', error);
+ return NextResponse.json(
+ { error: 'Failed to start agent' },
+ { status: 500 }
+ );
+ }
+}
diff --git a/examples/fal-smart-turn/client/src/app/globals.css b/examples/fal-smart-turn/client/src/app/globals.css
new file mode 100644
index 000000000..568839735
--- /dev/null
+++ b/examples/fal-smart-turn/client/src/app/globals.css
@@ -0,0 +1,82 @@
+body {
+ margin: 0;
+ padding: 20px;
+ font-family: Arial, sans-serif;
+ background-color: #f0f0f0;
+}
+
+.app {
+ max-width: 1200px;
+ margin: 0 auto;
+}
+
+.status-bar {
+ display: flex;
+ justify-content: space-between;
+ align-items: center;
+ padding: 10px;
+ background-color: #fff;
+ border-radius: 8px;
+ margin-bottom: 20px;
+}
+
+.controls button {
+ padding: 8px 16px;
+ margin-left: 10px;
+ border: none;
+ border-radius: 4px;
+ cursor: pointer;
+}
+
+button:disabled {
+ opacity: 0.5;
+ cursor: not-allowed;
+}
+
+.connect-btn {
+ background-color: #4caf50;
+ color: white;
+}
+
+.disconnect-btn {
+ background-color: #f44336;
+ color: white;
+}
+
+.main-content {
+ background-color: #fff;
+ border-radius: 8px;
+ padding: 20px;
+ margin-bottom: 20px;
+}
+
+.bot-container {
+ display: flex;
+ flex-direction: column;
+ align-items: center;
+}
+
+.video-container {
+ width: 640px;
+ height: 360px;
+ background-color: #ddd;
+ margin-bottom: 20px;
+ border-radius: 8px;
+ overflow: hidden;
+}
+
+.video-container video {
+ width: 100%;
+ height: 100%;
+ object-fit: cover;
+}
+
+.mic-enabled {
+ background-color: #4caf50;
+ color: white;
+}
+
+.mic-disabled {
+ background-color: #f44336;
+ color: white;
+}
diff --git a/examples/fal-smart-turn/client/src/app/layout.tsx b/examples/fal-smart-turn/client/src/app/layout.tsx
new file mode 100644
index 000000000..359c61c8f
--- /dev/null
+++ b/examples/fal-smart-turn/client/src/app/layout.tsx
@@ -0,0 +1,27 @@
+import './globals.css';
+import { RTVIProvider } from '@/providers/RTVIProvider';
+
+export const metadata = {
+ title: 'Pipecat React Client',
+ description: 'Pipecat RTVI Client using Next.js',
+ icons: {
+ icon: [{ url: '/favicon.svg', type: 'image/svg+xml' }],
+ },
+};
+
+export default function RootLayout({
+ children,
+}: {
+ children: React.ReactNode;
+}) {
+ return (
+
+
+ {children}
+
+
+ );
+}
diff --git a/examples/fal-smart-turn/client/src/app/page.tsx b/examples/fal-smart-turn/client/src/app/page.tsx
new file mode 100644
index 000000000..d4b69f1ab
--- /dev/null
+++ b/examples/fal-smart-turn/client/src/app/page.tsx
@@ -0,0 +1,41 @@
+'use client';
+
+import {
+ RTVIClientAudio,
+ RTVIClientVideo,
+ useRTVIClientTransportState,
+} from '@pipecat-ai/client-react';
+import { ConnectButton } from '../components/ConnectButton';
+import { StatusDisplay } from '../components/StatusDisplay';
+import { DebugDisplay } from '../components/DebugDisplay';
+
+function BotVideo() {
+ const transportState = useRTVIClientTransportState();
+ const isConnected = transportState !== 'disconnected';
+
+ return (
+
+
+ {isConnected &&
+
+
+ {isConnected ? 'Disconnect' : 'Connect'}
+
+
+ );
+}
diff --git a/examples/fal-smart-turn/client/src/components/DebugDisplay.css b/examples/fal-smart-turn/client/src/components/DebugDisplay.css
new file mode 100644
index 000000000..a9ecc66e2
--- /dev/null
+++ b/examples/fal-smart-turn/client/src/components/DebugDisplay.css
@@ -0,0 +1,26 @@
+.debug-panel {
+ background-color: #fff;
+ border-radius: 8px;
+ padding: 20px;
+}
+
+.debug-panel h3 {
+ margin: 0 0 10px 0;
+ font-size: 16px;
+ font-weight: bold;
+}
+
+.debug-log {
+ height: 200px;
+ overflow-y: auto;
+ background-color: #f8f8f8;
+ padding: 10px;
+ border-radius: 4px;
+ font-family: monospace;
+ font-size: 12px;
+ line-height: 1.4;
+}
+
+.debug-log div {
+ margin-bottom: 4px;
+}
diff --git a/examples/fal-smart-turn/client/src/components/DebugDisplay.tsx b/examples/fal-smart-turn/client/src/components/DebugDisplay.tsx
new file mode 100644
index 000000000..e19a23ab5
--- /dev/null
+++ b/examples/fal-smart-turn/client/src/components/DebugDisplay.tsx
@@ -0,0 +1,171 @@
+import { useRef, useCallback } from 'react';
+import {
+ Participant,
+ RTVIEvent,
+ TransportState,
+ TranscriptData,
+ BotLLMTextData,
+} from '@pipecat-ai/client-js';
+import { useRTVIClient, useRTVIClientEvent } from '@pipecat-ai/client-react';
+import './DebugDisplay.css';
+
+interface SmartTurnResultData {
+ type: 'smart_turn_result';
+ is_complete: boolean;
+ probability: number;
+ inference_time_ms: number; // Pure model inference time
+ server_total_time_ms: number; // Server processing time
+ e2e_processing_time_ms: number; // Complete end-to-end time
+}
+
+export function DebugDisplay() {
+ const debugLogRef = useRef(null);
+ const client = useRTVIClient();
+
+ const log = useCallback((message: string) => {
+ if (!debugLogRef.current) return;
+
+ const entry = document.createElement('div');
+ entry.textContent = `${new Date().toISOString()} - ${message}`;
+
+ // Add styling based on message type
+ if (message.startsWith('User: ')) {
+ entry.style.color = '#2196F3'; // blue for user
+ } else if (message.startsWith('Bot: ')) {
+ entry.style.color = '#4CAF50'; // green for bot
+ } else if (message.includes('Smart Turn:')) {
+ entry.style.color = '#9C27B0'; // purple for smart turn
+ }
+
+ debugLogRef.current.appendChild(entry);
+ debugLogRef.current.scrollTop = debugLogRef.current.scrollHeight;
+ }, []);
+
+ // Log transport state changes
+ useRTVIClientEvent(
+ RTVIEvent.TransportStateChanged,
+ useCallback(
+ (state: TransportState) => {
+ log(`Transport state changed: ${state}`);
+ },
+ [log]
+ )
+ );
+
+ // Log bot connection events
+ useRTVIClientEvent(
+ RTVIEvent.BotConnected,
+ useCallback(
+ (participant?: Participant) => {
+ log(`Bot connected: ${JSON.stringify(participant)}`);
+ },
+ [log]
+ )
+ );
+
+ useRTVIClientEvent(
+ RTVIEvent.BotDisconnected,
+ useCallback(
+ (participant?: Participant) => {
+ log(`Bot disconnected: ${JSON.stringify(participant)}`);
+ },
+ [log]
+ )
+ );
+
+ // Log track events
+ useRTVIClientEvent(
+ RTVIEvent.TrackStarted,
+ useCallback(
+ (track: MediaStreamTrack, participant?: Participant) => {
+ log(
+ `Track started: ${track.kind} from ${participant?.name || 'unknown'}`
+ );
+ },
+ [log]
+ )
+ );
+
+ useRTVIClientEvent(
+ RTVIEvent.TrackStopped,
+ useCallback(
+ (track: MediaStreamTrack, participant?: Participant) => {
+ log(
+ `Track stopped: ${track.kind} from ${participant?.name || 'unknown'}`
+ );
+ },
+ [log]
+ )
+ );
+
+ // Log bot ready state and check tracks
+ useRTVIClientEvent(
+ RTVIEvent.BotReady,
+ useCallback(() => {
+ log(`Bot ready`);
+
+ if (!client) return;
+
+ const tracks = client.tracks();
+ log(
+ `Available tracks: ${JSON.stringify({
+ local: {
+ audio: !!tracks.local.audio,
+ video: !!tracks.local.video,
+ },
+ bot: {
+ audio: !!tracks.bot?.audio,
+ video: !!tracks.bot?.video,
+ },
+ })}`
+ );
+ }, [client, log])
+ );
+
+ // Log transcripts
+ useRTVIClientEvent(
+ RTVIEvent.UserTranscript,
+ useCallback(
+ (data: TranscriptData) => {
+ // Only log final transcripts
+ if (data.final) {
+ log(`User: ${data.text}`);
+ }
+ },
+ [log]
+ )
+ );
+
+ useRTVIClientEvent(
+ RTVIEvent.BotTranscript,
+ useCallback(
+ (data: BotLLMTextData) => {
+ log(`Bot: ${data.text}`);
+ },
+ [log]
+ )
+ );
+
+ useRTVIClientEvent(
+ RTVIEvent.ServerMessage,
+ useCallback(
+ (data: SmartTurnResultData) => {
+ log(
+ `Smart Turn:
+ ${data.is_complete ? 'COMPLETE' : 'INCOMPLETE'},
+ Probability: ${(data.probability * 100).toFixed(1)}%,
+ Model inference: ${data.inference_time_ms?.toFixed(2) || 'N/A'}ms,
+ Server processing: ${data.server_total_time_ms?.toFixed(2) || 'N/A'}ms,
+ End-to-end: ${data.e2e_processing_time_ms?.toFixed(2) || 'N/A'}ms`
+ );
+ },
+ [log]
+ )
+ );
+ return (
+
+ );
+}
diff --git a/examples/fal-smart-turn/client/src/components/StatusDisplay.tsx b/examples/fal-smart-turn/client/src/components/StatusDisplay.tsx
new file mode 100644
index 000000000..f024378d9
--- /dev/null
+++ b/examples/fal-smart-turn/client/src/components/StatusDisplay.tsx
@@ -0,0 +1,11 @@
+import { useRTVIClientTransportState } from '@pipecat-ai/client-react';
+
+export function StatusDisplay() {
+ const transportState = useRTVIClientTransportState();
+
+ return (
+
+ Status: {transportState}
+
+ );
+}
diff --git a/examples/fal-smart-turn/client/src/providers/RTVIProvider.tsx b/examples/fal-smart-turn/client/src/providers/RTVIProvider.tsx
new file mode 100644
index 000000000..4dd805d36
--- /dev/null
+++ b/examples/fal-smart-turn/client/src/providers/RTVIProvider.tsx
@@ -0,0 +1,43 @@
+'use client';
+
+import { RTVIClient } from '@pipecat-ai/client-js';
+import { DailyTransport } from '@pipecat-ai/daily-transport';
+import { RTVIClientProvider } from '@pipecat-ai/client-react';
+import { PropsWithChildren, useEffect, useState } from 'react';
+
+// Get the API base URL from environment variables
+// Default to "/api" if not specified
+// "/api" is the default for Next.js API routes and used
+// for the Pipecat Cloud deployed agent
+const API_BASE_URL = process.env.NEXT_PUBLIC_API_BASE_URL || '/api';
+
+console.log('Using API base URL:', API_BASE_URL);
+
+export function RTVIProvider({ children }: PropsWithChildren) {
+ const [client, setClient] = useState(null);
+
+ useEffect(() => {
+ const transport = new DailyTransport();
+
+ const rtviClient = new RTVIClient({
+ transport,
+ params: {
+ baseUrl: API_BASE_URL,
+ endpoints: {
+ connect: '/connect',
+ },
+ requestData: { foo: 'bar' },
+ },
+ enableMic: true,
+ enableCam: false,
+ });
+
+ setClient(rtviClient);
+ }, []);
+
+ if (!client) {
+ return null;
+ }
+
+ return {children} ;
+}
diff --git a/examples/fal-smart-turn/client/tsconfig.json b/examples/fal-smart-turn/client/tsconfig.json
new file mode 100644
index 000000000..64e9ef605
--- /dev/null
+++ b/examples/fal-smart-turn/client/tsconfig.json
@@ -0,0 +1,28 @@
+{
+ "compilerOptions": {
+ "target": "ES2017",
+ "lib": ["dom", "dom.iterable", "esnext"],
+ "allowJs": true,
+ "skipLibCheck": true,
+ "strict": true,
+ "noEmit": true,
+ "esModuleInterop": true,
+ "module": "esnext",
+ "moduleResolution": "bundler",
+ "resolveJsonModule": true,
+ "isolatedModules": true,
+ "jsx": "preserve",
+ "incremental": true,
+ "plugins": [
+ {
+ "name": "next"
+ }
+ ],
+ "paths": {
+ "@/components/*": ["./src/components/*"],
+ "@/providers/*": ["./src/providers/*"]
+ }
+ },
+ "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts"],
+ "exclude": ["node_modules"]
+}
diff --git a/examples/fal-smart-turn/server/Dockerfile b/examples/fal-smart-turn/server/Dockerfile
new file mode 100644
index 000000000..48b7ff64e
--- /dev/null
+++ b/examples/fal-smart-turn/server/Dockerfile
@@ -0,0 +1,8 @@
+FROM dailyco/pipecat-base:latest
+
+COPY ./requirements.txt requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY ./assets assets
+COPY ./bot.py bot.py
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diff --git a/examples/fal-smart-turn/server/assets/robot09.png b/examples/fal-smart-turn/server/assets/robot09.png
new file mode 100644
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diff --git a/examples/fal-smart-turn/server/bot.py b/examples/fal-smart-turn/server/bot.py
new file mode 100644
index 000000000..8c3810535
--- /dev/null
+++ b/examples/fal-smart-turn/server/bot.py
@@ -0,0 +1,299 @@
+#
+# Copyright (c) 2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from PIL import Image
+from pipecatcloud.agent import DailySessionArguments
+
+from pipecat.audio.turn.smart_turn.fal_smart_turn import FalSmartTurnAnalyzer
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.audio.vad.vad_analyzer import VADParams
+from pipecat.frames.frames import (
+ BotStartedSpeakingFrame,
+ BotStoppedSpeakingFrame,
+ Frame,
+ MetricsFrame,
+ OutputImageRawFrame,
+ SpriteFrame,
+)
+from pipecat.metrics.metrics import SmartTurnMetricsData
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.processors.frameworks.rtvi import (
+ RTVIConfig,
+ RTVIObserver,
+ RTVIProcessor,
+ RTVIServerMessageFrame,
+)
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.google.llm import GoogleLLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+# Check if we're in local development mode
+LOCAL = os.getenv("LOCAL")
+
+logger.remove()
+logger.add(sys.stderr, level="DEBUG")
+
+sprites = []
+script_dir = os.path.dirname(__file__)
+
+# Load sequential animation frames
+for i in range(1, 26):
+ # Build the full path to the image file
+ full_path = os.path.join(script_dir, f"assets/robot0{i}.png")
+ # Get the filename without the extension to use as the dictionary key
+ # Open the image and convert it to bytes
+ with Image.open(full_path) as img:
+ sprites.append(OutputImageRawFrame(image=img.tobytes(), size=img.size, format=img.format))
+
+# Create a smooth animation by adding reversed frames
+flipped = sprites[::-1]
+sprites.extend(flipped)
+
+# Define static and animated states
+quiet_frame = sprites[0] # Static frame for when bot is listening
+talking_frame = SpriteFrame(images=sprites) # Animation sequence for when bot is talking
+
+
+class TalkingAnimation(FrameProcessor):
+ """Manages the bot's visual animation states.
+
+ Switches between static (listening) and animated (talking) states based on
+ the bot's current speaking status.
+ """
+
+ def __init__(self):
+ super().__init__()
+ self._is_talking = False
+
+ async def process_frame(self, frame: Frame, direction: FrameDirection):
+ """Process incoming frames and update animation state.
+
+ Args:
+ frame: The incoming frame to process
+ direction: The direction of frame flow in the pipeline
+ """
+ await super().process_frame(frame, direction)
+
+ # Switch to talking animation when bot starts speaking
+ if isinstance(frame, BotStartedSpeakingFrame):
+ if not self._is_talking:
+ await self.push_frame(talking_frame)
+ self._is_talking = True
+ # Return to static frame when bot stops speaking
+ elif isinstance(frame, BotStoppedSpeakingFrame):
+ await self.push_frame(quiet_frame)
+ self._is_talking = False
+
+ await self.push_frame(frame, direction)
+
+
+class SmartTurnMetricsProcessor(FrameProcessor):
+ """Processes the metrics data from Smart Turn Analyzer.
+
+ This processor is responsible for handling smart turn metrics data
+ and forwarding it to the client UI via RTVI.
+ """
+
+ async def process_frame(self, frame: Frame, direction: FrameDirection):
+ """Process incoming frames and handle Smart Turn metrics.
+
+ Args:
+ frame: The incoming frame to process
+ direction: The direction of frame flow in the pipeline
+ """
+ await super().process_frame(frame, direction)
+
+ # Handle Smart Turn metrics
+ if isinstance(frame, MetricsFrame):
+ for metrics in frame.data:
+ if isinstance(metrics, SmartTurnMetricsData):
+ logger.info(f"Smart Turn metrics: {metrics}")
+
+ # Create a payload with the smart turn prediction data
+ smart_turn_data = {
+ "type": "smart_turn_result",
+ "is_complete": metrics.is_complete,
+ "probability": metrics.probability,
+ "inference_time_ms": metrics.inference_time_ms,
+ "server_total_time_ms": metrics.server_total_time_ms,
+ "e2e_processing_time_ms": metrics.e2e_processing_time_ms,
+ }
+
+ # Send the data to the client via RTVI
+ rtvi_frame = RTVIServerMessageFrame(data=smart_turn_data)
+ await self.push_frame(rtvi_frame)
+
+ await self.push_frame(frame, direction)
+
+
+async def main(transport: DailyTransport):
+ # Configure your STT, LLM, and TTS services here
+ # Swap out different processors or properties to customize your bot
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+ llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"))
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
+
+ # Set up the initial context for the conversation
+ # You can specified initial system and assistant messages here
+ messages = [
+ {
+ "role": "system",
+ "content": "You are Chatbot, a friendly, helpful robot. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way, but keep your responses brief. Start by introducing yourself.",
+ },
+ ]
+
+ # This sets up the LLM context by providing messages and tools
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ ta = TalkingAnimation()
+ smart_turn_metrics_processor = SmartTurnMetricsProcessor()
+
+ # RTVI events for Pipecat client UI
+ rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
+
+ # A core voice AI pipeline
+ # Add additional processors to customize the bot's behavior
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ rtvi,
+ smart_turn_metrics_processor,
+ stt,
+ context_aggregator.user(),
+ llm,
+ tts,
+ ta,
+ transport.output(),
+ context_aggregator.assistant(),
+ ]
+ )
+
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ ),
+ observers=[RTVIObserver(rtvi)],
+ )
+
+ @rtvi.event_handler("on_client_ready")
+ async def on_client_ready(rtvi):
+ logger.debug("Client ready event received")
+ await rtvi.set_bot_ready()
+ # Kick off the conversation
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+ @transport.event_handler("on_first_participant_joined")
+ async def on_first_participant_joined(transport, participant):
+ logger.info("First participant joined: {}", participant["id"])
+ # Push a static frame to show the bot is listening
+ await task.queue_frame(quiet_frame)
+
+ @transport.event_handler("on_participant_left")
+ async def on_participant_left(transport, participant, reason):
+ logger.info("Participant left: {}", participant)
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False, force_gc=True)
+
+ await runner.run(task)
+
+
+async def bot(args: DailySessionArguments):
+ """Main bot entry point compatible with the FastAPI route handler.
+
+ Args:
+ room_url: The Daily room URL
+ token: The Daily room token
+ body: The configuration object from the request body
+ session_id: The session ID for logging
+ """
+ from pipecat.audio.filters.krisp_filter import KrispFilter
+
+ logger.info(f"Bot process initialized {args.room_url} {args.token}")
+ async with aiohttp.ClientSession() as session:
+ transport = DailyTransport(
+ args.room_url,
+ args.token,
+ "Smart Turn Bot",
+ params=DailyParams(
+ audio_in_enabled=True,
+ audio_in_filter=KrispFilter(),
+ audio_out_enabled=True,
+ video_out_enabled=True,
+ video_out_width=1024,
+ video_out_height=576,
+ vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
+ turn_analyzer=FalSmartTurnAnalyzer(
+ api_key=os.getenv("FAL_SMART_TURN_API_KEY"), aiohttp_session=session
+ ),
+ ),
+ )
+
+ try:
+ await main(transport)
+ logger.info("Bot process completed")
+ except Exception as e:
+ logger.exception(f"Error in bot process: {str(e)}")
+ raise
+
+
+# Local development
+async def local_daily():
+ """Daily transport for local development."""
+ from runner import configure
+
+ try:
+ async with aiohttp.ClientSession() as session:
+ (room_url, token) = await configure(session)
+ transport = DailyTransport(
+ room_url,
+ token,
+ "Smart Turn Bot",
+ params=DailyParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ video_out_enabled=True,
+ video_out_width=1024,
+ video_out_height=576,
+ vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
+ turn_analyzer=FalSmartTurnAnalyzer(
+ api_key=os.getenv("FAL_SMART_TURN_API_KEY"), aiohttp_session=session
+ ),
+ ),
+ )
+
+ await main(transport)
+ except Exception as e:
+ logger.exception(f"Error in local development mode: {e}")
+
+
+# Local development entry point
+if LOCAL and __name__ == "__main__":
+ try:
+ asyncio.run(local_daily())
+ except Exception as e:
+ logger.exception(f"Failed to run in local mode: {e}")
diff --git a/examples/fal-smart-turn/server/build.sh b/examples/fal-smart-turn/server/build.sh
new file mode 100755
index 000000000..346ebc267
--- /dev/null
+++ b/examples/fal-smart-turn/server/build.sh
@@ -0,0 +1,19 @@
+#!/bin/bash
+set -e
+
+VERSION="0.1"
+DOCKER_USERNAME=""
+AGENT_NAME="pcc-smart-turn"
+
+# Build the Docker image with the correct context
+echo "Building Docker image..."
+docker build --platform=linux/arm64 -t "$DOCKER_USERNAME/$AGENT_NAME:$VERSION" -t "$DOCKER_USERNAME/$AGENT_NAME:latest" .
+
+# Push the Docker images
+echo "Pushing Docker image $DOCKER_USERNAME/$AGENT_NAME:$VERSION..."
+docker push "$DOCKER_USERNAME/$AGENT_NAME:$VERSION"
+
+echo "Pushing Docker image $DOCKER_USERNAME/$AGENT_NAME:latest..."
+docker push "$DOCKER_USERNAME/$AGENT_NAME:latest"
+
+echo "Successfully built and pushed $DOCKER_USERNAME/$AGENT_NAME:$VERSION and $DOCKER_USERNAME/$AGENT_NAME:latest"
\ No newline at end of file
diff --git a/examples/fal-smart-turn/server/env.example b/examples/fal-smart-turn/server/env.example
new file mode 100644
index 000000000..bd12600ad
--- /dev/null
+++ b/examples/fal-smart-turn/server/env.example
@@ -0,0 +1,5 @@
+GOOGLE_API_KEY=
+CARTESIA_API_KEY=
+DEEPGRAM_API_KEY=
+DAILY_API_KEY=
+FAL_SMART_TURN_API_KEY=
\ No newline at end of file
diff --git a/examples/fal-smart-turn/server/pcc-deploy.toml b/examples/fal-smart-turn/server/pcc-deploy.toml
new file mode 100644
index 000000000..c2560f049
--- /dev/null
+++ b/examples/fal-smart-turn/server/pcc-deploy.toml
@@ -0,0 +1,7 @@
+agent_name = "pcc-smart-turn"
+image = "your-username/pcc-smart-turn:0.1"
+secret_set = "pcc-smart-turn-secrets"
+enable_krisp = true
+
+[scaling]
+ min_instances = 0
diff --git a/examples/fal-smart-turn/server/requirements.txt b/examples/fal-smart-turn/server/requirements.txt
new file mode 100644
index 000000000..d5e07f538
--- /dev/null
+++ b/examples/fal-smart-turn/server/requirements.txt
@@ -0,0 +1,3 @@
+pipecatcloud
+pipecat-ai[google,daily,deepgram,cartesia,silero]
+python-dotenv
diff --git a/examples/fal-smart-turn/server/runner.py b/examples/fal-smart-turn/server/runner.py
new file mode 100644
index 000000000..9e050c997
--- /dev/null
+++ b/examples/fal-smart-turn/server/runner.py
@@ -0,0 +1,56 @@
+#
+# Copyright (c) 2024β2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import os
+
+import aiohttp
+
+from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper
+
+
+async def configure(aiohttp_session: aiohttp.ClientSession):
+ """Configure the Daily room and Daily REST helper."""
+ parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")
+ parser.add_argument(
+ "-u", "--url", type=str, required=False, help="URL of the Daily room to join"
+ )
+ parser.add_argument(
+ "-k",
+ "--apikey",
+ type=str,
+ required=False,
+ help="Daily API Key (needed to create an owner token for the room)",
+ )
+
+ args, unknown = parser.parse_known_args()
+
+ url = args.url or os.getenv("DAILY_SAMPLE_ROOM_URL")
+ key = args.apikey or os.getenv("DAILY_API_KEY")
+
+ if not url:
+ raise Exception(
+ "No Daily room specified. use the -u/--url option from the command line, or set DAILY_SAMPLE_ROOM_URL in your environment to specify a Daily room URL."
+ )
+
+ if not key:
+ raise Exception(
+ "No Daily API key specified. use the -k/--apikey option from the command line, or set DAILY_API_KEY in your environment to specify a Daily API key, available from https://dashboard.daily.co/developers."
+ )
+
+ daily_rest_helper = DailyRESTHelper(
+ daily_api_key=key,
+ daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
+ aiohttp_session=aiohttp_session,
+ )
+
+ # Create a meeting token for the given room with an expiration 1 hour in
+ # the future.
+ expiry_time: float = 60 * 60
+
+ token = await daily_rest_helper.get_token(url, expiry_time)
+
+ return (url, token)
diff --git a/examples/fal-smart-turn/server/server.py b/examples/fal-smart-turn/server/server.py
new file mode 100644
index 000000000..97556b61a
--- /dev/null
+++ b/examples/fal-smart-turn/server/server.py
@@ -0,0 +1,228 @@
+#
+# Copyright (c) 2024β2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""RTVI Bot Server Implementation.
+
+This FastAPI server manages RTVI bot instances and provides endpoints for both
+direct browser access and RTVI client connections. It handles:
+- Creating Daily rooms
+- Managing bot processes
+- Providing connection credentials
+- Monitoring bot status
+
+Requirements:
+- Daily API key (set in .env file)
+- Python 3.10+
+- FastAPI
+- Running bot implementation
+"""
+
+import argparse
+import os
+import subprocess
+from contextlib import asynccontextmanager
+from typing import Any, Dict
+
+import aiohttp
+from dotenv import load_dotenv
+from fastapi import FastAPI, HTTPException, Request
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse, RedirectResponse
+
+from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper, DailyRoomParams
+
+# Load environment variables from .env file
+load_dotenv(override=True)
+
+# Maximum number of bot instances allowed per room
+MAX_BOTS_PER_ROOM = 1
+
+# Dictionary to track bot processes: {pid: (process, room_url)}
+bot_procs = {}
+
+# Store Daily API helpers
+daily_helpers = {}
+
+
+def cleanup():
+ """Cleanup function to terminate all bot processes.
+
+ Called during server shutdown.
+ """
+ for entry in bot_procs.values():
+ proc = entry[0]
+ proc.terminate()
+ proc.wait()
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+ """FastAPI lifespan manager that handles startup and shutdown tasks.
+
+ - Creates aiohttp session
+ - Initializes Daily API helper
+ - Cleans up resources on shutdown
+ """
+ aiohttp_session = aiohttp.ClientSession()
+ daily_helpers["rest"] = DailyRESTHelper(
+ daily_api_key=os.getenv("DAILY_API_KEY", ""),
+ daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
+ aiohttp_session=aiohttp_session,
+ )
+ yield
+ await aiohttp_session.close()
+ cleanup()
+
+
+# Initialize FastAPI app with lifespan manager
+app = FastAPI(lifespan=lifespan)
+
+# Configure CORS to allow requests from any origin
+app.add_middleware(
+ CORSMiddleware,
+ allow_origins=["*"],
+ allow_credentials=True,
+ allow_methods=["*"],
+ allow_headers=["*"],
+)
+
+
+async def create_room_and_token() -> tuple[str, str]:
+ """Helper function to create a Daily room and generate an access token.
+
+ Returns:
+ tuple[str, str]: A tuple containing (room_url, token)
+
+ Raises:
+ HTTPException: If room creation or token generation fails
+ """
+ room = await daily_helpers["rest"].create_room(DailyRoomParams())
+ if not room.url:
+ raise HTTPException(status_code=500, detail="Failed to create room")
+
+ token = await daily_helpers["rest"].get_token(room.url)
+ if not token:
+ raise HTTPException(status_code=500, detail=f"Failed to get token for room: {room.url}")
+
+ return room.url, token
+
+
+@app.get("/")
+async def start_agent(request: Request):
+ """Endpoint for direct browser access to the bot.
+
+ Creates a room, starts a bot instance, and redirects to the Daily room URL.
+
+ Returns:
+ RedirectResponse: Redirects to the Daily room URL
+
+ Raises:
+ HTTPException: If room creation, token generation, or bot startup fails
+ """
+ print("Creating room")
+ room_url, token = await create_room_and_token()
+ print(f"Room URL: {room_url}")
+
+ # Check if there is already an existing process running in this room
+ num_bots_in_room = sum(
+ 1 for proc in bot_procs.values() if proc[1] == room_url and proc[0].poll() is None
+ )
+ if num_bots_in_room >= MAX_BOTS_PER_ROOM:
+ raise HTTPException(status_code=500, detail=f"Max bot limit reached for room: {room_url}")
+
+ # Spawn a new bot process
+ try:
+ proc = subprocess.Popen(
+ [f"python3 bot.py -u {room_url} -t {token}"],
+ shell=True,
+ bufsize=1,
+ cwd=os.path.dirname(os.path.abspath(__file__)),
+ )
+ bot_procs[proc.pid] = (proc, room_url)
+ except Exception as e:
+ raise HTTPException(status_code=500, detail=f"Failed to start subprocess: {e}")
+
+ return RedirectResponse(room_url)
+
+
+@app.post("/connect")
+async def rtvi_connect(request: Request) -> Dict[Any, Any]:
+ """RTVI connect endpoint that creates a room and returns connection credentials.
+
+ This endpoint is called by RTVI clients to establish a connection.
+
+ Returns:
+ Dict[Any, Any]: Authentication bundle containing room_url and token
+
+ Raises:
+ HTTPException: If room creation, token generation, or bot startup fails
+ """
+ print("Creating room for RTVI connection")
+ room_url, token = await create_room_and_token()
+ print(f"Room URL: {room_url}")
+
+ # Start the bot process
+ try:
+ proc = subprocess.Popen(
+ [f"python3 -m bot -u {room_url} -t {token}"],
+ shell=True,
+ bufsize=1,
+ cwd=os.path.dirname(os.path.abspath(__file__)),
+ )
+ bot_procs[proc.pid] = (proc, room_url)
+ except Exception as e:
+ raise HTTPException(status_code=500, detail=f"Failed to start subprocess: {e}")
+
+ # Return the authentication bundle in format expected by DailyTransport
+ return {"room_url": room_url, "token": token}
+
+
+@app.get("/status/{pid}")
+def get_status(pid: int):
+ """Get the status of a specific bot process.
+
+ Args:
+ pid (int): Process ID of the bot
+
+ Returns:
+ JSONResponse: Status information for the bot
+
+ Raises:
+ HTTPException: If the specified bot process is not found
+ """
+ # Look up the subprocess
+ proc = bot_procs.get(pid)
+
+ # If the subprocess doesn't exist, return an error
+ if not proc:
+ raise HTTPException(status_code=404, detail=f"Bot with process id: {pid} not found")
+
+ # Check the status of the subprocess
+ status = "running" if proc[0].poll() is None else "finished"
+ return JSONResponse({"bot_id": pid, "status": status})
+
+
+if __name__ == "__main__":
+ import uvicorn
+
+ # Parse command line arguments for server configuration
+ default_host = os.getenv("HOST", "0.0.0.0")
+ default_port = int(os.getenv("FAST_API_PORT", "7860"))
+
+ parser = argparse.ArgumentParser(description="Daily Storyteller FastAPI server")
+ parser.add_argument("--host", type=str, default=default_host, help="Host address")
+ parser.add_argument("--port", type=int, default=default_port, help="Port number")
+ parser.add_argument("--reload", action="store_true", help="Reload code on change")
+
+ config = parser.parse_args()
+
+ # Start the FastAPI server
+ uvicorn.run(
+ "server:app",
+ host=config.host,
+ port=config.port,
+ reload=config.reload,
+ )
diff --git a/examples/foundational/01-say-one-thing-piper.py b/examples/foundational/01-say-one-thing-piper.py
index 0ae6b4b66..2c6d6eebb 100644
--- a/examples/foundational/01-say-one-thing-piper.py
+++ b/examples/foundational/01-say-one-thing-piper.py
@@ -4,54 +4,55 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.frames.frames import EndFrame, TTSSpeakFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.services.piper.tts import PiperTTSService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
+ # Create a transport using the WebRTC connection
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_out_enabled=True,
+ ),
+ )
+
+ # Create an HTTP session
async with aiohttp.ClientSession() as session:
- (room_url, _) = await configure(session)
-
- transport = DailyTransport(
- room_url, None, "Say One Thing", DailyParams(audio_out_enabled=True)
- )
-
tts = PiperTTSService(
base_url=os.getenv("PIPER_BASE_URL"), aiohttp_session=session, sample_rate=24000
)
- runner = PipelineRunner()
-
task = PipelineTask(Pipeline([tts, transport.output()]))
- # Register an event handler so we can play the audio when the
- # participant joins.
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await task.queue_frames(
- [TTSSpeakFrame(f"Hello there, how are you today ?"), EndFrame()]
- )
+ # Register an event handler so we can play the audio when the client joins
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ await task.queue_frames([TTSSpeakFrame(f"Hello there!"), EndFrame()])
+
+ runner = PipelineRunner(handle_sigint=False)
await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/01-say-one-thing-rime.py b/examples/foundational/01-say-one-thing-rime.py
new file mode 100644
index 000000000..46efbb3cd
--- /dev/null
+++ b/examples/foundational/01-say-one-thing-rime.py
@@ -0,0 +1,60 @@
+#
+# Copyright (c) 2024β2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import os
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.frames.frames import EndFrame, TTSSpeakFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineTask
+from pipecat.services.rime.tts import RimeHttpTTSService
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+
+load_dotenv(override=True)
+
+
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
+
+ # Create a transport using the WebRTC connection
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_out_enabled=True,
+ ),
+ )
+
+ # Create an HTTP session
+ async with aiohttp.ClientSession() as session:
+ tts = RimeHttpTTSService(
+ api_key=os.getenv("RIME_API_KEY", ""),
+ voice_id="rex",
+ aiohttp_session=session,
+ )
+
+ task = PipelineTask(Pipeline([tts, transport.output()]))
+
+ # Register an event handler so we can play the audio when the client joins
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ await task.queue_frames([TTSSpeakFrame(f"Hello there!"), EndFrame()])
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
+
+
+if __name__ == "__main__":
+ from run import main
+
+ main()
diff --git a/examples/foundational/01-say-one-thing.py b/examples/foundational/01-say-one-thing.py
index b21a2bf83..fbbf23b6c 100644
--- a/examples/foundational/01-say-one-thing.py
+++ b/examples/foundational/01-say-one-thing.py
@@ -4,56 +4,53 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.frames.frames import EndFrame, TTSSpeakFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, _) = await configure(session)
+ # Create a transport using the WebRTC connection
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_out_enabled=True,
+ ),
+ )
- transport = DailyTransport(
- room_url, None, "Say One Thing", DailyParams(audio_out_enabled=True)
- )
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ task = PipelineTask(Pipeline([tts, transport.output()]))
- runner = PipelineRunner()
+ # Register an event handler so we can play the audio when the client joins
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ await task.queue_frames([TTSSpeakFrame(f"Hello there!"), EndFrame()])
- task = PipelineTask(Pipeline([tts, transport.output()]))
+ runner = PipelineRunner(handle_sigint=False)
- # Register an event handler so we can play the audio when the
- # participant joins.
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- participant_name = participant.get("info", {}).get("userName", "")
- await task.queue_frames(
- [TTSSpeakFrame(f"Hello there, {participant_name}!"), EndFrame()]
- )
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/01c-fastpitch.py b/examples/foundational/01c-fastpitch.py
index 919ea0481..effed6f01 100644
--- a/examples/foundational/01c-fastpitch.py
+++ b/examples/foundational/01c-fastpitch.py
@@ -4,51 +4,50 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.frames.frames import EndFrame, TTSSpeakFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.services.riva.tts import FastPitchTTSService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, _) = await configure(session)
+ # Create a transport using the WebRTC connection
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_out_enabled=True,
+ ),
+ )
- transport = DailyTransport(
- room_url, None, "Say One Thing", DailyParams(audio_out_enabled=True)
- )
+ tts = FastPitchTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
- tts = FastPitchTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
+ task = PipelineTask(Pipeline([tts, transport.output()]))
- runner = PipelineRunner()
+ # Register an event handler so we can play the audio when the client joins
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ await task.queue_frames([TTSSpeakFrame(f"Hello there!"), EndFrame()])
- task = PipelineTask(Pipeline([tts, transport.output()]))
+ runner = PipelineRunner(handle_sigint=False)
- # Register an event handler so we can play the audio when the
- # participant joins.
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- participant_name = participant.get("info", {}).get("userName", "")
- await task.queue_frames([TTSSpeakFrame(f"Aloha, {participant_name}!"), EndFrame()])
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/02-llm-say-one-thing.py b/examples/foundational/02-llm-say-one-thing.py
index 326ea388a..3162ffef4 100644
--- a/examples/foundational/02-llm-say-one-thing.py
+++ b/examples/foundational/02-llm-say-one-thing.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.frames.frames import EndFrame, LLMMessagesFrame
from pipecat.pipeline.pipeline import Pipeline
@@ -19,46 +16,51 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, _) = await configure(session)
+ # Create a transport using the WebRTC connection
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_out_enabled=True,
+ ),
+ )
- transport = DailyTransport(
- room_url, None, "Say One Thing From an LLM", DailyParams(audio_out_enabled=True)
- )
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ messages = [
+ {
+ "role": "system",
+ "content": "You are an LLM in a WebRTC session, and this is a 'hello world' demo. Say hello to the world.",
+ }
+ ]
- messages = [
- {
- "role": "system",
- "content": "You are an LLM in a WebRTC session, and this is a 'hello world' demo. Say hello to the world.",
- }
- ]
+ task = PipelineTask(Pipeline([llm, tts, transport.output()]))
- runner = PipelineRunner()
+ # Register an event handler so we can play the audio when the client joins
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ await task.queue_frames([LLMMessagesFrame(messages), EndFrame()])
- task = PipelineTask(Pipeline([llm, tts, transport.output()]))
+ runner = PipelineRunner(handle_sigint=False)
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await task.queue_frames([LLMMessagesFrame(messages), EndFrame()])
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/03-still-frame.py b/examples/foundational/03-still-frame.py
index f758a3228..3b7ef84e3 100644
--- a/examples/foundational/03-still-frame.py
+++ b/examples/foundational/03-still-frame.py
@@ -4,59 +4,68 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.frames.frames import TextFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.services.fal.image import FalImageGenService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
+ # Create a transport using the WebRTC connection
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ video_out_enabled=True,
+ video_out_width=1024,
+ video_out_height=1024,
+ ),
+ )
+
+ # Create an HTTP session
async with aiohttp.ClientSession() as session:
- (room_url, _) = await configure(session)
-
- transport = DailyTransport(
- room_url,
- None,
- "Show a still frame image",
- DailyParams(camera_out_enabled=True, camera_out_width=1024, camera_out_height=1024),
- )
-
imagegen = FalImageGenService(
params=FalImageGenService.InputParams(image_size="square_hd"),
aiohttp_session=session,
key=os.getenv("FAL_KEY"),
)
- runner = PipelineRunner()
-
task = PipelineTask(Pipeline([imagegen, transport.output()]))
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
+ # Register an event handler so we can play the audio when the client joins
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
await task.queue_frame(TextFrame("a cat in the style of picasso"))
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
+
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
await task.cancel()
+ runner = PipelineRunner(handle_sigint=False)
+
await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/03a-local-still-frame.py b/examples/foundational/03a-local-still-frame.py
index cacb227ef..7ea02cca0 100644
--- a/examples/foundational/03a-local-still-frame.py
+++ b/examples/foundational/03a-local-still-frame.py
@@ -33,9 +33,7 @@ async def main():
transport = TkLocalTransport(
tk_root,
- TkTransportParams(
- camera_out_enabled=True, camera_out_width=1024, camera_out_height=1024
- ),
+ TkTransportParams(video_out_enabled=True, video_out_width=1024, video_out_height=1024),
)
imagegen = FalImageGenService(
diff --git a/examples/foundational/03b-still-frame-imagen.py b/examples/foundational/03b-still-frame-imagen.py
index 3968be5c2..783fd9be8 100644
--- a/examples/foundational/03b-still-frame-imagen.py
+++ b/examples/foundational/03b-still-frame-imagen.py
@@ -4,62 +4,68 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
-from pipecat.frames.frames import EndFrame, TextFrame
+from pipecat.frames.frames import TextFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.services.google.image import GoogleImageGenService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, _) = await configure(session)
+ # Create a transport using the WebRTC connection
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ video_out_enabled=True,
+ video_out_width=1024,
+ video_out_height=1024,
+ ),
+ )
- transport = DailyTransport(
- room_url,
- None,
- "Show a still frame image",
- DailyParams(camera_out_enabled=True, camera_out_width=1024, camera_out_height=1024),
- )
+ imagegen = GoogleImageGenService(
+ api_key=os.getenv("GOOGLE_API_KEY"),
+ )
- imagegen = GoogleImageGenService(
- api_key=os.getenv("GOOGLE_API_KEY"),
- )
+ task = PipelineTask(
+ Pipeline([imagegen, transport.output()]),
+ params=PipelineParams(enable_metrics=True),
+ )
- runner = PipelineRunner()
+ # Register an event handler so we can play the audio when the client joins
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ await task.queue_frame(TextFrame("a cat in the style of picasso"))
+ await task.queue_frame(TextFrame("a dog in the style of picasso"))
+ await task.queue_frame(TextFrame("a fish in the style of picasso"))
- task = PipelineTask(
- Pipeline([imagegen, transport.output()]),
- params=PipelineParams(enable_metrics=True),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await task.queue_frame(TextFrame("a cat in the style of picasso"))
- await task.queue_frame(TextFrame("a dog in the style of picasso"))
- await task.queue_frame(TextFrame("a fish in the style of picasso"))
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.queue_frame(EndFrame())
+ runner = PipelineRunner(handle_sigint=False)
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/04-utterance-and-speech.py b/examples/foundational/04-utterance-and-speech.py
index f2184b58f..f72bf5539 100644
--- a/examples/foundational/04-utterance-and-speech.py
+++ b/examples/foundational/04-utterance-and-speech.py
@@ -13,9 +13,9 @@ import os
import sys
import aiohttp
+from daily_runner import configure
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.frames.frames import EndPipeFrame, LLMMessagesFrame, TextFrame
from pipecat.pipeline.merge_pipeline import SequentialMergePipeline
diff --git a/examples/foundational/05-sync-speech-and-image.py b/examples/foundational/05-sync-speech-and-image.py
index 4e0202d3d..e91c49eb5 100644
--- a/examples/foundational/05-sync-speech-and-image.py
+++ b/examples/foundational/05-sync-speech-and-image.py
@@ -4,15 +4,13 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
from dataclasses import dataclass
import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.frames.frames import (
DataFrame,
@@ -30,13 +28,12 @@ from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.services.cartesia.tts import CartesiaHttpTTSService
from pipecat.services.fal.image import FalImageGenService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
@dataclass
class MonthFrame(DataFrame):
@@ -67,23 +64,29 @@ class MonthPrepender(FrameProcessor):
await self.push_frame(frame, direction)
-async def main():
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ """Run the Calendar Month Narration bot using WebRTC transport.
+
+ Args:
+ webrtc_connection: The WebRTC connection to use
+ room_name: Optional room name for display purposes
+ """
+ logger.info(f"Starting bot")
+
+ # Create a transport using the WebRTC connection
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_out_enabled=True,
+ video_out_enabled=True,
+ video_out_width=1024,
+ video_out_height=1024,
+ ),
+ )
+
+ # Create an HTTP session for API calls
async with aiohttp.ClientSession() as session:
- (room_url, _) = await configure(session)
-
- transport = DailyTransport(
- room_url,
- None,
- "Month Narration Bot",
- DailyParams(
- audio_out_enabled=True,
- camera_out_enabled=True,
- camera_out_width=1024,
- camera_out_height=1024,
- ),
- )
-
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
tts = CartesiaHttpTTSService(
api_key=os.getenv("CARTESIA_API_KEY"),
@@ -144,14 +147,30 @@ async def main():
frames.append(MonthFrame(month=month))
frames.append(LLMMessagesFrame(messages))
- runner = PipelineRunner()
-
task = PipelineTask(pipeline)
- await task.queue_frames(frames)
+ # Set up transport event handlers
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Start the month narration once connected
+ await task.queue_frames(frames)
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
+
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ # Run the pipeline
+ runner = PipelineRunner(handle_sigint=False)
await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/05a-local-sync-speech-and-image.py b/examples/foundational/05a-local-sync-speech-and-image.py
index dee4c3c6d..a01f07c0e 100644
--- a/examples/foundational/05a-local-sync-speech-and-image.py
+++ b/examples/foundational/05a-local-sync-speech-and-image.py
@@ -93,7 +93,7 @@ async def main():
self.frame = frame
await self.push_frame(frame, direction)
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
tts = CartesiaHttpTTSService(
api_key=os.getenv("CARTESIA_API_KEY"),
@@ -153,9 +153,9 @@ async def main():
tk_root,
TkTransportParams(
audio_out_enabled=True,
- camera_out_enabled=True,
- camera_out_width=1024,
- camera_out_height=1024,
+ video_out_enabled=True,
+ video_out_width=1024,
+ video_out_height=1024,
),
)
diff --git a/examples/foundational/06-listen-and-respond.py b/examples/foundational/06-listen-and-respond.py
index bc7160104..921144e85 100644
--- a/examples/foundational/06-listen-and-respond.py
+++ b/examples/foundational/06-listen-and-respond.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import Frame, MetricsFrame
@@ -27,14 +24,14 @@ from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
class MetricsLogger(FrameProcessor):
async def process_frame(self, frame: Frame, direction: FrameDirection):
@@ -56,76 +53,81 @@ class MetricsLogger(FrameProcessor):
await self.push_frame(frame, direction)
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- ml = MetricsLogger()
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
+ ml = MetricsLogger()
+
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ context_aggregator.user(),
+ llm,
+ tts,
+ ml,
+ transport.output(),
+ context_aggregator.assistant(),
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(),
- context_aggregator.user(),
- llm,
- tts,
- ml,
- transport.output(),
- context_aggregator.assistant(),
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- enable_metrics=True,
- enable_usage_metrics=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- messages.append({"role": "system", "content": "Please introduce yourself to the user."})
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
-
- runner = PipelineRunner()
-
- await runner.run(task)
+ runner = PipelineRunner(handle_sigint=False)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/06a-image-sync.py b/examples/foundational/06a-image-sync.py
index e8d21931c..a9d4e16b5 100644
--- a/examples/foundational/06a-image-sync.py
+++ b/examples/foundational/06a-image-sync.py
@@ -4,15 +4,12 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
from PIL import Image
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import (
@@ -20,7 +17,6 @@ from pipecat.frames.frames import (
BotStoppedSpeakingFrame,
Frame,
OutputImageRawFrame,
- TextFrame,
)
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
@@ -28,14 +24,14 @@ from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
class ImageSyncAggregator(FrameProcessor):
def __init__(self, speaking_path: str, waiting_path: str):
@@ -72,83 +68,88 @@ class ImageSyncAggregator(FrameProcessor):
await self.push_frame(frame)
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- camera_out_enabled=True,
- camera_out_width=1024,
- camera_out_height=1024,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ video_out_enabled=True,
+ video_out_width=1024,
+ video_out_height=1024,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ image_sync_aggregator = ImageSyncAggregator(
+ os.path.join(os.path.dirname(__file__), "assets", "speaking.png"),
+ os.path.join(os.path.dirname(__file__), "assets", "waiting.png"),
+ )
+
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ context_aggregator.user(),
+ llm,
+ tts,
+ image_sync_aggregator,
+ transport.output(),
+ context_aggregator.assistant(),
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- image_sync_aggregator = ImageSyncAggregator(
- os.path.join(os.path.dirname(__file__), "assets", "speaking.png"),
- os.path.join(os.path.dirname(__file__), "assets", "waiting.png"),
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- pipeline = Pipeline(
- [
- transport.input(),
- context_aggregator.user(),
- llm,
- tts,
- image_sync_aggregator,
- transport.output(),
- context_aggregator.assistant(),
- ]
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- participant_name = participant.get("info", {}).get("userName", "")
- await transport.capture_participant_transcription(participant["id"])
- await task.queue_frames([TextFrame(f"Hi there {participant_name}!")])
-
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
-
- runner = PipelineRunner()
-
- await runner.run(task)
+ runner = PipelineRunner(handle_sigint=False)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/07-interruptible-vad.py b/examples/foundational/07-interruptible-vad.py
deleted file mode 100644
index 2e340ce40..000000000
--- a/examples/foundational/07-interruptible-vad.py
+++ /dev/null
@@ -1,104 +0,0 @@
-#
-# Copyright (c) 2024β2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure
-
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.processors.audio.vad.silero import SileroVAD
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
-
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_in_enabled=True,
- audio_out_enabled=True,
- transcription_enabled=True,
- ),
- )
-
- vad = SileroVAD()
-
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
-
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
- ]
-
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
-
- pipeline = Pipeline(
- [
- transport.input(),
- vad,
- context_aggregator.user(),
- llm,
- tts,
- transport.output(),
- context_aggregator.assistant(),
- ]
- )
-
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
-
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- messages.append({"role": "system", "content": "Please introduce yourself to the user."})
- await task.queue_frames([context_aggregator.user().get_context_frame()])
-
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
-
- runner = PipelineRunner()
-
- await runner.run(task)
-
-
-if __name__ == "__main__":
- asyncio.run(main())
diff --git a/examples/foundational/07-interruptible.py b/examples/foundational/07-interruptible.py
index 00589df97..29ac495ea 100644
--- a/examples/foundational/07-interruptible.py
+++ b/examples/foundational/07-interruptible.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
@@ -19,84 +16,90 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt,
+ context_aggregator.user(), # User responses
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- context_aggregator.user(), # User responses
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- messages.append({"role": "system", "content": "Please introduce yourself to the user."})
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
+ runner = PipelineRunner(handle_sigint=False)
- runner = PipelineRunner()
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/07a-interruptible-vad.py b/examples/foundational/07a-interruptible-vad.py
new file mode 100644
index 000000000..90e8407b6
--- /dev/null
+++ b/examples/foundational/07a-interruptible-vad.py
@@ -0,0 +1,107 @@
+#
+# Copyright (c) 2024β2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.audio.vad.silero import SileroVAD
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+
+load_dotenv(override=True)
+
+
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
+
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ ),
+ )
+
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+ vad = SileroVAD()
+
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
+
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ vad,
+ context_aggregator.user(),
+ llm,
+ tts,
+ transport.output(),
+ context_aggregator.assistant(),
+ ]
+ )
+
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
+
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
+
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
+
+
+if __name__ == "__main__":
+ from run import main
+
+ main()
diff --git a/examples/foundational/07b-interruptible-langchain.py b/examples/foundational/07b-interruptible-langchain.py
index ba4f78adc..0b352719f 100644
--- a/examples/foundational/07b-interruptible-langchain.py
+++ b/examples/foundational/07b-interruptible-langchain.py
@@ -4,11 +4,9 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_community.chat_message_histories import ChatMessageHistory
@@ -16,7 +14,6 @@ from langchain_core.chat_history import BaseChatMessageHistory
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain_openai import ChatOpenAI
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import LLMMessagesFrame
@@ -29,14 +26,14 @@ from pipecat.processors.aggregators.llm_response import (
)
from pipecat.processors.frameworks.langchain import LangchainProcessor
from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
message_store = {}
@@ -46,90 +43,95 @@ def get_session_history(session_id: str) -> BaseChatMessageHistory:
return message_store[session_id]
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
+
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
+
+ prompt = ChatPromptTemplate.from_messages(
+ [
+ (
+ "system",
+ "Be nice and helpful. Answer very briefly and without special characters like `#` or `*`. "
+ "Your response will be synthesized to voice and those characters will create unnatural sounds.",
),
- )
+ MessagesPlaceholder("chat_history"),
+ ("human", "{input}"),
+ ]
+ )
+ chain = prompt | ChatOpenAI(model="gpt-4.1", temperature=0.7)
+ history_chain = RunnableWithMessageHistory(
+ chain,
+ get_session_history,
+ history_messages_key="chat_history",
+ input_messages_key="input",
+ )
+ lc = LangchainProcessor(history_chain)
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ tma_in = LLMUserResponseAggregator()
+ tma_out = LLMAssistantResponseAggregator()
- prompt = ChatPromptTemplate.from_messages(
- [
- (
- "system",
- "Be nice and helpful. Answer very briefly and without special characters like `#` or `*`. "
- "Your response will be synthesized to voice and those characters will create unnatural sounds.",
- ),
- MessagesPlaceholder("chat_history"),
- ("human", "{input}"),
- ]
- )
- chain = prompt | ChatOpenAI(model="gpt-4o", temperature=0.7)
- history_chain = RunnableWithMessageHistory(
- chain,
- get_session_history,
- history_messages_key="chat_history",
- input_messages_key="input",
- )
- lc = LangchainProcessor(history_chain)
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt,
+ tma_in, # User responses
+ lc, # Langchain
+ tts, # TTS
+ transport.output(), # Transport bot output
+ tma_out, # Assistant spoken responses
+ ]
+ )
- tma_in = LLMUserResponseAggregator()
- tma_out = LLMAssistantResponseAggregator()
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- tma_in, # User responses
- lc, # Langchain
- tts, # TTS
- transport.output(), # Transport bot output
- tma_out, # Assistant spoken responses
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ # the `LLMMessagesFrame` will be picked up by the LangchainProcessor using
+ # only the content of the last message to inject it in the prompt defined
+ # above. So no role is required here.
+ messages = [({"content": "Please briefly introduce yourself to the user."})]
+ await task.queue_frames([LLMMessagesFrame(messages)])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- lc.set_participant_id(participant["id"])
- # Kick off the conversation.
- # the `LLMMessagesFrame` will be picked up by the LangchainProcessor using
- # only the content of the last message to inject it in the prompt defined
- # above. So no role is required here.
- messages = [({"content": "Please briefly introduce yourself to the user."})]
- await task.queue_frames([LLMMessagesFrame(messages)])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
+ runner = PipelineRunner(handle_sigint=False)
- runner = PipelineRunner()
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/07c-interruptible-deepgram-vad.py b/examples/foundational/07c-interruptible-deepgram-vad.py
index cdfa78b27..a6d6ab4bb 100644
--- a/examples/foundational/07c-interruptible-deepgram-vad.py
+++ b/examples/foundational/07c-interruptible-deepgram-vad.py
@@ -4,15 +4,12 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from deepgram import LiveOptions
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.frames.frames import (
BotInterruptionFrame,
@@ -27,91 +24,95 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.deepgram.tts import DeepgramTTSService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, _) = await configure(session)
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ ),
+ )
- transport = DailyTransport(
- room_url,
- None,
- "Respond bot",
- DailyParams(
- audio_in_enabled=True,
- audio_out_enabled=True,
- ),
- )
+ stt = DeepgramSTTService(
+ api_key=os.getenv("DEEPGRAM_API_KEY"),
+ live_options=LiveOptions(vad_events=True, utterance_end_ms="1000"),
+ )
- stt = DeepgramSTTService(
- api_key=os.getenv("DEEPGRAM_API_KEY"),
- live_options=LiveOptions(vad_events=True, utterance_end_ms="1000"),
- )
+ tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
- tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt, # STT
+ context_aggregator.user(), # User responses
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- stt, # STT
- context_aggregator.user(), # User responses
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses
- ]
- )
+ @stt.event_handler("on_speech_started")
+ async def on_speech_started(stt, *args, **kwargs):
+ await task.queue_frames([BotInterruptionFrame(), UserStartedSpeakingFrame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @stt.event_handler("on_utterance_end")
+ async def on_utterance_end(stt, *args, **kwargs):
+ await task.queue_frames([StopInterruptionFrame(), UserStoppedSpeakingFrame()])
- @stt.event_handler("on_speech_started")
- async def on_speech_started(stt, *args, **kwargs):
- await task.queue_frames([BotInterruptionFrame(), UserStartedSpeakingFrame()])
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- @stt.event_handler("on_utterance_end")
- async def on_utterance_end(stt, *args, **kwargs):
- await task.queue_frames([StopInterruptionFrame(), UserStoppedSpeakingFrame()])
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- # Kick off the conversation.
- messages.append({"role": "system", "content": "Please introduce yourself to the user."})
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
+ runner = PipelineRunner(handle_sigint=False)
- runner = PipelineRunner()
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/07c-interruptible-deepgram.py b/examples/foundational/07c-interruptible-deepgram.py
index 6da37bdbc..3e02d8d77 100644
--- a/examples/foundational/07c-interruptible-deepgram.py
+++ b/examples/foundational/07c-interruptible-deepgram.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
@@ -21,82 +18,85 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.deepgram.tts import DeepgramTTSService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, _) = await configure(session)
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- transport = DailyTransport(
- room_url,
- None,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
- ),
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+ tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
- tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt, # STT
+ context_aggregator.user(), # User responses
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- stt, # STT
- context_aggregator.user(), # User responses
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- # Kick off the conversation.
- messages.append({"role": "system", "content": "Please introduce yourself to the user."})
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
+ runner = PipelineRunner(handle_sigint=False)
- runner = PipelineRunner()
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/07d-interruptible-elevenlabs-http.py b/examples/foundational/07d-interruptible-elevenlabs-http.py
index 13a60051a..9fadd2cf5 100644
--- a/examples/foundational/07d-interruptible-elevenlabs-http.py
+++ b/examples/foundational/07d-interruptible-elevenlabs-http.py
@@ -4,45 +4,43 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.elevenlabs.tts import ElevenLabsHttpTTSService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
+
+ # Create an HTTP session
async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
-
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
tts = ElevenLabsHttpTTSService(
api_key=os.getenv("ELEVENLABS_API_KEY", ""),
@@ -50,7 +48,7 @@ async def main():
aiohttp_session=session,
)
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
messages = [
{
@@ -65,6 +63,7 @@ async def main():
pipeline = Pipeline(
[
transport.input(), # Transport user input
+ stt,
context_aggregator.user(), # User responses
llm, # LLM
tts, # TTS
@@ -83,21 +82,28 @@ async def main():
),
)
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
# Kick off the conversation.
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
await task.queue_frames([context_aggregator.user().get_context_frame()])
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
+
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
await task.cancel()
- runner = PipelineRunner()
+ runner = PipelineRunner(handle_sigint=False)
await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/07d-interruptible-elevenlabs.py b/examples/foundational/07d-interruptible-elevenlabs.py
index e900b6809..885a034c0 100644
--- a/examples/foundational/07d-interruptible-elevenlabs.py
+++ b/examples/foundational/07d-interruptible-elevenlabs.py
@@ -4,99 +4,102 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- tts = ElevenLabsTTSService(
- api_key=os.getenv("ELEVENLABS_API_KEY", ""),
- voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
- )
+ tts = ElevenLabsTTSService(
+ api_key=os.getenv("ELEVENLABS_API_KEY", ""),
+ voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
+ )
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt,
+ context_aggregator.user(), # User responses
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- context_aggregator.user(), # User responses
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- messages.append({"role": "system", "content": "Please introduce yourself to the user."})
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
+ runner = PipelineRunner(handle_sigint=False)
- runner = PipelineRunner()
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/07e-interruptible-playht-http.py b/examples/foundational/07e-interruptible-playht-http.py
index a04fefaee..5ac99640e 100644
--- a/examples/foundational/07e-interruptible-playht-http.py
+++ b/examples/foundational/07e-interruptible-playht-http.py
@@ -4,100 +4,103 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.services.playht.tts import PlayHTHttpTTSService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- tts = PlayHTHttpTTSService(
- user_id=os.getenv("PLAYHT_USER_ID"),
- api_key=os.getenv("PLAYHT_API_KEY"),
- voice_url="s3://voice-cloning-zero-shot/d9ff78ba-d016-47f6-b0ef-dd630f59414e/female-cs/manifest.json",
- )
+ tts = PlayHTHttpTTSService(
+ user_id=os.getenv("PLAYHT_USER_ID"),
+ api_key=os.getenv("PLAYHT_API_KEY"),
+ voice_url="s3://voice-cloning-zero-shot/d9ff78ba-d016-47f6-b0ef-dd630f59414e/female-cs/manifest.json",
+ )
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt,
+ context_aggregator.user(), # User responses
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- context_aggregator.user(), # User responses
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- messages.append({"role": "system", "content": "Please introduce yourself to the user."})
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
+ runner = PipelineRunner(handle_sigint=False)
- runner = PipelineRunner()
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/07e-interruptible-playht.py b/examples/foundational/07e-interruptible-playht.py
index 5539d9df4..321a876c0 100644
--- a/examples/foundational/07e-interruptible-playht.py
+++ b/examples/foundational/07e-interruptible-playht.py
@@ -4,102 +4,105 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.services.playht.tts import PlayHTTTSService
from pipecat.transcriptions.language import Language
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- tts = PlayHTTTSService(
- user_id=os.getenv("PLAYHT_USER_ID"),
- api_key=os.getenv("PLAYHT_API_KEY"),
- voice_url="s3://voice-cloning-zero-shot/e46b4027-b38d-4d24-b292-38fbca2be0ef/original/manifest.json",
- params=PlayHTTTSService.InputParams(language=Language.EN),
- )
+ tts = PlayHTTTSService(
+ user_id=os.getenv("PLAYHT_USER_ID"),
+ api_key=os.getenv("PLAYHT_API_KEY"),
+ voice_url="s3://voice-cloning-zero-shot/e46b4027-b38d-4d24-b292-38fbca2be0ef/original/manifest.json",
+ params=PlayHTTTSService.InputParams(language=Language.EN),
+ )
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt,
+ context_aggregator.user(), # User responses
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- context_aggregator.user(), # User responses
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- messages.append({"role": "system", "content": "Please introduce yourself to the user."})
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
+ runner = PipelineRunner(handle_sigint=False)
- runner = PipelineRunner()
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/07f-interruptible-azure.py b/examples/foundational/07f-interruptible-azure.py
index 184d8eb7d..5c2f42247 100644
--- a/examples/foundational/07f-interruptible-azure.py
+++ b/examples/foundational/07f-interruptible-azure.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
@@ -21,93 +18,95 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.azure.llm import AzureLLMService
from pipecat.services.azure.stt import AzureSTTService
from pipecat.services.azure.tts import AzureTTSService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
- ),
- )
+ stt = AzureSTTService(
+ api_key=os.getenv("AZURE_SPEECH_API_KEY"),
+ region=os.getenv("AZURE_SPEECH_REGION"),
+ )
- stt = AzureSTTService(
- api_key=os.getenv("AZURE_SPEECH_API_KEY"),
- region=os.getenv("AZURE_SPEECH_REGION"),
- )
+ tts = AzureTTSService(
+ api_key=os.getenv("AZURE_SPEECH_API_KEY"),
+ region=os.getenv("AZURE_SPEECH_REGION"),
+ )
- tts = AzureTTSService(
- api_key=os.getenv("AZURE_SPEECH_API_KEY"),
- region=os.getenv("AZURE_SPEECH_REGION"),
- )
+ llm = AzureLLMService(
+ api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
+ endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
+ model=os.getenv("AZURE_CHATGPT_MODEL"),
+ )
- llm = AzureLLMService(
- api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
- endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
- model=os.getenv("AZURE_CHATGPT_MODEL"),
- )
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt, # STT
+ context_aggregator.user(), # User responses
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- stt, # STT
- context_aggregator.user(), # User responses
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- messages.append({"role": "system", "content": "Please introduce yourself to the user."})
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
+ runner = PipelineRunner(handle_sigint=False)
- runner = PipelineRunner()
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/07g-interruptible-openai.py b/examples/foundational/07g-interruptible-openai.py
index 476dd5cea..c89baa068 100644
--- a/examples/foundational/07g-interruptible-openai.py
+++ b/examples/foundational/07g-interruptible-openai.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
@@ -21,95 +18,90 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.services.openai.stt import OpenAISTTService
from pipecat.services.openai.tts import OpenAITTSService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- audio_out_sample_rate=24000,
- transcription_enabled=False,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
- ),
- )
+ stt = OpenAISTTService(
+ api_key=os.getenv("OPENAI_API_KEY"),
+ model="gpt-4o-transcribe",
+ prompt="Expect words related to dogs, such as breed names.",
+ )
- # You can use the OpenAI compatible API like Groq.
- # stt = OpenAISTTService(
- # base_url="https://api.groq.com/openai/v1",
- # api_key="gsk_***",
- # model="whisper-large-v3",
- # )
- stt = OpenAISTTService(
- api_key=os.getenv("OPENAI_API_KEY"),
- model="gpt-4o-transcribe-latest",
- prompt="Expect words related to dogs, such as breed names.",
- )
+ tts = OpenAITTSService(api_key=os.getenv("OPENAI_API_KEY"), voice="ballad")
- tts = OpenAITTSService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o-mini-tts-latest")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ messages = [
+ {
+ "role": "system",
+ "content": "You are very knowledgable about dogs. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
- messages = [
- {
- "role": "system",
- "content": "You are very knowledgable about dogs. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt, # STT
+ context_aggregator.user(), # User responses
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ audio_out_sample_rate=24000,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- stt, # STT
- context_aggregator.user(), # User responses
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- messages.append({"role": "system", "content": "Please introduce yourself to the user."})
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
+ runner = PipelineRunner(handle_sigint=False)
- runner = PipelineRunner()
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/07h-interruptible-openpipe.py b/examples/foundational/07h-interruptible-openpipe.py
index 38a71d75a..90daf9311 100644
--- a/examples/foundational/07h-interruptible-openpipe.py
+++ b/examples/foundational/07h-interruptible-openpipe.py
@@ -4,15 +4,12 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
import time
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
@@ -20,90 +17,95 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openpipe.llm import OpenPipeLLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- timestamp = int(time.time())
- llm = OpenPipeLLMService(
- api_key=os.getenv("OPENAI_API_KEY"),
- openpipe_api_key=os.getenv("OPENPIPE_API_KEY"),
- model="gpt-4o",
- tags={"conversation_id": f"pipecat-{timestamp}"},
- )
+ timestamp = int(time.time())
+ llm = OpenPipeLLMService(
+ api_key=os.getenv("OPENAI_API_KEY"),
+ openpipe_api_key=os.getenv("OPENPIPE_API_KEY"),
+ tags={"conversation_id": f"pipecat-{timestamp}"},
+ )
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt,
+ context_aggregator.user(), # User responses
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- context_aggregator.user(), # User responses
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- messages.append({"role": "system", "content": "Please introduce yourself to the user."})
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
+ runner = PipelineRunner(handle_sigint=False)
- runner = PipelineRunner()
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/07i-interruptible-xtts.py b/examples/foundational/07i-interruptible-xtts.py
index 8504261b5..1ca56b865 100644
--- a/examples/foundational/07i-interruptible-xtts.py
+++ b/examples/foundational/07i-interruptible-xtts.py
@@ -4,45 +4,43 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.services.xtts.tts import XTTSService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
+
+ # Create an HTTP session
async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
-
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
tts = XTTSService(
aiohttp_session=session,
@@ -50,7 +48,7 @@ async def main():
base_url="http://localhost:8000",
)
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
messages = [
{
@@ -65,6 +63,7 @@ async def main():
pipeline = Pipeline(
[
transport.input(), # Transport user input
+ stt,
context_aggregator.user(), # User responses
llm, # LLM
tts, # TTS
@@ -83,21 +82,28 @@ async def main():
),
)
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
# Kick off the conversation.
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
await task.queue_frames([context_aggregator.user().get_context_frame()])
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
+
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
await task.cancel()
- runner = PipelineRunner()
+ runner = PipelineRunner(handle_sigint=False)
await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/07j-interruptible-gladia.py b/examples/foundational/07j-interruptible-gladia.py
index 7dcd44a7a..757ae2697 100644
--- a/examples/foundational/07j-interruptible-gladia.py
+++ b/examples/foundational/07j-interruptible-gladia.py
@@ -4,106 +4,110 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.gladia import GladiaSTTService
-from pipecat.services.openai import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.gladia.config import GladiaInputParams, LanguageConfig
+from pipecat.services.gladia.stt import GladiaSTTService
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transcriptions.language import Language
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
- ),
- )
+ stt = GladiaSTTService(
+ api_key=os.getenv("GLADIA_API_KEY", ""),
+ params=GladiaInputParams(
+ language_config=LanguageConfig(
+ languages=[Language.EN],
+ )
+ ),
+ )
- stt = GladiaSTTService(
- api_key=os.getenv("GLADIA_API_KEY"),
- )
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY", ""),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY", ""))
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ messages = [
+ {
+ "role": "system",
+ "content": f"You are a helpful LLM. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt, # STT
+ context_aggregator.user(), # User responses
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- stt, # STT
- context_aggregator.user(), # User responses
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- messages.append({"role": "system", "content": "Please introduce yourself to the user."})
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- # Register an event handler to exit the application when the user leaves.
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
-
- runner = PipelineRunner()
-
- await runner.run(task)
+ runner = PipelineRunner(handle_sigint=False)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/07k-interruptible-lmnt.py b/examples/foundational/07k-interruptible-lmnt.py
index ed08d6ee5..7447f8257 100644
--- a/examples/foundational/07k-interruptible-lmnt.py
+++ b/examples/foundational/07k-interruptible-lmnt.py
@@ -4,96 +4,99 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.lmnt.tts import LmntTTSService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- tts = LmntTTSService(api_key=os.getenv("LMNT_API_KEY"), voice_id="morgan")
+ tts = LmntTTSService(api_key=os.getenv("LMNT_API_KEY"), voice_id="morgan")
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt,
+ context_aggregator.user(), # User respones
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- context_aggregator.user(), # User respones
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- messages.append({"role": "system", "content": "Please introduce yourself to the user."})
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
+ runner = PipelineRunner(handle_sigint=False)
- runner = PipelineRunner()
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/07l-interruptible-groq.py b/examples/foundational/07l-interruptible-groq.py
new file mode 100644
index 000000000..b6826d49c
--- /dev/null
+++ b/examples/foundational/07l-interruptible-groq.py
@@ -0,0 +1,101 @@
+#
+# Copyright (c) 2024β2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.groq.llm import GroqLLMService
+from pipecat.services.groq.stt import GroqSTTService
+from pipecat.services.groq.tts import GroqTTSService
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+
+load_dotenv(override=True)
+
+
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
+
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
+
+ stt = GroqSTTService(api_key=os.getenv("GROQ_API_KEY"))
+
+ llm = GroqLLMService(api_key=os.getenv("GROQ_API_KEY"), model="llama-3.3-70b-versatile")
+
+ tts = GroqTTSService(api_key=os.getenv("GROQ_API_KEY"))
+
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt,
+ context_aggregator.user(), # User responses
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
+ ]
+ )
+
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ ),
+ )
+
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
+
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
+
+
+if __name__ == "__main__":
+ from run import main
+
+ main()
diff --git a/examples/foundational/07l-interruptible-together.py b/examples/foundational/07l-interruptible-together.py
deleted file mode 100644
index 243fbe5f5..000000000
--- a/examples/foundational/07l-interruptible-together.py
+++ /dev/null
@@ -1,115 +0,0 @@
-#
-# Copyright (c) 2024β2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.together.llm import TogetherLLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
-
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
-
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
-
- llm = TogetherLLMService(
- api_key=os.getenv("TOGETHER_API_KEY"),
- model="meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
- params=TogetherLLMService.InputParams(
- temperature=1.0,
- top_p=0.9,
- top_k=40,
- extra={
- "frequency_penalty": 2.0,
- "presence_penalty": 0.0,
- },
- ),
- )
-
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond in plain language. Respond to what the user said in a creative and helpful way.",
- },
- ]
-
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
- user_aggregator = context_aggregator.user()
- assistant_aggregator = context_aggregator.assistant()
-
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- user_aggregator, # User responses
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- assistant_aggregator, # Assistant spoken responses
- ]
- )
-
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
-
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
-
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
-
- runner = PipelineRunner()
-
- await runner.run(task)
-
-
-if __name__ == "__main__":
- asyncio.run(main())
diff --git a/examples/foundational/07m-interruptible-polly.py b/examples/foundational/07m-interruptible-polly.py
index d5d2ec887..286fe5128 100644
--- a/examples/foundational/07m-interruptible-polly.py
+++ b/examples/foundational/07m-interruptible-polly.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
@@ -21,89 +18,91 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.aws.tts import PollyTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, _) = await configure(session)
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- transport = DailyTransport(
- room_url,
- None,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
- ),
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+ tts = PollyTTSService(
+ api_key=os.getenv("AWS_SECRET_ACCESS_KEY"),
+ aws_access_key_id=os.getenv("AWS_ACCESS_KEY_ID"),
+ region=os.getenv("AWS_REGION"),
+ voice_id="Amy",
+ params=PollyTTSService.InputParams(engine="neural", language="en-GB", rate="1.05"),
+ )
- tts = PollyTTSService(
- api_key=os.getenv("AWS_SECRET_ACCESS_KEY"),
- aws_access_key_id=os.getenv("AWS_ACCESS_KEY_ID"),
- region=os.getenv("AWS_REGION"),
- voice_id="Amy",
- params=PollyTTSService.InputParams(engine="neural", language="en-GB", rate="1.05"),
- )
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt, # STT
+ context_aggregator.user(), # User responses
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- stt, # STT
- context_aggregator.user(), # User responses
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- messages.append({"role": "system", "content": "Please introduce yourself to the user."})
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
+ runner = PipelineRunner(handle_sigint=False)
- runner = PipelineRunner()
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/07n-interruptible-google.py b/examples/foundational/07n-interruptible-google.py
index 0b974a691..36cb27193 100644
--- a/examples/foundational/07n-interruptible-google.py
+++ b/examples/foundational/07n-interruptible-google.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
@@ -22,88 +19,92 @@ from pipecat.services.google.llm import GoogleLLMService
from pipecat.services.google.stt import GoogleSTTService
from pipecat.services.google.tts import GoogleTTSService
from pipecat.transcriptions.language import Language
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, _) = await configure(session)
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- transport = DailyTransport(
- room_url,
- None,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
- ),
- )
+ stt = GoogleSTTService(
+ params=GoogleSTTService.InputParams(languages=Language.EN_US),
+ credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
+ )
- stt = GoogleSTTService(
- params=GoogleSTTService.InputParams(languages=Language.EN_US),
- )
+ tts = GoogleTTSService(
+ voice_id="en-US-Chirp3-HD-Charon",
+ params=GoogleTTSService.InputParams(language=Language.EN_US),
+ credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
+ )
- tts = GoogleTTSService(
- voice_id="en-US-Journey-F",
- params=GoogleTTSService.InputParams(language=Language.EN_US),
- )
+ llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"))
- llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"))
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt, # STT
+ context_aggregator.user(), # User respones
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- stt, # STT
- context_aggregator.user(), # User respones
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- messages.append({"role": "system", "content": "Please introduce yourself to the user."})
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
+ runner = PipelineRunner(handle_sigint=False)
- runner = PipelineRunner()
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/07o-interruptible-assemblyai.py b/examples/foundational/07o-interruptible-assemblyai.py
index 3c1ac6039..2b371be50 100644
--- a/examples/foundational/07o-interruptible-assemblyai.py
+++ b/examples/foundational/07o-interruptible-assemblyai.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
@@ -21,88 +18,90 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.assemblyai.stt import AssemblyAISTTService
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
- ),
- )
+ stt = AssemblyAISTTService(
+ api_key=os.getenv("ASSEMBLYAI_API_KEY"),
+ )
- stt = AssemblyAISTTService(
- api_key=os.getenv("ASSEMBLYAI_API_KEY"),
- )
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt, # STT
+ context_aggregator.user(), # User responses
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- stt, # STT
- context_aggregator.user(), # User responses
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- messages.append({"role": "system", "content": "Please introduce yourself to the user."})
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
+ runner = PipelineRunner(handle_sigint=False)
- runner = PipelineRunner()
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/07p-interruptible-krisp.py b/examples/foundational/07p-interruptible-krisp.py
index 7ca7e2fb6..baaef1852 100644
--- a/examples/foundational/07p-interruptible-krisp.py
+++ b/examples/foundational/07p-interruptible-krisp.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.filters.krisp_filter import KrispFilter
from pipecat.audio.vad.silero import SileroVADAnalyzer
@@ -22,83 +19,86 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.deepgram.tts import DeepgramTTSService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ audio_in_filter=KrispFilter(),
+ ),
+ )
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
- audio_in_filter=KrispFilter(),
- ),
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+ tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
- tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt, # STT
+ context_aggregator.user(), # User responses
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- stt, # STT
- context_aggregator.user(), # User responses
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- # Kick off the conversation.
- messages.append({"role": "system", "content": "Please introduce yourself to the user."})
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
+ runner = PipelineRunner(handle_sigint=False)
- runner = PipelineRunner()
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/07q-interruptible-rime-http.py b/examples/foundational/07q-interruptible-rime-http.py
index 3b0c99a21..19d032413 100644
--- a/examples/foundational/07q-interruptible-rime-http.py
+++ b/examples/foundational/07q-interruptible-rime-http.py
@@ -4,45 +4,43 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.services.rime.tts import RimeHttpTTSService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
+
+ # Create an HTTP session
async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
-
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
tts = RimeHttpTTSService(
api_key=os.getenv("RIME_API_KEY", ""),
@@ -50,7 +48,7 @@ async def main():
aiohttp_session=session,
)
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
messages = [
{
@@ -65,6 +63,7 @@ async def main():
pipeline = Pipeline(
[
transport.input(), # Transport user input
+ stt,
context_aggregator.user(), # User responses
llm, # LLM
tts, # TTS
@@ -83,21 +82,28 @@ async def main():
),
)
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
# Kick off the conversation.
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
await task.queue_frames([context_aggregator.user().get_context_frame()])
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
+
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
await task.cancel()
- runner = PipelineRunner()
+ runner = PipelineRunner(handle_sigint=False)
await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/07q-interruptible-rime.py b/examples/foundational/07q-interruptible-rime.py
index febb30a4e..27f678930 100644
--- a/examples/foundational/07q-interruptible-rime.py
+++ b/examples/foundational/07q-interruptible-rime.py
@@ -4,99 +4,102 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.services.rime.tts import RimeTTSService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- tts = RimeTTSService(
- api_key=os.getenv("RIME_API_KEY", ""),
- voice_id="rex",
- )
+ tts = RimeTTSService(
+ api_key=os.getenv("RIME_API_KEY", ""),
+ voice_id="rex",
+ )
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt,
+ context_aggregator.user(), # User responses
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- context_aggregator.user(), # User responses
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- messages.append({"role": "system", "content": "Please introduce yourself to the user."})
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
+ runner = PipelineRunner(handle_sigint=False)
- runner = PipelineRunner()
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/07r-interruptible-riva-nim.py b/examples/foundational/07r-interruptible-riva-nim.py
index 411a6ec80..915beda51 100644
--- a/examples/foundational/07r-interruptible-riva-nim.py
+++ b/examples/foundational/07r-interruptible-riva-nim.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
@@ -21,76 +18,85 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.nim.llm import NimLLMService
from pipecat.services.riva.stt import ParakeetSTTService
from pipecat.services.riva.tts import FastPitchTTSService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, _) = await configure(session)
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- transport = DailyTransport(
- room_url,
- None,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
- ),
- )
+ stt = ParakeetSTTService(api_key=os.getenv("NVIDIA_API_KEY"))
- stt = ParakeetSTTService(api_key=os.getenv("NVIDIA_API_KEY"))
+ llm = NimLLMService(api_key=os.getenv("NVIDIA_API_KEY"), model="meta/llama-3.1-405b-instruct")
- llm = NimLLMService(
- api_key=os.getenv("NVIDIA_API_KEY"), model="meta/llama-3.1-405b-instruct"
- )
+ tts = FastPitchTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
- tts = FastPitchTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt, # STT
+ context_aggregator.user(), # User responses
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- stt, # STT
- context_aggregator.user(), # User responses
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- # Kick off the conversation.
- messages.append({"role": "system", "content": "Please introduce yourself to the user."})
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
+ runner = PipelineRunner(handle_sigint=False)
- runner = PipelineRunner()
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/07s-interruptible-google-audio-in.py b/examples/foundational/07s-interruptible-google-audio-in.py
index 69bae75fc..360a5d350 100644
--- a/examples/foundational/07s-interruptible-google-audio-in.py
+++ b/examples/foundational/07s-interruptible-google-audio-in.py
@@ -4,16 +4,13 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
from dataclasses import dataclass
-import aiohttp
import google.ai.generativelanguage as glm
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import (
@@ -32,14 +29,15 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.frame_processor import FrameProcessor
-from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.google.llm import GoogleLLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.services.google.tts import GoogleTTSService
+from pipecat.transcriptions.language import Language
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
marker = "|----|"
system_message = f"""
@@ -193,85 +191,88 @@ class TanscriptionContextFixup(FrameProcessor):
await self.push_frame(frame, direction)
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- # No transcription at all. just audio input to Gemini!
- # transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"), model="gemini-2.0-flash-001")
- llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"), model="gemini-2.0-flash-001")
+ tts = GoogleTTSService(
+ voice_id="en-US-Chirp3-HD-Charon",
+ params=GoogleTTSService.InputParams(language=Language.EN_US),
+ credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
+ )
- messages = [
- {
- "role": "system",
- "content": system_message,
- },
- {
- "role": "user",
- "content": "Start by saying hello.",
- },
+ messages = [
+ {
+ "role": "system",
+ "content": system_message,
+ },
+ {
+ "role": "user",
+ "content": "Start by saying hello.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+ audio_collector = UserAudioCollector(context, context_aggregator.user())
+ pull_transcript_out_of_llm_output = TranscriptExtractor(context)
+ fixup_context_messages = TanscriptionContextFixup(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ audio_collector,
+ context_aggregator.user(), # User responses
+ llm, # LLM
+ pull_transcript_out_of_llm_output,
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
+ fixup_context_messages,
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
- audio_collector = UserAudioCollector(context, context_aggregator.user())
- pull_transcript_out_of_llm_output = TranscriptExtractor(context)
- fixup_context_messages = TanscriptionContextFixup(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- audio_collector,
- context_aggregator.user(), # User responses
- llm, # LLM
- pull_transcript_out_of_llm_output,
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses
- fixup_context_messages,
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
+ runner = PipelineRunner(handle_sigint=False)
- runner = PipelineRunner()
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/07t-interruptible-fish.py b/examples/foundational/07t-interruptible-fish.py
index 567e57ecf..58fb4cd61 100644
--- a/examples/foundational/07t-interruptible-fish.py
+++ b/examples/foundational/07t-interruptible-fish.py
@@ -4,99 +4,102 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.fish.tts import FishAudioTTSService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- tts = FishAudioTTSService(
- api_key=os.getenv("FISH_API_KEY"),
- model="4ce7e917cedd4bc2bb2e6ff3a46acaa1", # Barack Obama
- )
+ tts = FishAudioTTSService(
+ api_key=os.getenv("FISH_API_KEY"),
+ model="4ce7e917cedd4bc2bb2e6ff3a46acaa1", # Barack Obama
+ )
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt,
+ context_aggregator.user(), # User responses
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- context_aggregator.user(), # User responses
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- messages.append({"role": "system", "content": "Please introduce yourself to the user."})
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
+ runner = PipelineRunner(handle_sigint=False)
- runner = PipelineRunner()
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/07u-interruptible-ultravox.py b/examples/foundational/07u-interruptible-ultravox.py
index 1b3f853e0..b1e2e3756 100644
--- a/examples/foundational/07u-interruptible-ultravox.py
+++ b/examples/foundational/07u-interruptible-ultravox.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
@@ -20,7 +17,9 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.ultravox.stt import UltravoxSTTService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
@@ -28,64 +27,68 @@ load_dotenv(override=True)
# The Ultravox model is compute-intensive and performs best with GPU acceleration.
# This can be deployed on cloud GPU providers like Cerebrium.ai for optimal performance.
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
# Want to initialize the ultravox processor since it takes time to load the model and dont
# want to load it every time the pipeline is run
ultravox_processor = UltravoxSTTService(
- model_size="fixie-ai/ultravox-v0_4_1-llama-3_1-8b",
+ model_name="fixie-ai/ultravox-v0_5-llama-3_1-8b",
hf_token=os.getenv("HF_TOKEN"),
)
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=False,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
- vad_audio_passthrough=True,
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
+ ),
+ )
- tts = CartesiaTTSService(
- api_key=os.environ.get("CARTESIA_API_KEY"),
- voice_id="97f4b8fb-f2fe-444b-bb9a-c109783a857a",
- )
+ tts = CartesiaTTSService(
+ api_key=os.environ.get("CARTESIA_API_KEY"),
+ voice_id="97f4b8fb-f2fe-444b-bb9a-c109783a857a",
+ )
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- ultravox_processor,
- tts, # TTS
- transport.output(), # Transport bot output
- ]
- )
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ ultravox_processor,
+ tts, # TTS
+ transport.output(), # Transport bot output
+ ]
+ )
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- ),
- )
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ ),
+ )
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
- runner = PipelineRunner()
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- await runner.run(task)
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/07v-interruptible-neuphonic-http.py b/examples/foundational/07v-interruptible-neuphonic-http.py
index b89315ec5..24eafa2e5 100644
--- a/examples/foundational/07v-interruptible-neuphonic-http.py
+++ b/examples/foundational/07v-interruptible-neuphonic-http.py
@@ -4,99 +4,102 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.neuphonic.tts import NeuphonicHttpTTSService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- tts = NeuphonicHttpTTSService(
- api_key=os.getenv("NEUPHONIC_API_KEY"),
- voice_id="fc854436-2dac-4d21-aa69-ae17b54e98eb", # Emily
- )
+ tts = NeuphonicHttpTTSService(
+ api_key=os.getenv("NEUPHONIC_API_KEY"),
+ voice_id="fc854436-2dac-4d21-aa69-ae17b54e98eb", # Emily
+ )
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt,
+ context_aggregator.user(), # User responses
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- context_aggregator.user(), # User responses
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- messages.append({"role": "system", "content": "Please introduce yourself to the user."})
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
+ runner = PipelineRunner(handle_sigint=False)
- runner = PipelineRunner()
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/07v-interruptible-neuphonic.py b/examples/foundational/07v-interruptible-neuphonic.py
index 002b3f6cc..660925544 100644
--- a/examples/foundational/07v-interruptible-neuphonic.py
+++ b/examples/foundational/07v-interruptible-neuphonic.py
@@ -4,99 +4,102 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.neuphonic.tts import NeuphonicTTSService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- tts = NeuphonicTTSService(
- api_key=os.getenv("NEUPHONIC_API_KEY"),
- voice_id="fc854436-2dac-4d21-aa69-ae17b54e98eb", # Emily
- )
+ tts = NeuphonicTTSService(
+ api_key=os.getenv("NEUPHONIC_API_KEY"),
+ voice_id="fc854436-2dac-4d21-aa69-ae17b54e98eb", # Emily
+ )
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt,
+ context_aggregator.user(), # User responses
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- context_aggregator.user(), # User responses
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- messages.append({"role": "system", "content": "Please introduce yourself to the user."})
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
+ runner = PipelineRunner(handle_sigint=False)
- runner = PipelineRunner()
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/07w-interruptible-fal.py b/examples/foundational/07w-interruptible-fal.py
index f202d7245..754aac7e9 100644
--- a/examples/foundational/07w-interruptible-fal.py
+++ b/examples/foundational/07w-interruptible-fal.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
@@ -21,89 +18,90 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.fal.stt import FalSTTService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
- ),
- )
+ stt = FalSTTService(
+ api_key=os.getenv("FAL_KEY"),
+ )
- stt = FalSTTService(
- api_key=os.getenv("FAL_KEY"),
- )
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt, # STT
+ context_aggregator.user(), # User responses
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- stt, # STT
- context_aggregator.user(), # User responses
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- messages.append({"role": "system", "content": "Please introduce yourself to the user."})
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- # Register an event handler to exit the application when the user leaves.
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
+ runner = PipelineRunner(handle_sigint=False)
- runner = PipelineRunner()
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/07x-interruptible-local.py b/examples/foundational/07x-interruptible-local.py
index 989af1883..94097d7c9 100644
--- a/examples/foundational/07x-interruptible-local.py
+++ b/examples/foundational/07x-interruptible-local.py
@@ -32,9 +32,7 @@ async def main():
LocalAudioTransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
- vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
)
)
@@ -45,7 +43,7 @@ async def main():
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
)
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
messages = [
{
diff --git a/examples/foundational/07y-interruptible-groq.py b/examples/foundational/07y-interruptible-groq.py
deleted file mode 100644
index f0639412e..000000000
--- a/examples/foundational/07y-interruptible-groq.py
+++ /dev/null
@@ -1,103 +0,0 @@
-#
-# Copyright (c) 2024β2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.groq.llm import GroqLLMService
-from pipecat.services.groq.stt import GroqSTTService
-from pipecat.services.groq.tts import GroqTTSService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
-
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- # transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
- ),
- )
-
- stt = GroqSTTService(api_key=os.getenv("GROQ_API_KEY"))
-
- llm = GroqLLMService(api_key=os.getenv("GROQ_API_KEY"), model="llama-3.3-70b-versatile")
-
- tts = GroqTTSService(api_key=os.getenv("GROQ_API_KEY"))
-
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
- ]
-
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
-
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- stt,
- context_aggregator.user(), # User responses
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses
- ]
- )
-
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- ),
- )
-
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- messages.append({"role": "system", "content": "Please introduce yourself to the user."})
- await task.queue_frames([context_aggregator.user().get_context_frame()])
-
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
-
- runner = PipelineRunner()
-
- await runner.run(task)
-
-
-if __name__ == "__main__":
- asyncio.run(main())
diff --git a/examples/foundational/08-bots-arguing.py b/examples/foundational/08-bots-arguing.py
index a0472546e..ffb642d64 100644
--- a/examples/foundational/08-bots-arguing.py
+++ b/examples/foundational/08-bots-arguing.py
@@ -4,8 +4,8 @@ import os
from typing import Tuple
import aiohttp
+from daily_runner import configure
from dotenv import load_dotenv
-from runner import configure
from pipecat.frames.frames import AudioFrame, EndFrame, ImageFrame, LLMMessagesFrame, TextFrame
from pipecat.pipeline.pipeline import Pipeline
@@ -72,7 +72,8 @@ async def main():
async def get_text_and_audio(messages) -> Tuple[str, bytearray]:
"""This function streams text from the LLM and uses the TTS service to convert
- that text to speech as it's received."""
+ that text to speech as it's received.
+ """
source_queue = asyncio.Queue()
sink_queue = asyncio.Queue()
sentence_aggregator = SentenceAggregator()
diff --git a/examples/foundational/09-mirror.py b/examples/foundational/09-mirror.py
index dc060ccc4..2a62b5a88 100644
--- a/examples/foundational/09-mirror.py
+++ b/examples/foundational/09-mirror.py
@@ -4,13 +4,10 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
-import sys
+import argparse
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.frames.frames import (
Frame,
@@ -23,13 +20,12 @@ from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
class MirrorProcessor(FrameProcessor):
async def process_frame(self, frame: Frame, direction: FrameDirection):
@@ -51,42 +47,48 @@ class MirrorProcessor(FrameProcessor):
await self.push_frame(frame, direction)
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Test",
- DailyParams(
- audio_in_enabled=True,
- audio_out_enabled=True,
- camera_out_enabled=True,
- camera_out_is_live=True,
- camera_out_width=1280,
- camera_out_height=720,
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ video_in_enabled=True,
+ video_out_enabled=True,
+ video_out_is_live=True,
+ video_out_width=1280,
+ video_out_height=720,
+ ),
+ )
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_video(participant["id"])
+ pipeline = Pipeline([transport.input(), MirrorProcessor(), transport.output()])
- pipeline = Pipeline([transport.input(), MirrorProcessor(), transport.output()])
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(),
+ )
- runner = PipelineRunner()
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- audio_in_sample_rate=24000,
- audio_out_sample_rate=24000,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- await runner.run(task)
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/09a-local-mirror.py b/examples/foundational/09a-local-mirror.py
index eb6739f49..489f68888 100644
--- a/examples/foundational/09a-local-mirror.py
+++ b/examples/foundational/09a-local-mirror.py
@@ -4,14 +4,12 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
+import argparse
import asyncio
-import sys
import tkinter as tk
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.frames.frames import (
Frame,
@@ -24,14 +22,13 @@ from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.transports.base_transport import TransportParams
from pipecat.transports.local.tk import TkLocalTransport, TkTransportParams
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
class MirrorProcessor(FrameProcessor):
async def process_frame(self, frame: Frame, direction: FrameDirection):
@@ -53,52 +50,59 @@ class MirrorProcessor(FrameProcessor):
await self.push_frame(frame, direction)
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- tk_root = tk.Tk()
- tk_root.title("Local Mirror")
+ p2p_transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ video_in_enabled=True,
+ video_out_enabled=True,
+ video_out_is_live=True,
+ video_out_width=1280,
+ video_out_height=720,
+ ),
+ )
- daily_transport = DailyTransport(
- room_url, token, "Test", DailyParams(audio_in_enabled=True)
- )
+ tk_root = tk.Tk()
+ tk_root.title("Local Mirror")
- tk_transport = TkLocalTransport(
- tk_root,
- TkTransportParams(
- audio_out_enabled=True,
- camera_out_enabled=True,
- camera_out_is_live=True,
- camera_out_width=1280,
- camera_out_height=720,
- ),
- )
+ tk_transport = TkLocalTransport(
+ tk_root,
+ TkTransportParams(
+ audio_out_enabled=True,
+ video_out_enabled=True,
+ video_out_is_live=True,
+ video_out_width=1280,
+ video_out_height=720,
+ ),
+ )
- @daily_transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_video(participant["id"])
+ @p2p_transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
- pipeline = Pipeline([daily_transport.input(), MirrorProcessor(), tk_transport.output()])
+ pipeline = Pipeline([p2p_transport.input(), MirrorProcessor(), tk_transport.output()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- audio_in_sample_rate=24000,
- audio_out_sample_rate=24000,
- ),
- )
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(),
+ )
- async def run_tk():
- while not task.has_finished():
- tk_root.update()
- tk_root.update_idletasks()
- await asyncio.sleep(0.1)
+ async def run_tk():
+ while not task.has_finished():
+ tk_root.update()
+ tk_root.update_idletasks()
+ await asyncio.sleep(0.1)
- runner = PipelineRunner()
+ runner = PipelineRunner(handle_sigint=False)
- await asyncio.gather(runner.run(task), run_tk())
+ await asyncio.gather(runner.run(task), run_tk())
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/10-wake-phrase.py b/examples/foundational/10-wake-phrase.py
index a89d0a0db..66b4045d6 100644
--- a/examples/foundational/10-wake-phrase.py
+++ b/examples/foundational/10-wake-phrase.py
@@ -4,89 +4,98 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import TTSSpeakFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.filters.wake_check_filter import WakeCheckFilter
from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- transport = DailyTransport(
- room_url,
- token,
- "Robot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
- messages = [
- {
- "role": "system",
- "content": "You are a helpful assistant. Respond to what the user said in a creative and helpful way. Keep your responses brief.",
- },
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful assistant. Respond to what the user said in a creative and helpful way. Keep your responses brief.",
+ },
+ ]
+
+ hey_robot_filter = WakeCheckFilter(["hey robot", "hey, robot"])
+
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt, # STT
+ hey_robot_filter, # Filter out speech not directed at the robot
+ context_aggregator.user(), # User responses
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
]
+ )
- hey_robot_filter = WakeCheckFilter(["hey robot", "hey, robot"])
+ task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frame(TTSSpeakFrame("Hi! If you want to talk to me, just say 'Hey Robot'"))
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- hey_robot_filter, # Filter out speech not directed at the robot
- context_aggregator.user(), # User responses
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses
- ]
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- await tts.say("Hi! If you want to talk to me, just say 'Hey Robot'.")
+ runner = PipelineRunner(handle_sigint=False)
- runner = PipelineRunner()
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/11-sound-effects.py b/examples/foundational/11-sound-effects.py
index 5c25e4c33..d896ddfb9 100644
--- a/examples/foundational/11-sound-effects.py
+++ b/examples/foundational/11-sound-effects.py
@@ -4,21 +4,19 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
import wave
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import (
Frame,
LLMFullResponseEndFrame,
OutputAudioRawFrame,
+ TTSSpeakFrame,
)
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
@@ -30,14 +28,14 @@ from pipecat.processors.aggregators.openai_llm_context import (
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.processors.logger import FrameLogger
from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
sounds = {}
sound_files = ["ding1.wav", "ding2.wav"]
@@ -80,70 +78,81 @@ class InboundSoundEffectWrapper(FrameProcessor):
await self.push_frame(frame, direction)
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio. Respond to what the user said in a creative and helpful way.",
- },
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
+
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+ out_sound = OutboundSoundEffectWrapper()
+ in_sound = InboundSoundEffectWrapper()
+ fl = FrameLogger("LLM Out")
+ fl2 = FrameLogger("Transcription In")
+
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ context_aggregator.user(),
+ in_sound,
+ fl2,
+ llm,
+ fl,
+ tts,
+ out_sound,
+ transport.output(),
+ context_aggregator.assistant(),
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
- out_sound = OutboundSoundEffectWrapper()
- in_sound = InboundSoundEffectWrapper()
- fl = FrameLogger("LLM Out")
- fl2 = FrameLogger("Transcription In")
+ task = PipelineTask(pipeline)
- pipeline = Pipeline(
- [
- transport.input(),
- context_aggregator.user(),
- in_sound,
- fl2,
- llm,
- fl,
- tts,
- out_sound,
- transport.output(),
- context_aggregator.assistant(),
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frame(TTSSpeakFrame("Hi, I'm listening!"))
+ await transport.send_audio(sounds["ding1.wav"])
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- await tts.say("Hi, I'm listening!")
- await transport.send_audio(sounds["ding1.wav"])
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- runner = PipelineRunner()
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- task = PipelineTask(pipeline)
+ runner = PipelineRunner(handle_sigint=False)
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/12-describe-video.py b/examples/foundational/12-describe-video.py
index a0977167d..3afd6dd0c 100644
--- a/examples/foundational/12-describe-video.py
+++ b/examples/foundational/12-describe-video.py
@@ -4,15 +4,12 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
from typing import Optional
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import Frame, TextFrame, UserImageRequestFrame
@@ -23,14 +20,14 @@ from pipecat.processors.aggregators.user_response import UserResponseAggregator
from pipecat.processors.aggregators.vision_image_frame import VisionImageFrameAggregator
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.moondream.vision import MoondreamService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
class UserImageRequester(FrameProcessor):
def __init__(self, participant_id: Optional[str] = None):
@@ -50,61 +47,79 @@ class UserImageRequester(FrameProcessor):
await self.push_frame(frame, direction)
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ # Get WebRTC peer connection ID
+ webrtc_peer_id = webrtc_connection.pc_id
- transport = DailyTransport(
- room_url,
- token,
- "Describe participant video",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ logger.info(f"Starting bot with peer_id: {webrtc_peer_id}")
- user_response = UserResponseAggregator()
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ video_in_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- image_requester = UserImageRequester()
+ user_response = UserResponseAggregator()
- vision_aggregator = VisionImageFrameAggregator()
+ # Initialize the image requester without setting the participant ID yet
+ image_requester = UserImageRequester()
- # If you run into weird description, try with use_cpu=True
- moondream = MoondreamService()
+ vision_aggregator = VisionImageFrameAggregator()
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ # If you run into weird description, try with use_cpu=True
+ moondream = MoondreamService()
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await tts.say("Hi there! Feel free to ask me what I see.")
- await transport.capture_participant_video(participant["id"], framerate=0)
- await transport.capture_participant_transcription(participant["id"])
- image_requester.set_participant_id(participant["id"])
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- pipeline = Pipeline(
- [
- transport.input(),
- user_response,
- image_requester,
- vision_aggregator,
- moondream,
- tts,
- transport.output(),
- ]
- )
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- task = PipelineTask(pipeline)
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ user_response,
+ image_requester,
+ vision_aggregator,
+ moondream,
+ tts,
+ transport.output(),
+ ]
+ )
- runner = PipelineRunner()
+ task = PipelineTask(pipeline)
- await runner.run(task)
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected: {client}")
+
+ # Welcome message
+ await tts.say("Hi there! Feel free to ask me what I see.")
+
+ # Set the participant ID in the image requester
+ image_requester.set_participant_id(webrtc_peer_id)
+
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
+
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/12a-describe-video-gemini-flash.py b/examples/foundational/12a-describe-video-gemini-flash.py
index 131196e4f..f81655298 100644
--- a/examples/foundational/12a-describe-video-gemini-flash.py
+++ b/examples/foundational/12a-describe-video-gemini-flash.py
@@ -4,33 +4,30 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
from typing import Optional
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import Frame, TextFrame, UserImageRequestFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineTask
+from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.user_response import UserResponseAggregator
from pipecat.processors.aggregators.vision_image_frame import VisionImageFrameAggregator
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.google.llm import GoogleLLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
class UserImageRequester(FrameProcessor):
def __init__(self, participant_id: Optional[str] = None):
@@ -50,61 +47,82 @@ class UserImageRequester(FrameProcessor):
await self.push_frame(frame, direction)
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ # Get WebRTC peer connection ID
+ webrtc_peer_id = webrtc_connection.pc_id
- transport = DailyTransport(
- room_url,
- token,
- "Describe participant video",
- DailyParams(
- audio_in_enabled=True, # This is so Silero VAD can get audio data
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ logger.info(f"Starting bot with peer_id: {webrtc_peer_id}")
- user_response = UserResponseAggregator()
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ video_in_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- image_requester = UserImageRequester()
+ user_response = UserResponseAggregator()
- vision_aggregator = VisionImageFrameAggregator()
+ # Initialize the image requester without setting the participant ID yet
+ image_requester = UserImageRequester()
- google = GoogleLLMService(model="gemini-2.0-flash-001", api_key=os.getenv("GOOGLE_API_KEY"))
+ vision_aggregator = VisionImageFrameAggregator()
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await tts.say("Hi there! Feel free to ask me what I see.")
- await transport.capture_participant_video(participant["id"], framerate=0)
- await transport.capture_participant_transcription(participant["id"])
- image_requester.set_participant_id(participant["id"])
+ # Google Gemini model for vision analysis
+ google = GoogleLLMService(model="gemini-2.0-flash-001", api_key=os.getenv("GOOGLE_API_KEY"))
- pipeline = Pipeline(
- [
- transport.input(),
- user_response,
- image_requester,
- vision_aggregator,
- google,
- tts,
- transport.output(),
- ]
- )
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- task = PipelineTask(pipeline)
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ user_response,
+ image_requester,
+ vision_aggregator,
+ google,
+ tts,
+ transport.output(),
+ ]
+ )
- runner = PipelineRunner()
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(allow_interruptions=True),
+ )
- await runner.run(task)
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected: {client}")
+
+ # Welcome message
+ await tts.say("Hi there! Feel free to ask me what I see.")
+
+ # Set the participant ID in the image requester
+ image_requester.set_participant_id(webrtc_peer_id)
+
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
+
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/12b-describe-video-gpt-4o.py b/examples/foundational/12b-describe-video-gpt-4o.py
index a8eed5a1a..00d64863c 100644
--- a/examples/foundational/12b-describe-video-gpt-4o.py
+++ b/examples/foundational/12b-describe-video-gpt-4o.py
@@ -4,33 +4,30 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
from typing import Optional
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import Frame, TextFrame, UserImageRequestFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineTask
+from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.user_response import UserResponseAggregator
from pipecat.processors.aggregators.vision_image_frame import VisionImageFrameAggregator
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
class UserImageRequester(FrameProcessor):
def __init__(self, participant_id: Optional[str] = None):
@@ -50,60 +47,82 @@ class UserImageRequester(FrameProcessor):
await self.push_frame(frame, direction)
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ # Get WebRTC peer connection ID
+ webrtc_peer_id = webrtc_connection.pc_id
- transport = DailyTransport(
- room_url,
- token,
- "Describe participant video",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ logger.info(f"Starting bot with peer_id: {webrtc_peer_id}")
- user_response = UserResponseAggregator()
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ video_in_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- image_requester = UserImageRequester()
+ user_response = UserResponseAggregator()
- vision_aggregator = VisionImageFrameAggregator()
+ # Initialize the image requester without setting the participant ID yet
+ image_requester = UserImageRequester()
- openai = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ vision_aggregator = VisionImageFrameAggregator()
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await tts.say("Hi there! Feel free to ask me what I see.")
- await transport.capture_participant_video(participant["id"], framerate=0)
- await transport.capture_participant_transcription(participant["id"])
- image_requester.set_participant_id(participant["id"])
+ # OpenAI GPT-4o for vision analysis
+ openai = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
- pipeline = Pipeline(
- [
- transport.input(),
- user_response,
- image_requester,
- vision_aggregator,
- openai,
- tts,
- transport.output(),
- ]
- )
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- task = PipelineTask(pipeline)
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ user_response,
+ image_requester,
+ vision_aggregator,
+ openai,
+ tts,
+ transport.output(),
+ ]
+ )
- runner = PipelineRunner()
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(allow_interruptions=True),
+ )
- await runner.run(task)
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected: {client}")
+
+ # Welcome message
+ await tts.say("Hi there! Feel free to ask me what I see.")
+
+ # Set the participant ID in the image requester
+ image_requester.set_participant_id(webrtc_peer_id)
+
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
+
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/12c-describe-video-anthropic.py b/examples/foundational/12c-describe-video-anthropic.py
index e53908357..cc454d2e1 100644
--- a/examples/foundational/12c-describe-video-anthropic.py
+++ b/examples/foundational/12c-describe-video-anthropic.py
@@ -4,33 +4,30 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
from typing import Optional
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import Frame, TextFrame, UserImageRequestFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineTask
+from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.user_response import UserResponseAggregator
from pipecat.processors.aggregators.vision_image_frame import VisionImageFrameAggregator
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.services.anthropic.llm import AnthropicLLMService
from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
class UserImageRequester(FrameProcessor):
def __init__(self, participant_id: Optional[str] = None):
@@ -50,60 +47,82 @@ class UserImageRequester(FrameProcessor):
await self.push_frame(frame, direction)
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ # Get WebRTC peer connection ID
+ webrtc_peer_id = webrtc_connection.pc_id
- transport = DailyTransport(
- room_url,
- token,
- "Describe participant video",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ logger.info(f"Starting bot with peer_id: {webrtc_peer_id}")
- user_response = UserResponseAggregator()
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ video_in_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- image_requester = UserImageRequester()
+ user_response = UserResponseAggregator()
- vision_aggregator = VisionImageFrameAggregator()
+ # Initialize the image requester without setting the participant ID yet
+ image_requester = UserImageRequester()
- anthropic = AnthropicLLMService(api_key=os.getenv("ANTHROPIC_API_KEY"))
+ vision_aggregator = VisionImageFrameAggregator()
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await tts.say("Hi there! Feel free to ask me what I see.")
- await transport.capture_participant_video(participant["id"], framerate=0)
- await transport.capture_participant_transcription(participant["id"])
- image_requester.set_participant_id(participant["id"])
+ # Anthropic for vision analysis
+ anthropic = AnthropicLLMService(api_key=os.getenv("ANTHROPIC_API_KEY"))
- pipeline = Pipeline(
- [
- transport.input(),
- user_response,
- image_requester,
- vision_aggregator,
- anthropic,
- tts,
- transport.output(),
- ]
- )
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- task = PipelineTask(pipeline)
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ user_response,
+ image_requester,
+ vision_aggregator,
+ anthropic,
+ tts,
+ transport.output(),
+ ]
+ )
- runner = PipelineRunner()
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(allow_interruptions=True),
+ )
- await runner.run(task)
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected: {client}")
+
+ # Welcome message
+ await tts.say("Hi there! Feel free to ask me what I see.")
+
+ # Set the participant ID in the image requester
+ image_requester.set_participant_id(webrtc_peer_id)
+
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
+
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/13-whisper-transcription.py b/examples/foundational/13-whisper-transcription.py
index e9fe7400b..1cee7a00b 100644
--- a/examples/foundational/13-whisper-transcription.py
+++ b/examples/foundational/13-whisper-transcription.py
@@ -4,13 +4,10 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
-import sys
+import argparse
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import Frame, TranscriptionFrame
@@ -19,13 +16,12 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.services.whisper.stt import WhisperSTTService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
class TranscriptionLogger(FrameProcessor):
async def process_frame(self, frame: Frame, direction: FrameDirection):
@@ -35,34 +31,40 @@ class TranscriptionLogger(FrameProcessor):
print(f"Transcription: {frame.text}")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, _) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- None,
- "Transcription bot",
- DailyParams(
- audio_in_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- stt = WhisperSTTService()
+ stt = WhisperSTTService()
- tl = TranscriptionLogger()
+ tl = TranscriptionLogger()
- pipeline = Pipeline([transport.input(), stt, tl])
+ pipeline = Pipeline([transport.input(), stt, tl])
- task = PipelineTask(pipeline)
+ task = PipelineTask(pipeline)
- runner = PipelineRunner()
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- await runner.run(task)
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/13a-whisper-local.py b/examples/foundational/13a-whisper-local.py
index ba556916b..34cce6796 100644
--- a/examples/foundational/13a-whisper-local.py
+++ b/examples/foundational/13a-whisper-local.py
@@ -37,9 +37,7 @@ async def main():
transport = LocalAudioTransport(
LocalAudioTransportParams(
audio_in_enabled=True,
- vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
)
)
diff --git a/examples/foundational/13b-deepgram-transcription.py b/examples/foundational/13b-deepgram-transcription.py
index 32452d9f2..4c7e75dc1 100644
--- a/examples/foundational/13b-deepgram-transcription.py
+++ b/examples/foundational/13b-deepgram-transcription.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.frames.frames import Frame, TranscriptionFrame
from pipecat.pipeline.pipeline import Pipeline
@@ -19,13 +16,12 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.services.deepgram.stt import DeepgramSTTService, Language, LiveOptions
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
class TranscriptionLogger(FrameProcessor):
async def process_frame(self, frame: Frame, direction: FrameDirection):
@@ -35,29 +31,40 @@ class TranscriptionLogger(FrameProcessor):
print(f"Transcription: {frame.text}")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, _) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url, None, "Transcription bot", DailyParams(audio_in_enabled=True)
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(audio_in_enabled=True),
+ )
- stt = DeepgramSTTService(
- api_key=os.getenv("DEEPGRAM_API_KEY"),
- # live_options=LiveOptions(language=Language.FR),
- )
+ stt = DeepgramSTTService(
+ api_key=os.getenv("DEEPGRAM_API_KEY"),
+ live_options=LiveOptions(language=Language.EN),
+ )
- tl = TranscriptionLogger()
+ tl = TranscriptionLogger()
- pipeline = Pipeline([transport.input(), stt, tl])
+ pipeline = Pipeline([transport.input(), stt, tl])
- task = PipelineTask(pipeline)
+ task = PipelineTask(pipeline)
- runner = PipelineRunner()
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- await runner.run(task)
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/13c-gladia-transcription.py b/examples/foundational/13c-gladia-transcription.py
index d982c20d1..a0a6264bf 100644
--- a/examples/foundational/13c-gladia-transcription.py
+++ b/examples/foundational/13c-gladia-transcription.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.frames.frames import Frame, TranscriptionFrame
from pipecat.pipeline.pipeline import Pipeline
@@ -19,13 +16,12 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.services.gladia import GladiaSTTService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
class TranscriptionLogger(FrameProcessor):
async def process_frame(self, frame: Frame, direction: FrameDirection):
@@ -35,29 +31,40 @@ class TranscriptionLogger(FrameProcessor):
print(f"Transcription: {frame.text}")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, _) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url, None, "Transcription bot", DailyParams(audio_in_enabled=True)
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(audio_in_enabled=True),
+ )
- stt = GladiaSTTService(
- api_key=os.getenv("GLADIA_API_KEY"),
- # live_options=LiveOptions(language=Language.FR),
- )
+ stt = GladiaSTTService(
+ api_key=os.getenv("GLADIA_API_KEY"),
+ # live_options=LiveOptions(language=Language.FR),
+ )
- tl = TranscriptionLogger()
+ tl = TranscriptionLogger()
- pipeline = Pipeline([transport.input(), stt, tl])
+ pipeline = Pipeline([transport.input(), stt, tl])
- task = PipelineTask(pipeline)
+ task = PipelineTask(pipeline)
- runner = PipelineRunner()
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- await runner.run(task)
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/13d-assemblyai-transcription.py b/examples/foundational/13d-assemblyai-transcription.py
index 18ba8d44f..8fa99d8de 100644
--- a/examples/foundational/13d-assemblyai-transcription.py
+++ b/examples/foundational/13d-assemblyai-transcription.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.frames.frames import Frame, TranscriptionFrame
from pipecat.pipeline.pipeline import Pipeline
@@ -19,13 +16,12 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.services.assemblyai.stt import AssemblyAISTTService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
class TranscriptionLogger(FrameProcessor):
async def process_frame(self, frame: Frame, direction: FrameDirection):
@@ -35,28 +31,39 @@ class TranscriptionLogger(FrameProcessor):
print(f"Transcription: {frame.text}")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, _) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url, None, "Transcription bot", DailyParams(audio_in_enabled=True)
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(audio_in_enabled=True),
+ )
- stt = AssemblyAISTTService(
- api_key=os.getenv("ASSEMBLYAI_API_KEY"),
- )
+ stt = AssemblyAISTTService(
+ api_key=os.getenv("ASSEMBLYAI_API_KEY"),
+ )
- tl = TranscriptionLogger()
+ tl = TranscriptionLogger()
- pipeline = Pipeline([transport.input(), stt, tl])
+ pipeline = Pipeline([transport.input(), stt, tl])
- task = PipelineTask(pipeline)
+ task = PipelineTask(pipeline)
- runner = PipelineRunner()
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- await runner.run(task)
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/13e-whisper-mlx.py b/examples/foundational/13e-whisper-mlx.py
index 4ed2bb2c4..9ab7bc82b 100644
--- a/examples/foundational/13e-whisper-mlx.py
+++ b/examples/foundational/13e-whisper-mlx.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
-import sys
+import argparse
import time
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
@@ -21,13 +18,12 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.services.whisper.stt import MLXModel, WhisperSTTServiceMLX
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
STOP_SECS = 2.0
@@ -56,40 +52,46 @@ class TranscriptionLogger(FrameProcessor):
self._last_transcription_time = time.time()
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, _) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- None,
- "Transcription bot",
- DailyParams(
- audio_in_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=STOP_SECS)),
- vad_audio_passthrough=True,
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=STOP_SECS)),
+ ),
+ )
- stt = WhisperSTTServiceMLX(model=MLXModel.LARGE_V3_TURBO)
+ stt = WhisperSTTServiceMLX(model=MLXModel.LARGE_V3_TURBO)
- tl = TranscriptionLogger()
+ tl = TranscriptionLogger()
- pipeline = Pipeline([transport.input(), stt, tl])
+ pipeline = Pipeline([transport.input(), stt, tl])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- enable_metrics=True,
- report_only_initial_ttfb=False,
- ),
- )
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ enable_metrics=True,
+ report_only_initial_ttfb=False,
+ ),
+ )
- runner = PipelineRunner()
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- await runner.run(task)
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/14-function-calling.py b/examples/foundational/14-function-calling.py
index 942995248..d8ddf51c0 100644
--- a/examples/foundational/14-function-calling.py
+++ b/examples/foundational/14-function-calling.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
@@ -22,106 +19,116 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
await llm.push_frame(TTSSpeakFrame("Let me check on that."))
await result_callback({"conditions": "nice", "temperature": "75"})
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- # You can also register a function_name of None to get all functions
- # sent to the same callback with an additional function_name parameter.
- llm.register_function("get_current_weather", fetch_weather_from_api)
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
- weather_function = FunctionSchema(
- name="get_current_weather",
- description="Get the current weather",
- properties={
- "location": {
- "type": "string",
- "description": "The city and state, e.g. San Francisco, CA",
- },
- "format": {
- "type": "string",
- "enum": ["celsius", "fahrenheit"],
- "description": "The temperature unit to use. Infer this from the user's location.",
- },
+ # You can also register a function_name of None to get all functions
+ # sent to the same callback with an additional function_name parameter.
+ llm.register_function("get_current_weather", fetch_weather_from_api)
+
+ weather_function = FunctionSchema(
+ name="get_current_weather",
+ description="Get the current weather",
+ properties={
+ "location": {
+ "type": "string",
+ "description": "The city and state, e.g. San Francisco, CA",
},
- required=["location", "format"],
- )
- tools = ToolsSchema(standard_tools=[weather_function])
-
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ "format": {
+ "type": "string",
+ "enum": ["celsius", "fahrenheit"],
+ "description": "The temperature unit to use. Infer this from the user's location.",
},
+ },
+ required=["location", "format"],
+ )
+ tools = ToolsSchema(standard_tools=[weather_function])
+
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages, tools)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ context_aggregator.user(),
+ llm,
+ tts,
+ transport.output(),
+ context_aggregator.assistant(),
]
+ )
- context = OpenAILLMContext(messages, tools)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(),
- context_aggregator.user(),
- llm,
- tts,
- transport.output(),
- context_aggregator.assistant(),
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- runner = PipelineRunner()
+ runner = PipelineRunner(handle_sigint=False)
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/14a-function-calling-anthropic.py b/examples/foundational/14a-function-calling-anthropic.py
index e7fcc6a34..986dd2420 100644
--- a/examples/foundational/14a-function-calling-anthropic.py
+++ b/examples/foundational/14a-function-calling-anthropic.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
@@ -22,99 +19,109 @@ from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.anthropic.llm import AnthropicLLMService
from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
async def get_weather(function_name, tool_call_id, arguments, llm, context, result_callback):
location = arguments["location"]
await result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- llm = AnthropicLLMService(
- api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-7-sonnet-latest"
- )
- llm.register_function("get_weather", get_weather)
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- weather_function = FunctionSchema(
- name="get_weather",
- description="Get the current weather",
- properties={
- "location": {
- "type": "string",
- "description": "The city and state, e.g. San Francisco, CA",
- },
+ llm = AnthropicLLMService(
+ api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-7-sonnet-latest"
+ )
+ llm.register_function("get_weather", get_weather)
+
+ weather_function = FunctionSchema(
+ name="get_weather",
+ description="Get the current weather",
+ properties={
+ "location": {
+ "type": "string",
+ "description": "The city and state, e.g. San Francisco, CA",
},
- required=["location"],
- )
- tools = ToolsSchema(standard_tools=[weather_function])
+ },
+ required=["location"],
+ )
+ tools = ToolsSchema(standard_tools=[weather_function])
- # todo: test with very short initial user message
+ # todo: test with very short initial user message
- # messages = [{"role": "system",
- # "content": "You are a helpful assistant who can report the weather in any location in the universe. Respond concisely. Your response will be turned into speech so use only simple words and punctuation."},
- # {"role": "user",
- # "content": " Start the conversation by introducing yourself."}]
+ # messages = [{"role": "system",
+ # "content": "You are a helpful assistant who can report the weather in any location in the universe. Respond concisely. Your response will be turned into speech so use only simple words and punctuation."},
+ # {"role": "user",
+ # "content": " Start the conversation by introducing yourself."}]
- messages = [{"role": "user", "content": "Say 'hello' to start the conversation."}]
+ messages = [{"role": "user", "content": "Say 'hello' to start the conversation."}]
- context = OpenAILLMContext(messages, tools)
- context_aggregator = llm.create_context_aggregator(context)
+ context = OpenAILLMContext(messages, tools)
+ context_aggregator = llm.create_context_aggregator(context)
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- context_aggregator.user(), # User spoken responses
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses and tool context
- ]
- )
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt,
+ context_aggregator.user(), # User spoken responses
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses and tool context
+ ]
+ )
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- ),
- )
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ ),
+ )
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- runner = PipelineRunner()
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- await runner.run(task)
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/14b-function-calling-anthropic-video.py b/examples/foundational/14b-function-calling-anthropic-video.py
index 92d570c1e..65876982a 100644
--- a/examples/foundational/14b-function-calling-anthropic-video.py
+++ b/examples/foundational/14b-function-calling-anthropic-video.py
@@ -4,14 +4,12 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
+import argparse
import asyncio
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
@@ -22,14 +20,16 @@ from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.anthropic.llm import AnthropicLLMService
from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-video_participant_id = None
+# Global variable to store the peer connection ID
+webrtc_peer_id = None
async def get_weather(function_name, tool_call_id, arguments, llm, context, result_callback):
@@ -39,72 +39,81 @@ async def get_weather(function_name, tool_call_id, arguments, llm, context, resu
async def get_image(function_name, tool_call_id, arguments, llm, context, result_callback):
question = arguments["question"]
+ logger.debug(f"Requesting image with user_id={webrtc_peer_id}, question={question}")
+
+ # Request the image frame
await llm.request_image_frame(
- user_id=video_participant_id,
+ user_id=webrtc_peer_id,
function_name=function_name,
tool_call_id=tool_call_id,
text_content=question,
)
+ # Wait a short time for the frame to be processed
+ await asyncio.sleep(0.5)
-async def main():
- global llm
+ # Return a result to complete the function call
+ await result_callback(
+ f"I've captured an image from your camera and I'm analyzing what you asked about: {question}"
+ )
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ global webrtc_peer_id
+ webrtc_peer_id = webrtc_connection.pc_id
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ logger.info(f"Starting bot with peer_id: {webrtc_peer_id}")
- llm = AnthropicLLMService(
- api_key=os.getenv("ANTHROPIC_API_KEY"),
- model="claude-3-7-sonnet-latest",
- enable_prompt_caching_beta=True,
- )
- llm.register_function("get_weather", get_weather)
- llm.register_function("get_image", get_image)
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ video_in_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- weather_function = FunctionSchema(
- name="get_weather",
- description="Get the current weather",
- properties={
- "location": {
- "type": "string",
- "description": "The city and state, e.g. San Francisco, CA",
- },
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
+
+ llm = AnthropicLLMService(
+ api_key=os.getenv("ANTHROPIC_API_KEY"),
+ model="claude-3-7-sonnet-latest",
+ enable_prompt_caching_beta=True,
+ )
+ llm.register_function("get_weather", get_weather)
+ llm.register_function("get_image", get_image)
+
+ weather_function = FunctionSchema(
+ name="get_weather",
+ description="Get the current weather",
+ properties={
+ "location": {
+ "type": "string",
+ "description": "The city and state, e.g. San Francisco, CA",
},
- required=["location"],
- )
- get_image_function = FunctionSchema(
- name="get_image",
- description="Get an image from the video stream.",
- properties={
- "question": {
- "type": "string",
- "description": "The question that the user is asking about the image.",
- }
- },
- required=["question"],
- )
- tools = ToolsSchema(standard_tools=[weather_function, get_image_function])
+ },
+ required=["location"],
+ )
+ get_image_function = FunctionSchema(
+ name="get_image",
+ description="Get an image from the video stream.",
+ properties={
+ "question": {
+ "type": "string",
+ "description": "The question that the user is asking about the image.",
+ }
+ },
+ required=["question"],
+ )
+ tools = ToolsSchema(standard_tools=[weather_function, get_image_function])
- # todo: test with very short initial user message
-
- system_prompt = """\
+ system_prompt = """\
You are a helpful assistant who converses with a user and answers questions. Respond concisely to general questions.
Your response will be turned into speech so use only simple words and punctuation.
@@ -115,63 +124,73 @@ You can respond to questions about the weather using the get_weather tool.
You can answer questions about the user's video stream using the get_image tool. Some examples of phrases that \
indicate you should use the get_image tool are:
- - What do you see?
- - What's in the video?
- - Can you describe the video?
- - Tell me about what you see.
- - Tell me something interesting about what you see.
- - What's happening in the video?
+- What do you see?
+- What's in the video?
+- Can you describe the video?
+- Tell me about what you see.
+- Tell me something interesting about what you see.
+- What's happening in the video?
If you need to use a tool, simply use the tool. Do not tell the user the tool you are using. Be brief and concise.
- """
+ """
- messages = [
- {
- "role": "system",
- "content": [
- {
- "type": "text",
- "text": system_prompt,
- }
- ],
- },
- {"role": "user", "content": "Start the conversation by introducing yourself."},
+ messages = [
+ {
+ "role": "system",
+ "content": [
+ {
+ "type": "text",
+ "text": system_prompt,
+ }
+ ],
+ },
+ {"role": "user", "content": "Start the conversation by introducing yourself."},
+ ]
+
+ context = OpenAILLMContext(messages, tools)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt, # STT
+ context_aggregator.user(), # User speech to text
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses and tool context
]
+ )
- context = OpenAILLMContext(messages, tools)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- context_aggregator.user(), # User speech to text
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses and tool context
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected: {client}")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- global video_participant_id
- video_participant_id = participant["id"]
- await transport.capture_participant_transcription(video_participant_id)
- await transport.capture_participant_video(video_participant_id, framerate=0)
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- runner = PipelineRunner()
- await runner.run(task)
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/14c-function-calling-together.py b/examples/foundational/14c-function-calling-together.py
index dcceb973b..670f6f500 100644
--- a/examples/foundational/14c-function-calling-together.py
+++ b/examples/foundational/14c-function-calling-together.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
@@ -22,99 +19,109 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.together.llm import TogetherLLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
await llm.push_frame(TTSSpeakFrame("Let me check on that."))
await result_callback({"conditions": "nice", "temperature": "75"})
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- llm = TogetherLLMService(
- api_key=os.getenv("TOGETHER_API_KEY"),
- model="meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
- )
- # You can also register a function_name of None to get all functions
- # sent to the same callback with an additional function_name parameter.
- llm.register_function("get_current_weather", fetch_weather_from_api)
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- weather_function = FunctionSchema(
- name="get_current_weather",
- description="Get the current weather",
- properties={
- "location": {
- "type": "string",
- "description": "The city and state, e.g. San Francisco, CA",
- },
- "format": {
- "type": "string",
- "enum": ["celsius", "fahrenheit"],
- "description": "The temperature unit to use. Infer this from the user's location.",
- },
+ llm = TogetherLLMService(
+ api_key=os.getenv("TOGETHER_API_KEY"),
+ model="meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
+ )
+ # You can also register a function_name of None to get all functions
+ # sent to the same callback with an additional function_name parameter.
+ llm.register_function("get_current_weather", fetch_weather_from_api)
+
+ weather_function = FunctionSchema(
+ name="get_current_weather",
+ description="Get the current weather",
+ properties={
+ "location": {
+ "type": "string",
+ "description": "The city and state, e.g. San Francisco, CA",
},
- required=["location", "format"],
- )
- tools = ToolsSchema(standard_tools=[weather_function])
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ "format": {
+ "type": "string",
+ "enum": ["celsius", "fahrenheit"],
+ "description": "The temperature unit to use. Infer this from the user's location.",
},
+ },
+ required=["location", "format"],
+ )
+ tools = ToolsSchema(standard_tools=[weather_function])
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages, tools)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ context_aggregator.user(),
+ llm,
+ tts,
+ transport.output(),
+ context_aggregator.assistant(),
]
+ )
- context = OpenAILLMContext(messages, tools)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(pipeline)
- pipeline = Pipeline(
- [
- transport.input(),
- context_aggregator.user(),
- llm,
- tts,
- transport.output(),
- context_aggregator.assistant(),
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(pipeline)
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- runner = PipelineRunner()
+ runner = PipelineRunner(handle_sigint=False)
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/14d-function-calling-video.py b/examples/foundational/14d-function-calling-video.py
index e50ea5b31..c0278cd92 100644
--- a/examples/foundational/14d-function-calling-video.py
+++ b/examples/foundational/14d-function-calling-video.py
@@ -4,14 +4,12 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
+import argparse
import asyncio
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
@@ -21,15 +19,17 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-video_participant_id = None
+# Global variable to store the peer connection ID
+webrtc_peer_id = None
async def get_weather(function_name, tool_call_id, arguments, llm, context, result_callback):
@@ -38,71 +38,83 @@ async def get_weather(function_name, tool_call_id, arguments, llm, context, resu
async def get_image(function_name, tool_call_id, arguments, llm, context, result_callback):
- logger.debug(f"!!! IN get_image {video_participant_id}, {arguments}")
question = arguments["question"]
+ logger.debug(f"Requesting image with user_id={webrtc_peer_id}, question={question}")
+
+ # Request the image frame
await llm.request_image_frame(
- user_id=video_participant_id,
+ user_id=webrtc_peer_id,
function_name=function_name,
tool_call_id=tool_call_id,
text_content=question,
)
+ # Wait a short time for the frame to be processed
+ await asyncio.sleep(0.5)
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+ # Return a result to complete the function call
+ await result_callback(
+ f"I've captured an image from your camera and I'm analyzing what you asked about: {question}"
+ )
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ global webrtc_peer_id
+ webrtc_peer_id = webrtc_connection.pc_id
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
- llm.register_function("get_weather", get_weather)
- llm.register_function("get_image", get_image)
+ logger.info(f"Starting bot with peer_id: {webrtc_peer_id}")
- weather_function = FunctionSchema(
- name="get_weather",
- description="Get the current weather",
- properties={
- "location": {
- "type": "string",
- "description": "The city and state, e.g. San Francisco, CA",
- },
- "format": {
- "type": "string",
- "enum": ["celsius", "fahrenheit"],
- "description": "The temperature unit to use. Infer this from the user's location.",
- },
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ video_in_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
+
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
+
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+ llm.register_function("get_weather", get_weather)
+ llm.register_function("get_image", get_image)
+
+ weather_function = FunctionSchema(
+ name="get_weather",
+ description="Get the current weather",
+ properties={
+ "location": {
+ "type": "string",
+ "description": "The city and state, e.g. San Francisco, CA",
},
- required=["location"],
- )
- get_image_function = FunctionSchema(
- name="get_image",
- description="Get an image from the video stream.",
- properties={
- "question": {
- "type": "string",
- "description": "The question that the user is asking about the image.",
- }
+ "format": {
+ "type": "string",
+ "enum": ["celsius", "fahrenheit"],
+ "description": "The temperature unit to use. Infer this from the user's location.",
},
- required=["question"],
- )
- tools = ToolsSchema(standard_tools=[weather_function, get_image_function])
+ },
+ required=["location"],
+ )
+ get_image_function = FunctionSchema(
+ name="get_image",
+ description="Get an image from the video stream.",
+ properties={
+ "question": {
+ "type": "string",
+ "description": "The question that the user is asking about the image.",
+ }
+ },
+ required=["question"],
+ )
+ tools = ToolsSchema(standard_tools=[weather_function, get_image_function])
- system_prompt = """\
+ system_prompt = """\
You are a helpful assistant who converses with a user and answers questions. Respond concisely to general questions.
Your response will be turned into speech so use only simple words and punctuation.
@@ -113,46 +125,55 @@ You can respond to questions about the weather using the get_weather tool.
You can answer questions about the user's video stream using the get_image tool. Some examples of phrases that \
indicate you should use the get_image tool are:
- - What do you see?
- - What's in the video?
- - Can you describe the video?
- - Tell me about what you see.
- - Tell me something interesting about what you see.
- - What's happening in the video?
+- What do you see?
+- What's in the video?
+- Can you describe the video?
+- Tell me about what you see.
+- Tell me something interesting about what you see.
+- What's happening in the video?
"""
- messages = [
- {"role": "system", "content": system_prompt},
+ messages = [
+ {"role": "system", "content": system_prompt},
+ ]
+
+ context = OpenAILLMContext(messages, tools)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ context_aggregator.user(),
+ llm,
+ tts,
+ transport.output(),
+ context_aggregator.assistant(),
]
+ )
- context = OpenAILLMContext(messages, tools)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(pipeline)
- pipeline = Pipeline(
- [
- transport.input(),
- context_aggregator.user(),
- llm,
- tts,
- transport.output(),
- context_aggregator.assistant(),
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(pipeline)
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- global video_participant_id
- video_participant_id = participant["id"]
- await transport.capture_participant_transcription(participant["id"])
- await transport.capture_participant_video(video_participant_id, framerate=0)
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- runner = PipelineRunner()
+ runner = PipelineRunner(handle_sigint=False)
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/14e-function-calling-gemini.py b/examples/foundational/14e-function-calling-gemini.py
index 9853c07f6..2cf950c89 100644
--- a/examples/foundational/14e-function-calling-gemini.py
+++ b/examples/foundational/14e-function-calling-gemini.py
@@ -4,14 +4,12 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
+import argparse
import asyncio
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
@@ -22,15 +20,17 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.google.llm import GoogleLLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-video_participant_id = None
+# Global variable to store the peer connection ID
+webrtc_peer_id = None
async def get_weather(function_name, tool_call_id, arguments, llm, context, result_callback):
@@ -40,71 +40,83 @@ async def get_weather(function_name, tool_call_id, arguments, llm, context, resu
async def get_image(function_name, tool_call_id, arguments, llm, context, result_callback):
- logger.debug(f"!!! IN get_image {video_participant_id}, {arguments}")
question = arguments["question"]
+ logger.debug(f"Requesting image with user_id={webrtc_peer_id}, question={question}")
+
+ # Request the image frame
await llm.request_image_frame(
- user_id=video_participant_id,
+ user_id=webrtc_peer_id,
function_name=function_name,
tool_call_id=tool_call_id,
text_content=question,
)
+ # Wait a short time for the frame to be processed
+ await asyncio.sleep(0.5)
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+ # Return a result to complete the function call
+ await result_callback(
+ f"I've captured an image from your camera and I'm analyzing what you asked about: {question}"
+ )
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ global webrtc_peer_id
+ webrtc_peer_id = webrtc_connection.pc_id
- llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"), model="gemini-2.0-flash-001")
- llm.register_function("get_weather", get_weather)
- llm.register_function("get_image", get_image)
+ logger.info(f"Starting bot with peer_id: {webrtc_peer_id}")
- weather_function = FunctionSchema(
- name="get_weather",
- description="Get the current weather",
- properties={
- "location": {
- "type": "string",
- "description": "The city and state, e.g. San Francisco, CA",
- },
- "format": {
- "type": "string",
- "enum": ["celsius", "fahrenheit"],
- "description": "The temperature unit to use. Infer this from the user's location.",
- },
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ video_in_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
+
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
+
+ llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"), model="gemini-2.0-flash-001")
+ llm.register_function("get_weather", get_weather)
+ llm.register_function("get_image", get_image)
+
+ weather_function = FunctionSchema(
+ name="get_weather",
+ description="Get the current weather",
+ properties={
+ "location": {
+ "type": "string",
+ "description": "The city and state, e.g. San Francisco, CA",
},
- required=["location", "format"],
- )
- get_image_function = FunctionSchema(
- name="get_image",
- description="Get an image from the video stream.",
- properties={
- "question": {
- "type": "string",
- "description": "The question that the user is asking about the image.",
- }
+ "format": {
+ "type": "string",
+ "enum": ["celsius", "fahrenheit"],
+ "description": "The temperature unit to use. Infer this from the user's location.",
},
- required=["question"],
- )
- tools = ToolsSchema(standard_tools=[weather_function, get_image_function])
+ },
+ required=["location", "format"],
+ )
+ get_image_function = FunctionSchema(
+ name="get_image",
+ description="Get an image from the video stream.",
+ properties={
+ "question": {
+ "type": "string",
+ "description": "The question that the user is asking about the image.",
+ }
+ },
+ required=["question"],
+ )
+ tools = ToolsSchema(standard_tools=[weather_function, get_image_function])
- system_prompt = """\
+ system_prompt = """\
You are a helpful assistant who converses with a user and answers questions. Respond concisely to general questions.
Your response will be turned into speech so use only simple words and punctuation.
@@ -115,54 +127,63 @@ You can respond to questions about the weather using the get_weather tool.
You can answer questions about the user's video stream using the get_image tool. Some examples of phrases that \
indicate you should use the get_image tool are:
- - What do you see?
- - What's in the video?
- - Can you describe the video?
- - Tell me about what you see.
- - Tell me something interesting about what you see.
- - What's happening in the video?
+- What do you see?
+- What's in the video?
+- Can you describe the video?
+- Tell me about what you see.
+- Tell me something interesting about what you see.
+- What's happening in the video?
"""
- messages = [
- {"role": "system", "content": system_prompt},
- {"role": "user", "content": "Say hello."},
+ messages = [
+ {"role": "system", "content": system_prompt},
+ {"role": "user", "content": "Say hello."},
+ ]
+
+ context = OpenAILLMContext(messages, tools)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ context_aggregator.user(),
+ llm,
+ tts,
+ transport.output(),
+ context_aggregator.assistant(),
]
+ )
- context = OpenAILLMContext(messages, tools)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(),
- context_aggregator.user(),
- llm,
- tts,
- transport.output(),
- context_aggregator.assistant(),
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected: {client}")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- global video_participant_id
- video_participant_id = participant["id"]
- await transport.capture_participant_transcription(participant["id"])
- await transport.capture_participant_video(video_participant_id, framerate=0)
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- runner = PipelineRunner()
+ runner = PipelineRunner(handle_sigint=False)
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/14f-function-calling-groq.py b/examples/foundational/14f-function-calling-groq.py
index 839c9460c..525f623f2 100644
--- a/examples/foundational/14f-function-calling-groq.py
+++ b/examples/foundational/14f-function-calling-groq.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
@@ -24,105 +21,111 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.groq.llm import GroqLLMService
from pipecat.services.groq.stt import GroqSTTService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
await llm.push_frame(TTSSpeakFrame("Let me check on that."))
await result_callback({"conditions": "nice", "temperature": "75"})
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- stt = GroqSTTService(api_key=os.getenv("GROQ_API_KEY"), model="distil-whisper-large-v3-en")
+ stt = GroqSTTService(api_key=os.getenv("GROQ_API_KEY"), model="distil-whisper-large-v3-en")
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- llm = GroqLLMService(api_key=os.getenv("GROQ_API_KEY"), model="llama-3.3-70b-versatile")
- # You can also register a function_name of None to get all functions
- # sent to the same callback with an additional function_name parameter.
- llm.register_function("get_current_weather", fetch_weather_from_api)
+ llm = GroqLLMService(api_key=os.getenv("GROQ_API_KEY"), model="llama-3.3-70b-versatile")
+ # You can also register a function_name of None to get all functions
+ # sent to the same callback with an additional function_name parameter.
+ llm.register_function("get_current_weather", fetch_weather_from_api)
- weather_function = FunctionSchema(
- name="get_current_weather",
- description="Get the current weather",
- properties={
- "location": {
- "type": "string",
- "description": "The city and state, e.g. San Francisco, CA",
- },
- "format": {
- "type": "string",
- "enum": ["celsius", "fahrenheit"],
- "description": "The temperature unit to use. Infer this from the user's location.",
- },
+ weather_function = FunctionSchema(
+ name="get_current_weather",
+ description="Get the current weather",
+ properties={
+ "location": {
+ "type": "string",
+ "description": "The city and state, e.g. San Francisco, CA",
},
- required=["location"],
- )
- tools = ToolsSchema(standard_tools=[weather_function])
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ "format": {
+ "type": "string",
+ "enum": ["celsius", "fahrenheit"],
+ "description": "The temperature unit to use. Infer this from the user's location.",
},
+ },
+ required=["location"],
+ )
+ tools = ToolsSchema(standard_tools=[weather_function])
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages, tools)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ context_aggregator.user(),
+ llm,
+ tts,
+ transport.output(),
+ context_aggregator.assistant(),
]
+ )
- context = OpenAILLMContext(messages, tools)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(),
- stt,
- context_aggregator.user(),
- llm,
- tts,
- transport.output(),
- context_aggregator.assistant(),
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- runner = PipelineRunner()
+ runner = PipelineRunner(handle_sigint=False)
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/14g-function-calling-grok.py b/examples/foundational/14g-function-calling-grok.py
index f287b8380..a42e775b4 100644
--- a/examples/foundational/14g-function-calling-grok.py
+++ b/examples/foundational/14g-function-calling-grok.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
@@ -21,102 +18,112 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.grok.llm import GrokLLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
await result_callback({"conditions": "nice", "temperature": "75"})
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- llm = GrokLLMService(api_key=os.getenv("GROK_API_KEY"))
- # You can also register a function_name of None to get all functions
- # sent to the same callback with an additional function_name parameter.
- llm.register_function("get_current_weather", fetch_weather_from_api)
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- weather_function = FunctionSchema(
- name="get_current_weather",
- description="Get the current weather",
- properties={
- "location": {
- "type": "string",
- "description": "The city and state, e.g. San Francisco, CA",
- },
- "format": {
- "type": "string",
- "enum": ["celsius", "fahrenheit"],
- "description": "The temperature unit to use. Infer this from the user's location.",
- },
+ llm = GrokLLMService(api_key=os.getenv("GROK_API_KEY"))
+ # You can also register a function_name of None to get all functions
+ # sent to the same callback with an additional function_name parameter.
+ llm.register_function("get_current_weather", fetch_weather_from_api)
+
+ weather_function = FunctionSchema(
+ name="get_current_weather",
+ description="Get the current weather",
+ properties={
+ "location": {
+ "type": "string",
+ "description": "The city and state, e.g. San Francisco, CA",
},
- required=["location", "format"],
- )
- tools = ToolsSchema(standard_tools=[weather_function])
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ "format": {
+ "type": "string",
+ "enum": ["celsius", "fahrenheit"],
+ "description": "The temperature unit to use. Infer this from the user's location.",
},
+ },
+ required=["location", "format"],
+ )
+ tools = ToolsSchema(standard_tools=[weather_function])
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages, tools)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ context_aggregator.user(),
+ llm,
+ tts,
+ transport.output(),
+ context_aggregator.assistant(),
]
+ )
- context = OpenAILLMContext(messages, tools)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(),
- context_aggregator.user(),
- llm,
- tts,
- transport.output(),
- context_aggregator.assistant(),
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- runner = PipelineRunner()
+ runner = PipelineRunner(handle_sigint=False)
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/14h-function-calling-azure.py b/examples/foundational/14h-function-calling-azure.py
index e10b4ac02..fbacfadf8 100644
--- a/examples/foundational/14h-function-calling-azure.py
+++ b/examples/foundational/14h-function-calling-azure.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
@@ -23,106 +20,116 @@ from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.azure.llm import AzureLLMService
from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
await llm.push_frame(TTSSpeakFrame("Let me check on that."))
await result_callback({"conditions": "nice", "temperature": "75"})
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- llm = AzureLLMService(
- api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
- endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
- model=os.getenv("AZURE_CHATGPT_MODEL"),
- )
- # You can also register a function_name of None to get all functions
- # sent to the same callback with an additional function_name parameter.
- llm.register_function("get_current_weather", fetch_weather_from_api)
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- weather_function = FunctionSchema(
- name="get_current_weather",
- description="Get the current weather",
- properties={
- "location": {
- "type": "string",
- "description": "The city and state, e.g. San Francisco, CA",
- },
- "format": {
- "type": "string",
- "enum": ["celsius", "fahrenheit"],
- "description": "The temperature unit to use. Infer this from the user's location.",
- },
+ llm = AzureLLMService(
+ api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
+ endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
+ model=os.getenv("AZURE_CHATGPT_MODEL"),
+ )
+ # You can also register a function_name of None to get all functions
+ # sent to the same callback with an additional function_name parameter.
+ llm.register_function("get_current_weather", fetch_weather_from_api)
+
+ weather_function = FunctionSchema(
+ name="get_current_weather",
+ description="Get the current weather",
+ properties={
+ "location": {
+ "type": "string",
+ "description": "The city and state, e.g. San Francisco, CA",
},
- required=["location", "format"],
- )
- tools = ToolsSchema(standard_tools=[weather_function])
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ "format": {
+ "type": "string",
+ "enum": ["celsius", "fahrenheit"],
+ "description": "The temperature unit to use. Infer this from the user's location.",
},
+ },
+ required=["location", "format"],
+ )
+ tools = ToolsSchema(standard_tools=[weather_function])
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages, tools)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ context_aggregator.user(),
+ llm,
+ tts,
+ transport.output(),
+ context_aggregator.assistant(),
]
+ )
- context = OpenAILLMContext(messages, tools)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(),
- context_aggregator.user(),
- llm,
- tts,
- transport.output(),
- context_aggregator.assistant(),
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- runner = PipelineRunner()
+ runner = PipelineRunner(handle_sigint=False)
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/14i-function-calling-fireworks.py b/examples/foundational/14i-function-calling-fireworks.py
index 3d2f1e876..22f89b299 100644
--- a/examples/foundational/14i-function-calling-fireworks.py
+++ b/examples/foundational/14i-function-calling-fireworks.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
@@ -22,106 +19,116 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.fireworks.llm import FireworksLLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
await llm.push_frame(TTSSpeakFrame("Let me check on that."))
await result_callback({"conditions": "nice", "temperature": "75"})
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- llm = FireworksLLMService(
- api_key=os.getenv("FIREWORKS_API_KEY"),
- model="accounts/fireworks/models/llama-v3p1-405b-instruct",
- )
- # You can also register a function_name of None to get all functions
- # sent to the same callback with an additional function_name parameter.
- llm.register_function("get_current_weather", fetch_weather_from_api)
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- weather_function = FunctionSchema(
- name="get_current_weather",
- description="Get the current weather",
- properties={
- "location": {
- "type": "string",
- "description": "The city and state, e.g. San Francisco, CA",
- },
- "format": {
- "type": "string",
- "enum": ["celsius", "fahrenheit"],
- "description": "The temperature unit to use. Infer this from the user's location.",
- },
+ llm = FireworksLLMService(
+ api_key=os.getenv("FIREWORKS_API_KEY"),
+ model="accounts/fireworks/models/llama-v3p1-405b-instruct",
+ )
+ # You can also register a function_name of None to get all functions
+ # sent to the same callback with an additional function_name parameter.
+ llm.register_function("get_current_weather", fetch_weather_from_api)
+
+ weather_function = FunctionSchema(
+ name="get_current_weather",
+ description="Get the current weather",
+ properties={
+ "location": {
+ "type": "string",
+ "description": "The city and state, e.g. San Francisco, CA",
},
- required=["location", "format"],
- )
- tools = ToolsSchema(standard_tools=[weather_function])
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ "format": {
+ "type": "string",
+ "enum": ["celsius", "fahrenheit"],
+ "description": "The temperature unit to use. Infer this from the user's location.",
},
+ },
+ required=["location", "format"],
+ )
+ tools = ToolsSchema(standard_tools=[weather_function])
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages, tools)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ context_aggregator.user(),
+ llm,
+ tts,
+ transport.output(),
+ context_aggregator.assistant(),
]
+ )
- context = OpenAILLMContext(messages, tools)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(),
- context_aggregator.user(),
- llm,
- tts,
- transport.output(),
- context_aggregator.assistant(),
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- runner = PipelineRunner()
+ runner = PipelineRunner(handle_sigint=False)
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/14j-function-calling-nim.py b/examples/foundational/14j-function-calling-nim.py
index 174b92aa3..f8d5b6ee1 100644
--- a/examples/foundational/14j-function-calling-nim.py
+++ b/examples/foundational/14j-function-calling-nim.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
@@ -22,106 +19,114 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.nim.llm import NimLLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
await llm.push_frame(TTSSpeakFrame("Let me check on that."))
await result_callback({"conditions": "nice", "temperature": "75"})
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- # text_filters=[MarkdownTextFilter()],
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- llm = NimLLMService(
- api_key=os.getenv("NVIDIA_API_KEY"), model="meta/llama-3.3-70b-instruct"
- )
- # You can also register a function_name of None to get all functions
- # sent to the same callback with an additional function_name parameter.
- llm.register_function("get_current_weather", fetch_weather_from_api)
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ # text_filters=[MarkdownTextFilter()],
+ )
- weather_function = FunctionSchema(
- name="get_current_weather",
- description="Get the current weather",
- properties={
- "location": {
- "type": "string",
- "description": "The city and state, e.g. San Francisco, CA",
- },
- "format": {
- "type": "string",
- "enum": ["celsius", "fahrenheit"],
- "description": "The temperature unit to use. Infer this from the user's location.",
- },
+ llm = NimLLMService(api_key=os.getenv("NVIDIA_API_KEY"), model="meta/llama-3.3-70b-instruct")
+ # You can also register a function_name of None to get all functions
+ # sent to the same callback with an additional function_name parameter.
+ llm.register_function("get_current_weather", fetch_weather_from_api)
+
+ weather_function = FunctionSchema(
+ name="get_current_weather",
+ description="Get the current weather",
+ properties={
+ "location": {
+ "type": "string",
+ "description": "The city and state, e.g. San Francisco, CA",
},
- required=["location", "format"],
- )
- tools = ToolsSchema(standard_tools=[weather_function])
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ "format": {
+ "type": "string",
+ "enum": ["celsius", "fahrenheit"],
+ "description": "The temperature unit to use. Infer this from the user's location.",
},
+ },
+ required=["location", "format"],
+ )
+ tools = ToolsSchema(standard_tools=[weather_function])
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages, tools)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ context_aggregator.user(),
+ llm,
+ tts,
+ transport.output(),
+ context_aggregator.assistant(),
]
+ )
- context = OpenAILLMContext(messages, tools)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(),
- context_aggregator.user(),
- llm,
- tts,
- transport.output(),
- context_aggregator.assistant(),
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- runner = PipelineRunner()
+ runner = PipelineRunner(handle_sigint=False)
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/14k-function-calling-cerebras.py b/examples/foundational/14k-function-calling-cerebras.py
index fbe27f35b..151ec5ae8 100644
--- a/examples/foundational/14k-function-calling-cerebras.py
+++ b/examples/foundational/14k-function-calling-cerebras.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
@@ -23,66 +20,64 @@ from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.cerebras.llm import CerebrasLLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
await llm.push_frame(TTSSpeakFrame("Let me check on that."))
await result_callback({"conditions": "nice", "temperature": "75"})
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- llm = CerebrasLLMService(api_key=os.getenv("CEREBRAS_API_KEY"), model="llama-3.3-70b")
- # You can also register a function_name of None to get all functions
- # sent to the same callback with an additional function_name parameter.
- llm.register_function("get_current_weather", fetch_weather_from_api)
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- weather_function = FunctionSchema(
- name="get_current_weather",
- description="Get the current weather",
- properties={
- "location": {
- "type": "string",
- "description": "The city and state, e.g. San Francisco, CA",
- },
- "format": {
- "type": "string",
- "enum": ["celsius", "fahrenheit"],
- "description": "The temperature unit to use. Infer this from the user's location.",
- },
+ llm = CerebrasLLMService(api_key=os.getenv("CEREBRAS_API_KEY"), model="llama-3.3-70b")
+ # You can also register a function_name of None to get all functions
+ # sent to the same callback with an additional function_name parameter.
+ llm.register_function("get_current_weather", fetch_weather_from_api)
+
+ weather_function = FunctionSchema(
+ name="get_current_weather",
+ description="Get the current weather",
+ properties={
+ "location": {
+ "type": "string",
+ "description": "The city and state, e.g. San Francisco, CA",
},
- required=["location", "format"],
- )
- tools = ToolsSchema(standard_tools=[weather_function])
- messages = [
- {
- "role": "system",
- "content": """You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way.
+ "format": {
+ "type": "string",
+ "enum": ["celsius", "fahrenheit"],
+ "description": "The temperature unit to use. Infer this from the user's location.",
+ },
+ },
+ required=["location", "format"],
+ )
+ tools = ToolsSchema(standard_tools=[weather_function])
+ messages = [
+ {
+ "role": "system",
+ "content": """You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way.
You have one functions available:
@@ -92,44 +87,56 @@ Infer whether to use Fahrenheit or Celsius automatically based on the location,
Start by asking me for my location. Then, use 'get_weather_current' to give me a forecast.
- Respond to what the user said in a creative and helpful way.""",
- },
+ Respond to what the user said in a creative and helpful way.""",
+ },
+ ]
+
+ context = OpenAILLMContext(messages, tools)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ context_aggregator.user(),
+ llm,
+ tts,
+ transport.output(),
+ context_aggregator.assistant(),
]
+ )
- context = OpenAILLMContext(messages, tools)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(),
- context_aggregator.user(),
- llm,
- tts,
- transport.output(),
- context_aggregator.assistant(),
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- runner = PipelineRunner()
+ runner = PipelineRunner(handle_sigint=False)
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/14l-function-calling-deepseek.py b/examples/foundational/14l-function-calling-deepseek.py
index 674579b75..61ee57072 100644
--- a/examples/foundational/14l-function-calling-deepseek.py
+++ b/examples/foundational/14l-function-calling-deepseek.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
@@ -22,67 +19,65 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.deepseek.llm import DeepSeekLLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
await llm.push_frame(TTSSpeakFrame("Let me check on that."))
await result_callback({"conditions": "nice", "temperature": "75"})
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- llm = DeepSeekLLMService(api_key=os.getenv("DEEPSEEK_API_KEY"), model="deepseek-chat")
- # You can also register a function_name of None to get all functions
- # sent to the same callback with an additional function_name parameter.
- llm.register_function("get_current_weather", fetch_weather_from_api)
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- weather_function = FunctionSchema(
- name="get_current_weather",
- description="Get the current weather",
- properties={
- "location": {
- "type": "string",
- "description": "The city and state, e.g. San Francisco, CA",
- },
- "format": {
- "type": "string",
- "enum": ["celsius", "fahrenheit"],
- "description": "The temperature unit to use. Infer this from the user's location.",
- },
+ llm = DeepSeekLLMService(api_key=os.getenv("DEEPSEEK_API_KEY"), model="deepseek-chat")
+ # You can also register a function_name of None to get all functions
+ # sent to the same callback with an additional function_name parameter.
+ llm.register_function("get_current_weather", fetch_weather_from_api)
+
+ weather_function = FunctionSchema(
+ name="get_current_weather",
+ description="Get the current weather",
+ properties={
+ "location": {
+ "type": "string",
+ "description": "The city and state, e.g. San Francisco, CA",
},
- required=["location", "format"],
- )
- tools = ToolsSchema(standard_tools=[weather_function])
- messages = [
- {
- "role": "system",
- "content": """You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way.
+ "format": {
+ "type": "string",
+ "enum": ["celsius", "fahrenheit"],
+ "description": "The temperature unit to use. Infer this from the user's location.",
+ },
+ },
+ required=["location", "format"],
+ )
+ tools = ToolsSchema(standard_tools=[weather_function])
+ messages = [
+ {
+ "role": "system",
+ "content": """You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way.
You have one functions available:
@@ -92,44 +87,56 @@ Infer whether to use Fahrenheit or Celsius automatically based on the location,
Start by asking me for my location. Then, use 'get_weather_current' to give me a forecast.
- Respond to what the user said in a creative and helpful way.""",
- },
+ Respond to what the user said in a creative and helpful way.""",
+ },
+ ]
+
+ context = OpenAILLMContext(messages, tools)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ context_aggregator.user(),
+ llm,
+ tts,
+ transport.output(),
+ context_aggregator.assistant(),
]
+ )
- context = OpenAILLMContext(messages, tools)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(),
- context_aggregator.user(),
- llm,
- tts,
- transport.output(),
- context_aggregator.assistant(),
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- runner = PipelineRunner()
+ runner = PipelineRunner(handle_sigint=False)
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/14m-function-calling-openrouter.py b/examples/foundational/14m-function-calling-openrouter.py
index 6ae4c4e7a..9ff11e741 100644
--- a/examples/foundational/14m-function-calling-openrouter.py
+++ b/examples/foundational/14m-function-calling-openrouter.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
@@ -22,108 +19,118 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.azure.tts import AzureTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openrouter.llm import OpenRouterLLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
await llm.push_frame(TTSSpeakFrame("Let me check on that."))
await result_callback({"conditions": "nice", "temperature": "75"})
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- tts = AzureTTSService(
- api_key=os.getenv("AZURE_API_KEY"),
- region="eastus",
- voice="en-US-JennyNeural",
- params=AzureTTSService.InputParams(language="en-US", rate="1.1", style="cheerful"),
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- llm = OpenRouterLLMService(
- api_key=os.getenv("OPENROUTER_API_KEY"), model="openai/gpt-4o-2024-11-20"
- )
- # You can also register a function_name of None to get all functions
- # sent to the same callback with an additional function_name parameter.
- llm.register_function("get_current_weather", fetch_weather_from_api)
+ tts = AzureTTSService(
+ api_key=os.getenv("AZURE_API_KEY"),
+ region="eastus",
+ voice="en-US-JennyNeural",
+ params=AzureTTSService.InputParams(language="en-US", rate="1.1", style="cheerful"),
+ )
- weather_function = FunctionSchema(
- name="get_current_weather",
- description="Get the current weather",
- properties={
- "location": {
- "type": "string",
- "description": "The city and state, e.g. San Francisco, CA",
- },
- "format": {
- "type": "string",
- "enum": ["celsius", "fahrenheit"],
- "description": "The temperature unit to use. Infer this from the user's location.",
- },
+ llm = OpenRouterLLMService(
+ api_key=os.getenv("OPENROUTER_API_KEY"), model="openai/gpt-4o-2024-11-20"
+ )
+ # You can also register a function_name of None to get all functions
+ # sent to the same callback with an additional function_name parameter.
+ llm.register_function("get_current_weather", fetch_weather_from_api)
+
+ weather_function = FunctionSchema(
+ name="get_current_weather",
+ description="Get the current weather",
+ properties={
+ "location": {
+ "type": "string",
+ "description": "The city and state, e.g. San Francisco, CA",
},
- required=["location", "format"],
- )
- tools = ToolsSchema(standard_tools=[weather_function])
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ "format": {
+ "type": "string",
+ "enum": ["celsius", "fahrenheit"],
+ "description": "The temperature unit to use. Infer this from the user's location.",
},
+ },
+ required=["location", "format"],
+ )
+ tools = ToolsSchema(standard_tools=[weather_function])
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages, tools)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ context_aggregator.user(),
+ llm,
+ tts,
+ transport.output(),
+ context_aggregator.assistant(),
]
+ )
- context = OpenAILLMContext(messages, tools)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(),
- context_aggregator.user(),
- llm,
- tts,
- transport.output(),
- context_aggregator.assistant(),
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- runner = PipelineRunner()
+ runner = PipelineRunner(handle_sigint=False)
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/14n-function-calling-perplexity.py b/examples/foundational/14n-function-calling-perplexity.py
index 47afe5918..168441611 100644
--- a/examples/foundational/14n-function-calling-perplexity.py
+++ b/examples/foundational/14n-function-calling-perplexity.py
@@ -11,14 +11,11 @@ currently support function calling. The example shows basic chat completion func
using Perplexity's API while maintaining compatibility with the OpenAI interface.
"""
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
@@ -26,79 +23,89 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.perplexity.llm import PerplexityLLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- llm = PerplexityLLMService(api_key=os.getenv("PERPLEXITY_API_KEY"), model="sonar")
+ llm = PerplexityLLMService(api_key=os.getenv("PERPLEXITY_API_KEY"), model="sonar")
- messages = [
- {
- "role": "user",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
+ messages = [
+ {
+ "role": "user",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ context_aggregator.user(),
+ llm,
+ tts,
+ transport.output(),
+ context_aggregator.assistant(),
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(),
- context_aggregator.user(),
- llm,
- tts,
- transport.output(),
- context_aggregator.assistant(),
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- runner = PipelineRunner()
+ runner = PipelineRunner(handle_sigint=False)
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/14o-function-calling-gemini-openai-format.py b/examples/foundational/14o-function-calling-gemini-openai-format.py
index bbd205e41..d82dcc706 100644
--- a/examples/foundational/14o-function-calling-gemini-openai-format.py
+++ b/examples/foundational/14o-function-calling-gemini-openai-format.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
@@ -21,104 +18,114 @@ from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
from pipecat.services.google.llm_openai import GoogleLLMOpenAIBetaService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
await llm.push_frame(TTSSpeakFrame("Let me check on that."))
await result_callback({"conditions": "nice", "temperature": "75"})
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- tts = ElevenLabsTTSService(
- api_key=os.getenv("ELEVENLABS_API_KEY", ""),
- voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- llm = GoogleLLMOpenAIBetaService(api_key=os.getenv("GEMINI_API_KEY"))
- # You can aslo register a function_name of None to get all functions
- # sent to the same callback with an additional function_name parameter.
- llm.register_function("get_current_weather", fetch_weather_from_api)
+ tts = ElevenLabsTTSService(
+ api_key=os.getenv("ELEVENLABS_API_KEY", ""),
+ voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
+ )
- weather_function = FunctionSchema(
- name="get_current_weather",
- description="Get the current weather",
- properties={
- "location": {
- "type": "string",
- "description": "The city and state, e.g. San Francisco, CA",
- },
- "format": {
- "type": "string",
- "enum": ["celsius", "fahrenheit"],
- "description": "The temperature unit to use. Infer this from the user's location.",
- },
+ llm = GoogleLLMOpenAIBetaService(api_key=os.getenv("GEMINI_API_KEY"))
+ # You can aslo register a function_name of None to get all functions
+ # sent to the same callback with an additional function_name parameter.
+ llm.register_function("get_current_weather", fetch_weather_from_api)
+
+ weather_function = FunctionSchema(
+ name="get_current_weather",
+ description="Get the current weather",
+ properties={
+ "location": {
+ "type": "string",
+ "description": "The city and state, e.g. San Francisco, CA",
},
- required=["location", "format"],
- )
- tools = ToolsSchema(standard_tools=[weather_function])
- messages = [
- {
- "role": "user",
- "content": "Start a conversation with 'Hey there' to get the current weather.",
+ "format": {
+ "type": "string",
+ "enum": ["celsius", "fahrenheit"],
+ "description": "The temperature unit to use. Infer this from the user's location.",
},
+ },
+ required=["location", "format"],
+ )
+ tools = ToolsSchema(standard_tools=[weather_function])
+ messages = [
+ {
+ "role": "user",
+ "content": "Start a conversation with 'Hey there' to get the current weather.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages, tools)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ context_aggregator.user(),
+ llm,
+ tts,
+ transport.output(),
+ context_aggregator.assistant(),
]
+ )
- context = OpenAILLMContext(messages, tools)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(),
- context_aggregator.user(),
- llm,
- tts,
- transport.output(),
- context_aggregator.assistant(),
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- runner = PipelineRunner()
+ runner = PipelineRunner(handle_sigint=False)
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/14p-function-calling-gemini-vertex-ai.py b/examples/foundational/14p-function-calling-gemini-vertex-ai.py
index edb9bed5a..19738a827 100644
--- a/examples/foundational/14p-function-calling-gemini-vertex-ai.py
+++ b/examples/foundational/14p-function-calling-gemini-vertex-ai.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
@@ -21,110 +18,120 @@ from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
from pipecat.services.google.llm_vertex import GoogleVertexLLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
await llm.push_frame(TTSSpeakFrame("Let me check on that."))
await result_callback({"conditions": "nice", "temperature": "75"})
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
+
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+ tts = ElevenLabsTTSService(
+ api_key=os.getenv("ELEVENLABS_API_KEY", ""),
+ voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
+ )
+
+ llm = GoogleVertexLLMService(
+ # credentials="",
+ params=GoogleVertexLLMService.InputParams(
+ project_id="",
)
+ )
+ # You can aslo register a function_name of None to get all functions
+ # sent to the same callback with an additional function_name parameter.
+ llm.register_function("get_current_weather", fetch_weather_from_api)
- tts = ElevenLabsTTSService(
- api_key=os.getenv("ELEVENLABS_API_KEY", ""),
- voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
- )
-
- llm = GoogleVertexLLMService(
- # credentials="",
- params=GoogleVertexLLMService.InputParams(
- project_id="",
- )
- )
- # You can aslo register a function_name of None to get all functions
- # sent to the same callback with an additional function_name parameter.
- llm.register_function("get_current_weather", fetch_weather_from_api)
-
- weather_function = FunctionSchema(
- name="get_current_weather",
- description="Get the current weather",
- properties={
- "location": {
- "type": "string",
- "description": "The city and state, e.g. San Francisco, CA",
- },
- "format": {
- "type": "string",
- "enum": ["celsius", "fahrenheit"],
- "description": "The temperature unit to use. Infer this from the user's location.",
- },
+ weather_function = FunctionSchema(
+ name="get_current_weather",
+ description="Get the current weather",
+ properties={
+ "location": {
+ "type": "string",
+ "description": "The city and state, e.g. San Francisco, CA",
},
- required=["location", "format"],
- )
- tools = ToolsSchema(standard_tools=[weather_function])
-
- messages = [
- {
- "role": "user",
- "content": "Start a conversation with 'Hey there' to get the current weather.",
+ "format": {
+ "type": "string",
+ "enum": ["celsius", "fahrenheit"],
+ "description": "The temperature unit to use. Infer this from the user's location.",
},
+ },
+ required=["location", "format"],
+ )
+ tools = ToolsSchema(standard_tools=[weather_function])
+
+ messages = [
+ {
+ "role": "user",
+ "content": "Start a conversation with 'Hey there' to get the current weather.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages, tools)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ context_aggregator.user(),
+ llm,
+ tts,
+ transport.output(),
+ context_aggregator.assistant(),
]
+ )
- context = OpenAILLMContext(messages, tools)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(),
- context_aggregator.user(),
- llm,
- tts,
- transport.output(),
- context_aggregator.assistant(),
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- runner = PipelineRunner()
+ runner = PipelineRunner(handle_sigint=False)
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/14q-function-calling-qwen.py b/examples/foundational/14q-function-calling-qwen.py
index 43b290040..1ef29a369 100644
--- a/examples/foundational/14q-function-calling-qwen.py
+++ b/examples/foundational/14q-function-calling-qwen.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
@@ -22,106 +19,116 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.qwen.llm import QwenLLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
await llm.push_frame(TTSSpeakFrame("Let me check on that."))
await result_callback({"conditions": "nice", "temperature": "75"})
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- llm = QwenLLMService(api_key=os.getenv("QWEN_API_KEY"), model="qwen2.5-72b-instruct")
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- # You can also register a function_name of None to get all functions
- # sent to the same callback with an additional function_name parameter.
- llm.register_function("get_current_weather", fetch_weather_from_api)
+ llm = QwenLLMService(api_key=os.getenv("QWEN_API_KEY"), model="qwen2.5-72b-instruct")
- weather_function = FunctionSchema(
- name="get_current_weather",
- description="Get the current weather",
- properties={
- "location": {
- "type": "string",
- "description": "The city and state, e.g. San Francisco, CA",
- },
- "format": {
- "type": "string",
- "enum": ["celsius", "fahrenheit"],
- "description": "The temperature unit to use. Infer this from the user's location.",
- },
+ # You can also register a function_name of None to get all functions
+ # sent to the same callback with an additional function_name parameter.
+ llm.register_function("get_current_weather", fetch_weather_from_api)
+
+ weather_function = FunctionSchema(
+ name="get_current_weather",
+ description="Get the current weather",
+ properties={
+ "location": {
+ "type": "string",
+ "description": "The city and state, e.g. San Francisco, CA",
},
- required=["location", "format"],
- )
- tools = ToolsSchema(standard_tools=[weather_function])
-
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ "format": {
+ "type": "string",
+ "enum": ["celsius", "fahrenheit"],
+ "description": "The temperature unit to use. Infer this from the user's location.",
},
+ },
+ required=["location", "format"],
+ )
+ tools = ToolsSchema(standard_tools=[weather_function])
+
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages, tools)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ context_aggregator.user(),
+ llm,
+ tts,
+ transport.output(),
+ context_aggregator.assistant(),
]
+ )
- context = OpenAILLMContext(messages, tools)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(),
- context_aggregator.user(),
- llm,
- tts,
- transport.output(),
- context_aggregator.assistant(),
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- runner = PipelineRunner()
+ runner = PipelineRunner(handle_sigint=False)
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/15-switch-voices.py b/examples/foundational/15-switch-voices.py
index 8743f6aad..b429b6ddc 100644
--- a/examples/foundational/15-switch-voices.py
+++ b/examples/foundational/15-switch-voices.py
@@ -4,15 +4,12 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
from openai.types.chat import ChatCompletionToolParam
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.parallel_pipeline import ParallelPipeline
@@ -22,13 +19,14 @@ from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.filters.function_filter import FunctionFilter
from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
current_voice = "News Lady"
@@ -55,105 +53,115 @@ async def barbershop_man_filter(frame) -> bool:
return current_voice == "Barbershop Man"
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Pipecat",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- news_lady = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="bf991597-6c13-47e4-8411-91ec2de5c466", # Newslady
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- british_lady = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ news_lady = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="bf991597-6c13-47e4-8411-91ec2de5c466", # Newslady
+ )
- barbershop_man = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="a0e99841-438c-4a64-b679-ae501e7d6091", # Barbershop Man
- )
+ british_lady = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
- llm.register_function("switch_voice", switch_voice)
+ barbershop_man = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="a0e99841-438c-4a64-b679-ae501e7d6091", # Barbershop Man
+ )
- tools = [
- ChatCompletionToolParam(
- type="function",
- function={
- "name": "switch_voice",
- "description": "Switch your voice only when the user asks you to",
- "parameters": {
- "type": "object",
- "properties": {
- "voice": {
- "type": "string",
- "description": "The voice the user wants you to use",
- },
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+ llm.register_function("switch_voice", switch_voice)
+
+ tools = [
+ ChatCompletionToolParam(
+ type="function",
+ function={
+ "name": "switch_voice",
+ "description": "Switch your voice only when the user asks you to",
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "voice": {
+ "type": "string",
+ "description": "The voice the user wants you to use",
},
- "required": ["voice"],
},
+ "required": ["voice"],
},
- )
+ },
+ )
+ ]
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities. Respond to what the user said in a creative and helpful way. Your output should not include non-alphanumeric characters. You can do the following voices: 'News Lady', 'British Lady' and 'Barbershop Man'.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages, tools)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt,
+ context_aggregator.user(), # User responses
+ llm, # LLM
+ ParallelPipeline( # TTS (one of the following vocies)
+ [FunctionFilter(news_lady_filter), news_lady], # News Lady voice
+ [
+ FunctionFilter(british_lady_filter),
+ british_lady,
+ ], # British Reading Lady voice
+ [FunctionFilter(barbershop_man_filter), barbershop_man], # Barbershop Man voice
+ ),
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
]
- messages = [
+ )
+
+ task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
+
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append(
{
"role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities. Respond to what the user said in a creative and helpful way. Your output should not include non-alphanumeric characters. You can do the following voices: 'News Lady', 'British Lady' and 'Barbershop Man'.",
- },
- ]
-
- context = OpenAILLMContext(messages, tools)
- context_aggregator = llm.create_context_aggregator(context)
-
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- context_aggregator.user(), # User responses
- llm, # LLM
- ParallelPipeline( # TTS (one of the following vocies)
- [FunctionFilter(news_lady_filter), news_lady], # News Lady voice
- [
- FunctionFilter(british_lady_filter),
- british_lady,
- ], # British Reading Lady voice
- [FunctionFilter(barbershop_man_filter), barbershop_man], # Barbershop Man voice
- ),
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses
- ]
+ "content": f"Please introduce yourself to the user and let them know the voices you can do. Your initial responses should be as if you were a {current_voice}.",
+ }
)
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- messages.append(
- {
- "role": "system",
- "content": f"Please introduce yourself to the user and let them know the voices you can do. Your initial responses should be as if you were a {current_voice}.",
- }
- )
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- runner = PipelineRunner()
+ runner = PipelineRunner(handle_sigint=False)
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/15a-switch-languages.py b/examples/foundational/15a-switch-languages.py
index dd094f8ea..3e74a8dc1 100644
--- a/examples/foundational/15a-switch-languages.py
+++ b/examples/foundational/15a-switch-languages.py
@@ -4,16 +4,13 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from deepgram import LiveOptions
from dotenv import load_dotenv
from loguru import logger
from openai.types.chat import ChatCompletionToolParam
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.parallel_pipeline import ParallelPipeline
@@ -25,12 +22,12 @@ from pipecat.processors.filters.function_filter import FunctionFilter
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
current_language = "English"
@@ -49,101 +46,108 @@ async def spanish_filter(frame) -> bool:
return current_language == "Spanish"
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Pipecat",
- DailyParams(
- audio_out_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- stt = DeepgramSTTService(
- api_key=os.getenv("DEEPGRAM_API_KEY"), live_options=LiveOptions(language="multi")
- )
+ stt = DeepgramSTTService(
+ api_key=os.getenv("DEEPGRAM_API_KEY"), live_options=LiveOptions(language="multi")
+ )
- english_tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ english_tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- spanish_tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="846d6cb0-2301-48b6-9683-48f5618ea2f6", # Spanish-speaking Lady
- )
+ spanish_tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="d4db5fb9-f44b-4bd1-85fa-192e0f0d75f9", # Spanish-speaking Lady
+ )
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
- llm.register_function("switch_language", switch_language)
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+ llm.register_function("switch_language", switch_language)
- tools = [
- ChatCompletionToolParam(
- type="function",
- function={
- "name": "switch_language",
- "description": "Switch to another language when the user asks you to",
- "parameters": {
- "type": "object",
- "properties": {
- "language": {
- "type": "string",
- "description": "The language the user wants you to speak",
- },
+ tools = [
+ ChatCompletionToolParam(
+ type="function",
+ function={
+ "name": "switch_language",
+ "description": "Switch to another language when the user asks you to",
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "language": {
+ "type": "string",
+ "description": "The language the user wants you to speak",
},
- "required": ["language"],
},
+ "required": ["language"],
},
- )
+ },
+ )
+ ]
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities. Respond to what the user said in a creative and helpful way. Your output should not include non-alphanumeric characters. You can speak the following languages: 'English' and 'Spanish'.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages, tools)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt, # STT
+ context_aggregator.user(), # User responses
+ llm, # LLM
+ ParallelPipeline( # TTS (bot will speak the chosen language)
+ [FunctionFilter(english_filter), english_tts], # English
+ [FunctionFilter(spanish_filter), spanish_tts], # Spanish
+ ),
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
]
- messages = [
+ )
+
+ task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
+
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append(
{
"role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities. Respond to what the user said in a creative and helpful way. Your output should not include non-alphanumeric characters. You can speak the following languages: 'English' and 'Spanish'.",
- },
- ]
-
- context = OpenAILLMContext(messages, tools)
- context_aggregator = llm.create_context_aggregator(context)
-
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- stt, # STT
- context_aggregator.user(), # User responses
- llm, # LLM
- ParallelPipeline( # TTS (bot will speak the chosen language)
- [FunctionFilter(english_filter), english_tts], # English
- [FunctionFilter(spanish_filter), spanish_tts], # Spanish
- ),
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses
- ]
+ "content": f"Please introduce yourself to the user and let them know the languages you speak. Your initial responses should be in {current_language}.",
+ }
)
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- messages.append(
- {
- "role": "system",
- "content": f"Please introduce yourself to the user and let them know the languages you speak. Your initial responses should be in {current_language}.",
- }
- )
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- runner = PipelineRunner()
+ runner = PipelineRunner(handle_sigint=False)
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/16-gpu-container-local-bot.py b/examples/foundational/16-gpu-container-local-bot.py
index 07afcfa7b..d5e560010 100644
--- a/examples/foundational/16-gpu-container-local-bot.py
+++ b/examples/foundational/16-gpu-container-local-bot.py
@@ -4,139 +4,138 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.deepgram.tts import DeepgramTTSService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import (
- DailyParams,
- DailyTransport,
- DailyTransportMessageFrame,
-)
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+from pipecat.transports.services.daily import DailyTransportMessageFrame
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- tts = DeepgramTTSService(
- aiohttp_session=session,
- api_key=os.getenv("DEEPGRAM_API_KEY"),
- voice="aura-asteria-en",
- base_url="http://0.0.0.0:8080/v1/speak",
- )
+ tts = DeepgramTTSService(
+ api_key=os.getenv("DEEPGRAM_API_KEY"),
+ voice="aura-asteria-en",
+ base_url="http://0.0.0.0:8080",
+ )
- llm = OpenAILLMService(
- # To use OpenAI
- # api_key=os.getenv("OPENAI_API_KEY"),
- # model="gpt-4o"
- # Or, to use a local vLLM (or similar) api server
- model="meta-llama/Meta-Llama-3-8B-Instruct",
- base_url="http://0.0.0.0:8000/v1",
- )
+ llm = OpenAILLMService(
+ # To use OpenAI
+ # api_key=os.getenv("OPENAI_API_KEY"),
+ # Or, to use a local vLLM (or similar) api server
+ model="meta-llama/Meta-Llama-3-8B-Instruct",
+ base_url="http://0.0.0.0:8000/v1",
+ )
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt, # STT
+ context_aggregator.user(),
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(),
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- context_aggregator.user(),
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(),
- ]
- )
+ # When the first participant joins, the bot should introduce itself.
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- ),
- )
-
- # When a participant joins, start transcription for that participant so the
- # bot can "hear" and respond to them.
- @transport.event_handler("on_participant_joined")
- async def on_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
-
- # When the first participant joins, the bot should introduce itself.
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- messages.append({"role": "system", "content": "Please introduce yourself to the user."})
- await task.queue_frames([context_aggregator.user().get_context_frame()])
-
- # Handle "latency-ping" messages. The client will send app messages that look like
- # this:
- # { "latency-ping": { ts: }}
- #
- # We want to send an immediate pong back to the client from this handler function.
- # Also, we will push a frame into the top of the pipeline and send it after the
- #
- @transport.event_handler("on_app_message")
- async def on_app_message(transport, message, sender):
- try:
- if "latency-ping" in message:
- logger.debug(f"Received latency ping app message: {message}")
- ts = message["latency-ping"]["ts"]
- # Send immediately
- transport.output().send_message(
- DailyTransportMessageFrame(
- message={"latency-pong-msg-handler": {"ts": ts}}, participant_id=sender
- )
+ # Handle "latency-ping" messages. The client will send app messages that look like
+ # this:
+ # { "latency-ping": { ts: }}
+ #
+ # We want to send an immediate pong back to the client from this handler function.
+ # Also, we will push a frame into the top of the pipeline and send it after the
+ #
+ @transport.event_handler("on_app_message")
+ async def on_app_message(transport, message, sender):
+ try:
+ if "latency-ping" in message:
+ logger.debug(f"Received latency ping app message: {message}")
+ ts = message["latency-ping"]["ts"]
+ # Send immediately
+ transport.output().send_message(
+ DailyTransportMessageFrame(
+ message={"latency-pong-msg-handler": {"ts": ts}}, participant_id=sender
)
- # And push to the pipeline for the Daily transport.output to send
- await task.queue_frame(
- DailyTransportMessageFrame(
- message={"latency-pong-pipeline-delivery": {"ts": ts}},
- participant_id=sender,
- )
+ )
+ # And push to the pipeline for the Daily transport.output to send
+ await task.queue_frame(
+ DailyTransportMessageFrame(
+ message={"latency-pong-pipeline-delivery": {"ts": ts}},
+ participant_id=sender,
)
- except Exception as e:
- logger.debug(f"message handling error: {e} - {message}")
+ )
+ except Exception as e:
+ logger.debug(f"message handling error: {e} - {message}")
- runner = PipelineRunner()
- await runner.run(task)
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
+
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/17-detect-user-idle.py b/examples/foundational/17-detect-user-idle.py
index 41cd84d67..96189805e 100644
--- a/examples/foundational/17-detect-user-idle.py
+++ b/examples/foundational/17-detect-user-idle.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame, LLMMessagesFrame, TTSSpeakFrame
@@ -21,111 +18,121 @@ from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.user_idle_processor import UserIdleProcessor
from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ async def handle_user_idle(user_idle: UserIdleProcessor, retry_count: int) -> bool:
+ if retry_count == 1:
+ # First attempt: Add a gentle prompt to the conversation
+ messages.append(
+ {
+ "role": "system",
+ "content": "The user has been quiet. Politely and briefly ask if they're still there.",
+ }
+ )
+ await user_idle.push_frame(LLMMessagesFrame(messages))
+ return True
+ elif retry_count == 2:
+ # Second attempt: More direct prompt
+ messages.append(
+ {
+ "role": "system",
+ "content": "The user is still inactive. Ask if they'd like to continue our conversation.",
+ }
+ )
+ await user_idle.push_frame(LLMMessagesFrame(messages))
+ return True
+ else:
+ # Third attempt: End the conversation
+ await user_idle.push_frame(
+ TTSSpeakFrame("It seems like you're busy right now. Have a nice day!")
+ )
+ await task.queue_frame(EndFrame())
+ return False
+
+ user_idle = UserIdleProcessor(callback=handle_user_idle, timeout=5.0)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt,
+ user_idle, # Idle user check-in
+ context_aggregator.user(),
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(),
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- async def handle_user_idle(user_idle: UserIdleProcessor, retry_count: int) -> bool:
- if retry_count == 1:
- # First attempt: Add a gentle prompt to the conversation
- messages.append(
- {
- "role": "system",
- "content": "The user has been quiet. Politely and briefly ask if they're still there.",
- }
- )
- await user_idle.push_frame(LLMMessagesFrame(messages))
- return True
- elif retry_count == 2:
- # Second attempt: More direct prompt
- messages.append(
- {
- "role": "system",
- "content": "The user is still inactive. Ask if they'd like to continue our conversation.",
- }
- )
- await user_idle.push_frame(LLMMessagesFrame(messages))
- return True
- else:
- # Third attempt: End the conversation
- await user_idle.push_frame(
- TTSSpeakFrame("It seems like you're busy right now. Have a nice day!")
- )
- await task.queue_frame(EndFrame())
- return False
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- user_idle = UserIdleProcessor(callback=handle_user_idle, timeout=5.0)
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- user_idle, # Idle user check-in
- context_aggregator.user(),
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(),
- ]
- )
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ runner = PipelineRunner(handle_sigint=False)
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- messages.append({"role": "system", "content": "Please introduce yourself to the user."})
- await task.queue_frames([context_aggregator.user().get_context_frame()])
-
- runner = PipelineRunner()
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/18-gstreamer-filesrc.py b/examples/foundational/18-gstreamer-filesrc.py
index 12bec25bd..92dcf973e 100644
--- a/examples/foundational/18-gstreamer-filesrc.py
+++ b/examples/foundational/18-gstreamer-filesrc.py
@@ -5,67 +5,61 @@
#
import argparse
-import asyncio
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure_with_args
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.processors.gstreamer.pipeline_source import GStreamerPipelineSource
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, args: argparse.Namespace):
+ logger.info(f"Starting bot with video input: {args.input}")
-async def main():
- async with aiohttp.ClientSession() as session:
- parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")
- parser.add_argument("-i", "--input", type=str, required=True, help="Input video file")
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_out_enabled=True,
+ video_out_enabled=True,
+ video_out_is_live=True,
+ video_out_width=1280,
+ video_out_height=720,
+ ),
+ )
- (room_url, _, args) = await configure_with_args(session, parser)
+ gst = GStreamerPipelineSource(
+ pipeline=f"filesrc location={args.input}",
+ out_params=GStreamerPipelineSource.OutputParams(
+ video_width=1280,
+ video_height=720,
+ ),
+ )
- transport = DailyTransport(
- room_url,
- None,
- "GStreamer",
- DailyParams(
- audio_out_enabled=True,
- camera_out_enabled=True,
- camera_out_width=1280,
- camera_out_height=720,
- camera_out_is_live=True,
- ),
- )
+ pipeline = Pipeline(
+ [
+ gst, # GStreamer file source
+ transport.output(), # Transport bot output
+ ]
+ )
- gst = GStreamerPipelineSource(
- pipeline=f"filesrc location={args.input}",
- out_params=GStreamerPipelineSource.OutputParams(
- video_width=1280,
- video_height=720,
- ),
- )
+ task = PipelineTask(pipeline)
- pipeline = Pipeline(
- [
- gst, # GStreamer file source
- transport.output(), # Transport bot output
- ]
- )
+ runner = PipelineRunner(handle_sigint=False)
- task = PipelineTask(pipeline)
-
- runner = PipelineRunner()
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ parser = argparse.ArgumentParser(description="Pipecat Bot Runner")
+ parser.add_argument("-i", "--input", type=str, required=True, help="Input video file")
+
+ main(parser)
diff --git a/examples/foundational/18a-gstreamer-videotestsrc.py b/examples/foundational/18a-gstreamer-videotestsrc.py
index b1139c12d..ece124667 100644
--- a/examples/foundational/18a-gstreamer-videotestsrc.py
+++ b/examples/foundational/18a-gstreamer-videotestsrc.py
@@ -4,62 +4,58 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
-import sys
+import argparse
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.processors.gstreamer.pipeline_source import GStreamerPipelineSource
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot with video test source")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, _) = await configure(session)
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ video_out_enabled=True,
+ video_out_is_live=True,
+ video_out_width=1280,
+ video_out_height=720,
+ ),
+ )
- transport = DailyTransport(
- room_url,
- None,
- "GStreamer",
- DailyParams(
- camera_out_enabled=True,
- camera_out_width=1280,
- camera_out_height=720,
- camera_out_is_live=True,
- ),
- )
+ gst = GStreamerPipelineSource(
+ pipeline='videotestsrc ! capsfilter caps="video/x-raw,width=1280,height=720,framerate=30/1"',
+ out_params=GStreamerPipelineSource.OutputParams(
+ video_width=1280, video_height=720, clock_sync=False
+ ),
+ )
- gst = GStreamerPipelineSource(
- pipeline='videotestsrc ! capsfilter caps="video/x-raw,width=1280,height=720,framerate=30/1"',
- out_params=GStreamerPipelineSource.OutputParams(
- video_width=1280, video_height=720, clock_sync=False
- ),
- )
+ pipeline = Pipeline(
+ [
+ gst, # GStreamer test source
+ transport.output(), # Transport bot output
+ ]
+ )
- pipeline = Pipeline(
- [
- gst, # GStreamer file source
- transport.output(), # Transport bot output
- ]
- )
+ task = PipelineTask(pipeline)
- task = PipelineTask(pipeline)
+ runner = PipelineRunner(handle_sigint=False)
- runner = PipelineRunner()
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/19-openai-realtime-beta.py b/examples/foundational/19-openai-realtime-beta.py
index 3aff14e65..076b25ec7 100644
--- a/examples/foundational/19-openai-realtime-beta.py
+++ b/examples/foundational/19-openai-realtime-beta.py
@@ -4,15 +4,12 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
from datetime import datetime
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
@@ -29,13 +26,12 @@ from pipecat.services.openai_realtime_beta import (
SemanticTurnDetection,
SessionProperties,
)
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
temperature = 75 if args["format"] == "fahrenheit" else 24
@@ -70,34 +66,28 @@ weather_function = FunctionSchema(
tools = ToolsSchema(standard_tools=[weather_function])
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_in_enabled=True,
- audio_out_enabled=True,
- transcription_enabled=False,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.8)),
- vad_audio_passthrough=True,
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.8)),
+ ),
+ )
- session_properties = SessionProperties(
- input_audio_transcription=InputAudioTranscription(),
- # Set openai TurnDetection parameters. Not setting this at all will turn it
- # on by default
- turn_detection=SemanticTurnDetection(),
- # Or set to False to disable openai turn detection and use transport VAD
- # turn_detection=False,
- input_audio_noise_reduction=InputAudioNoiseReduction(type="near_field"),
- # tools=tools,
- instructions="""Your knowledge cutoff is 2023-10. You are a helpful and friendly AI.
+ session_properties = SessionProperties(
+ input_audio_transcription=InputAudioTranscription(),
+ # Set openai TurnDetection parameters. Not setting this at all will turn it
+ # on by default
+ turn_detection=SemanticTurnDetection(),
+ # Or set to False to disable openai turn detection and use transport VAD
+ # turn_detection=False,
+ input_audio_noise_reduction=InputAudioNoiseReduction(type="near_field"),
+ # tools=tools,
+ instructions="""Your knowledge cutoff is 2023-10. You are a helpful and friendly AI.
Act like a human, but remember that you aren't a human and that you can't do human
things in the real world. Your voice and personality should be warm and engaging, with a lively and
@@ -111,68 +101,79 @@ You are participating in a voice conversation. Keep your responses concise, shor
unless specifically asked to elaborate on a topic.
Remember, your responses should be short. Just one or two sentences, usually.""",
- )
+ )
- llm = OpenAIRealtimeBetaLLMService(
- api_key=os.getenv("OPENAI_API_KEY"),
- session_properties=session_properties,
- start_audio_paused=False,
- )
+ llm = OpenAIRealtimeBetaLLMService(
+ api_key=os.getenv("OPENAI_API_KEY"),
+ session_properties=session_properties,
+ start_audio_paused=False,
+ )
- # you can either register a single function for all function calls, or specific functions
- # llm.register_function(None, fetch_weather_from_api)
- llm.register_function("get_current_weather", fetch_weather_from_api)
+ # you can either register a single function for all function calls, or specific functions
+ # llm.register_function(None, fetch_weather_from_api)
+ llm.register_function("get_current_weather", fetch_weather_from_api)
- # Create a standard OpenAI LLM context object using the normal messages format. The
- # OpenAIRealtimeBetaLLMService will convert this internally to messages that the
- # openai WebSocket API can understand.
- context = OpenAILLMContext(
- [{"role": "user", "content": "Say hello!"}],
- # [{"role": "user", "content": [{"type": "text", "text": "Say hello!"}]}],
- # [
- # {
- # "role": "user",
- # "content": [
- # {"type": "text", "text": "Say"},
- # {"type": "text", "text": "yo what's up!"},
- # ],
- # }
- # ],
- tools,
- )
+ # Create a standard OpenAI LLM context object using the normal messages format. The
+ # OpenAIRealtimeBetaLLMService will convert this internally to messages that the
+ # openai WebSocket API can understand.
+ context = OpenAILLMContext(
+ [{"role": "user", "content": "Say hello!"}],
+ # [{"role": "user", "content": [{"type": "text", "text": "Say hello!"}]}],
+ # [
+ # {
+ # "role": "user",
+ # "content": [
+ # {"type": "text", "text": "Say"},
+ # {"type": "text", "text": "yo what's up!"},
+ # ],
+ # }
+ # ],
+ tools,
+ )
- context_aggregator = llm.create_context_aggregator(context)
+ context_aggregator = llm.create_context_aggregator(context)
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- context_aggregator.user(),
- llm, # LLM
- transport.output(), # Transport bot output
- context_aggregator.assistant(),
- ]
- )
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ context_aggregator.user(),
+ llm, # LLM
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(),
+ ]
+ )
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- # report_only_initial_ttfb=True,
- ),
- )
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- runner = PipelineRunner()
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- await runner.run(task)
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/19a-azure-realtime-beta.py b/examples/foundational/19a-azure-realtime-beta.py
index 2eefd4ec9..ab4a83e92 100644
--- a/examples/foundational/19a-azure-realtime-beta.py
+++ b/examples/foundational/19a-azure-realtime-beta.py
@@ -4,15 +4,12 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
from datetime import datetime
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
@@ -27,13 +24,12 @@ from pipecat.services.openai_realtime_beta import (
InputAudioTranscription,
SessionProperties,
)
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
temperature = 75 if args["format"] == "fahrenheit" else 24
@@ -69,33 +65,27 @@ weather_function = FunctionSchema(
tools = ToolsSchema(standard_tools=[weather_function])
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_in_enabled=True,
- audio_out_enabled=True,
- transcription_enabled=False,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.8)),
- vad_audio_passthrough=True,
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.8)),
+ ),
+ )
- session_properties = SessionProperties(
- input_audio_transcription=InputAudioTranscription(),
- # Set openai TurnDetection parameters. Not setting this at all will turn it
- # on by default
- # turn_detection=TurnDetection(silence_duration_ms=1000),
- # Or set to False to disable openai turn detection and use transport VAD
- # turn_detection=False,
- # tools=tools,
- instructions="""Your knowledge cutoff is 2023-10. You are a helpful and friendly AI.
+ session_properties = SessionProperties(
+ input_audio_transcription=InputAudioTranscription(model="whisper-1"),
+ # Set openai TurnDetection parameters. Not setting this at all will turn it
+ # on by default
+ # turn_detection=TurnDetection(silence_duration_ms=1000),
+ # Or set to False to disable openai turn detection and use transport VAD
+ # turn_detection=False,
+ # tools=tools,
+ instructions="""Your knowledge cutoff is 2023-10. You are a helpful and friendly AI.
Act like a human, but remember that you aren't a human and that you can't do human
things in the real world. Your voice and personality should be warm and engaging, with a lively and
@@ -109,69 +99,80 @@ You are participating in a voice conversation. Keep your responses concise, shor
unless specifically asked to elaborate on a topic.
Remember, your responses should be short. Just one or two sentences, usually.""",
- )
+ )
- llm = AzureRealtimeBetaLLMService(
- api_key=os.getenv("AZURE_REALTIME_API_KEY"),
- base_url=os.getenv("AZURE_REALTIME_BASE_URL"),
- session_properties=session_properties,
- start_audio_paused=False,
- )
+ llm = AzureRealtimeBetaLLMService(
+ api_key=os.getenv("AZURE_REALTIME_API_KEY"),
+ base_url=os.getenv("AZURE_REALTIME_BASE_URL"),
+ session_properties=session_properties,
+ start_audio_paused=False,
+ )
- # you can either register a single function for all function calls, or specific functions
- # llm.register_function(None, fetch_weather_from_api)
- llm.register_function("get_current_weather", fetch_weather_from_api)
+ # you can either register a single function for all function calls, or specific functions
+ # llm.register_function(None, fetch_weather_from_api)
+ llm.register_function("get_current_weather", fetch_weather_from_api)
- # Create a standard OpenAI LLM context object using the normal messages format. The
- # OpenAIRealtimeBetaLLMService will convert this internally to messages that the
- # openai WebSocket API can understand.
- context = OpenAILLMContext(
- [{"role": "user", "content": "Say hello!"}],
- # [{"role": "user", "content": [{"type": "text", "text": "Say hello!"}]}],
- # [
- # {
- # "role": "user",
- # "content": [
- # {"type": "text", "text": "Say"},
- # {"type": "text", "text": "yo what's up!"},
- # ],
- # }
- # ],
- tools,
- )
+ # Create a standard OpenAI LLM context object using the normal messages format. The
+ # OpenAIRealtimeBetaLLMService will convert this internally to messages that the
+ # openai WebSocket API can understand.
+ context = OpenAILLMContext(
+ [{"role": "user", "content": "Say hello!"}],
+ # [{"role": "user", "content": [{"type": "text", "text": "Say hello!"}]}],
+ # [
+ # {
+ # "role": "user",
+ # "content": [
+ # {"type": "text", "text": "Say"},
+ # {"type": "text", "text": "yo what's up!"},
+ # ],
+ # }
+ # ],
+ tools,
+ )
- context_aggregator = llm.create_context_aggregator(context)
+ context_aggregator = llm.create_context_aggregator(context)
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- context_aggregator.user(),
- llm, # LLM
- context_aggregator.assistant(),
- transport.output(), # Transport bot output
- ]
- )
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ context_aggregator.user(),
+ llm, # LLM
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(),
+ ]
+ )
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- # report_only_initial_ttfb=True,
- ),
- )
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- runner = PipelineRunner()
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- await runner.run(task)
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/20a-persistent-context-openai.py b/examples/foundational/20a-persistent-context-openai.py
index 7ea2d5df1..92873d44e 100644
--- a/examples/foundational/20a-persistent-context-openai.py
+++ b/examples/foundational/20a-persistent-context-openai.py
@@ -4,17 +4,14 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import glob
import json
import os
-import sys
from datetime import datetime
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
@@ -25,13 +22,14 @@ from pipecat.processors.aggregators.openai_llm_context import (
OpenAILLMContext,
)
from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
BASE_FILENAME = "/tmp/pipecat_conversation_"
tts = None
@@ -165,71 +163,82 @@ tools = [
]
-async def main():
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
+
global tts
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.8)),
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.8)),
+ ),
+ )
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- # you can either register a single function for all function calls, or specific functions
- # llm.register_function(None, fetch_weather_from_api)
- llm.register_function("get_current_weather", fetch_weather_from_api)
- llm.register_function("save_conversation", save_conversation)
- llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames)
- llm.register_function("load_conversation", load_conversation)
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
- context = OpenAILLMContext(messages, tools)
- context_aggregator = llm.create_context_aggregator(context)
+ # you can either register a single function for all function calls, or specific functions
+ # llm.register_function(None, fetch_weather_from_api)
+ llm.register_function("get_current_weather", fetch_weather_from_api)
+ llm.register_function("save_conversation", save_conversation)
+ llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames)
+ llm.register_function("load_conversation", load_conversation)
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- context_aggregator.user(),
- llm, # LLM
- tts,
- context_aggregator.assistant(),
- transport.output(), # Transport bot output
- ]
- )
+ context = OpenAILLMContext(messages, tools)
+ context_aggregator = llm.create_context_aggregator(context)
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- # report_only_initial_ttfb=True,
- ),
- )
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt, # STT
+ context_aggregator.user(),
+ llm, # LLM
+ tts,
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(),
+ ]
+ )
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- runner = PipelineRunner()
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- await runner.run(task)
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
+
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/20b-persistent-context-openai-realtime.py b/examples/foundational/20b-persistent-context-openai-realtime.py
index ef82bd567..79c16b83f 100644
--- a/examples/foundational/20b-persistent-context-openai-realtime.py
+++ b/examples/foundational/20b-persistent-context-openai-realtime.py
@@ -4,17 +4,15 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
+import argparse
import asyncio
import glob
import json
import os
-import sys
from datetime import datetime
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
@@ -24,19 +22,19 @@ from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import (
OpenAILLMContext,
)
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai_realtime_beta import (
InputAudioTranscription,
OpenAIRealtimeBetaLLMService,
SessionProperties,
TurnDetection,
)
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
BASE_FILENAME = "/tmp/pipecat_conversation_"
@@ -167,33 +165,29 @@ tools = [
]
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_in_enabled=True,
- audio_out_enabled=True,
- transcription_enabled=False,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.8)),
- vad_audio_passthrough=True,
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.8)),
+ ),
+ )
- session_properties = SessionProperties(
- input_audio_transcription=InputAudioTranscription(),
- # Set openai TurnDetection parameters. Not setting this at all will turn it
- # on by default
- turn_detection=TurnDetection(silence_duration_ms=1000),
- # Or set to False to disable openai turn detection and use transport VAD
- # turn_detection=False,
- # tools=tools,
- instructions="""Your knowledge cutoff is 2023-10. You are a helpful and friendly AI.
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+ session_properties = SessionProperties(
+ input_audio_transcription=InputAudioTranscription(),
+ # Set openai TurnDetection parameters. Not setting this at all will turn it
+ # on by default
+ turn_detection=TurnDetection(silence_duration_ms=1000),
+ # Or set to False to disable openai turn detection and use transport VAD
+ # turn_detection=False,
+ # tools=tools,
+ instructions="""Your knowledge cutoff is 2023-10. You are a helpful and friendly AI.
Act like a human, but remember that you aren't a human and that you can't do human
things in the real world. Your voice and personality should be warm and engaging, with a lively and
@@ -207,54 +201,66 @@ You are participating in a voice conversation. Keep your responses concise, shor
unless specifically asked to elaborate on a topic.
Remember, your responses should be short. Just one or two sentences, usually.""",
- )
+ )
- llm = OpenAIRealtimeBetaLLMService(
- api_key=os.getenv("OPENAI_API_KEY"),
- session_properties=session_properties,
- start_audio_paused=False,
- )
+ llm = OpenAIRealtimeBetaLLMService(
+ api_key=os.getenv("OPENAI_API_KEY"),
+ session_properties=session_properties,
+ start_audio_paused=False,
+ )
- # you can either register a single function for all function calls, or specific functions
- # llm.register_function(None, fetch_weather_from_api)
- llm.register_function("get_current_weather", fetch_weather_from_api)
- llm.register_function("save_conversation", save_conversation)
- llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames)
- llm.register_function("load_conversation", load_conversation)
+ # you can either register a single function for all function calls, or specific functions
+ # llm.register_function(None, fetch_weather_from_api)
+ llm.register_function("get_current_weather", fetch_weather_from_api)
+ llm.register_function("save_conversation", save_conversation)
+ llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames)
+ llm.register_function("load_conversation", load_conversation)
- context = OpenAILLMContext([], tools)
- context_aggregator = llm.create_context_aggregator(context)
+ context = OpenAILLMContext([], tools)
+ context_aggregator = llm.create_context_aggregator(context)
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- context_aggregator.user(),
- llm, # LLM
- context_aggregator.assistant(),
- transport.output(), # Transport bot output
- ]
- )
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt, # STT
+ context_aggregator.user(),
+ llm, # LLM
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(),
+ ]
+ )
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- # report_only_initial_ttfb=True,
- ),
- )
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- runner = PipelineRunner()
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- await runner.run(task)
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/20c-persistent-context-anthropic.py b/examples/foundational/20c-persistent-context-anthropic.py
index 0d33472e6..edec70932 100644
--- a/examples/foundational/20c-persistent-context-anthropic.py
+++ b/examples/foundational/20c-persistent-context-anthropic.py
@@ -4,17 +4,14 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import glob
import json
import os
-import sys
from datetime import datetime
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
@@ -26,12 +23,13 @@ from pipecat.processors.aggregators.openai_llm_context import (
)
from pipecat.services.anthropic.llm import AnthropicLLMService
from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
BASE_FILENAME = "/tmp/pipecat_conversation_"
tts = None
@@ -160,73 +158,84 @@ tools = [
]
-async def main():
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
+
global tts
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.8)),
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.8)),
+ ),
+ )
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- llm = AnthropicLLMService(
- api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-5-sonnet-latest"
- )
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- # you can either register a single function for all function calls, or specific functions
- # llm.register_function(None, fetch_weather_from_api)
- llm.register_function("get_current_weather", fetch_weather_from_api)
- llm.register_function("save_conversation", save_conversation)
- llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames)
- llm.register_function("load_conversation", load_conversation)
+ llm = AnthropicLLMService(
+ api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-5-sonnet-latest"
+ )
- context = OpenAILLMContext(messages, tools)
- context_aggregator = llm.create_context_aggregator(context)
+ # you can either register a single function for all function calls, or specific functions
+ # llm.register_function(None, fetch_weather_from_api)
+ llm.register_function("get_current_weather", fetch_weather_from_api)
+ llm.register_function("save_conversation", save_conversation)
+ llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames)
+ llm.register_function("load_conversation", load_conversation)
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- context_aggregator.user(),
- llm, # LLM
- tts,
- context_aggregator.assistant(),
- transport.output(), # Transport bot output
- ]
- )
+ context = OpenAILLMContext(messages, tools)
+ context_aggregator = llm.create_context_aggregator(context)
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- # report_only_initial_ttfb=True,
- ),
- )
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt, # STT
+ context_aggregator.user(),
+ llm, # LLM
+ tts,
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(),
+ ]
+ )
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ # report_only_initial_ttfb=True,
+ ),
+ )
- runner = PipelineRunner()
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- await runner.run(task)
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
+
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/20d-persistent-context-gemini.py b/examples/foundational/20d-persistent-context-gemini.py
index a40500971..52b137985 100644
--- a/examples/foundational/20d-persistent-context-gemini.py
+++ b/examples/foundational/20d-persistent-context-gemini.py
@@ -4,17 +4,14 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import glob
import json
import os
-import sys
from datetime import datetime
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
@@ -25,19 +22,21 @@ from pipecat.processors.aggregators.openai_llm_context import (
OpenAILLMContext,
)
from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.google.llm import GoogleLLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
video_participant_id = None
BASE_FILENAME = "/tmp/pipecat_conversation_"
tts = None
+webrtc_peer_id = None
async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
@@ -54,8 +53,11 @@ async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context
async def get_image(function_name, tool_call_id, arguments, llm, context, result_callback):
question = arguments["question"]
+ logger.debug(f"Requesting image with user_id={webrtc_peer_id}, question={question}")
+
+ # Request the image frame
await llm.request_image_frame(
- user_id=video_participant_id,
+ user_id=webrtc_peer_id,
function_name=function_name,
tool_call_id=tool_call_id,
text_content=question,
@@ -220,75 +222,85 @@ tools = [
]
-async def main():
- global tts
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ global tts, webrtc_peer_id
+ webrtc_peer_id = webrtc_connection.pc_id
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.8)),
- ),
- )
+ logger.info(f"Starting bot with peer_id: {webrtc_peer_id}")
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ video_in_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.8)),
+ ),
+ )
- llm = GoogleLLMService(model="gemini-2.0-flash-001", api_key=os.getenv("GOOGLE_API_KEY"))
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- # you can either register a single function for all function calls, or specific functions
- # llm.register_function(None, fetch_weather_from_api)
- llm.register_function("get_current_weather", fetch_weather_from_api)
- llm.register_function("save_conversation", save_conversation)
- llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames)
- llm.register_function("load_conversation", load_conversation)
- llm.register_function("get_image", get_image)
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- context = OpenAILLMContext(messages, tools)
- context_aggregator = llm.create_context_aggregator(context)
+ llm = GoogleLLMService(model="gemini-2.0-flash-001", api_key=os.getenv("GOOGLE_API_KEY"))
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- context_aggregator.user(),
- llm, # LLM
- tts,
- context_aggregator.assistant(),
- transport.output(), # Transport bot output
- ]
- )
+ # you can either register a single function for all function calls, or specific functions
+ # llm.register_function(None, fetch_weather_from_api)
+ llm.register_function("get_current_weather", fetch_weather_from_api)
+ llm.register_function("save_conversation", save_conversation)
+ llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames)
+ llm.register_function("load_conversation", load_conversation)
+ llm.register_function("get_image", get_image)
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- # report_only_initial_ttfb=True,
- ),
- )
+ context = OpenAILLMContext(messages, tools)
+ context_aggregator = llm.create_context_aggregator(context)
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- global video_participant_id
- video_participant_id = participant["id"]
- await transport.capture_participant_transcription(participant["id"])
- await transport.capture_participant_video(video_participant_id, framerate=0)
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt, # STT
+ context_aggregator.user(),
+ llm, # LLM
+ tts,
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(),
+ ]
+ )
- runner = PipelineRunner()
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ # report_only_initial_ttfb=True,
+ ),
+ )
- await runner.run(task)
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
+
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/21-tavus-layer.py b/examples/foundational/21-tavus-layer.py
index d3721642f..ffe95e074 100644
--- a/examples/foundational/21-tavus-layer.py
+++ b/examples/foundational/21-tavus-layer.py
@@ -47,9 +47,8 @@ async def main():
token=None,
bot_name="Pipecat bot",
params=DailyParams(
- vad_enabled=True,
+ audio_in_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
),
)
diff --git a/examples/foundational/22-natural-conversation.py b/examples/foundational/22-natural-conversation.py
index 1640f8fdf..75683e41c 100644
--- a/examples/foundational/22-natural-conversation.py
+++ b/examples/foundational/22-natural-conversation.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import TextFrame
@@ -28,142 +25,144 @@ from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.sync.event_notifier import EventNotifier
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, _) = await configure(session)
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- transport = DailyTransport(
- room_url,
- None,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
+
+ # This is the LLM that will be used to detect if the user has finished a
+ # statement. This doesn't really need to be an LLM, we could use NLP
+ # libraries for that, but it was easier as an example because we
+ # leverage the context aggregators.
+ statement_llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+
+ statement_messages = [
+ {
+ "role": "system",
+ "content": "Determine if the user's statement is a complete sentence or question, ending in a natural pause or punctuation. Return 'YES' if it is complete and 'NO' if it seems to leave a thought unfinished.",
+ },
+ ]
+
+ statement_context = OpenAILLMContext(statement_messages)
+ statement_context_aggregator = statement_llm.create_context_aggregator(statement_context)
+
+ # This is the regular LLM.
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ # We have instructed the LLM to return 'YES' if it thinks the user
+ # completed a sentence. So, if it's 'YES' we will return true in this
+ # predicate which will wake up the notifier.
+ async def wake_check_filter(frame):
+ return frame.text == "YES"
+
+ # This is a notifier that we use to synchronize the two LLMs.
+ notifier = EventNotifier()
+
+ # This a filter that will wake up the notifier if the given predicate
+ # (wake_check_filter) returns true.
+ completness_check = WakeNotifierFilter(notifier, types=(TextFrame,), filter=wake_check_filter)
+
+ # This processor keeps the last context and will let it through once the
+ # notifier is woken up. We start with the gate open because we send an
+ # initial context frame to start the conversation.
+ gated_context_aggregator = GatedOpenAILLMContextAggregator(notifier=notifier, start_open=True)
+
+ # Notify if the user hasn't said anything.
+ async def user_idle_notifier(frame):
+ await notifier.notify()
+
+ # Sometimes the LLM will fail detecting if a user has completed a
+ # sentence, this will wake up the notifier if that happens.
+ user_idle = UserIdleProcessor(callback=user_idle_notifier, timeout=3.0)
+
+ # The ParallePipeline input are the user transcripts. We have two
+ # contexts. The first one will be used to determine if the user finished
+ # a statement and if so the notifier will be woken up. The second
+ # context is simply the regular context but it's gated waiting for the
+ # notifier to be woken up.
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt,
+ ParallelPipeline(
+ [
+ statement_context_aggregator.user(),
+ statement_llm,
+ completness_check,
+ NullFilter(),
+ ],
+ [context_aggregator.user(), gated_context_aggregator, llm],
),
- )
-
- stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
-
- # This is the LLM that will be used to detect if the user has finished a
- # statement. This doesn't really need to be an LLM, we could use NLP
- # libraries for that, but it was easier as an example because we
- # leverage the context aggregators.
- statement_llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-
- statement_messages = [
- {
- "role": "system",
- "content": "Determine if the user's statement is a complete sentence or question, ending in a natural pause or punctuation. Return 'YES' if it is complete and 'NO' if it seems to leave a thought unfinished.",
- },
+ user_idle,
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
]
+ )
- statement_context = OpenAILLMContext(statement_messages)
- statement_context_aggregator = statement_llm.create_context_aggregator(statement_context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- # This is the regular LLM.
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
- ]
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- # We have instructed the LLM to return 'YES' if it thinks the user
- # completed a sentence. So, if it's 'YES' we will return true in this
- # predicate which will wake up the notifier.
- async def wake_check_filter(frame):
- return frame.text == "YES"
+ runner = PipelineRunner(handle_sigint=False)
- # This is a notifier that we use to synchronize the two LLMs.
- notifier = EventNotifier()
-
- # This a filter that will wake up the notifier if the given predicate
- # (wake_check_filter) returns true.
- completness_check = WakeNotifierFilter(
- notifier, types=(TextFrame,), filter=wake_check_filter
- )
-
- # This processor keeps the last context and will let it through once the
- # notifier is woken up. We start with the gate open because we send an
- # initial context frame to start the conversation.
- gated_context_aggregator = GatedOpenAILLMContextAggregator(
- notifier=notifier, start_open=True
- )
-
- # Notify if the user hasn't said anything.
- async def user_idle_notifier(frame):
- await notifier.notify()
-
- # Sometimes the LLM will fail detecting if a user has completed a
- # sentence, this will wake up the notifier if that happens.
- user_idle = UserIdleProcessor(callback=user_idle_notifier, timeout=3.0)
-
- # The ParallePipeline input are the user transcripts. We have two
- # contexts. The first one will be used to determine if the user finished
- # a statement and if so the notifier will be woken up. The second
- # context is simply the regular context but it's gated waiting for the
- # notifier to be woken up.
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- stt,
- ParallelPipeline(
- [
- statement_context_aggregator.user(),
- statement_llm,
- completness_check,
- NullFilter(),
- ],
- [context_aggregator.user(), gated_context_aggregator, llm],
- ),
- user_idle,
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses
- ]
- )
-
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
-
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- messages.append({"role": "system", "content": "Please introduce yourself to the user."})
- await task.queue_frames([context_aggregator.user().get_context_frame()])
-
- runner = PipelineRunner()
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/22b-natural-conversation-proposal.py b/examples/foundational/22b-natural-conversation-proposal.py
index 21ce29279..0dad114b4 100644
--- a/examples/foundational/22b-natural-conversation-proposal.py
+++ b/examples/foundational/22b-natural-conversation-proposal.py
@@ -4,16 +4,14 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
+import argparse
import asyncio
import os
-import sys
import time
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
from openai.types.chat import ChatCompletionToolParam
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import (
@@ -49,13 +47,12 @@ from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.sync.base_notifier import BaseNotifier
from pipecat.sync.event_notifier import EventNotifier
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
classifier_statement = "Determine if the user's statement ends with a complete thought and you should respond. The user text is transcribed speech. It may contain multiple fragments concatentated together. You are trying to determine only the completeness of the last user statement. The previous assistant statement is provided only for context. Categorize the text as either complete with the user now expecting a response, or incomplete. Return 'YES' if text is likely complete and the user is expecting a response. Return 'NO' if the text seems to be a partial expression or unfinished thought."
@@ -204,186 +201,192 @@ async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context
await result_callback({"conditions": "nice", "temperature": "75"})
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, _) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- None,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- # This is the LLM that will be used to detect if the user has finished a
- # statement. This doesn't really need to be an LLM, we could use NLP
- # libraries for that, but we have the machinery to use an LLM, so we might as well!
- statement_llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ # This is the LLM that will be used to detect if the user has finished a
+ # statement. This doesn't really need to be an LLM, we could use NLP
+ # libraries for that, but we have the machinery to use an LLM, so we might as well!
+ statement_llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
- # This is the regular LLM.
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
- # You can also register a function_name of None to get all functions
- # sent to the same callback with an additional function_name parameter.
- llm.register_function("get_current_weather", fetch_weather_from_api)
+ # This is the regular LLM.
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+ # You can also register a function_name of None to get all functions
+ # sent to the same callback with an additional function_name parameter.
+ llm.register_function("get_current_weather", fetch_weather_from_api)
- tools = [
- ChatCompletionToolParam(
- type="function",
- function={
- "name": "get_current_weather",
- "description": "Get the current weather",
- "parameters": {
- "type": "object",
- "properties": {
- "location": {
- "type": "string",
- "description": "The city and state, e.g. San Francisco, CA",
- },
- "format": {
- "type": "string",
- "enum": ["celsius", "fahrenheit"],
- "description": "The temperature unit to use. Infer this from the users location.",
- },
+ tools = [
+ ChatCompletionToolParam(
+ type="function",
+ function={
+ "name": "get_current_weather",
+ "description": "Get the current weather",
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "location": {
+ "type": "string",
+ "description": "The city and state, e.g. San Francisco, CA",
+ },
+ "format": {
+ "type": "string",
+ "enum": ["celsius", "fahrenheit"],
+ "description": "The temperature unit to use. Infer this from the users location.",
},
- "required": ["location", "format"],
},
+ "required": ["location", "format"],
},
- )
- ]
-
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
},
+ )
+ ]
+
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages, tools)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ # We have instructed the LLM to return 'YES' if it thinks the user
+ # completed a sentence. So, if it's 'YES' we will return true in this
+ # predicate which will wake up the notifier.
+ async def wake_check_filter(frame):
+ logger.debug(f"Completeness check frame: {frame}")
+ return frame.text == "YES"
+
+ # This is a notifier that we use to synchronize the two LLMs.
+ notifier = EventNotifier()
+
+ # This turns the LLM context into an inference request to classify the user's speech
+ # as complete or incomplete.
+ statement_judge_context_filter = StatementJudgeContextFilter(notifier=notifier)
+
+ # This sends a UserStoppedSpeakingFrame and triggers the notifier event
+ completeness_check = CompletenessCheck(notifier=notifier)
+
+ # # Notify if the user hasn't said anything.
+ async def user_idle_notifier(frame):
+ await notifier.notify()
+
+ # Sometimes the LLM will fail detecting if a user has completed a
+ # sentence, this will wake up the notifier if that happens.
+ user_idle = UserIdleProcessor(callback=user_idle_notifier, timeout=5.0)
+
+ # We start with the gate open because we send an initial context frame
+ # to start the conversation.
+ bot_output_gate = OutputGate(notifier=notifier, start_open=True)
+
+ async def block_user_stopped_speaking(frame):
+ return not isinstance(frame, UserStoppedSpeakingFrame)
+
+ async def pass_only_llm_trigger_frames(frame):
+ return (
+ isinstance(frame, OpenAILLMContextFrame)
+ or isinstance(frame, LLMMessagesFrame)
+ or isinstance(frame, StartInterruptionFrame)
+ or isinstance(frame, StopInterruptionFrame)
+ or isinstance(frame, FunctionCallInProgressFrame)
+ or isinstance(frame, FunctionCallResultFrame)
+ )
+
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ context_aggregator.user(),
+ ParallelPipeline(
+ [
+ # Pass everything except UserStoppedSpeaking to the elements after
+ # this ParallelPipeline
+ FunctionFilter(filter=block_user_stopped_speaking),
+ ],
+ [
+ # Ignore everything except an OpenAILLMContextFrame. Pass a specially constructed
+ # LLMMessagesFrame to the statement classifier LLM. The only frame this
+ # sub-pipeline will output is a UserStoppedSpeakingFrame.
+ statement_judge_context_filter,
+ statement_llm,
+ completeness_check,
+ ],
+ [
+ # Block everything except OpenAILLMContextFrame and LLMMessagesFrame
+ FunctionFilter(filter=pass_only_llm_trigger_frames),
+ llm,
+ bot_output_gate, # Buffer all llm/tts output until notified.
+ ],
+ ),
+ tts,
+ user_idle,
+ transport.output(),
+ context_aggregator.assistant(),
]
+ )
- context = OpenAILLMContext(messages, tools)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- # We have instructed the LLM to return 'YES' if it thinks the user
- # completed a sentence. So, if it's 'YES' we will return true in this
- # predicate which will wake up the notifier.
- async def wake_check_filter(frame):
- logger.debug(f"Completeness check frame: {frame}")
- return frame.text == "YES"
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- # This is a notifier that we use to synchronize the two LLMs.
- notifier = EventNotifier()
+ @transport.event_handler("on_app_message")
+ async def on_app_message(transport, message):
+ logger.debug(f"Received app message: {message}")
+ if "message" not in message:
+ return
- # This turns the LLM context into an inference request to classify the user's speech
- # as complete or incomplete.
- statement_judge_context_filter = StatementJudgeContextFilter(notifier=notifier)
-
- # This sends a UserStoppedSpeakingFrame and triggers the notifier event
- completeness_check = CompletenessCheck(notifier=notifier)
-
- # # Notify if the user hasn't said anything.
- async def user_idle_notifier(frame):
- await notifier.notify()
-
- # Sometimes the LLM will fail detecting if a user has completed a
- # sentence, this will wake up the notifier if that happens.
- user_idle = UserIdleProcessor(callback=user_idle_notifier, timeout=5.0)
-
- # We start with the gate open because we send an initial context frame
- # to start the conversation.
- bot_output_gate = OutputGate(notifier=notifier, start_open=True)
-
- async def block_user_stopped_speaking(frame):
- return not isinstance(frame, UserStoppedSpeakingFrame)
-
- async def pass_only_llm_trigger_frames(frame):
- return (
- isinstance(frame, OpenAILLMContextFrame)
- or isinstance(frame, LLMMessagesFrame)
- or isinstance(frame, StartInterruptionFrame)
- or isinstance(frame, StopInterruptionFrame)
- or isinstance(frame, FunctionCallInProgressFrame)
- or isinstance(frame, FunctionCallResultFrame)
- )
-
- pipeline = Pipeline(
+ await task.queue_frames(
[
- transport.input(),
- stt,
- context_aggregator.user(),
- ParallelPipeline(
- [
- # Pass everything except UserStoppedSpeaking to the elements after
- # this ParallelPipeline
- FunctionFilter(filter=block_user_stopped_speaking),
- ],
- [
- # Ignore everything except an OpenAILLMContextFrame. Pass a specially constructed
- # LLMMessagesFrame to the statement classifier LLM. The only frame this
- # sub-pipeline will output is a UserStoppedSpeakingFrame.
- statement_judge_context_filter,
- statement_llm,
- completeness_check,
- ],
- [
- # Block everything except OpenAILLMContextFrame and LLMMessagesFrame
- FunctionFilter(filter=pass_only_llm_trigger_frames),
- llm,
- bot_output_gate, # Buffer all llm/tts output until notified.
- ],
- ),
- tts,
- user_idle,
- transport.output(),
- context_aggregator.assistant(),
+ UserStartedSpeakingFrame(),
+ TranscriptionFrame(user_id="", timestamp=time.time(), text=message["message"]),
+ UserStoppedSpeakingFrame(),
]
)
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- messages.append({"role": "system", "content": "Please introduce yourself to the user."})
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- @transport.event_handler("on_app_message")
- async def on_app_message(transport, message, sender):
- logger.debug(f"Received app message: {message} - {sender}")
- if "message" not in message:
- return
+ runner = PipelineRunner(handle_sigint=False)
- await task.queue_frames(
- [
- UserStartedSpeakingFrame(),
- TranscriptionFrame(
- user_id=sender, timestamp=time.time(), text=message["message"]
- ),
- UserStoppedSpeakingFrame(),
- ]
- )
-
- runner = PipelineRunner()
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/22c-natural-conversation-mixed-llms.py b/examples/foundational/22c-natural-conversation-mixed-llms.py
index 71b4bb078..6475c3902 100644
--- a/examples/foundational/22c-natural-conversation-mixed-llms.py
+++ b/examples/foundational/22c-natural-conversation-mixed-llms.py
@@ -4,16 +4,14 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
+import argparse
import asyncio
import os
-import sys
import time
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
from openai.types.chat import ChatCompletionToolParam
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import (
@@ -50,13 +48,12 @@ from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.sync.base_notifier import BaseNotifier
from pipecat.sync.event_notifier import EventNotifier
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
classifier_statement = """CRITICAL INSTRUCTION:
You are a BINARY CLASSIFIER that must ONLY output "YES" or "NO".
@@ -408,195 +405,195 @@ async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context
await result_callback({"conditions": "nice", "temperature": "75"})
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, _) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- None,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- # This is the LLM that will be used to detect if the user has finished a
- # statement. This doesn't really need to be an LLM, we could use NLP
- # libraries for that, but we have the machinery to use an LLM, so we might as well!
- statement_llm = AnthropicLLMService(
- api_key=os.getenv("ANTHROPIC_API_KEY"),
- model="claude-3-5-sonnet-20241022",
- )
+ # This is the LLM that will be used to detect if the user has finished a
+ # statement. This doesn't really need to be an LLM, we could use NLP
+ # libraries for that, but we have the machinery to use an LLM, so we might as well!
+ statement_llm = AnthropicLLMService(api_key=os.getenv("ANTHROPIC_API_KEY"))
- # This is the regular LLM.
- llm = OpenAILLMService(
- api_key=os.getenv("OPENAI_API_KEY"),
- model="gpt-4o",
- )
- # Register a function_name of None to get all functions
- # sent to the same callback with an additional function_name parameter.
- llm.register_function("get_current_weather", fetch_weather_from_api)
+ # This is the regular LLM.
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+ # Register a function_name of None to get all functions
+ # sent to the same callback with an additional function_name parameter.
+ llm.register_function("get_current_weather", fetch_weather_from_api)
- tools = [
- ChatCompletionToolParam(
- type="function",
- function={
- "name": "get_current_weather",
- "description": "Get the current weather",
- "parameters": {
- "type": "object",
- "properties": {
- "location": {
- "type": "string",
- "description": "The city and state, e.g. San Francisco, CA",
- },
- "format": {
- "type": "string",
- "enum": ["celsius", "fahrenheit"],
- "description": "The temperature unit to use. Infer this from the users location.",
- },
+ tools = [
+ ChatCompletionToolParam(
+ type="function",
+ function={
+ "name": "get_current_weather",
+ "description": "Get the current weather",
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "location": {
+ "type": "string",
+ "description": "The city and state, e.g. San Francisco, CA",
+ },
+ "format": {
+ "type": "string",
+ "enum": ["celsius", "fahrenheit"],
+ "description": "The temperature unit to use. Infer this from the users location.",
},
- "required": ["location", "format"],
},
+ "required": ["location", "format"],
},
- )
- ]
-
- messages = [
- {
- "role": "system",
- "content": conversational_system_message,
},
+ )
+ ]
+
+ messages = [
+ {
+ "role": "system",
+ "content": conversational_system_message,
+ },
+ ]
+
+ context = OpenAILLMContext(messages, tools)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ # We have instructed the LLM to return 'YES' if it thinks the user
+ # completed a sentence. So, if it's 'YES' we will return true in this
+ # predicate which will wake up the notifier.
+ async def wake_check_filter(frame):
+ return frame.text == "YES"
+
+ # This is a notifier that we use to synchronize the two LLMs.
+ notifier = EventNotifier()
+
+ # This turns the LLM context into an inference request to classify the user's speech
+ # as complete or incomplete.
+ statement_judge_context_filter = StatementJudgeContextFilter(notifier=notifier)
+
+ # This sends a UserStoppedSpeakingFrame and triggers the notifier event
+ completeness_check = CompletenessCheck(notifier=notifier)
+
+ # # Notify if the user hasn't said anything.
+ async def user_idle_notifier(frame):
+ await notifier.notify()
+
+ # Sometimes the LLM will fail detecting if a user has completed a
+ # sentence, this will wake up the notifier if that happens.
+ user_idle = UserIdleProcessor(callback=user_idle_notifier, timeout=5.0)
+
+ # We start with the gate open because we send an initial context frame
+ # to start the conversation.
+ bot_output_gate = OutputGate(notifier=notifier, start_open=True)
+
+ async def block_user_stopped_speaking(frame):
+ return not isinstance(frame, UserStoppedSpeakingFrame)
+
+ async def pass_only_llm_trigger_frames(frame):
+ return (
+ isinstance(frame, OpenAILLMContextFrame)
+ or isinstance(frame, LLMMessagesFrame)
+ or isinstance(frame, StartInterruptionFrame)
+ or isinstance(frame, StopInterruptionFrame)
+ or isinstance(frame, FunctionCallInProgressFrame)
+ or isinstance(frame, FunctionCallResultFrame)
+ )
+
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ context_aggregator.user(),
+ ParallelPipeline(
+ [
+ # Pass everything except UserStoppedSpeaking to the elements after
+ # this ParallelPipeline
+ FunctionFilter(filter=block_user_stopped_speaking),
+ ],
+ [
+ # Ignore everything except an OpenAILLMContextFrame. Pass a specially constructed
+ # LLMMessagesFrame to the statement classifier LLM. The only frame this
+ # sub-pipeline will output is a UserStoppedSpeakingFrame.
+ statement_judge_context_filter,
+ statement_llm,
+ completeness_check,
+ ],
+ [
+ # Block everything except OpenAILLMContextFrame and LLMMessagesFrame
+ FunctionFilter(filter=pass_only_llm_trigger_frames),
+ llm,
+ bot_output_gate, # Buffer all llm/tts output until notified.
+ ],
+ ),
+ tts,
+ user_idle,
+ transport.output(),
+ context_aggregator.assistant(),
]
+ )
- context = OpenAILLMContext(messages, tools)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ ),
+ )
- # We have instructed the LLM to return 'YES' if it thinks the user
- # completed a sentence. So, if it's 'YES' we will return true in this
- # predicate which will wake up the notifier.
- async def wake_check_filter(frame):
- return frame.text == "YES"
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append(
+ {
+ "role": "user",
+ "content": "Start by just saying \"Hello I'm ready.\" Don't say anything else.",
+ }
+ )
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- # This is a notifier that we use to synchronize the two LLMs.
- notifier = EventNotifier()
+ @transport.event_handler("on_app_message")
+ async def on_app_message(transport, message):
+ logger.debug(f"Received app message: {message}")
+ if "message" not in message:
+ return
- # This turns the LLM context into an inference request to classify the user's speech
- # as complete or incomplete.
- statement_judge_context_filter = StatementJudgeContextFilter(notifier=notifier)
-
- # This sends a UserStoppedSpeakingFrame and triggers the notifier event
- completeness_check = CompletenessCheck(notifier=notifier)
-
- # # Notify if the user hasn't said anything.
- async def user_idle_notifier(frame):
- await notifier.notify()
-
- # Sometimes the LLM will fail detecting if a user has completed a
- # sentence, this will wake up the notifier if that happens.
- user_idle = UserIdleProcessor(callback=user_idle_notifier, timeout=5.0)
-
- # We start with the gate open because we send an initial context frame
- # to start the conversation.
- bot_output_gate = OutputGate(notifier=notifier, start_open=True)
-
- async def block_user_stopped_speaking(frame):
- return not isinstance(frame, UserStoppedSpeakingFrame)
-
- async def pass_only_llm_trigger_frames(frame):
- return (
- isinstance(frame, OpenAILLMContextFrame)
- or isinstance(frame, LLMMessagesFrame)
- or isinstance(frame, StartInterruptionFrame)
- or isinstance(frame, StopInterruptionFrame)
- or isinstance(frame, FunctionCallInProgressFrame)
- or isinstance(frame, FunctionCallResultFrame)
- )
-
- pipeline = Pipeline(
+ await task.queue_frames(
[
- transport.input(),
- stt,
- context_aggregator.user(),
- ParallelPipeline(
- [
- # Pass everything except UserStoppedSpeaking to the elements after
- # this ParallelPipeline
- FunctionFilter(filter=block_user_stopped_speaking),
- ],
- [
- # Ignore everything except an OpenAILLMContextFrame. Pass a specially constructed
- # LLMMessagesFrame to the statement classifier LLM. The only frame this
- # sub-pipeline will output is a UserStoppedSpeakingFrame.
- statement_judge_context_filter,
- statement_llm,
- completeness_check,
- ],
- [
- # Block everything except OpenAILLMContextFrame and LLMMessagesFrame
- FunctionFilter(filter=pass_only_llm_trigger_frames),
- llm,
- bot_output_gate, # Buffer all llm/tts output until notified.
- ],
- ),
- tts,
- user_idle,
- transport.output(),
- context_aggregator.assistant(),
+ UserStartedSpeakingFrame(),
+ TranscriptionFrame(user_id="", timestamp=time.time(), text=message["message"]),
+ UserStoppedSpeakingFrame(),
]
)
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- messages.append(
- {
- "role": "user",
- "content": "Start by just saying \"Hello I'm ready.\" Don't say anything else.",
- }
- )
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- @transport.event_handler("on_app_message")
- async def on_app_message(transport, message, sender):
- logger.debug(f"Received app message: {message} - {sender}")
- if "message" not in message:
- return
+ runner = PipelineRunner(handle_sigint=False)
- await task.queue_frames(
- [
- UserStartedSpeakingFrame(),
- TranscriptionFrame(
- user_id=sender, timestamp=time.time(), text=message["message"]
- ),
- UserStoppedSpeakingFrame(),
- ]
- )
-
- runner = PipelineRunner()
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/22d-natural-conversation-gemini-audio.py b/examples/foundational/22d-natural-conversation-gemini-audio.py
index 71bc58782..3460e079c 100644
--- a/examples/foundational/22d-natural-conversation-gemini-audio.py
+++ b/examples/foundational/22d-natural-conversation-gemini-audio.py
@@ -4,16 +4,14 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
+import argparse
import asyncio
import os
-import sys
import time
-import aiohttp
import google.ai.generativelanguage as glm
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import (
@@ -36,7 +34,10 @@ from pipecat.pipeline.parallel_pipeline import ParallelPipeline
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response import LLMAssistantResponseAggregator
+from pipecat.processors.aggregators.llm_response import (
+ LLMAssistantAggregatorParams,
+ LLMAssistantResponseAggregator,
+)
from pipecat.processors.aggregators.openai_llm_context import (
OpenAILLMContext,
OpenAILLMContextFrame,
@@ -47,12 +48,12 @@ from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.google.llm import GoogleLLMContext, GoogleLLMService
from pipecat.sync.base_notifier import BaseNotifier
from pipecat.sync.event_notifier import EventNotifier
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
TRANSCRIBER_MODEL = "gemini-2.0-flash-001"
CLASSIFIER_MODEL = "gemini-2.0-flash-001"
@@ -481,7 +482,7 @@ class LLMAggregatorBuffer(LLMAssistantResponseAggregator):
"""Buffers the output of the transcription LLM. Used by the bot output gate."""
def __init__(self, **kwargs):
- super().__init__(expect_stripped_words=False)
+ super().__init__(params=LLMAssistantAggregatorParams(expect_stripped_words=False))
self._transcription = ""
async def process_frame(self, frame: Frame, direction: FrameDirection):
@@ -626,149 +627,153 @@ class OutputGate(FrameProcessor):
break
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, _) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- None,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- # This is the LLM that will transcribe user speech.
- tx_llm = GoogleLLMService(
- name="Transcriber",
- model=TRANSCRIBER_MODEL,
- api_key=os.getenv("GOOGLE_API_KEY"),
- temperature=0.0,
- system_instruction=transcriber_system_instruction,
- )
+ # This is the LLM that will transcribe user speech.
+ tx_llm = GoogleLLMService(
+ name="Transcriber",
+ model=TRANSCRIBER_MODEL,
+ api_key=os.getenv("GOOGLE_API_KEY"),
+ temperature=0.0,
+ system_instruction=transcriber_system_instruction,
+ )
- # This is the LLM that will classify user speech as complete or incomplete.
- classifier_llm = GoogleLLMService(
- name="Classifier",
- model=CLASSIFIER_MODEL,
- api_key=os.getenv("GOOGLE_API_KEY"),
- temperature=0.0,
- system_instruction=classifier_system_instruction,
- )
+ # This is the LLM that will classify user speech as complete or incomplete.
+ classifier_llm = GoogleLLMService(
+ name="Classifier",
+ model=CLASSIFIER_MODEL,
+ api_key=os.getenv("GOOGLE_API_KEY"),
+ temperature=0.0,
+ system_instruction=classifier_system_instruction,
+ )
- # This is the regular LLM that responds conversationally.
- conversation_llm = GoogleLLMService(
- name="Conversation",
- model=CONVERSATION_MODEL,
- api_key=os.getenv("GOOGLE_API_KEY"),
- system_instruction=conversation_system_instruction,
- )
+ # This is the regular LLM that responds conversationally.
+ conversation_llm = GoogleLLMService(
+ name="Conversation",
+ model=CONVERSATION_MODEL,
+ api_key=os.getenv("GOOGLE_API_KEY"),
+ system_instruction=conversation_system_instruction,
+ )
- context = OpenAILLMContext()
- context_aggregator = conversation_llm.create_context_aggregator(context)
+ context = OpenAILLMContext()
+ context_aggregator = conversation_llm.create_context_aggregator(context)
- # This is a notifier that we use to synchronize the two LLMs.
- notifier = EventNotifier()
+ # This is a notifier that we use to synchronize the two LLMs.
+ notifier = EventNotifier()
- # This turns the LLM context into an inference request to classify the user's speech
- # as complete or incomplete.
- # statement_judge_context_filter = StatementJudgeAudioContextAccumulator(notifier=notifier)
+ # This turns the LLM context into an inference request to classify the user's speech
+ # as complete or incomplete.
+ # statement_judge_context_filter = StatementJudgeAudioContextAccumulator(notifier=notifier)
- audio_accumulater = AudioAccumulator()
- # This sends a UserStoppedSpeakingFrame and triggers the notifier event
- completeness_check = CompletenessCheck(
- notifier=notifier, audio_accumulator=audio_accumulater
- )
+ audio_accumulater = AudioAccumulator()
+ # This sends a UserStoppedSpeakingFrame and triggers the notifier event
+ completeness_check = CompletenessCheck(notifier=notifier, audio_accumulator=audio_accumulater)
- async def block_user_stopped_speaking(frame):
- return not isinstance(frame, UserStoppedSpeakingFrame)
+ async def block_user_stopped_speaking(frame):
+ return not isinstance(frame, UserStoppedSpeakingFrame)
- conversation_audio_context_assembler = ConversationAudioContextAssembler(context=context)
+ conversation_audio_context_assembler = ConversationAudioContextAssembler(context=context)
- llm_aggregator_buffer = LLMAggregatorBuffer()
+ llm_aggregator_buffer = LLMAggregatorBuffer()
- bot_output_gate = OutputGate(
- notifier=notifier, context=context, llm_transcription_buffer=llm_aggregator_buffer
- )
+ bot_output_gate = OutputGate(
+ notifier=notifier, context=context, llm_transcription_buffer=llm_aggregator_buffer
+ )
- pipeline = Pipeline(
- [
- transport.input(),
- audio_accumulater,
- ParallelPipeline(
- [
- # Pass everything except UserStoppedSpeaking to the elements after
- # this ParallelPipeline
- FunctionFilter(filter=block_user_stopped_speaking),
- ],
- [
- ParallelPipeline(
- [
- classifier_llm,
- completeness_check,
- ],
- [
- tx_llm,
- llm_aggregator_buffer,
- ],
- )
- ],
- [
- conversation_audio_context_assembler,
- conversation_llm,
- bot_output_gate, # buffer output until notified, then flush frames and update context
- # TempPrinter(),
- ],
- ),
- tts,
- transport.output(),
- context_aggregator.assistant(),
- ],
- )
-
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- ),
- )
-
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
-
- @transport.event_handler("on_app_message")
- async def on_app_message(transport, message, sender):
- logger.debug(f"Received app message: {message} - {sender}")
- if "message" not in message:
- return
-
- await task.queue_frames(
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ audio_accumulater,
+ ParallelPipeline(
[
- UserStartedSpeakingFrame(),
- TranscriptionFrame(
- user_id=sender, timestamp=time.time(), text=message["message"]
- ),
- UserStoppedSpeakingFrame(),
- ]
- )
+ # Pass everything except UserStoppedSpeaking to the elements after
+ # this ParallelPipeline
+ FunctionFilter(filter=block_user_stopped_speaking),
+ ],
+ [
+ ParallelPipeline(
+ [
+ classifier_llm,
+ completeness_check,
+ ],
+ [
+ tx_llm,
+ llm_aggregator_buffer,
+ ],
+ )
+ ],
+ [
+ conversation_audio_context_assembler,
+ conversation_llm,
+ bot_output_gate, # buffer output until notified, then flush frames and update context
+ # TempPrinter(),
+ ],
+ ),
+ tts,
+ transport.output(),
+ context_aggregator.assistant(),
+ ],
+ )
- runner = PipelineRunner()
- await runner.run(task)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ ),
+ )
+
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+ @transport.event_handler("on_app_message")
+ async def on_app_message(transport, message):
+ logger.debug(f"Received app message: {message}")
+ if "message" not in message:
+ return
+
+ await task.queue_frames(
+ [
+ UserStartedSpeakingFrame(),
+ TranscriptionFrame(user_id="", timestamp=time.time(), text=message["message"]),
+ UserStoppedSpeakingFrame(),
+ ]
+ )
+
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
+
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/23-bot-background-sound.py b/examples/foundational/23-bot-background-sound-daily.py
similarity index 97%
rename from examples/foundational/23-bot-background-sound.py
rename to examples/foundational/23-bot-background-sound-daily.py
index c58fe5f13..67c03386f 100644
--- a/examples/foundational/23-bot-background-sound.py
+++ b/examples/foundational/23-bot-background-sound-daily.py
@@ -10,9 +10,9 @@ import os
import sys
import aiohttp
+from daily_runner import configure_with_args
from dotenv import load_dotenv
from loguru import logger
-from runner import configure_with_args
from pipecat.audio.mixers.soundfile_mixer import SoundfileMixer
from pipecat.audio.vad.silero import SileroVADAnalyzer
@@ -49,10 +49,10 @@ async def main():
token,
"Respond bot",
DailyParams(
+ audio_in_enabled=True,
audio_out_enabled=True,
audio_out_mixer=soundfile_mixer,
transcription_enabled=True,
- vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
),
)
@@ -62,7 +62,7 @@ async def main():
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
)
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
messages = [
{
diff --git a/examples/foundational/23-bot-background-sound-p2p.py b/examples/foundational/23-bot-background-sound-p2p.py
new file mode 100644
index 000000000..1d75c305e
--- /dev/null
+++ b/examples/foundational/23-bot-background-sound-p2p.py
@@ -0,0 +1,137 @@
+#
+# Copyright (c) 2024β2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""Usage
+-----
+Set the path to your background audio file using the `INPUT_AUDIO_PATH` environment variable, then run the bot using:
+
+ INPUT_AUDIO_PATH=path/to/your_audio.mp3 python 23-bot-background-sound.py
+
+Example:
+ INPUT_AUDIO_PATH=my_audio.mp3 python 23-bot-background-sound.py
+"""
+
+import argparse
+import asyncio
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.mixers.soundfile_mixer import SoundfileMixer
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import MixerEnableFrame, MixerUpdateSettingsFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+
+load_dotenv(override=True)
+
+audio_path = os.getenv("INPUT_AUDIO_PATH")
+if not audio_path:
+ raise ValueError("No INPUT_AUDIO_PATH specified in environment variables")
+
+
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
+
+ soundfile_mixer = SoundfileMixer(
+ sound_files={"office": audio_path},
+ default_sound="office",
+ volume=2.0,
+ )
+
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ audio_out_mixer=soundfile_mixer,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
+
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
+
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt, # STT service
+ context_aggregator.user(), # User responses
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
+ ]
+ )
+
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
+
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected: {client}")
+ # Show how to use mixer control frames.
+ await asyncio.sleep(10.0)
+ await task.queue_frame(MixerUpdateSettingsFrame({"volume": 0.5}))
+ await asyncio.sleep(5.0)
+ await task.queue_frame(MixerEnableFrame(False))
+ await asyncio.sleep(5.0)
+ await task.queue_frame(MixerEnableFrame(True))
+ await asyncio.sleep(5.0)
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
+
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
+
+
+if __name__ == "__main__":
+ from run import main
+
+ main()
diff --git a/examples/foundational/24-stt-mute-filter.py b/examples/foundational/24-stt-mute-filter.py
index 7a0c9777c..2002a05a1 100644
--- a/examples/foundational/24-stt-mute-filter.py
+++ b/examples/foundational/24-stt-mute-filter.py
@@ -4,16 +4,15 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
+import argparse
import asyncio
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from openai.types.chat import ChatCompletionToolParam
-from runner import configure
+from pipecat.adapters.schemas.function_schema import FunctionSchema
+from pipecat.adapters.schemas.tools_schema import ToolsSchema
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
@@ -23,13 +22,12 @@ from pipecat.processors.filters.stt_mute_filter import STTMuteConfig, STTMuteFil
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.deepgram.tts import DeepgramTTSService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
# Add a delay to test interruption during function calls
@@ -39,103 +37,105 @@ async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context
await result_callback({"conditions": "nice", "temperature": "75"})
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, _) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- None,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- # Configure the mute processor with both strategies
- stt_mute_processor = STTMuteFilter(
- config=STTMuteConfig(
- strategies={
- STTMuteStrategy.MUTE_UNTIL_FIRST_BOT_COMPLETE,
- STTMuteStrategy.FUNCTION_CALL,
- }
- ),
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
+ # Configure the mute processor with both strategies
+ stt_mute_processor = STTMuteFilter(
+ config=STTMuteConfig(
+ strategies={
+ STTMuteStrategy.MUTE_UNTIL_FIRST_BOT_COMPLETE,
+ STTMuteStrategy.FUNCTION_CALL,
+ }
+ ),
+ )
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
- llm.register_function("get_current_weather", fetch_weather_from_api)
+ tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
- tools = [
- ChatCompletionToolParam(
- type="function",
- function={
- "name": "get_current_weather",
- "description": "Get the current weather",
- "parameters": {
- "type": "object",
- "properties": {
- "location": {
- "type": "string",
- "description": "The city and state, e.g. San Francisco, CA",
- },
- "format": {
- "type": "string",
- "enum": ["celsius", "fahrenheit"],
- "description": "The temperature unit to use. Infer this from the users location.",
- },
- },
- "required": ["location", "format"],
- },
- },
- )
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+ llm.register_function("get_current_weather", fetch_weather_from_api)
+
+ weather_function = FunctionSchema(
+ name="get_current_weather",
+ description="Get the current weather",
+ properties={
+ "location": {
+ "type": "string",
+ "description": "The city and state, e.g. San Francisco, CA",
+ },
+ "format": {
+ "type": "string",
+ "enum": ["celsius", "fahrenheit"],
+ "description": "The temperature unit to use. Infer this from the user's location.",
+ },
+ },
+ required=["location", "format"],
+ )
+ tools = ToolsSchema(standard_tools=[weather_function])
+
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful assistant who can check the weather. Always check the weather when a location is mentioned. Respond concisely and naturally. Your output will be converted to audio so use only simple words and punctuation.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages, tools)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt_mute_processor, # Add the mute processor before STT
+ stt, # STT
+ context_aggregator.user(), # User responses
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
]
+ )
- messages = [
+ task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
+
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation with a weather-related prompt
+ messages.append(
{
"role": "system",
- "content": "You are a helpful assistant who can check the weather. Always check the weather when a location is mentioned. Respond concisely and naturally. Your output will be converted to audio so use only simple words and punctuation.",
- },
- ]
-
- context = OpenAILLMContext(messages, tools)
- context_aggregator = llm.create_context_aggregator(context)
-
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- stt_mute_processor, # Add the mute processor before STT
- stt, # STT
- context_aggregator.user(), # User responses
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses
- ]
+ "content": "Ask the user what city they'd like to know the weather for.",
+ }
)
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- # Kick off the conversation with a weather-related prompt
- messages.append(
- {
- "role": "system",
- "content": "Ask the user what city they'd like to know the weather for.",
- }
- )
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- runner = PipelineRunner()
+ runner = PipelineRunner(handle_sigint=False)
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/25-google-audio-in.py b/examples/foundational/25-google-audio-in.py
index 80c0545ee..eea8c5a2a 100644
--- a/examples/foundational/25-google-audio-in.py
+++ b/examples/foundational/25-google-audio-in.py
@@ -4,16 +4,13 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
from dataclasses import dataclass
-import aiohttp
import google.ai.generativelanguage as glm
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import (
@@ -37,13 +34,12 @@ from pipecat.processors.aggregators.openai_llm_context import (
from pipecat.processors.frame_processor import FrameProcessor
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.google.llm import GoogleLLMContext, GoogleLLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
#
# The system prompt for the main conversation.
#
@@ -273,102 +269,108 @@ class TranscriptionContextFixup(FrameProcessor):
await self.push_frame(frame, direction)
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- # No transcription at all. just audio input to Gemini!
- # transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
+
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
+
+ conversation_llm = GoogleLLMService(
+ name="Conversation",
+ model="gemini-2.0-flash-001",
+ # model="gemini-exp-1121",
+ api_key=os.getenv("GOOGLE_API_KEY"),
+ # we can give the GoogleLLMService a system instruction to use directly
+ # in the GenerativeModel constructor. Let's do that rather than put
+ # our system message in the messages list.
+ system_instruction=conversation_system_message,
+ )
+
+ input_transcription_llm = GoogleLLMService(
+ name="Transcription",
+ model="gemini-2.0-flash-001",
+ # model="gemini-exp-1121",
+ api_key=os.getenv("GOOGLE_API_KEY"),
+ system_instruction=transcriber_system_message,
+ )
+
+ messages = [
+ {
+ "role": "user",
+ "content": "Start by saying hello.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages)
+ context_aggregator = conversation_llm.create_context_aggregator(context)
+ audio_collector = UserAudioCollector(context, context_aggregator.user())
+ input_transcription_context_filter = InputTranscriptionContextFilter()
+ transcription_frames_emitter = InputTranscriptionFrameEmitter()
+ fixup_context_messages = TranscriptionContextFixup(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ audio_collector,
+ context_aggregator.user(),
+ ParallelPipeline(
+ [ # transcribe
+ input_transcription_context_filter,
+ input_transcription_llm,
+ transcription_frames_emitter,
+ ],
+ [ # conversation inference
+ conversation_llm,
+ ],
),
- )
-
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
-
- conversation_llm = GoogleLLMService(
- name="Conversation",
- model="gemini-2.0-flash-001",
- # model="gemini-exp-1121",
- api_key=os.getenv("GOOGLE_API_KEY"),
- # we can give the GoogleLLMService a system instruction to use directly
- # in the GenerativeModel constructor. Let's do that rather than put
- # our system message in the messages list.
- system_instruction=conversation_system_message,
- )
-
- input_transcription_llm = GoogleLLMService(
- name="Transcription",
- model="gemini-2.0-flash-001",
- # model="gemini-exp-1121",
- api_key=os.getenv("GOOGLE_API_KEY"),
- system_instruction=transcriber_system_message,
- )
-
- messages = [
- {
- "role": "user",
- "content": "Start by saying hello.",
- },
+ tts,
+ transport.output(),
+ context_aggregator.assistant(),
+ fixup_context_messages,
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = conversation_llm.create_context_aggregator(context)
- audio_collector = UserAudioCollector(context, context_aggregator.user())
- input_transcription_context_filter = InputTranscriptionContextFilter()
- transcription_frames_emitter = InputTranscriptionFrameEmitter()
- fixup_context_messages = TranscriptionContextFixup(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(),
- audio_collector,
- context_aggregator.user(),
- ParallelPipeline(
- [ # transcribe
- input_transcription_context_filter,
- input_transcription_llm,
- transcription_frames_emitter,
- ],
- [ # conversation inference
- conversation_llm,
- ],
- ),
- tts,
- transport.output(),
- context_aggregator.assistant(),
- fixup_context_messages,
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- runner = PipelineRunner()
+ runner = PipelineRunner(handle_sigint=False)
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/26-gemini-multimodal-live.py b/examples/foundational/26-gemini-multimodal-live.py
index c26d6ad2f..3fa11424c 100644
--- a/examples/foundational/26-gemini-multimodal-live.py
+++ b/examples/foundational/26-gemini-multimodal-live.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
@@ -20,75 +17,98 @@ from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.services.gemini_multimodal_live.gemini import GeminiMultimodalLiveLLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+# Load environment variables
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+ # Initialize the SmallWebRTCTransport with the connection
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ video_in_enabled=False,
+ # set stop_secs to something roughly similar to the internal setting
+ # of the Multimodal Live api, just to align events.
+ vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
+ ),
+ )
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- vad_enabled=True,
- vad_audio_passthrough=True,
- # set stop_secs to something roughly similar to the internal setting
- # of the Multimodal Live api, just to align events. This doesn't really
- # matter because we can only use the Multimodal Live API's phrase
- # endpointing, for now.
- vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
- ),
- )
+ # Create the Gemini Multimodal Live LLM service
+ system_instruction = f"""
+ You are a helpful AI assistant.
+ Your goal is to demonstrate your capabilities in a helpful and engaging way.
+ Your output will be converted to audio so don't include special characters in your answers.
+ Respond to what the user said in a creative and helpful way.
+ """
- llm = GeminiMultimodalLiveLLMService(
- api_key=os.getenv("GOOGLE_API_KEY"),
- # system_instruction="Talk like a pirate."
- )
+ llm = GeminiMultimodalLiveLLMService(
+ api_key=os.getenv("GOOGLE_API_KEY"),
+ system_instruction=system_instruction,
+ voice_id="Puck", # Aoede, Charon, Fenrir, Kore, Puck
+ transcribe_user_audio=True,
+ )
- pipeline = Pipeline(
+ # Build the pipeline
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ llm,
+ transport.output(),
+ ]
+ )
+
+ # Configure the pipeline task
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ ),
+ )
+
+ # Handle client connection event
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frames(
[
- transport.input(),
- llm,
- transport.output(),
+ LLMMessagesAppendFrame(
+ messages=[
+ {
+ "role": "user",
+ "content": f"Greet the user and introduce yourself.",
+ }
+ ]
+ )
]
)
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- ),
- )
+ # Handle client disconnection events
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await task.queue_frames(
- [
- LLMMessagesAppendFrame(
- messages=[
- {
- "role": "user",
- "content": "Greet the user.",
- }
- ]
- )
- ]
- )
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- runner = PipelineRunner()
-
- await runner.run(task)
+ # Run the pipeline
+ runner = PipelineRunner(handle_sigint=False)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/26a-gemini-multimodal-live-transcription.py b/examples/foundational/26a-gemini-multimodal-live-transcription.py
index aa07ace93..18a484a81 100644
--- a/examples/foundational/26a-gemini-multimodal-live-transcription.py
+++ b/examples/foundational/26a-gemini-multimodal-live-transcription.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
@@ -20,90 +17,98 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.gemini_multimodal_live.gemini import GeminiMultimodalLiveLLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+ # Initialize the SmallWebRTCTransport with the connection
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ # set stop_secs to something roughly similar to the internal setting
+ # of the Multimodal Live api, just to align events. This doesn't really
+ # matter because we can only use the Multimodal Live API's phrase
+ # endpointing, for now.
+ vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
+ ),
+ )
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- vad_enabled=True,
- vad_audio_passthrough=True,
- # set stop_secs to something roughly similar to the internal setting
- # of the Multimodal Live api, just to align events. This doesn't really
- # matter because we can only use the Multimodal Live API's phrase
- # endpointing, for now.
- vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
- ),
- )
+ llm = GeminiMultimodalLiveLLMService(
+ api_key=os.getenv("GOOGLE_API_KEY"),
+ voice_id="Aoede", # Puck, Charon, Kore, Fenrir, Aoede
+ # system_instruction="Talk like a pirate."
+ transcribe_user_audio=True,
+ # inference_on_context_initialization=False,
+ )
- llm = GeminiMultimodalLiveLLMService(
- api_key=os.getenv("GOOGLE_API_KEY"),
- voice_id="Aoede", # Puck, Charon, Kore, Fenrir, Aoede
- # system_instruction="Talk like a pirate."
- transcribe_user_audio=True,
- transcribe_model_audio=True,
- # inference_on_context_initialization=False,
- )
+ context = OpenAILLMContext(
+ [
+ {
+ "role": "user",
+ "content": "Say hello. Then ask if I want to hear a joke.",
+ },
+ # {"role": "assistant", "content": "Hello! Why don't scientists trust atoms?"},
+ # {
+ # "role": "user",
+ # "content": [
+ # {
+ # "type": "text",
+ # "text": "Oh, I know this one: because they make up everything.",
+ # }
+ # ],
+ # },
+ ],
+ )
+ context_aggregator = llm.create_context_aggregator(context)
- context = OpenAILLMContext(
- [
- {
- "role": "user",
- "content": "Say hello. Then ask if I want to hear a joke.",
- },
- # {"role": "assistant", "content": "Hello! Why don't scientists trust atoms?"},
- # {
- # "role": "user",
- # "content": [
- # {
- # "type": "text",
- # "text": "Oh, I know this one: because they make up everything.",
- # }
- # ],
- # },
- ],
- )
- context_aggregator = llm.create_context_aggregator(context)
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ context_aggregator.user(),
+ llm,
+ transport.output(),
+ context_aggregator.assistant(),
+ ]
+ )
- pipeline = Pipeline(
- [
- transport.input(),
- context_aggregator.user(),
- llm,
- transport.output(),
- context_aggregator.assistant(),
- ]
- )
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ ),
+ )
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- ),
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- runner = PipelineRunner()
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- await runner.run(task)
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/26b-gemini-multimodal-live-function-calling.py b/examples/foundational/26b-gemini-multimodal-live-function-calling.py
index 3aaad44ce..c9faa751c 100644
--- a/examples/foundational/26b-gemini-multimodal-live-function-calling.py
+++ b/examples/foundational/26b-gemini-multimodal-live-function-calling.py
@@ -4,15 +4,12 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
from datetime import datetime
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import AdapterType, ToolsSchema
@@ -23,13 +20,12 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.gemini_multimodal_live.gemini import GeminiMultimodalLiveLLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
temperature = 75 if args["format"] == "fahrenheit" else 24
@@ -51,87 +47,97 @@ for the weather, call this function.
"""
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- vad_enabled=True,
- vad_audio_passthrough=True,
- # set stop_secs to something roughly similar to the internal setting
- # of the Multimodal Live api, just to align events. This doesn't really
- # matter because we can only use the Multimodal Live API's phrase
- # endpointing, for now.
- vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
- ),
- )
+ # Initialize the SmallWebRTCTransport with the connection
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ # set stop_secs to something roughly similar to the internal setting
+ # of the Multimodal Live api, just to align events. This doesn't really
+ # matter because we can only use the Multimodal Live API's phrase
+ # endpointing, for now.
+ vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
+ ),
+ )
- weather_function = FunctionSchema(
- name="get_current_weather",
- description="Get the current weather",
- properties={
- "location": {
- "type": "string",
- "description": "The city and state, e.g. San Francisco, CA",
- },
- "format": {
- "type": "string",
- "enum": ["celsius", "fahrenheit"],
- "description": "The temperature unit to use. Infer this from the user's location.",
- },
+ weather_function = FunctionSchema(
+ name="get_current_weather",
+ description="Get the current weather",
+ properties={
+ "location": {
+ "type": "string",
+ "description": "The city and state, e.g. San Francisco, CA",
},
- required=["location", "format"],
- )
- search_tool = {"google_search": {}}
- tools = ToolsSchema(
- standard_tools=[weather_function], custom_tools={AdapterType.GEMINI: [search_tool]}
- )
+ "format": {
+ "type": "string",
+ "enum": ["celsius", "fahrenheit"],
+ "description": "The temperature unit to use. Infer this from the user's location.",
+ },
+ },
+ required=["location", "format"],
+ )
+ search_tool = {"google_search": {}}
+ tools = ToolsSchema(
+ standard_tools=[weather_function], custom_tools={AdapterType.GEMINI: [search_tool]}
+ )
- llm = GeminiMultimodalLiveLLMService(
- api_key=os.getenv("GOOGLE_API_KEY"),
- system_instruction=system_instruction,
- tools=tools,
- )
+ llm = GeminiMultimodalLiveLLMService(
+ api_key=os.getenv("GOOGLE_API_KEY"),
+ system_instruction=system_instruction,
+ tools=tools,
+ )
- llm.register_function("get_current_weather", fetch_weather_from_api)
+ llm.register_function("get_current_weather", fetch_weather_from_api)
- context = OpenAILLMContext(
- [{"role": "user", "content": "Say hello."}],
- )
- context_aggregator = llm.create_context_aggregator(context)
+ context = OpenAILLMContext(
+ [{"role": "user", "content": "Say hello."}],
+ )
+ context_aggregator = llm.create_context_aggregator(context)
- pipeline = Pipeline(
- [
- transport.input(),
- context_aggregator.user(),
- llm,
- context_aggregator.assistant(),
- transport.output(),
- ]
- )
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ context_aggregator.user(),
+ llm,
+ transport.output(),
+ context_aggregator.assistant(),
+ ]
+ )
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- ),
- )
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ ),
+ )
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- runner = PipelineRunner()
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- await runner.run(task)
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/26c-gemini-multimodal-live-video.py b/examples/foundational/26c-gemini-multimodal-live-video.py
index 4ec8663c4..7949f189e 100644
--- a/examples/foundational/26c-gemini-multimodal-live-video.py
+++ b/examples/foundational/26c-gemini-multimodal-live-video.py
@@ -9,9 +9,9 @@ import os
import sys
import aiohttp
+from daily_runner import configure
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
@@ -37,9 +37,8 @@ async def main():
token,
"Respond bot",
DailyParams(
+ audio_in_enabled=True,
audio_out_enabled=True,
- vad_enabled=True,
- vad_audio_passthrough=True,
# set stop_secs to something roughly similar to the internal setting
# of the Multimodal Live api, just to align events. This doesn't really
# matter because we can only use the Multimodal Live API's phrase
@@ -53,7 +52,6 @@ async def main():
voice_id="Aoede", # Puck, Charon, Kore, Fenrir, Aoede
# system_instruction="Talk like a pirate."
transcribe_user_audio=True,
- transcribe_model_audio=True,
# inference_on_context_initialization=False,
)
diff --git a/examples/foundational/26d-gemini-multimodal-live-text.py b/examples/foundational/26d-gemini-multimodal-live-text.py
index 150bb1613..2cc36b68e 100644
--- a/examples/foundational/26d-gemini-multimodal-live-text.py
+++ b/examples/foundational/26d-gemini-multimodal-live-text.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
@@ -25,12 +22,12 @@ from pipecat.services.gemini_multimodal_live.gemini import (
GeminiMultimodalModalities,
InputParams,
)
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
SYSTEM_INSTRUCTION = f"""
"You are Gemini Chatbot, a friendly, helpful robot.
@@ -43,90 +40,93 @@ Respond to what the user said in a creative and helpful way. Keep your responses
"""
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- vad_enabled=True,
- vad_audio_passthrough=True,
- # set stop_secs to something roughly similar to the internal setting
- # of the Multimodal Live api, just to align events. This doesn't really
- # matter because we can only use the Multimodal Live API's phrase
- # endpointing, for now.
- vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
- ),
- )
+ # Initialize the SmallWebRTCTransport with the connection
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ # set stop_secs to something roughly similar to the internal setting
+ # of the Multimodal Live api, just to align events. This doesn't really
+ # matter because we can only use the Multimodal Live API's phrase
+ # endpointing, for now.
+ vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
+ ),
+ )
- llm = GeminiMultimodalLiveLLMService(
- api_key=os.getenv("GOOGLE_API_KEY"),
- transcribe_user_audio=True,
- transcribe_model_audio=True,
- system_instruction=SYSTEM_INSTRUCTION,
- tools=[{"google_search": {}}, {"code_execution": {}}],
- params=InputParams(modalities=GeminiMultimodalModalities.TEXT),
- )
+ llm = GeminiMultimodalLiveLLMService(
+ api_key=os.getenv("GOOGLE_API_KEY"),
+ transcribe_user_audio=True,
+ system_instruction=SYSTEM_INSTRUCTION,
+ tools=[{"google_search": {}}, {"code_execution": {}}],
+ params=InputParams(modalities=GeminiMultimodalModalities.TEXT),
+ )
- # Optionally, you can set the response modalities via a function
- # llm.set_model_modalities(
- # GeminiMultimodalModalities.TEXT
- # )
+ # Optionally, you can set the response modalities via a function
+ # llm.set_model_modalities(
+ # GeminiMultimodalModalities.TEXT
+ # )
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121"
- )
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121"
+ )
- messages = [
- {
- "role": "user",
- "content": 'Start by saying "Hello, I\'m Gemini".',
- },
+ messages = [
+ {
+ "role": "user",
+ "content": 'Start by saying "Hello, I\'m Gemini".',
+ },
+ ]
+
+ # Set up conversation context and management
+ # The context_aggregator will automatically collect conversation context
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ context_aggregator.user(),
+ llm,
+ tts,
+ transport.output(),
+ context_aggregator.assistant(),
]
+ )
- # Set up conversation context and management
- # The context_aggregator will automatically collect conversation context
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(),
- context_aggregator.user(),
- llm,
- tts,
- transport.output(),
- context_aggregator.assistant(),
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- print(f"Participant left: {participant}")
- await task.cancel()
+ runner = PipelineRunner(handle_sigint=False)
- runner = PipelineRunner()
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/26e-gemini-multimodal-google-search.py b/examples/foundational/26e-gemini-multimodal-google-search.py
index 7bfd017d1..87e3d7372 100644
--- a/examples/foundational/26e-gemini-multimodal-google-search.py
+++ b/examples/foundational/26e-gemini-multimodal-google-search.py
@@ -4,12 +4,9 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-from pathlib import Path
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
@@ -19,15 +16,12 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.gemini_multimodal_live.gemini import GeminiMultimodalLiveLLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-sys.path.append(str(Path(__file__).parent.parent))
-from runner import configure
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
# Function handlers for the LLM
search_tool = {"google_search": {}}
@@ -47,62 +41,71 @@ Start each interaction by asking the user about which place they would like to k
"""
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Latest news!",
- DailyParams(
- audio_out_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
- ),
- )
+ # Initialize the SmallWebRTCTransport with the connection
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- # Initialize the Gemini Multimodal Live model
- llm = GeminiMultimodalLiveLLMService(
- api_key=os.getenv("GOOGLE_API_KEY"),
- voice_id="Puck", # Aoede, Charon, Fenrir, Kore, Puck
- transcribe_user_audio=True,
- transcribe_model_audio=True,
- system_instruction=system_instruction,
- tools=tools,
- )
+ # Initialize the Gemini Multimodal Live model
+ llm = GeminiMultimodalLiveLLMService(
+ api_key=os.getenv("GOOGLE_API_KEY"),
+ voice_id="Puck", # Aoede, Charon, Fenrir, Kore, Puck
+ transcribe_user_audio=True,
+ system_instruction=system_instruction,
+ tools=tools,
+ )
- context = OpenAILLMContext(
- [
- {
- "role": "user",
- "content": "Start by greeting the user warmly, introducing yourself, and mentioning the current day. Be friendly and engaging to set a positive tone for the interaction.",
- }
- ],
- )
- context_aggregator = llm.create_context_aggregator(context)
+ context = OpenAILLMContext(
+ [
+ {
+ "role": "user",
+ "content": "Start by greeting the user warmly, introducing yourself, and mentioning the current day. Be friendly and engaging to set a positive tone for the interaction.",
+ }
+ ],
+ )
+ context_aggregator = llm.create_context_aggregator(context)
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- context_aggregator.user(), # User responses
- llm, # LLM
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses
- ]
- )
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ context_aggregator.user(), # User responses
+ llm, # LLM
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
+ ]
+ )
- task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
+ task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- runner = PipelineRunner()
- await runner.run(task)
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
+
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/27-simli-layer.py b/examples/foundational/27-simli-layer.py
index d13ea705a..513bbc239 100644
--- a/examples/foundational/27-simli-layer.py
+++ b/examples/foundational/27-simli-layer.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from simli import SimliConfig
from pipecat.audio.vad.silero import SileroVADAnalyzer
@@ -20,83 +17,95 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.services.simli.video import SimliVideoService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
- async with aiohttp.ClientSession() as session:
- room, token = await configure(session)
- transport = DailyTransport(
- room,
- token,
- "Simli",
- DailyParams(
- audio_out_enabled=True,
- camera_out_enabled=True,
- camera_out_width=512,
- camera_out_height=512,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- transcription_enabled=True,
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ video_out_enabled=True,
+ video_out_width=512,
+ video_out_height=512,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="a167e0f3-df7e-4d52-a9c3-f949145efdab",
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- simli_ai = SimliVideoService(
- SimliConfig(os.getenv("SIMLI_API_KEY"), os.getenv("SIMLI_FACE_ID"))
- )
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="a167e0f3-df7e-4d52-a9c3-f949145efdab",
+ )
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o-mini")
+ simli_ai = SimliVideoService(
+ SimliConfig(os.getenv("SIMLI_API_KEY"), os.getenv("SIMLI_FACE_ID"))
+ )
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o-mini")
+
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ context_aggregator.user(),
+ llm,
+ tts,
+ simli_ai,
+ transport.output(),
+ context_aggregator.assistant(),
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(),
- context_aggregator.user(),
- llm,
- tts,
- simli_ai,
- transport.output(),
- context_aggregator.assistant(),
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Start conversation - empty prompt to let LLM follow system instructions
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- runner = PipelineRunner()
- await runner.run(task)
+ runner = PipelineRunner(handle_sigint=False)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/28-transcription-processor.py b/examples/foundational/28-transcription-processor.py
index bdb0da104..e538c20e9 100644
--- a/examples/foundational/28-transcription-processor.py
+++ b/examples/foundational/28-transcription-processor.py
@@ -4,15 +4,12 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
from typing import List, Optional
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import TranscriptionMessage, TranscriptionUpdateFrame
@@ -24,13 +21,12 @@ from pipecat.processors.transcript_processor import TranscriptProcessor
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
class TranscriptHandler:
"""Handles real-time transcript processing and output.
@@ -93,85 +89,83 @@ class TranscriptHandler:
await self.save_message(msg)
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- None,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- llm = OpenAILLMService(
- api_key=os.getenv("OPENAI_API_KEY"),
- model="gpt-4o",
- )
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative, helpful, and brief way. Say hello.",
- },
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative, helpful, and brief way. Say hello.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ # Create transcript processor and handler
+ transcript = TranscriptProcessor()
+ transcript_handler = TranscriptHandler() # Output to log only
+ # transcript_handler = TranscriptHandler(output_file="transcript.txt") # Output to file and log
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt, # STT
+ transcript.user(), # User transcripts
+ context_aggregator.user(), # User responses
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ transcript.assistant(), # Assistant transcripts
+ context_aggregator.assistant(), # Assistant spoken responses
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
- # Create transcript processor and handler
- transcript = TranscriptProcessor()
- transcript_handler = TranscriptHandler() # Output to log only
- # transcript_handler = TranscriptHandler(output_file="transcript.txt") # Output to file and log
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Start conversation - empty prompt to let LLM follow system instructions
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- stt, # STT
- transcript.user(), # User transcripts
- context_aggregator.user(), # User responses
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- transcript.assistant(), # Assistant transcripts
- context_aggregator.assistant(), # Assistant spoken responses
- ]
- )
+ # Register event handler for transcript updates
+ @transcript.event_handler("on_transcript_update")
+ async def on_transcript_update(processor, frame):
+ await transcript_handler.on_transcript_update(processor, frame)
- task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- # Register event handler for transcript updates
- @transcript.event_handler("on_transcript_update")
- async def on_transcript_update(processor, frame):
- await transcript_handler.on_transcript_update(processor, frame)
-
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- # Stop the pipeline immediately when the participant leaves
- await task.cancel()
-
- runner = PipelineRunner()
-
- await runner.run(task)
+ runner = PipelineRunner(handle_sigint=False)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/29-livekit-audio-chat.py b/examples/foundational/29-livekit-audio-chat.py
index d761e5418..ef2679ab8 100644
--- a/examples/foundational/29-livekit-audio-chat.py
+++ b/examples/foundational/29-livekit-audio-chat.py
@@ -115,8 +115,6 @@ async def main():
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
- vad_enabled=True,
- vad_audio_passthrough=True,
),
)
@@ -127,7 +125,7 @@ async def main():
),
)
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
tts = CartesiaTTSService(
api_key=os.getenv("CARTESIA_API_KEY"),
diff --git a/examples/foundational/30-observer.py b/examples/foundational/30-observer.py
index 82dcff0ef..d8c2ec100 100644
--- a/examples/foundational/30-observer.py
+++ b/examples/foundational/30-observer.py
@@ -4,14 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import (
@@ -28,14 +25,14 @@ from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
class DebugObserver(BaseObserver):
"""Observer to log interruptions and bot speaking events to the console.
@@ -71,76 +68,82 @@ class DebugObserver(BaseObserver):
logger.info(f"π€ BOT STOP SPEAKING: {src} {arrow} {dst} at {time_sec:.2f}s")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- messages = [
- {
- "role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
- },
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt,
+ context_aggregator.user(), # User responses
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ observers=[DebugObserver(), LLMLogObserver()],
+ )
- pipeline = Pipeline(
- [
- transport.input(), # Transport user input
- context_aggregator.user(), # User responses
- llm, # LLM
- tts, # TTS
- transport.output(), # Transport bot output
- context_aggregator.assistant(), # Assistant spoken responses
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- observers=[DebugObserver(), LLMLogObserver()],
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- messages.append({"role": "system", "content": "Please introduce yourself to the user."})
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await task.cancel()
+ runner = PipelineRunner(handle_sigint=False)
- runner = PipelineRunner()
-
- await runner.run(task)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/31-heartbeats.py b/examples/foundational/31-heartbeats.py
index 2487c13e7..70f6829da 100644
--- a/examples/foundational/31-heartbeats.py
+++ b/examples/foundational/31-heartbeats.py
@@ -25,11 +25,11 @@ class NullProcessor(FrameProcessor):
async def main():
- """This test shows heartbeat monitoring by displaying a warning when
- heartbeats are not received.
+ """This test shows heartbeat monitoring.
+ A warning is dispalyed when heartbeats are not received within the
+ default (5 seconds) timeout.
"""
-
pipeline = Pipeline([NullProcessor()])
task = PipelineTask(pipeline, params=PipelineParams(enable_heartbeats=True))
diff --git a/examples/foundational/32-gemini-grounding-metadata.py b/examples/foundational/32-gemini-grounding-metadata.py
index 1f7fb1ab3..0c9f4cf38 100644
--- a/examples/foundational/32-gemini-grounding-metadata.py
+++ b/examples/foundational/32-gemini-grounding-metadata.py
@@ -4,14 +4,14 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
import sys
from pathlib import Path
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
+from openai import audio
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import Frame
@@ -23,15 +23,14 @@ from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.google.llm import GoogleLLMService, LLMSearchResponseFrame
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
sys.path.append(str(Path(__file__).parent.parent))
-from runner import configure
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
# Function handlers for the LLM
search_tool = {"google_search_retrieval": {}}
@@ -61,71 +60,80 @@ class LLMSearchLoggerProcessor(FrameProcessor):
await self.push_frame(frame)
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Latest news!",
- DailyParams(
- audio_out_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
- )
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
- # Initialize the Gemini Multimodal Live model
- llm = GoogleLLMService(
- api_key=os.getenv("GOOGLE_API_KEY"),
- system_instruction=system_instruction,
- tools=tools,
- model="gemini-1.5-flash-002",
- )
+ # Initialize the Gemini Multimodal Live model
+ llm = GoogleLLMService(
+ api_key=os.getenv("GOOGLE_API_KEY"),
+ system_instruction=system_instruction,
+ tools=tools,
+ model="gemini-1.5-flash-002",
+ )
- context = OpenAILLMContext(
- [
- {
- "role": "user",
- "content": "Start by greeting the user warmly, introducing yourself, and mentioning the current day. Be friendly and engaging to set a positive tone for the interaction.",
- }
- ],
- )
- context_aggregator = llm.create_context_aggregator(context)
+ context = OpenAILLMContext(
+ [
+ {
+ "role": "user",
+ "content": "Start by greeting the user warmly, introducing yourself, and mentioning the current day. Be friendly and engaging to set a positive tone for the interaction.",
+ }
+ ],
+ )
+ context_aggregator = llm.create_context_aggregator(context)
- llm_search_logger = LLMSearchLoggerProcessor()
+ llm_search_logger = LLMSearchLoggerProcessor()
- pipeline = Pipeline(
- [
- transport.input(),
- stt,
- context_aggregator.user(),
- llm,
- llm_search_logger,
- tts,
- transport.output(),
- context_aggregator.assistant(),
- ]
- )
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ context_aggregator.user(),
+ llm,
+ llm_search_logger,
+ tts,
+ transport.output(),
+ context_aggregator.assistant(),
+ ]
+ )
- task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
+ task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Start conversation - empty prompt to let LLM follow system instructions
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- runner = PipelineRunner()
- await runner.run(task)
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
+
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/33-gemini-rag.py b/examples/foundational/33-gemini-rag.py
index 74905084b..2c16b1dbf 100644
--- a/examples/foundational/33-gemini-rag.py
+++ b/examples/foundational/33-gemini-rag.py
@@ -47,17 +47,14 @@ Customization options:
- change the function calling logic
"""
-import asyncio
+import argparse
import json
import os
-import sys
import time
-import aiohttp
import google.generativeai as genai
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
@@ -65,16 +62,14 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.google.llm import GoogleLLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="INFO")
-
-video_participant_id = None
-
def get_rag_content():
"""Get the RAG content from the file."""
@@ -158,97 +153,104 @@ async def query_knowledge_base(
await result_callback(response.text)
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Gemini RAG Bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="f9836c6e-a0bd-460e-9d3c-f7299fa60f94", # Southern Lady
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- llm = GoogleLLMService(
- model=VOICE_MODEL,
- api_key=os.getenv("GOOGLE_API_KEY"),
- )
- llm.register_function("query_knowledge_base", query_knowledge_base)
- tools = [
- {
- "function_declarations": [
- {
- "name": "query_knowledge_base",
- "description": "Query the knowledge base for the answer to the question.",
- "parameters": {
- "type": "object",
- "properties": {
- "question": {
- "type": "string",
- "description": "The question to query the knowledge base with.",
- },
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="f9836c6e-a0bd-460e-9d3c-f7299fa60f94", # Southern Lady
+ )
+
+ llm = GoogleLLMService(
+ model=VOICE_MODEL,
+ api_key=os.getenv("GOOGLE_API_KEY"),
+ )
+ llm.register_function("query_knowledge_base", query_knowledge_base)
+ tools = [
+ {
+ "function_declarations": [
+ {
+ "name": "query_knowledge_base",
+ "description": "Query the knowledge base for the answer to the question.",
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "question": {
+ "type": "string",
+ "description": "The question to query the knowledge base with.",
},
},
},
- ],
- },
- ]
- system_prompt = """\
+ },
+ ],
+ },
+ ]
+ system_prompt = """\
You are a helpful assistant who converses with a user and answers questions.
You have access to the tool, query_knowledge_base, that allows you to query the knowledge base for the answer to the user's question.
Your response will be turned into speech so use only simple words and punctuation.
"""
- messages = [
- {"role": "system", "content": system_prompt},
- {"role": "user", "content": "Greet the user."},
+ messages = [
+ {"role": "system", "content": system_prompt},
+ {"role": "user", "content": "Greet the user."},
+ ]
+
+ context = OpenAILLMContext(messages, tools)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ context_aggregator.user(),
+ llm,
+ tts,
+ transport.output(),
+ context_aggregator.assistant(),
]
+ )
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ ),
+ )
- context = OpenAILLMContext(messages, tools)
- context_aggregator = llm.create_context_aggregator(context)
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Start conversation - empty prompt to let LLM follow system instructions
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- pipeline = Pipeline(
- [
- transport.input(),
- context_aggregator.user(),
- llm,
- tts,
- transport.output(),
- context_aggregator.assistant(),
- ]
- )
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- global video_participant_id
- video_participant_id = participant["id"]
- await transport.capture_participant_transcription(participant["id"])
- await transport.capture_participant_video(video_participant_id, framerate=0)
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- runner = PipelineRunner()
- await runner.run(task)
+ runner = PipelineRunner(handle_sigint=False)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/34-audio-recording.py b/examples/foundational/34-audio-recording.py
index 09b9e3827..a7d07e7b2 100644
--- a/examples/foundational/34-audio-recording.py
+++ b/examples/foundational/34-audio-recording.py
@@ -46,18 +46,15 @@ Note:
handling merged and separate audio tracks respectively.
"""
-import asyncio
+import argparse
import datetime
import io
import os
-import sys
import wave
import aiofiles
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
@@ -68,11 +65,11 @@ from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
async def save_audio_file(audio: bytes, filename: str, sample_rate: int, num_channels: int):
@@ -89,102 +86,99 @@ async def save_audio_file(audio: bytes, filename: str, sample_rate: int, num_cha
logger.info(f"Audio saved to {filename}")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Recording bot",
- DailyParams(
- # audio_in_enabled=True,
- audio_out_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"), audio_passthrough=True)
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"), audio_passthrough=True)
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",
- )
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",
+ )
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4")
- # Create audio buffer processor
- audiobuffer = AudioBufferProcessor()
+ # Create audio buffer processor
+ audiobuffer = AudioBufferProcessor()
- messages = [
- {
- "role": "system",
- "content": "You are a helpful assistant demonstrating audio recording capabilities. Keep your responses brief and clear.",
- },
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful assistant demonstrating audio recording capabilities. Keep your responses brief and clear.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ context_aggregator.user(),
+ llm,
+ tts,
+ transport.output(),
+ audiobuffer, # Add audio buffer to pipeline
+ context_aggregator.assistant(),
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
- pipeline = Pipeline(
- [
- transport.input(),
- stt,
- context_aggregator.user(),
- llm,
- tts,
- transport.output(),
- audiobuffer, # Add audio buffer to pipeline
- context_aggregator.assistant(),
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Start recording audio
+ await audiobuffer.start_recording()
+ # Start conversation - empty prompt to let LLM follow system instructions
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- await audiobuffer.start_recording()
- messages.append(
- {
- "role": "system",
- "content": "Greet the user and explain that this conversation will be recorded.",
- }
- )
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- await audiobuffer.stop_recording()
- await task.cancel()
+ # Handler for merged audio
+ @audiobuffer.event_handler("on_audio_data")
+ async def on_audio_data(buffer, audio, sample_rate, num_channels):
+ timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+ filename = f"recordings/merged_{timestamp}.wav"
+ os.makedirs("recordings", exist_ok=True)
+ await save_audio_file(audio, filename, sample_rate, num_channels)
- # Handler for merged audio
- @audiobuffer.event_handler("on_audio_data")
- async def on_audio_data(buffer, audio, sample_rate, num_channels):
- timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
- filename = f"recordings/merged_{timestamp}.wav"
- os.makedirs("recordings", exist_ok=True)
- await save_audio_file(audio, filename, sample_rate, num_channels)
+ # Handler for separate tracks
+ @audiobuffer.event_handler("on_track_audio_data")
+ async def on_track_audio_data(buffer, user_audio, bot_audio, sample_rate, num_channels):
+ timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+ os.makedirs("recordings", exist_ok=True)
- # Handler for separate tracks
- @audiobuffer.event_handler("on_track_audio_data")
- async def on_track_audio_data(buffer, user_audio, bot_audio, sample_rate, num_channels):
- timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
- os.makedirs("recordings", exist_ok=True)
+ # Save user audio
+ user_filename = f"recordings/user_{timestamp}.wav"
+ await save_audio_file(user_audio, user_filename, sample_rate, 1)
- # Save user audio
- user_filename = f"recordings/user_{timestamp}.wav"
- await save_audio_file(user_audio, user_filename, sample_rate, 1)
+ # Save bot audio
+ bot_filename = f"recordings/bot_{timestamp}.wav"
+ await save_audio_file(bot_audio, bot_filename, sample_rate, 1)
- # Save bot audio
- bot_filename = f"recordings/bot_{timestamp}.wav"
- await save_audio_file(bot_audio, bot_filename, sample_rate, 1)
-
- runner = PipelineRunner()
- await runner.run(task)
+ runner = PipelineRunner(handle_sigint=False)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/35-pattern-pair-voice-switching.py b/examples/foundational/35-pattern-pair-voice-switching.py
index 29218b2c3..b99b543a9 100644
--- a/examples/foundational/35-pattern-pair-voice-switching.py
+++ b/examples/foundational/35-pattern-pair-voice-switching.py
@@ -44,14 +44,11 @@ Note:
such as formatting instructions, command recognition, or structured data extraction.
"""
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
@@ -59,14 +56,15 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
from pipecat.utils.text.pattern_pair_aggregator import PatternMatch, PatternPairAggregator
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
# Define voice IDs
VOICE_IDS = {
@@ -76,57 +74,55 @@ VOICE_IDS = {
}
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Multi-voice storyteller",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- # Create pattern pair aggregator for voice switching
- pattern_aggregator = PatternPairAggregator()
+ # Create pattern pair aggregator for voice switching
+ pattern_aggregator = PatternPairAggregator()
- # Add pattern for voice switching
- pattern_aggregator.add_pattern_pair(
- pattern_id="voice_tag",
- start_pattern="",
- end_pattern=" ",
- remove_match=True,
- )
+ # Add pattern for voice switching
+ pattern_aggregator.add_pattern_pair(
+ pattern_id="voice_tag",
+ start_pattern="",
+ end_pattern=" ",
+ remove_match=True,
+ )
- # Register handler for voice switching
- def on_voice_tag(match: PatternMatch):
- voice_name = match.content.strip().lower()
- if voice_name in VOICE_IDS:
- voice_id = VOICE_IDS[voice_name]
- tts.set_voice(voice_id)
- logger.info(f"Switched to {voice_name} voice")
- else:
- logger.warning(f"Unknown voice: {voice_name}")
+ # Register handler for voice switching
+ def on_voice_tag(match: PatternMatch):
+ voice_name = match.content.strip().lower()
+ if voice_name in VOICE_IDS:
+ voice_id = VOICE_IDS[voice_name]
+ tts.set_voice(voice_id)
+ logger.info(f"Switched to {voice_name} voice")
+ else:
+ logger.warning(f"Unknown voice: {voice_name}")
- pattern_aggregator.on_pattern_match("voice_tag", on_voice_tag)
+ pattern_aggregator.on_pattern_match("voice_tag", on_voice_tag)
- # Initialize TTS with narrator voice as default
- tts = CartesiaTTSService(
- api_key=os.getenv("CARTESIA_API_KEY"),
- voice_id=VOICE_IDS["narrator"],
- text_aggregator=pattern_aggregator,
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- # Initialize LLM
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ # Initialize TTS with narrator voice as default
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id=VOICE_IDS["narrator"],
+ text_aggregator=pattern_aggregator,
+ )
- # System prompt for storytelling with voice switching
- system_prompt = """You are an engaging storyteller that uses different voices to bring stories to life.
+ # Initialize LLM
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+
+ # System prompt for storytelling with voice switching
+ system_prompt = """You are an engaging storyteller that uses different voices to bring stories to life.
You have three voices to use, but each has a specific purpose:
@@ -173,58 +169,60 @@ FOLLOW THESE RULES:
Remember: Use narrator voice for EVERYTHING except the actual quoted dialogue."""
- # Set up LLM context
- messages = [
- {
- "role": "system",
- "content": system_prompt,
- },
+ # Set up LLM context
+ messages = [
+ {
+ "role": "system",
+ "content": system_prompt,
+ },
+ ]
+
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ # Create pipeline
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ context_aggregator.user(),
+ llm,
+ tts, # TTS with pattern aggregator
+ transport.output(),
+ context_aggregator.assistant(),
]
+ )
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- # Create pipeline
- pipeline = Pipeline(
- [
- transport.input(),
- context_aggregator.user(),
- llm,
- tts, # TTS with pattern aggregator
- transport.output(),
- context_aggregator.assistant(),
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Start conversation - empty prompt to let LLM follow system instructions
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- logger.info(f"First participant joined: {participant['id']}")
- await transport.capture_participant_transcription(participant["id"])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- # Start conversation - empty prompt to let LLM follow system instructions
- await task.queue_frames([context_aggregator.user().get_context_frame()])
-
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- logger.info(f"Participant left: {participant['id']}")
- await task.cancel()
-
- logger.info(f"Starting storytelling bot at: {room_url}")
- logger.info("Join the room to interact with the bot!")
-
- runner = PipelineRunner()
- await runner.run(task)
+ runner = PipelineRunner(handle_sigint=False)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/36-user-email-gathering.py b/examples/foundational/36-user-email-gathering.py
index 54a5cb480..11c74d117 100644
--- a/examples/foundational/36-user-email-gathering.py
+++ b/examples/foundational/36-user-email-gathering.py
@@ -4,15 +4,12 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
from openai.types.chat import ChatCompletionToolParam
-from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
@@ -20,123 +17,131 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.services.rime.tts import RimeHttpTTSService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
async def store_user_emails(function_name, tool_call_id, args, llm, context, result_callback):
print(f"User emails: {args}")
-async def main():
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
- audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- ),
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- # Cartesia offers a ` tags, for example a@a.com .",
- # Rime spell()
- # "content": "You need to gather a valid email or emails from the user. Your output will be converted to audio so don't include special characters in your answers. If the user provides one or more email addresses confirm them with the user. Enclose all emails with spell(), for example spell(a@a.com).",
},
+ )
+ ]
+ messages = [
+ {
+ "role": "system",
+ # Cartesia tags, for example a@a.com .",
+ # Rime spell()
+ # "content": "You need to gather a valid email or emails from the user. Your output will be converted to audio so don't include special characters in your answers. If the user provides one or more email addresses confirm them with the user. Enclose all emails with spell(), for example spell(a@a.com).",
+ },
+ ]
+
+ context = OpenAILLMContext(messages, tools)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ stt,
+ context_aggregator.user(),
+ llm,
+ tts,
+ transport.output(),
+ context_aggregator.assistant(),
]
+ )
- context = OpenAILLMContext(messages, tools)
- context_aggregator = llm.create_context_aggregator(context)
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
- pipeline = Pipeline(
- [
- transport.input(),
- context_aggregator.user(),
- llm,
- tts,
- transport.output(),
- context_aggregator.assistant(),
- ]
- )
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Start conversation - empty prompt to let LLM follow system instructions
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- report_only_initial_ttfb=True,
- ),
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- runner = PipelineRunner()
-
- await runner.run(task)
+ runner = PipelineRunner(handle_sigint=False)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/37-mem0.py b/examples/foundational/37-mem0.py
index 05e576495..a9ce685c9 100644
--- a/examples/foundational/37-mem0.py
+++ b/examples/foundational/37-mem0.py
@@ -36,14 +36,12 @@ Requirements:
The bot runs as part of a pipeline that processes audio frames and manages the conversation flow.
"""
-import asyncio
+import argparse
import os
-import sys
-import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
+from openai import audio
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
@@ -51,13 +49,13 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
+from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
from pipecat.services.mem0.memory import Mem0MemoryService
from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
@@ -115,7 +113,7 @@ async def get_initial_greeting(
return "Hello! How can I help you today?"
-async def main():
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
"""Main bot execution function.
Sets up and runs the bot pipeline including:
@@ -127,116 +125,119 @@ async def main():
"""
# Note: You can pass the user_id as a parameter in API call
USER_ID = "pipecat-demo-user"
- async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
- # Set up Daily transport with video/audio parameters
- transport = DailyTransport(
- room_url,
- token,
- "Chatbot",
- DailyParams(
- audio_out_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
- transcription_enabled=True,
- ),
- )
+ logger.info(f"Starting bot")
- # Initialize text-to-speech service
- tts = ElevenLabsTTSService(
- api_key=os.getenv("ELEVENLABS_API_KEY"),
- voice_id="pNInz6obpgDQGcFmaJgB",
- )
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ ),
+ )
- # Initialize Mem0 memory service
- memory = Mem0MemoryService(
- api_key=os.getenv("MEM0_API_KEY"),
- user_id=USER_ID, # Unique identifier for the user
- # agent_id="agent1", # Optional identifier for the agent
- # run_id="session1", # Optional identifier for the run
- params=Mem0MemoryService.InputParams(
- search_limit=10,
- search_threshold=0.3,
- api_version="v2",
- system_prompt="Based on previous conversations, I recall: \n\n",
- add_as_system_message=True,
- position=1,
- ),
- )
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
- # Initialize LLM service
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o-mini")
+ # Initialize text-to-speech service
+ tts = ElevenLabsTTSService(
+ api_key=os.getenv("ELEVENLABS_API_KEY"),
+ voice_id="pNInz6obpgDQGcFmaJgB",
+ )
- messages = [
- {
- "role": "system",
- "content": """You are a personal assistant. You can remember things about the person you are talking to.
- Some Guidelines:
- - Make sure your responses are friendly yet short and concise.
- - If the user asks you to remember something, make sure to remember it.
- - Greet the user by their name if you know about it.
- """,
- },
+ # Initialize Mem0 memory service
+ memory = Mem0MemoryService(
+ api_key=os.getenv("MEM0_API_KEY"),
+ user_id=USER_ID, # Unique identifier for the user
+ # agent_id="agent1", # Optional identifier for the agent
+ # run_id="session1", # Optional identifier for the run
+ params=Mem0MemoryService.InputParams(
+ search_limit=10,
+ search_threshold=0.3,
+ api_version="v2",
+ system_prompt="Based on previous conversations, I recall: \n\n",
+ add_as_system_message=True,
+ position=1,
+ ),
+ )
+
+ # Initialize LLM service
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o-mini")
+
+ messages = [
+ {
+ "role": "system",
+ "content": """You are a personal assistant. You can remember things about the person you are talking to.
+ Some Guidelines:
+ - Make sure your responses are friendly yet short and concise.
+ - If the user asks you to remember something, make sure to remember it.
+ - Greet the user by their name if you know about it.
+ """,
+ },
+ ]
+
+ # Set up conversation context and management
+ # The context_aggregator will automatically collect conversation context
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+ rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
+
+ pipeline = Pipeline(
+ [
+ transport.input(),
+ rtvi,
+ stt,
+ context_aggregator.user(),
+ memory,
+ llm,
+ tts,
+ transport.output(),
+ context_aggregator.assistant(),
]
+ )
- # Set up conversation context and management
- # The context_aggregator will automatically collect conversation context
- context = OpenAILLMContext(messages)
- context_aggregator = llm.create_context_aggregator(context)
- rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ ),
+ observers=[RTVIObserver(rtvi)],
+ )
- pipeline = Pipeline(
- [
- transport.input(),
- rtvi,
- context_aggregator.user(),
- memory,
- llm,
- tts,
- transport.output(),
- context_aggregator.assistant(),
- ]
+ @rtvi.event_handler("on_client_ready")
+ async def on_client_ready(rtvi):
+ await rtvi.set_bot_ready()
+ # Get personalized greeting based on user memories. Can pass agent_id and run_id as per requirement of the application to manage short term memory or agent specific memory.
+ greeting = await get_initial_greeting(
+ memory_client=memory.memory_client, user_id=USER_ID, agent_id=None, run_id=None
)
- task = PipelineTask(
- pipeline,
- params=PipelineParams(
- allow_interruptions=True,
- enable_metrics=True,
- enable_usage_metrics=True,
- ),
- observers=[RTVIObserver(rtvi)],
- )
+ # Add the greeting as an assistant message to start the conversation
+ context.add_message({"role": "assistant", "content": greeting})
- @rtvi.event_handler("on_client_ready")
- async def on_client_ready(rtvi):
- await rtvi.set_bot_ready()
+ # Queue the context frame to start the conversation
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
- # Get personalized greeting based on user memories. Can pass agent_id and run_id as per requirement of the application to manage short term memory or agent specific memory.
- greeting = await get_initial_greeting(
- memory_client=memory.memory_client, user_id=USER_ID, agent_id=None, run_id=None
- )
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
- # Add the greeting as an assistant message to start the conversation
- context.add_message({"role": "assistant", "content": greeting})
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
- # Queue the context frame to start the conversation
- await task.queue_frames([context_aggregator.user().get_context_frame()])
-
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
- print(f"Participant left: {participant}")
- await task.cancel()
-
- runner = PipelineRunner()
-
- await runner.run(task)
+ runner = PipelineRunner(handle_sigint=False)
+ await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/07a-interruptible-anthropic.py b/examples/foundational/38-smart-turn-fal.py
similarity index 54%
rename from examples/foundational/07a-interruptible-anthropic.py
rename to examples/foundational/38-smart-turn-fal.py
index 0f135b8fa..b4a0708cb 100644
--- a/examples/foundational/07a-interruptible-anthropic.py
+++ b/examples/foundational/38-smart-turn-fal.py
@@ -4,62 +4,59 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
+import argparse
import os
-import sys
import aiohttp
from dotenv import load_dotenv
from loguru import logger
-from runner import configure
+from pipecat.audio.turn.smart_turn.fal_smart_turn import FalSmartTurnAnalyzer
from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.audio.vad.vad_analyzer import VADParams
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.anthropic.llm import AnthropicLLMService
from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
-async def main():
async with aiohttp.ClientSession() as session:
- (room_url, token) = await configure(session)
-
- transport = DailyTransport(
- room_url,
- token,
- "Respond bot",
- DailyParams(
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
audio_out_enabled=True,
- transcription_enabled=True,
- vad_enabled=True,
- vad_analyzer=SileroVADAnalyzer(),
+ vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
+ turn_analyzer=FalSmartTurnAnalyzer(
+ api_key=os.getenv("FAL_SMART_TURN_API_KEY"), aiohttp_session=session
+ ),
),
)
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
tts = CartesiaTTSService(
api_key=os.getenv("CARTESIA_API_KEY"),
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
)
- llm = AnthropicLLMService(
- api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-opus-20240229"
- )
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
- # todo: think more about how to handle system prompts in a more general way. OpenAI,
- # Google, and Anthropic all have slightly different approaches to providing a system
- # prompt.
messages = [
{
"role": "system",
- "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative, helpful, and brief way. Say hello.",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
},
]
@@ -69,6 +66,7 @@ async def main():
pipeline = Pipeline(
[
transport.input(), # Transport user input
+ stt,
context_aggregator.user(), # User responses
llm, # LLM
tts, # TTS
@@ -87,20 +85,28 @@ async def main():
),
)
- @transport.event_handler("on_first_participant_joined")
- async def on_first_participant_joined(transport, participant):
- await transport.capture_participant_transcription(participant["id"])
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
# Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
await task.queue_frames([context_aggregator.user().get_context_frame()])
- @transport.event_handler("on_participant_left")
- async def on_participant_left(transport, participant, reason):
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
+
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
await task.cancel()
- runner = PipelineRunner()
+ runner = PipelineRunner(handle_sigint=False)
await runner.run(task)
if __name__ == "__main__":
- asyncio.run(main())
+ from run import main
+
+ main()
diff --git a/examples/foundational/38a-smart-turn-local-coreml.py b/examples/foundational/38a-smart-turn-local-coreml.py
new file mode 100644
index 000000000..4168e8bbc
--- /dev/null
+++ b/examples/foundational/38a-smart-turn-local-coreml.py
@@ -0,0 +1,128 @@
+#
+# Copyright (c) 2024β2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
+from pipecat.audio.turn.smart_turn.local_coreml_smart_turn import LocalCoreMLSmartTurnAnalyzer
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.audio.vad.vad_analyzer import VADParams
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+
+load_dotenv(override=True)
+
+
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+ logger.info(f"Starting bot")
+
+ # To use this locally, set the environment variable LOCAL_SMART_TURN_MODEL_PATH
+ # to the path where the smart-turn repo is cloned.
+ #
+ # Example setup:
+ #
+ # # Git LFS (Large File Storage)
+ # brew install git-lfs
+ # # Hugging Face uses LFS to store large model files, including .mlpackage
+ # git lfs install
+ # # Clone the repo with the smart_turn_classifier.mlpackage
+ # git clone https://huggingface.co/pipecat-ai/smart-turn
+ #
+ # Then set the env variable:
+ # export LOCAL_SMART_TURN_MODEL_PATH=./smart-turn
+ # or add it to your .env file
+ smart_turn_model_path = os.getenv("LOCAL_SMART_TURN_MODEL_PATH")
+
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
+ turn_analyzer=LocalCoreMLSmartTurnAnalyzer(
+ smart_turn_model_path=smart_turn_model_path, params=SmartTurnParams()
+ ),
+ ),
+ )
+
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
+
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+
+ messages = [
+ {
+ "role": "system",
+ "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+ },
+ ]
+
+ context = OpenAILLMContext(messages)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt,
+ context_aggregator.user(), # User responses
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses
+ ]
+ )
+
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ enable_usage_metrics=True,
+ report_only_initial_ttfb=True,
+ ),
+ )
+
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected")
+ # Kick off the conversation.
+ messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
+
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
+
+
+if __name__ == "__main__":
+ from run import main
+
+ main()
diff --git a/examples/foundational/39-mcp-stdio.py b/examples/foundational/39-mcp-stdio.py
new file mode 100644
index 000000000..e32f0c7ff
--- /dev/null
+++ b/examples/foundational/39-mcp-stdio.py
@@ -0,0 +1,192 @@
+#
+# Copyright (c) 2024β2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import io
+import os
+import re
+import shutil
+import sys
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from mcp import StdioServerParameters
+from PIL import Image
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import (
+ Frame,
+ FunctionCallResultFrame,
+ URLImageRawFrame,
+)
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.services.anthropic.llm import AnthropicLLMService
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.mcp_service import MCPClient
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+
+load_dotenv(override=True)
+
+
+class UrlToImageProcessor(FrameProcessor):
+ def __init__(self, aiohttp_session: aiohttp.ClientSession, **kwargs):
+ super().__init__(**kwargs)
+ self._aiohttp_session = aiohttp_session
+
+ async def process_frame(self, frame: Frame, direction: FrameDirection):
+ await super().process_frame(frame, direction)
+
+ if isinstance(frame, FunctionCallResultFrame):
+ await self.push_frame(frame, direction)
+ image_url = self.extract_url(frame.result)
+ if image_url:
+ await self.run_image_process(image_url)
+ # sometimes we get multiple image urls- process 1 at a time
+ await asyncio.sleep(1)
+ else:
+ await self.push_frame(frame, direction)
+
+ def extract_url(self, text: str):
+ pattern = r"!\[[^\]]*\]\((https?://[^)]+\.(png|jpg|jpeg|PNG|JPG|JPEG))\)"
+ match = re.search(pattern, text)
+ if match:
+ return match.group(1)
+ return None
+
+ async def run_image_process(self, image_url: str):
+ try:
+ logger.debug(f"handling image from url: '{image_url}'")
+ async with self._aiohttp_session.get(image_url) as response:
+ image_stream = io.BytesIO(await response.content.read())
+ image = Image.open(image_stream)
+ image = image.convert("RGB")
+ frame = URLImageRawFrame(
+ url=image_url, image=image.tobytes(), size=image.size, format="RGB"
+ )
+ await self.push_frame(frame)
+ except Exception as e:
+ error_msg = f"Error handling image url {image_url}: {str(e)}"
+ logger.error(error_msg)
+
+
+async def run_bot(webrtc_connection: SmallWebRTCConnection):
+ logger.info(f"Starting bot")
+
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ camera_out_enabled=True,
+ camera_out_width=1024,
+ camera_out_height=1024,
+ vad_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ vad_audio_passthrough=True,
+ ),
+ )
+
+ # Create an HTTP session for API calls
+ async with aiohttp.ClientSession() as session:
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
+
+ llm = AnthropicLLMService(
+ api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-7-sonnet-latest"
+ )
+
+ try:
+ mcp = MCPClient(
+ server_params=StdioServerParameters(
+ command=shutil.which("npx"),
+ args=["-y", "@programcomputer/nasa-mcp-server@latest"],
+ # https://api.nasa.gov
+ env={"NASA_API_KEY": os.getenv("NASA_API_KEY")},
+ )
+ )
+ except Exception as e:
+ logger.error(f"error setting up mcp")
+ logger.exception("error trace:")
+
+ mcp_image = UrlToImageProcessor(aiohttp_session=session)
+
+ tools = await mcp.register_tools(llm)
+
+ system = f"""
+ You are a helpful LLM in a WebRTC call.
+ Your goal is to demonstrate your capabilities in a succinct way.
+ You have access to a number of tools provided by NASA MCP. Use any and all tools to help users.
+ When asked for the astronomy picture of the day, PASS in NO date to the API.
+ This ensures we get the latest picture available. If as specific date is asked for, you
+ can pass in that date to the API.
+ Your output will be converted to audio so don't include special characters in your answers.
+ Respond to what the user said in a creative and helpful way.
+ Don't overexplain what you are doing.
+ Just respond with short sentences when you are carrying out tool calls.
+ """
+
+ messages = [{"role": "system", "content": system}]
+
+ context = OpenAILLMContext(messages, tools)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt,
+ context_aggregator.user(), # User spoken responses
+ llm, # LLM
+ tts, # TTS
+ mcp_image, # URL image -> output
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses and tool context
+ ]
+ )
+
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ ),
+ )
+
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected: {client}")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
+
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
+
+
+if __name__ == "__main__":
+ from run import main
+
+ main()
diff --git a/examples/foundational/39a-mcp-run-sse.py b/examples/foundational/39a-mcp-run-sse.py
new file mode 100644
index 000000000..a624bc7b8
--- /dev/null
+++ b/examples/foundational/39a-mcp-run-sse.py
@@ -0,0 +1,124 @@
+#
+# Copyright (c) 2024β2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import os
+import sys
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.services.anthropic.llm import AnthropicLLMService
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.mcp_service import MCPClient
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+
+load_dotenv(override=True)
+
+
+async def run_bot(webrtc_connection: SmallWebRTCConnection):
+ logger.info(f"Starting bot")
+
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ vad_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ vad_audio_passthrough=True,
+ ),
+ )
+
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
+
+ llm = AnthropicLLMService(
+ api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-7-sonnet-latest"
+ )
+
+ try:
+ # https://docs.mcp.run/integrating/tutorials/mcp-run-sse-openai-agents/
+ mcp = MCPClient(server_params=os.getenv("MCP_RUN_SSE_URL"))
+ except Exception as e:
+ logger.error(f"error setting up mcp")
+ logger.exception("error trace:")
+
+ tools = await mcp.register_tools(llm)
+
+ system = f"""
+ You are a helpful LLM in a WebRTC call.
+ Your goal is to demonstrate your capabilities in a succinct way.
+ You have access to a number of tools provided by mcp.run. Use any and all tools to help users.
+ Your output will be converted to audio so don't include special characters in your answers.
+ Respond to what the user said in a creative and helpful way.
+ When asked for today's date, use 'https://www.datetoday.net/'.
+ Don't overexplain what you are doing.
+ Just respond with short sentences when you are carrying out tool calls.
+ """
+
+ messages = [{"role": "system", "content": system}]
+
+ context = OpenAILLMContext(messages, tools)
+ context_aggregator = llm.create_context_aggregator(context)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt,
+ context_aggregator.user(), # User spoken responses
+ llm, # LLM
+ tts, # TTS
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses and tool context
+ ]
+ )
+
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ ),
+ )
+
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected: {client}")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
+
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
+
+
+if __name__ == "__main__":
+ from run import main
+
+ main()
diff --git a/examples/foundational/39b-multiple-mcp.py b/examples/foundational/39b-multiple-mcp.py
new file mode 100644
index 000000000..473054009
--- /dev/null
+++ b/examples/foundational/39b-multiple-mcp.py
@@ -0,0 +1,203 @@
+#
+# Copyright (c) 2024β2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import io
+import os
+import re
+import shutil
+import sys
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from mcp import StdioServerParameters
+from PIL import Image
+
+from pipecat.adapters.schemas.tools_schema import ToolsSchema
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import (
+ Frame,
+ FunctionCallResultFrame,
+ URLImageRawFrame,
+)
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.services.anthropic.llm import AnthropicLLMService
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.mcp_service import MCPClient
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+
+load_dotenv(override=True)
+
+
+class UrlToImageProcessor(FrameProcessor):
+ def __init__(self, aiohttp_session: aiohttp.ClientSession, **kwargs):
+ super().__init__(**kwargs)
+ self._aiohttp_session = aiohttp_session
+
+ async def process_frame(self, frame: Frame, direction: FrameDirection):
+ await super().process_frame(frame, direction)
+
+ if isinstance(frame, FunctionCallResultFrame):
+ await self.push_frame(frame, direction)
+ image_url = self.extract_url(frame.result)
+ if image_url:
+ await self.run_image_process(image_url)
+ # sometimes we get multiple image urls- process 1 at a time
+ await asyncio.sleep(1)
+ else:
+ await self.push_frame(frame, direction)
+
+ def extract_url(self, text: str):
+ pattern = r"!\[[^\]]*\]\((https?://[^)]+\.(png|jpg|jpeg|PNG|JPG|JPEG|gif))\)"
+ match = re.search(pattern, text)
+ if match:
+ return match.group(1)
+ return None
+
+ async def run_image_process(self, image_url: str):
+ try:
+ logger.debug(f"handling image from url: '{image_url}'")
+ async with self._aiohttp_session.get(image_url) as response:
+ image_stream = io.BytesIO(await response.content.read())
+ image = Image.open(image_stream)
+ image = image.convert("RGB")
+ frame = URLImageRawFrame(
+ url=image_url, image=image.tobytes(), size=image.size, format="RGB"
+ )
+ await self.push_frame(frame)
+ except Exception as e:
+ error_msg = f"Error handling image url {image_url}: {str(e)}"
+ logger.error(error_msg)
+
+
+async def run_bot(webrtc_connection: SmallWebRTCConnection):
+ logger.info(f"Starting bot")
+
+ transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ camera_out_enabled=True,
+ camera_out_width=1024,
+ camera_out_height=1024,
+ vad_enabled=True,
+ vad_analyzer=SileroVADAnalyzer(),
+ vad_audio_passthrough=True,
+ ),
+ )
+
+ # Create an HTTP session for API calls
+ async with aiohttp.ClientSession() as session:
+ stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+ tts = CartesiaTTSService(
+ api_key=os.getenv("CARTESIA_API_KEY"),
+ voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
+ )
+
+ llm = AnthropicLLMService(
+ api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-7-sonnet-latest"
+ )
+
+ system = f"""
+ You are a helpful LLM in a WebRTC call.
+ Your goal is to demonstrate your capabilities in a succinct way.
+ You have access to a number of tools provided by NASA MCP. Use any and all tools to help users.
+ When asked for today's date, use 'https://www.datetoday.net/'.
+ When asked for the astronomy picture of the day, use 'https://www.datetoday.net/', to get today's date.
+ Your output will be converted to audio so don't include special characters in your answers.
+ Respond to what the user said in a creative and helpful way.
+ Don't overexplain what you are doing.
+ Just respond with short sentences when you are carrying out tool calls.
+ """
+
+ messages = [{"role": "system", "content": system}]
+
+ try:
+ mcp = MCPClient(
+ server_params=StdioServerParameters(
+ command=shutil.which("npx"),
+ args=["-y", "@programcomputer/nasa-mcp-server@latest"],
+ # https://api.nasa.gov
+ env={"NASA_API_KEY": os.getenv("NASA_API_KEY")},
+ )
+ )
+ except Exception as e:
+ logger.error(f"error setting up nasa mcp")
+ logger.exception("error trace:")
+ try:
+ # https://docs.mcp.run/integrating/tutorials/mcp-run-sse-openai-agents/
+ # ie. "https://www.mcp.run/api/mcp/sse?..."
+ # ensure the profile has a tool or few installed
+ mcp_run = MCPClient(server_params=os.getenv("MCP_RUN_SSE_URL"))
+ except Exception as e:
+ logger.error(f"error setting up mcp.run")
+ logger.exception("error trace:")
+
+ tools = await mcp.register_tools(llm)
+ run_tools = await mcp_run.register_tools(llm)
+
+ all_standard_tools = run_tools.standard_tools + tools.standard_tools
+ all_tools = ToolsSchema(standard_tools=all_standard_tools)
+
+ context = OpenAILLMContext(messages, all_tools)
+ context_aggregator = llm.create_context_aggregator(context)
+ mcp_image_processor = UrlToImageProcessor(aiohttp_session=session)
+
+ pipeline = Pipeline(
+ [
+ transport.input(), # Transport user input
+ stt,
+ context_aggregator.user(), # User spoken responses
+ llm, # LLM
+ tts, # TTS
+ mcp_image_processor, # URL image -> output
+ transport.output(), # Transport bot output
+ context_aggregator.assistant(), # Assistant spoken responses and tool context
+ ]
+ )
+
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=True,
+ enable_metrics=True,
+ ),
+ )
+
+ @transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info(f"Client connected: {client}")
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+ @transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info(f"Client disconnected")
+
+ @transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info(f"Client closed connection")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
+
+
+if __name__ == "__main__":
+ from run import main
+
+ main()
diff --git a/examples/foundational/README.md b/examples/foundational/README.md
new file mode 100644
index 000000000..14323d189
--- /dev/null
+++ b/examples/foundational/README.md
@@ -0,0 +1,122 @@
+# Pipecat Foundational Examples
+
+This directory contains examples showing how to build voice and multimodal agents with Pipecat. Each example demonstrates specific features, progressing from basic to advanced concepts.
+
+## Learning Paths
+
+Depending on what you're trying to build, these learning paths will guide you through relevant examples:
+
+- **New to Pipecat**: Start with examples [01](https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/01-say-one-thing.py), [02](https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/02-llm-say-one-thing.py), [07](https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/07-interruptible.py)
+- **Building conversational bots**: [07](https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/07-interruptible.py), [10](https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/10-wake-phrase.py), [38](https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/38-smart-turn-fal.py)
+- **Common add-on capabilities**: [17](https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/17-detect-user-idle.py), [24](https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/24-stt-mute-filter.py), [28](https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/28-transcription-processor.py), [34](https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/34-audio-recording.py)
+- **Adding visual capabilities**: [03](https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/03-still-frame.py), [12](https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/12a-describe-video-gemini-flash.py), [26](https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/26c-gemini-multimodal-live-video.py)
+- **Advanced agent capabilities**: [14](https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/14-function-calling.py), [20](https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/20a-persistent-context-openai.py), [37](https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/37-mem0.py)
+
+## Quick Start
+
+1. Set up a virtual environment:
+
+ ```bash
+ python -m venv venv
+ source venv/bin/activate # On Windows: venv\Scripts\activate
+ ```
+
+2. Install dependencies:
+
+ ```bash
+ pip install -r requirements.txt
+ ```
+
+3. Create a `.env` file with your API keys.
+
+4. Run any example:
+
+ ```bash
+ python run.py 01-say-one-thing.py
+ ```
+
+5. Open the web interface at http://localhost:7860 and click "Connect"
+
+## Examples by Feature
+
+### Basics
+
+- **[01-say-one-thing.py](./01-say-one-thing.py)**: Most basic bot that says one phrase and exits (Transport, TTS, Event handlers)
+- **[02-llm-say-one-thing.py](./02-llm-say-one-thing.py)**: Bot generates a response with an LLM (LLM initialization)
+- **[03-still-frame.py](./03-still-frame.py)**: Displays a static image (Video transport, Image service)
+- **[04-transport.py](./04-transport.py)**: Different transport options (WebRTC, Daily, Livekit)
+
+### Conversational AI
+
+- **[07-interruptible.py](./07-interruptible.py)**: Basic voice assistant bot (STT, TTS, LLM, Interruptible speech)
+- **[10-wake-phrase.py](./10-wake-phrase.py)**: Bot activated by wake phrase (WakeCheckFilter)
+- **[22-natural-conversation.py](./22-natural-conversation.py)**: Smart turn detection (Multiple LLMs, Turn management)
+- **[38-smart-turn-fal.py](./38-smart-turn-fal.py)**: ML-based turn detection (Fal service, Local models)
+
+### Common Utilities
+
+- **[17-detect-user-idle.py](./17-detect-user-idle.py)**: Handle inactive users (UserIdleProcessor)
+- **[24-stt-mute-filter.py](./24-stt-mute-filter.py)**: Selectively mute user input (STTMuteFilter)
+- **[28-transcription-processor.py](./28-transcription-processor.py)**: Record conversation text (TranscriptProcessor)
+- **[30-observer.py](./30-observer.py)**: Access frame data (Custom observers)
+- **[31-heartbeats.py](./31-heartbeats.py)**: Detect idle pipelines (Pipeline monitoring)
+- **[34-audio-recording.py](./34-audio-recording.py)**: Record conversation audio (Composite and track-level recording)
+
+### Advanced LLM Features
+
+- **[14-function-calling.py](./14-function-calling.py)**: Bot with tool usage (Function schemas, Tool registration)
+- **[20a-persistent-context-openai.py](./20a-persistent-context-openai.py)**: Persistent conversation context (Memory management)
+- **[32-gemini-grounding-metadata.py](./32-gemini-grounding-metadata.py)**: Web search capabilities (Google search integration)
+- **[33-gemini-rag.py](./33-gemini-rag.py)**: Retrieval-augmented generation (Data sources, Grounding)
+- **[37-mem0.py](./37-mem0.py)**: Long-term agent memory (Mem0 service integration)
+
+### Media Handling
+
+- **[05-sync-speech-and-images.py](./05-sync-speech-and-images.py)**: Synchronized narration with images (Custom processors, SyncParallelPipeline)
+- **[06a-image-sync.py](./06a-image-sync.py)**: Dynamic image updates while speaking (Synchronized A/V pipelines)
+- **[09-mirror.py](./09-mirror.py)**: Mirror user's audio and video (Custom frame processors)
+- **[11-sound-effects.py](./11-sound-effects.py)**: Add sounds when bot speaks (Sound playback, Event synchronization)
+- **[23-bot-background-sound.py](./23-bot-background-sound.py)**: Play background audio (SoundfileMixer)
+
+### Vision & Multimodal
+
+- **[12a-describe-video-gemini-flash.py](./12a-describe-video-gemini-flash.py)**: Bot describes user's video (Video input, Multimodal LLMs)
+- **[26c-gemini-multimodal-live-video.py](./26c-gemini-multimodal-live-video.py)**: Gemini with video input (Streaming video, Function calls)
+
+### Voice & Language
+
+- **[13-transcription.py](./13-transcription.py)**: Speech transcription demo (STT providers, Real-time transcription)
+- **[15-switch-voices.py](./15-switch-voices.py)**: Dynamic voice/language changing (ParallelPipelines, FunctionFilters)
+- **[25-google-audio-in.py](./25-google-audio-in.py)**: Gemini for speech recognition (Alternative transcription)
+- **[35-pattern-pair-voice-switching.py](./35-pattern-pair-voice-switching.py)**: Dynamic TTS voice switching (XML parsing, PatternPairAggregator)
+- **[36-user-email-gathering.py](./36-user-email-gathering.py)**: Spelling mode for TTS (Confirmation patterns, XML tags)
+
+### Integration Examples
+
+- **[18-gstreamer-filesrc.py](./18-gstreamer-filesrc.py)**: GStreamer video streaming (Video processing)
+- **[19-openai-realtime-beta.py](./19-openai-realtime-beta.py)**: OpenAI Speech-to-Speech (Direct S2S, Function calls)
+- **[21-tavus-layer.py](./21-tavus-layer.py)**: Tavus digital twin (Avatar integration)
+- **[27-simli-layer.py](./27-simli-layer.py)**: Simli avatar integration (Video synchronization)
+
+### Performance & Optimization
+
+- **[16-gpu-container-local-bot.py](./16-gpu-container-local-bot.py)**: GPU-accelerated local bot (Performance measurement)
+
+### Utilities
+
+## Advanced Usage
+
+### Customizing Network Settings
+
+```bash
+python run.py --host 0.0.0.0 --port 8080
+```
+
+### Troubleshooting
+
+- **No audio/video**: Check browser permissions for microphone and camera
+- **Connection errors**: Verify API keys in `.env` file
+- **Missing dependencies**: Run `pip install -r requirements.txt`
+- **Port conflicts**: Use `--port` to change the port
+
+For more examples, visit our [GitHub repository](https://github.com/pipecat-ai/pipecat/tree/main/examples).
diff --git a/examples/foundational/runner.py b/examples/foundational/daily_runner.py
similarity index 100%
rename from examples/foundational/runner.py
rename to examples/foundational/daily_runner.py
diff --git a/examples/foundational/requirements.txt b/examples/foundational/requirements.txt
new file mode 100644
index 000000000..9f352efbc
--- /dev/null
+++ b/examples/foundational/requirements.txt
@@ -0,0 +1,5 @@
+fastapi
+uvicorn
+python-dotenv
+pipecat-ai[webrtc]
+pipecat-ai-small-webrtc-prebuilt
\ No newline at end of file
diff --git a/examples/foundational/run.py b/examples/foundational/run.py
new file mode 100644
index 000000000..fc8b631b4
--- /dev/null
+++ b/examples/foundational/run.py
@@ -0,0 +1,201 @@
+#
+# Copyright (c) 2024β2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import asyncio
+import importlib.util
+import os
+import sys
+from contextlib import asynccontextmanager
+from inspect import iscoroutinefunction, signature
+from typing import Any, Callable, Dict, Optional, Tuple
+
+import uvicorn
+from dotenv import load_dotenv
+from fastapi import BackgroundTasks, FastAPI
+from fastapi.responses import RedirectResponse
+from loguru import logger
+from pipecat_ai_small_webrtc_prebuilt.frontend import SmallWebRTCPrebuiltUI
+
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+
+# Load environment variables
+load_dotenv(override=True)
+
+app = FastAPI()
+
+# Store connections by pc_id
+pcs_map: Dict[str, SmallWebRTCConnection] = {}
+
+ice_servers = ["stun:stun.l.google.com:19302"]
+
+# Mount the frontend at /
+app.mount("/client", SmallWebRTCPrebuiltUI)
+
+# Store program arguments
+args: argparse.Namespace = argparse.Namespace()
+
+# Store the bot module and function info
+bot_module: Any = None
+run_bot_func: Optional[Callable] = None
+is_webrtc_bot: bool = True
+
+
+def import_bot_file(file_path: str) -> Tuple[Any, Callable, bool]:
+ """Dynamically import the bot file and determine how to run it.
+
+ Returns:
+ tuple: (module, run_function, is_webrtc_bot)
+ - module: The imported module
+ - run_function: Either run_bot or main function
+ - is_webrtc_bot: True if run_bot function exists and accepts a WebRTC connection
+ """
+ if not os.path.exists(file_path):
+ raise FileNotFoundError(f"Bot file not found: {file_path}")
+
+ # Extract module name without extension
+ module_name = os.path.splitext(os.path.basename(file_path))[0]
+
+ # Load the module
+ spec = importlib.util.spec_from_file_location(module_name, file_path)
+ if not spec or not spec.loader:
+ raise ImportError(f"Could not load spec for {file_path}")
+
+ module = importlib.util.module_from_spec(spec)
+ sys.modules[module_name] = module
+ spec.loader.exec_module(module)
+
+ # Check for run_bot function first
+ if hasattr(module, "run_bot"):
+ run_func = module.run_bot
+ # Check if the function accepts a WebRTC connection
+ sig = signature(run_func)
+ is_webrtc = len(sig.parameters) > 0
+ return module, run_func, is_webrtc
+
+ # Fall back to main function
+ if hasattr(module, "main") and iscoroutinefunction(module.main):
+ return module, module.main, False
+
+ raise AttributeError(f"No run_bot or async main function found in {file_path}")
+
+
+@app.get("/", include_in_schema=False)
+async def root_redirect():
+ return RedirectResponse(url="/client/")
+
+
+@app.post("/api/offer")
+async def offer(request: dict, background_tasks: BackgroundTasks):
+ global run_bot_func, is_webrtc_bot
+
+ if not run_bot_func:
+ raise RuntimeError("No bot file has been loaded")
+
+ if not is_webrtc_bot:
+ return {
+ "error": "This bot doesn't support WebRTC connections, it's running in standalone mode"
+ }
+
+ pc_id = request.get("pc_id")
+
+ if pc_id and pc_id in pcs_map:
+ pipecat_connection = pcs_map[pc_id]
+ logger.info(f"Reusing existing connection for pc_id: {pc_id}")
+ await pipecat_connection.renegotiate(
+ sdp=request["sdp"], type=request["type"], restart_pc=request.get("restart_pc", False)
+ )
+ else:
+ pipecat_connection = SmallWebRTCConnection(ice_servers)
+ await pipecat_connection.initialize(sdp=request["sdp"], type=request["type"])
+
+ @pipecat_connection.event_handler("closed")
+ async def handle_disconnected(webrtc_connection: SmallWebRTCConnection):
+ logger.info(f"Discarding peer connection for pc_id: {webrtc_connection.pc_id}")
+ pcs_map.pop(webrtc_connection.pc_id, None)
+
+ # We've already checked that run_bot_func exists
+ assert run_bot_func is not None
+ background_tasks.add_task(run_bot_func, pipecat_connection, args)
+
+ answer = pipecat_connection.get_answer()
+ # Updating the peer connection inside the map
+ pcs_map[answer["pc_id"]] = pipecat_connection
+
+ return answer
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+ yield # Run app
+ coros = [pc.close() for pc in pcs_map.values()]
+ await asyncio.gather(*coros)
+ pcs_map.clear()
+
+
+async def run_standalone_bot() -> None:
+ """Run a standalone bot that doesn't require WebRTC"""
+ global run_bot_func
+ if run_bot_func is not None:
+ await run_bot_func()
+ else:
+ raise RuntimeError("No bot function available to run")
+
+
+def main(parser: Optional[argparse.ArgumentParser] = None):
+ global args
+
+ if not parser:
+ parser = argparse.ArgumentParser(description="Pipecat Bot Runner")
+ parser.add_argument("bot_file", nargs="?", help="Path to the bot file", default=None)
+ parser.add_argument(
+ "--host", default="localhost", help="Host for HTTP server (default: localhost)"
+ )
+ parser.add_argument(
+ "--port", type=int, default=7860, help="Port for HTTP server (default: 7860)"
+ )
+ parser.add_argument("--verbose", "-v", action="count", default=0)
+ args = parser.parse_args()
+
+ logger.remove(0)
+ if args.verbose:
+ logger.add(sys.stderr, level="TRACE")
+ else:
+ logger.add(sys.stderr, level="DEBUG")
+
+ # Infer the bot file from the caller if not provided explicitly
+ bot_file = args.bot_file
+ if bot_file is None:
+ # Get the __file__ of the script that called main()
+ import inspect
+
+ caller_frame = inspect.stack()[1]
+ caller_globals = caller_frame.frame.f_globals
+ bot_file = caller_globals.get("__file__")
+
+ if not bot_file:
+ print("β Could not determine the bot file. Pass it explicitly to main().")
+ sys.exit(1)
+
+ # Import the bot file
+ try:
+ global run_bot_func, bot_module, is_webrtc_bot
+ bot_module, run_bot_func, is_webrtc_bot = import_bot_file(bot_file)
+ logger.info(f"Successfully loaded bot from {bot_file}")
+
+ if is_webrtc_bot:
+ logger.info("Detected WebRTC-compatible bot, starting web server...")
+ uvicorn.run(app, host=args.host, port=args.port)
+ else:
+ logger.info("Detected standalone bot, running directly...")
+ asyncio.run(run_standalone_bot())
+ except Exception as e:
+ logger.error(f"Error loading bot file: {e}")
+ sys.exit(1)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/examples/instant-voice/client/javascript/package-lock.json b/examples/instant-voice/client/javascript/package-lock.json
index 54ed9131c..0f7d3381a 100644
--- a/examples/instant-voice/client/javascript/package-lock.json
+++ b/examples/instant-voice/client/javascript/package-lock.json
@@ -9,8 +9,8 @@
"version": "1.0.0",
"license": "ISC",
"dependencies": {
- "@pipecat-ai/client-js": "^0.3.2",
- "@pipecat-ai/daily-transport": "^0.3.5"
+ "@pipecat-ai/client-js": "^0.3.5",
+ "@pipecat-ai/daily-transport": "^0.3.8"
},
"devDependencies": {
"@types/node": "^22.13.1",
@@ -20,9 +20,9 @@
}
},
"node_modules/@babel/runtime": {
- "version": "7.26.0",
- "resolved": "https://registry.npmjs.org/@babel/runtime/-/runtime-7.26.0.tgz",
- "integrity": "sha512-FDSOghenHTiToteC/QRlv2q3DhPZ/oOXTBoirfWNx1Cx3TMVcGWQtMMmQcSvb/JjpNeGzx8Pq/b4fKEJuWm1sw==",
+ "version": "7.27.0",
+ "resolved": "https://registry.npmjs.org/@babel/runtime/-/runtime-7.27.0.tgz",
+ "integrity": "sha512-VtPOkrdPHZsKc/clNqyi9WUA8TINkZ4cGk63UUE3u4pmB2k+ZMQRDuIOagv8UVd6j7k0T3+RRIb7beKTebNbcw==",
"license": "MIT",
"dependencies": {
"regenerator-runtime": "^0.14.0"
@@ -32,9 +32,9 @@
}
},
"node_modules/@daily-co/daily-js": {
- "version": "0.73.0",
- "resolved": "https://registry.npmjs.org/@daily-co/daily-js/-/daily-js-0.73.0.tgz",
- "integrity": "sha512-Wz8c60hgmkx8fcEeDAi4L4J0rbafiihWKyXFyhYoFYPsw2OdChHpA4RYwIB+1enRws5IK+/HdmzFDYLQsB4A6w==",
+ "version": "0.77.0",
+ "resolved": "https://registry.npmjs.org/@daily-co/daily-js/-/daily-js-0.77.0.tgz",
+ "integrity": "sha512-icNXKieKAkRR/C5dcPjrCkL1jQGFp5C5WtLHy5uHAdTztm+mo9wlPJuehbWaGOM3TV24mgWHZ/+8jOys1G0I4w==",
"license": "BSD-2-Clause",
"dependencies": {
"@babel/runtime": "^7.12.5",
@@ -47,74 +47,6 @@
"node": ">=10.0.0"
}
},
- "node_modules/@esbuild/aix-ppc64": {
- "version": "0.24.0",
- "resolved": "https://registry.npmjs.org/@esbuild/aix-ppc64/-/aix-ppc64-0.24.0.tgz",
- "integrity": "sha512-WtKdFM7ls47zkKHFVzMz8opM7LkcsIp9amDUBIAWirg70RM71WRSjdILPsY5Uv1D42ZpUfaPILDlfactHgsRkw==",
- "cpu": [
- "ppc64"
- ],
- "dev": true,
- "license": "MIT",
- "optional": true,
- "os": [
- "aix"
- ],
- "engines": {
- "node": ">=18"
- }
- },
- "node_modules/@esbuild/android-arm": {
- "version": "0.24.0",
- "resolved": "https://registry.npmjs.org/@esbuild/android-arm/-/android-arm-0.24.0.tgz",
- "integrity": "sha512-arAtTPo76fJ/ICkXWetLCc9EwEHKaeya4vMrReVlEIUCAUncH7M4bhMQ+M9Vf+FFOZJdTNMXNBrWwW+OXWpSew==",
- "cpu": [
- "arm"
- ],
- "dev": true,
- "license": "MIT",
- "optional": true,
- "os": [
- "android"
- ],
- "engines": {
- "node": ">=18"
- }
- },
- "node_modules/@esbuild/android-arm64": {
- "version": "0.24.0",
- "resolved": "https://registry.npmjs.org/@esbuild/android-arm64/-/android-arm64-0.24.0.tgz",
- "integrity": "sha512-Vsm497xFM7tTIPYK9bNTYJyF/lsP590Qc1WxJdlB6ljCbdZKU9SY8i7+Iin4kyhV/KV5J2rOKsBQbB77Ab7L/w==",
- "cpu": [
- "arm64"
- ],
- "dev": true,
- "license": "MIT",
- "optional": true,
- "os": [
- "android"
- ],
- "engines": {
- "node": ">=18"
- }
- },
- "node_modules/@esbuild/android-x64": {
- "version": "0.24.0",
- "resolved": "https://registry.npmjs.org/@esbuild/android-x64/-/android-x64-0.24.0.tgz",
- "integrity": "sha512-t8GrvnFkiIY7pa7mMgJd7p8p8qqYIz1NYiAoKc75Zyv73L3DZW++oYMSHPRarcotTKuSs6m3hTOa5CKHaS02TQ==",
- "cpu": [
- "x64"
- ],
- "dev": true,
- "license": "MIT",
- "optional": true,
- "os": [
- "android"
- ],
- "engines": {
- "node": ">=18"
- }
- },
"node_modules/@esbuild/darwin-arm64": {
"version": "0.24.0",
"resolved": "https://registry.npmjs.org/@esbuild/darwin-arm64/-/darwin-arm64-0.24.0.tgz",
@@ -132,333 +64,10 @@
"node": ">=18"
}
},
- "node_modules/@esbuild/darwin-x64": {
- "version": "0.24.0",
- "resolved": "https://registry.npmjs.org/@esbuild/darwin-x64/-/darwin-x64-0.24.0.tgz",
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- "cpu": [
- "x64"
- ],
- "dev": true,
- "license": "MIT",
- "optional": true,
- "os": [
- "darwin"
- ],
- "engines": {
- "node": ">=18"
- }
- },
- "node_modules/@esbuild/freebsd-arm64": {
- "version": "0.24.0",
- "resolved": "https://registry.npmjs.org/@esbuild/freebsd-arm64/-/freebsd-arm64-0.24.0.tgz",
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- "cpu": [
- "arm64"
- ],
- "dev": true,
- "license": "MIT",
- "optional": true,
- "os": [
- "freebsd"
- ],
- "engines": {
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- }
- },
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+ "integrity": "sha512-6g7jpbefjHYs821Z+EBJ8r4Z7LT5h80YSWRJaylGS4nW5W5Z2KXzpdnyFarv37O7QjauzVC2E+PABmpkw5/JGA==",
"license": "MIT",
"engines": {
"node": ">=14.18"
@@ -863,159 +234,6 @@
"node": ">=10"
}
},
- "node_modules/@swc/core-darwin-x64": {
- "version": "1.10.14",
- "resolved": "https://registry.npmjs.org/@swc/core-darwin-x64/-/core-darwin-x64-1.10.14.tgz",
- "integrity": "sha512-KpzotL/I0O12RE3tF8NmQErINv0cQe/0mnN/Q50ESFzB5kU6bLgp2HMnnwDTm/XEZZRJCNe0oc9WJ5rKbAJFRQ==",
- "cpu": [
- "x64"
- ],
- "dev": true,
- "license": "Apache-2.0 AND MIT",
- "optional": true,
- "os": [
- "darwin"
- ],
- "engines": {
- "node": ">=10"
- }
- },
- "node_modules/@swc/core-linux-arm-gnueabihf": {
- "version": "1.10.14",
- "resolved": "https://registry.npmjs.org/@swc/core-linux-arm-gnueabihf/-/core-linux-arm-gnueabihf-1.10.14.tgz",
- "integrity": "sha512-20yRXZjMJVz1wp1TcscKiGTVXistG+saIaxOmxSNQia1Qun3hSWLL+u6+5kXbfYGr7R2N6kqSwtZbIfJI25r9Q==",
- "cpu": [
- "arm"
- ],
- "dev": true,
- "license": "Apache-2.0",
- "optional": true,
- "os": [
- "linux"
- ],
- "engines": {
- "node": ">=10"
- }
- },
- "node_modules/@swc/core-linux-arm64-gnu": {
- "version": "1.10.14",
- "resolved": "https://registry.npmjs.org/@swc/core-linux-arm64-gnu/-/core-linux-arm64-gnu-1.10.14.tgz",
- "integrity": "sha512-Gy7cGrNkiMfPxQyLGxdgXPwyWzNzbHuWycJFcoKBihxZKZIW8hkPBttkGivuLC+0qOgsV2/U+S7tlvAju7FtmQ==",
- "cpu": [
- "arm64"
- ],
- "dev": true,
- "license": "Apache-2.0 AND MIT",
- "optional": true,
- "os": [
- "linux"
- ],
- "engines": {
- "node": ">=10"
- }
- },
- "node_modules/@swc/core-linux-arm64-musl": {
- "version": "1.10.14",
- "resolved": "https://registry.npmjs.org/@swc/core-linux-arm64-musl/-/core-linux-arm64-musl-1.10.14.tgz",
- "integrity": "sha512-+oYVqJvFw62InZ8PIy1rBACJPC2WTe4vbVb9kM1jJj2D7dKLm9acnnYIVIDsM5Wo7Uab8RvPHXVbs19IBurzuw==",
- "cpu": [
- "arm64"
- ],
- "dev": true,
- "license": "Apache-2.0 AND MIT",
- "optional": true,
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- "linux"
- ],
- "engines": {
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- }
- },
- "node_modules/@swc/core-linux-x64-gnu": {
- "version": "1.10.14",
- "resolved": "https://registry.npmjs.org/@swc/core-linux-x64-gnu/-/core-linux-x64-gnu-1.10.14.tgz",
- "integrity": "sha512-OmEbVEKQFLQVHwo4EJl9osmlulURy46k232Opfpn/1ji0t2KcNCci3POsnfMuoZjLkGJv8vGNJdPQxX+CP+wSA==",
- "cpu": [
- "x64"
- ],
- "dev": true,
- "license": "Apache-2.0 AND MIT",
- "optional": true,
- "os": [
- "linux"
- ],
- "engines": {
- "node": ">=10"
- }
- },
- "node_modules/@swc/core-linux-x64-musl": {
- "version": "1.10.14",
- "resolved": "https://registry.npmjs.org/@swc/core-linux-x64-musl/-/core-linux-x64-musl-1.10.14.tgz",
- "integrity": "sha512-OZW+Icm8DMPqHbhdxplkuG8qrNnPk5i7xJOZWYi1y5bTjgGFI4nEzrsmmeHKMdQTaWwsFrm3uK1rlyQ48MmXmg==",
- "cpu": [
- "x64"
- ],
- "dev": true,
- "license": "Apache-2.0 AND MIT",
- "optional": true,
- "os": [
- "linux"
- ],
- "engines": {
- "node": ">=10"
- }
- },
- "node_modules/@swc/core-win32-arm64-msvc": {
- "version": "1.10.14",
- "resolved": "https://registry.npmjs.org/@swc/core-win32-arm64-msvc/-/core-win32-arm64-msvc-1.10.14.tgz",
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- "cpu": [
- "arm64"
- ],
- "dev": true,
- "license": "Apache-2.0 AND MIT",
- "optional": true,
- "os": [
- "win32"
- ],
- "engines": {
- "node": ">=10"
- }
- },
- "node_modules/@swc/core-win32-ia32-msvc": {
- "version": "1.10.14",
- "resolved": "https://registry.npmjs.org/@swc/core-win32-ia32-msvc/-/core-win32-ia32-msvc-1.10.14.tgz",
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- "cpu": [
- "ia32"
- ],
- "dev": true,
- "license": "Apache-2.0 AND MIT",
- "optional": true,
- "os": [
- "win32"
- ],
- "engines": {
- "node": ">=10"
- }
- },
- "node_modules/@swc/core-win32-x64-msvc": {
- "version": "1.10.14",
- "resolved": "https://registry.npmjs.org/@swc/core-win32-x64-msvc/-/core-win32-x64-msvc-1.10.14.tgz",
- "integrity": "sha512-TYtWkUSMkjs0jGPeWdtWbex4B+DlQZmN/ySVLiPI+EltYCLEXsFMkVFq6aWn48dqFHggFK0UYfvDrJUR2c3Qxg==",
- "cpu": [
- "x64"
- ],
- "dev": true,
- "license": "Apache-2.0 AND MIT",
- "optional": true,
- "os": [
- "win32"
- ],
- "engines": {
- "node": ">=10"
- }
- },
"node_modules/@swc/counter": {
"version": "0.1.3",
"resolved": "https://registry.npmjs.org/@swc/counter/-/counter-0.1.3.tgz",
diff --git a/examples/instant-voice/client/javascript/package.json b/examples/instant-voice/client/javascript/package.json
index 9c3afb89a..2c0d937bd 100644
--- a/examples/instant-voice/client/javascript/package.json
+++ b/examples/instant-voice/client/javascript/package.json
@@ -18,7 +18,7 @@
"vite": "^6.0.2"
},
"dependencies": {
- "@pipecat-ai/client-js": "^0.3.2",
- "@pipecat-ai/daily-transport": "^0.3.5"
+ "@pipecat-ai/client-js": "^0.3.5",
+ "@pipecat-ai/daily-transport": "^0.3.8"
}
}
diff --git a/examples/instant-voice/client/javascript/src/app.ts b/examples/instant-voice/client/javascript/src/app.ts
index bb587307c..6ce1a91b3 100644
--- a/examples/instant-voice/client/javascript/src/app.ts
+++ b/examples/instant-voice/client/javascript/src/app.ts
@@ -23,15 +23,14 @@ import {
RTVIEvent,
} from '@pipecat-ai/client-js';
import { DailyTransport } from '@pipecat-ai/daily-transport';
-import SoundUtils from "./util/soundUtils";
-import { InstantVoiceHelper } from "./util/instantVoiceHelper";
+import SoundUtils from './util/soundUtils';
+import { InstantVoiceHelper } from './util/instantVoiceHelper';
/**
* InstantVoiceClient handles the connection and media management for a real-time
* voice and video interaction with an AI bot.
*/
class InstantVoiceClient {
-
private declare rtviClient: RTVIClient;
private connectBtn: HTMLButtonElement | null = null;
private disconnectBtn: HTMLButtonElement | null = null;
@@ -54,8 +53,12 @@ class InstantVoiceClient {
* Set up references to DOM elements and create necessary media elements
*/
private setupDOMElements(): void {
- this.connectBtn = document.getElementById('connect-btn') as HTMLButtonElement;
- this.disconnectBtn = document.getElementById('disconnect-btn') as HTMLButtonElement;
+ this.connectBtn = document.getElementById(
+ 'connect-btn'
+ ) as HTMLButtonElement;
+ this.disconnectBtn = document.getElementById(
+ 'disconnect-btn'
+ ) as HTMLButtonElement;
this.statusSpan = document.getElementById('connection-status');
this.bufferingAudioSpan = document.getElementById('buffering-status');
this.debugLog = document.getElementById('debug-log');
@@ -70,11 +73,10 @@ class InstantVoiceClient {
}
private initializeRTVIClient(): void {
- const transport = new DailyTransport({
- bufferLocalAudioUntilBotReady: true
- });
const RTVIConfig: RTVIClientOptions = {
- transport,
+ transport: new DailyTransport({
+ bufferLocalAudioUntilBotReady: true,
+ }),
params: {
// The baseURL and endpoint of your bot server that the client will connect to
baseUrl: 'http://localhost:7860',
@@ -95,7 +97,7 @@ class InstantVoiceClient {
if (this.disconnectBtn) this.disconnectBtn.disabled = true;
this.log('Client disconnected');
},
- onBotConnected: (participant: Participant) => {
+ onBotConnected: (participant: Participant) => {
this.log(`onBotConnected, timeTaken: ${Date.now() - this.startTime}`);
},
onBotReady: (data) => {
@@ -112,23 +114,29 @@ class InstantVoiceClient {
onMessageError: (error) => console.error('Message error:', error),
onError: (error) => console.error('Error:', error),
},
- }
+ };
this.rtviClient = new RTVIClient(RTVIConfig);
- this.rtviClient.registerHelper("transport", new InstantVoiceHelper({
- callbacks: {
- onAudioBufferingStarted: () => {
- SoundUtils.beep()
- this.updateBufferingStatus('Yes');
- this.log(`onMicCaptureStarted, timeTaken: ${Date.now() - this.startTime}`);
- },
- onAudioBufferingStopped: () => {
- this.updateBufferingStatus('No');
- this.log(`onMicCaptureStopped, timeTaken: ${Date.now() - this.startTime}`);
- }
- }
- }
- ));
+ this.rtviClient.registerHelper(
+ 'transport',
+ new InstantVoiceHelper({
+ callbacks: {
+ onAudioBufferingStarted: () => {
+ SoundUtils.beep();
+ this.updateBufferingStatus('Yes');
+ this.log(
+ `onMicCaptureStarted, timeTaken: ${Date.now() - this.startTime}`
+ );
+ },
+ onAudioBufferingStopped: () => {
+ this.updateBufferingStatus('No');
+ this.log(
+ `onMicCaptureStopped, timeTaken: ${Date.now() - this.startTime}`
+ );
+ },
+ },
+ })
+ );
this.setupTrackListeners();
}
@@ -198,7 +206,9 @@ class InstantVoiceClient {
// Listen for tracks stopping
this.rtviClient.on(RTVIEvent.TrackStopped, (track, participant) => {
- this.log(`Track stopped: ${track.kind} from ${participant?.name || 'unknown'}`);
+ this.log(
+ `Track stopped: ${track.kind} from ${participant?.name || 'unknown'}`
+ );
});
}
@@ -208,7 +218,10 @@ class InstantVoiceClient {
*/
private setupAudioTrack(track: MediaStreamTrack): void {
this.log('Setting up audio track');
- if (this.botAudio.srcObject && "getAudioTracks" in this.botAudio.srcObject) {
+ if (
+ this.botAudio.srcObject &&
+ 'getAudioTracks' in this.botAudio.srcObject
+ ) {
const oldTrack = this.botAudio.srcObject.getAudioTracks()[0];
if (oldTrack?.id === track.id) return;
}
@@ -246,8 +259,13 @@ class InstantVoiceClient {
public async disconnect(): Promise {
try {
await this.rtviClient.disconnect();
- if (this.botAudio.srcObject && "getAudioTracks" in this.botAudio.srcObject) {
- this.botAudio.srcObject.getAudioTracks().forEach((track) => track.stop());
+ if (
+ this.botAudio.srcObject &&
+ 'getAudioTracks' in this.botAudio.srcObject
+ ) {
+ this.botAudio.srcObject
+ .getAudioTracks()
+ .forEach((track) => track.stop());
this.botAudio.srcObject = null;
}
} catch (error) {
diff --git a/examples/instant-voice/client/javascript/yarn.lock b/examples/instant-voice/client/javascript/yarn.lock
index e74aef8b7..8a92c8778 100644
--- a/examples/instant-voice/client/javascript/yarn.lock
+++ b/examples/instant-voice/client/javascript/yarn.lock
@@ -3,16 +3,16 @@
"@babel/runtime@^7.12.5":
- version "7.26.0"
- resolved "https://registry.npmjs.org/@babel/runtime/-/runtime-7.26.0.tgz"
- integrity sha512-FDSOghenHTiToteC/QRlv2q3DhPZ/oOXTBoirfWNx1Cx3TMVcGWQtMMmQcSvb/JjpNeGzx8Pq/b4fKEJuWm1sw==
+ version "7.27.0"
+ resolved "https://registry.npmjs.org/@babel/runtime/-/runtime-7.27.0.tgz"
+ integrity sha512-VtPOkrdPHZsKc/clNqyi9WUA8TINkZ4cGk63UUE3u4pmB2k+ZMQRDuIOagv8UVd6j7k0T3+RRIb7beKTebNbcw==
dependencies:
regenerator-runtime "^0.14.0"
-"@daily-co/daily-js@^0.73.0":
- version "0.73.0"
- resolved "https://registry.npmjs.org/@daily-co/daily-js/-/daily-js-0.73.0.tgz"
- integrity sha512-Wz8c60hgmkx8fcEeDAi4L4J0rbafiihWKyXFyhYoFYPsw2OdChHpA4RYwIB+1enRws5IK+/HdmzFDYLQsB4A6w==
+"@daily-co/daily-js@^0.77.0":
+ version "0.77.0"
+ resolved "https://registry.npmjs.org/@daily-co/daily-js/-/daily-js-0.77.0.tgz"
+ integrity sha512-icNXKieKAkRR/C5dcPjrCkL1jQGFp5C5WtLHy5uHAdTztm+mo9wlPJuehbWaGOM3TV24mgWHZ/+8jOys1G0I4w==
dependencies:
"@babel/runtime" "^7.12.5"
"@sentry/browser" "^8.33.1"
@@ -25,10 +25,10 @@
resolved "https://registry.npmjs.org/@esbuild/darwin-arm64/-/darwin-arm64-0.24.0.tgz"
integrity sha512-CKyDpRbK1hXwv79soeTJNHb5EiG6ct3efd/FTPdzOWdbZZfGhpbcqIpiD0+vwmpu0wTIL97ZRPZu8vUt46nBSw==
-"@pipecat-ai/client-js@^0.3.2", "@pipecat-ai/client-js@~0.3.2":
- version "0.3.2"
- resolved "https://registry.npmjs.org/@pipecat-ai/client-js/-/client-js-0.3.2.tgz"
- integrity sha512-psunOVrJjPka2SWlq53vxVWCA0Vt8pSXsXtn8pOLC0YTKFsUx+b7Z6quYUJcDZjCe1aAg9cKETek3Xal3Co8Tg==
+"@pipecat-ai/client-js@^0.3.5", "@pipecat-ai/client-js@~0.3.5":
+ version "0.3.5"
+ resolved "https://registry.npmjs.org/@pipecat-ai/client-js/-/client-js-0.3.5.tgz"
+ integrity sha512-qmhnDjwY2XUtLjww35ShsYf5TF9BCuAk0tIj0oHjpTe6v6QOlgKQt8JVCAdc32p5ycouzSZOeDFtBd2aNWuq1g==
dependencies:
"@types/events" "^3.0.3"
clone-deep "^4.0.1"
@@ -36,63 +36,63 @@
typed-emitter "^2.1.0"
uuid "^10.0.0"
-"@pipecat-ai/daily-transport@^0.3.5":
- version "0.3.5"
- resolved "https://registry.npmjs.org/@pipecat-ai/daily-transport/-/daily-transport-0.3.5.tgz"
- integrity sha512-nJ0TvWPCqXPmU81U8cXOqk5mUEEvEuI06Mis+N0jN8KZUrNy1pP08iWbs07ObmIXdnQcoL+kQmHOerT4q/bF0w==
+"@pipecat-ai/daily-transport@^0.3.8":
+ version "0.3.8"
+ resolved "https://registry.npmjs.org/@pipecat-ai/daily-transport/-/daily-transport-0.3.8.tgz"
+ integrity sha512-AcRP51LGOsEA7DH0yPaZTqX/pozfTpkJbKC0itgWLv6uCM8dAnNtBj/m1CdFKRsE7QObhEOa+cRp5PUAyF4wCA==
dependencies:
- "@daily-co/daily-js" "^0.73.0"
+ "@daily-co/daily-js" "^0.77.0"
"@rollup/rollup-darwin-arm64@4.28.0":
version "4.28.0"
resolved "https://registry.npmjs.org/@rollup/rollup-darwin-arm64/-/rollup-darwin-arm64-4.28.0.tgz"
integrity sha512-lmKx9yHsppblnLQZOGxdO66gT77bvdBtr/0P+TPOseowE7D9AJoBw8ZDULRasXRWf1Z86/gcOdpBrV6VDUY36Q==
-"@sentry-internal/browser-utils@8.49.0":
- version "8.49.0"
- resolved "https://registry.npmjs.org/@sentry-internal/browser-utils/-/browser-utils-8.49.0.tgz"
- integrity sha512-XkPHHdFqsN7EPaB+QGUOEmpFqXiqP67t2rRZ1HG1UwJoe0PhJEKNy7b4+WRwmT7ODSt+PvFk1gNBlJBpThwH7Q==
+"@sentry-internal/browser-utils@8.55.0":
+ version "8.55.0"
+ resolved "https://registry.npmjs.org/@sentry-internal/browser-utils/-/browser-utils-8.55.0.tgz"
+ integrity sha512-ROgqtQfpH/82AQIpESPqPQe0UyWywKJsmVIqi3c5Fh+zkds5LUxnssTj3yNd1x+kxaPDVB023jAP+3ibNgeNDw==
dependencies:
- "@sentry/core" "8.49.0"
+ "@sentry/core" "8.55.0"
-"@sentry-internal/feedback@8.49.0":
- version "8.49.0"
- resolved "https://registry.npmjs.org/@sentry-internal/feedback/-/feedback-8.49.0.tgz"
- integrity sha512-v/wf7WvPxEvZUB7xrCnecI3fhevVo84hw8WlxgZIz6mLUHXEIX8xYWc9H8Yet/KKJ2uEB8GQ8aDsY6S1hVEIUA==
+"@sentry-internal/feedback@8.55.0":
+ version "8.55.0"
+ resolved "https://registry.npmjs.org/@sentry-internal/feedback/-/feedback-8.55.0.tgz"
+ integrity sha512-cP3BD/Q6pquVQ+YL+rwCnorKuTXiS9KXW8HNKu4nmmBAyf7urjs+F6Hr1k9MXP5yQ8W3yK7jRWd09Yu6DHWOiw==
dependencies:
- "@sentry/core" "8.49.0"
+ "@sentry/core" "8.55.0"
-"@sentry-internal/replay-canvas@8.49.0":
- version "8.49.0"
- resolved "https://registry.npmjs.org/@sentry-internal/replay-canvas/-/replay-canvas-8.49.0.tgz"
- integrity sha512-/yXxI7f+Wu24FIYoRE7A0AidNxORuhAyPzb5ey1wFqMXP72nG8dXhOpcl0w+bi554FkqkLjdeUDhSOBWYZXH9g==
+"@sentry-internal/replay-canvas@8.55.0":
+ version "8.55.0"
+ resolved "https://registry.npmjs.org/@sentry-internal/replay-canvas/-/replay-canvas-8.55.0.tgz"
+ integrity sha512-nIkfgRWk1091zHdu4NbocQsxZF1rv1f7bbp3tTIlZYbrH62XVZosx5iHAuZG0Zc48AETLE7K4AX9VGjvQj8i9w==
dependencies:
- "@sentry-internal/replay" "8.49.0"
- "@sentry/core" "8.49.0"
+ "@sentry-internal/replay" "8.55.0"
+ "@sentry/core" "8.55.0"
-"@sentry-internal/replay@8.49.0":
- version "8.49.0"
- resolved "https://registry.npmjs.org/@sentry-internal/replay/-/replay-8.49.0.tgz"
- integrity sha512-BDiiCBxskkktTd6FNplBc9V8l14R4T/AwRIZj2itX4xnuHewTTDjVbeyvGol4roA4r+V0Mzoi31hLEGI6yFQ5Q==
+"@sentry-internal/replay@8.55.0":
+ version "8.55.0"
+ resolved "https://registry.npmjs.org/@sentry-internal/replay/-/replay-8.55.0.tgz"
+ integrity sha512-roCDEGkORwolxBn8xAKedybY+Jlefq3xYmgN2fr3BTnsXjSYOPC7D1/mYqINBat99nDtvgFvNfRcZPiwwZ1hSw==
dependencies:
- "@sentry-internal/browser-utils" "8.49.0"
- "@sentry/core" "8.49.0"
+ "@sentry-internal/browser-utils" "8.55.0"
+ "@sentry/core" "8.55.0"
"@sentry/browser@^8.33.1":
- version "8.49.0"
- resolved "https://registry.npmjs.org/@sentry/browser/-/browser-8.49.0.tgz"
- integrity sha512-dS4Sw2h8EixHeXOIR++XEVMTen6xCGcIQ/XhJbsjqvddXeIijW0WkxSeTfPkfs0dsqFHSisWmlmo0xhHbXvEsQ==
+ version "8.55.0"
+ resolved "https://registry.npmjs.org/@sentry/browser/-/browser-8.55.0.tgz"
+ integrity sha512-1A31mCEWCjaMxJt6qGUK+aDnLDcK6AwLAZnqpSchNysGni1pSn1RWSmk9TBF8qyTds5FH8B31H480uxMPUJ7Cw==
dependencies:
- "@sentry-internal/browser-utils" "8.49.0"
- "@sentry-internal/feedback" "8.49.0"
- "@sentry-internal/replay" "8.49.0"
- "@sentry-internal/replay-canvas" "8.49.0"
- "@sentry/core" "8.49.0"
+ "@sentry-internal/browser-utils" "8.55.0"
+ "@sentry-internal/feedback" "8.55.0"
+ "@sentry-internal/replay" "8.55.0"
+ "@sentry-internal/replay-canvas" "8.55.0"
+ "@sentry/core" "8.55.0"
-"@sentry/core@8.49.0":
- version "8.49.0"
- resolved "https://registry.npmjs.org/@sentry/core/-/core-8.49.0.tgz"
- integrity sha512-/OAm6LdHhh8TvfDAucWfSJV7M03IOHrJm5LVjrrKr4gwQ1HKd4CDbARsBbPwHIzSRAle0IgG3sbJxEvv52JUIw==
+"@sentry/core@8.55.0":
+ version "8.55.0"
+ resolved "https://registry.npmjs.org/@sentry/core/-/core-8.55.0.tgz"
+ integrity sha512-6g7jpbefjHYs821Z+EBJ8r4Z7LT5h80YSWRJaylGS4nW5W5Z2KXzpdnyFarv37O7QjauzVC2E+PABmpkw5/JGA==
"@swc/core-darwin-arm64@1.10.14":
version "1.10.14"
diff --git a/examples/instant-voice/server/src/single_bot.py b/examples/instant-voice/server/src/single_bot.py
index 55ba20377..6c4e2b1b4 100644
--- a/examples/instant-voice/server/src/single_bot.py
+++ b/examples/instant-voice/server/src/single_bot.py
@@ -15,7 +15,7 @@ from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIProcessor
+from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
from pipecat.services.gemini_multimodal_live import GeminiMultimodalLiveLLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport
@@ -58,10 +58,9 @@ async def main():
token,
"Instant voice Chatbot",
DailyParams(
+ audio_in_enabled=True,
audio_out_enabled=True,
- vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
),
)
@@ -69,7 +68,6 @@ async def main():
api_key=os.getenv("GOOGLE_API_KEY"),
voice_id="Puck", # Aoede, Charon, Fenrir, Kore, Puck
transcribe_user_audio=True,
- transcribe_model_audio=True,
system_instruction=SYSTEM_INSTRUCTION,
)
@@ -93,20 +91,22 @@ async def main():
task = PipelineTask(
pipeline,
params=PipelineParams(allow_interruptions=True),
- observers=[rtvi.observer()],
+ observers=[RTVIObserver(rtvi)],
)
@rtvi.event_handler("on_client_ready")
async def on_client_ready(rtvi):
await rtvi.set_bot_ready()
+ # Kick off the conversation
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
@daily_transport.event_handler("on_first_participant_joined")
async def on_first_participant_joined(transport, participant):
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ logger.debug("First participant joined: {}", participant["id"])
@daily_transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
- print(f"Participant left: {participant}")
+ logger.debug(f"Participant left: {participant}")
await task.cancel()
runner = PipelineRunner(handle_sigint=False)
diff --git a/examples/moondream-chatbot/bot.py b/examples/moondream-chatbot/bot.py
index 4b2303959..d5b24aec2 100644
--- a/examples/moondream-chatbot/bot.py
+++ b/examples/moondream-chatbot/bot.py
@@ -141,12 +141,12 @@ async def main():
token,
"Chatbot",
DailyParams(
+ audio_in_enabled=True,
audio_out_enabled=True,
- camera_out_enabled=True,
- camera_out_width=1024,
- camera_out_height=576,
+ video_out_enabled=True,
+ video_out_width=1024,
+ video_out_height=576,
transcription_enabled=True,
- vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
),
)
@@ -156,7 +156,7 @@ async def main():
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
)
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
ta = TalkingAnimation()
diff --git a/examples/news-chatbot/client/javascript/package-lock.json b/examples/news-chatbot/client/javascript/package-lock.json
index 2c51d3cc7..c0945d858 100644
--- a/examples/news-chatbot/client/javascript/package-lock.json
+++ b/examples/news-chatbot/client/javascript/package-lock.json
@@ -9,17 +9,17 @@
"version": "1.0.0",
"license": "ISC",
"dependencies": {
- "@pipecat-ai/client-js": "^0.3.2",
- "@pipecat-ai/daily-transport": "^0.3.4"
+ "@pipecat-ai/client-js": "^0.3.5",
+ "@pipecat-ai/daily-transport": "^0.3.8"
},
"devDependencies": {
"vite": "^6.0.9"
}
},
"node_modules/@babel/runtime": {
- "version": "7.26.0",
- "resolved": "https://registry.npmjs.org/@babel/runtime/-/runtime-7.26.0.tgz",
- "integrity": "sha512-FDSOghenHTiToteC/QRlv2q3DhPZ/oOXTBoirfWNx1Cx3TMVcGWQtMMmQcSvb/JjpNeGzx8Pq/b4fKEJuWm1sw==",
+ "version": "7.27.0",
+ "resolved": "https://registry.npmjs.org/@babel/runtime/-/runtime-7.27.0.tgz",
+ "integrity": "sha512-VtPOkrdPHZsKc/clNqyi9WUA8TINkZ4cGk63UUE3u4pmB2k+ZMQRDuIOagv8UVd6j7k0T3+RRIb7beKTebNbcw==",
"license": "MIT",
"dependencies": {
"regenerator-runtime": "^0.14.0"
@@ -29,9 +29,9 @@
}
},
"node_modules/@daily-co/daily-js": {
- "version": "0.73.0",
- "resolved": "https://registry.npmjs.org/@daily-co/daily-js/-/daily-js-0.73.0.tgz",
- "integrity": "sha512-Wz8c60hgmkx8fcEeDAi4L4J0rbafiihWKyXFyhYoFYPsw2OdChHpA4RYwIB+1enRws5IK+/HdmzFDYLQsB4A6w==",
+ "version": "0.77.0",
+ "resolved": "https://registry.npmjs.org/@daily-co/daily-js/-/daily-js-0.77.0.tgz",
+ "integrity": "sha512-icNXKieKAkRR/C5dcPjrCkL1jQGFp5C5WtLHy5uHAdTztm+mo9wlPJuehbWaGOM3TV24mgWHZ/+8jOys1G0I4w==",
"license": "BSD-2-Clause",
"dependencies": {
"@babel/runtime": "^7.12.5",
@@ -45,13 +45,14 @@
}
},
"node_modules/@esbuild/aix-ppc64": {
- "version": "0.24.2",
- "resolved": "https://registry.npmjs.org/@esbuild/aix-ppc64/-/aix-ppc64-0.24.2.tgz",
- "integrity": "sha512-thpVCb/rhxE/BnMLQ7GReQLLN8q9qbHmI55F4489/ByVg2aQaQ6kbcLb6FHkocZzQhxc4gx0sCk0tJkKBFzDhA==",
+ "version": "0.25.2",
+ "resolved": "https://registry.npmjs.org/@esbuild/aix-ppc64/-/aix-ppc64-0.25.2.tgz",
+ "integrity": "sha512-wCIboOL2yXZym2cgm6mlA742s9QeJ8DjGVaL39dLN4rRwrOgOyYSnOaFPhKZGLb2ngj4EyfAFjsNJwPXZvseag==",
"cpu": [
"ppc64"
],
"dev": true,
+ "license": "MIT",
"optional": true,
"os": [
"aix"
@@ -61,13 +62,14 @@
}
},
"node_modules/@esbuild/android-arm": {
- "version": "0.24.2",
- "resolved": "https://registry.npmjs.org/@esbuild/android-arm/-/android-arm-0.24.2.tgz",
- "integrity": "sha512-tmwl4hJkCfNHwFB3nBa8z1Uy3ypZpxqxfTQOcHX+xRByyYgunVbZ9MzUUfb0RxaHIMnbHagwAxuTL+tnNM+1/Q==",
+ "version": "0.25.2",
+ "resolved": "https://registry.npmjs.org/@esbuild/android-arm/-/android-arm-0.25.2.tgz",
+ "integrity": "sha512-NQhH7jFstVY5x8CKbcfa166GoV0EFkaPkCKBQkdPJFvo5u+nGXLEH/ooniLb3QI8Fk58YAx7nsPLozUWfCBOJA==",
"cpu": [
"arm"
],
"dev": true,
+ "license": "MIT",
"optional": true,
"os": [
"android"
@@ -77,13 +79,14 @@
}
},
"node_modules/@esbuild/android-arm64": {
- "version": "0.24.2",
- "resolved": "https://registry.npmjs.org/@esbuild/android-arm64/-/android-arm64-0.24.2.tgz",
- "integrity": "sha512-cNLgeqCqV8WxfcTIOeL4OAtSmL8JjcN6m09XIgro1Wi7cF4t/THaWEa7eL5CMoMBdjoHOTh/vwTO/o2TRXIyzg==",
+ "version": "0.25.2",
+ "resolved": "https://registry.npmjs.org/@esbuild/android-arm64/-/android-arm64-0.25.2.tgz",
+ "integrity": "sha512-5ZAX5xOmTligeBaeNEPnPaeEuah53Id2tX4c2CVP3JaROTH+j4fnfHCkr1PjXMd78hMst+TlkfKcW/DlTq0i4w==",
"cpu": [
"arm64"
],
"dev": true,
+ "license": "MIT",
"optional": true,
"os": [
"android"
@@ -93,13 +96,14 @@
}
},
"node_modules/@esbuild/android-x64": {
- "version": "0.24.2",
- "resolved": "https://registry.npmjs.org/@esbuild/android-x64/-/android-x64-0.24.2.tgz",
- "integrity": "sha512-B6Q0YQDqMx9D7rvIcsXfmJfvUYLoP722bgfBlO5cGvNVb5V/+Y7nhBE3mHV9OpxBf4eAS2S68KZztiPaWq4XYw==",
+ "version": "0.25.2",
+ "resolved": "https://registry.npmjs.org/@esbuild/android-x64/-/android-x64-0.25.2.tgz",
+ "integrity": "sha512-Ffcx+nnma8Sge4jzddPHCZVRvIfQ0kMsUsCMcJRHkGJ1cDmhe4SsrYIjLUKn1xpHZybmOqCWwB0zQvsjdEHtkg==",
"cpu": [
"x64"
],
"dev": true,
+ "license": "MIT",
"optional": true,
"os": [
"android"
@@ -109,13 +113,14 @@
}
},
"node_modules/@esbuild/darwin-arm64": {
- "version": "0.24.2",
- "resolved": "https://registry.npmjs.org/@esbuild/darwin-arm64/-/darwin-arm64-0.24.2.tgz",
- "integrity": "sha512-kj3AnYWc+CekmZnS5IPu9D+HWtUI49hbnyqk0FLEJDbzCIQt7hg7ucF1SQAilhtYpIujfaHr6O0UHlzzSPdOeA==",
+ "version": "0.25.2",
+ "resolved": "https://registry.npmjs.org/@esbuild/darwin-arm64/-/darwin-arm64-0.25.2.tgz",
+ "integrity": "sha512-MpM6LUVTXAzOvN4KbjzU/q5smzryuoNjlriAIx+06RpecwCkL9JpenNzpKd2YMzLJFOdPqBpuub6eVRP5IgiSA==",
"cpu": [
"arm64"
],
"dev": true,
+ "license": "MIT",
"optional": true,
"os": [
"darwin"
@@ -125,13 +130,14 @@
}
},
"node_modules/@esbuild/darwin-x64": {
- "version": "0.24.2",
- "resolved": "https://registry.npmjs.org/@esbuild/darwin-x64/-/darwin-x64-0.24.2.tgz",
- "integrity": "sha512-WeSrmwwHaPkNR5H3yYfowhZcbriGqooyu3zI/3GGpF8AyUdsrrP0X6KumITGA9WOyiJavnGZUwPGvxvwfWPHIA==",
+ "version": "0.25.2",
+ "resolved": "https://registry.npmjs.org/@esbuild/darwin-x64/-/darwin-x64-0.25.2.tgz",
+ "integrity": "sha512-5eRPrTX7wFyuWe8FqEFPG2cU0+butQQVNcT4sVipqjLYQjjh8a8+vUTfgBKM88ObB85ahsnTwF7PSIt6PG+QkA==",
"cpu": [
"x64"
],
"dev": true,
+ "license": "MIT",
"optional": true,
"os": [
"darwin"
@@ -141,13 +147,14 @@
}
},
"node_modules/@esbuild/freebsd-arm64": {
- "version": "0.24.2",
- "resolved": "https://registry.npmjs.org/@esbuild/freebsd-arm64/-/freebsd-arm64-0.24.2.tgz",
- "integrity": "sha512-UN8HXjtJ0k/Mj6a9+5u6+2eZ2ERD7Edt1Q9IZiB5UZAIdPnVKDoG7mdTVGhHJIeEml60JteamR3qhsr1r8gXvg==",
+ "version": "0.25.2",
+ "resolved": "https://registry.npmjs.org/@esbuild/freebsd-arm64/-/freebsd-arm64-0.25.2.tgz",
+ "integrity": "sha512-mLwm4vXKiQ2UTSX4+ImyiPdiHjiZhIaE9QvC7sw0tZ6HoNMjYAqQpGyui5VRIi5sGd+uWq940gdCbY3VLvsO1w==",
"cpu": [
"arm64"
],
"dev": true,
+ "license": "MIT",
"optional": true,
"os": [
"freebsd"
@@ -157,13 +164,14 @@
}
},
"node_modules/@esbuild/freebsd-x64": {
- "version": "0.24.2",
- "resolved": "https://registry.npmjs.org/@esbuild/freebsd-x64/-/freebsd-x64-0.24.2.tgz",
- "integrity": "sha512-TvW7wE/89PYW+IevEJXZ5sF6gJRDY/14hyIGFXdIucxCsbRmLUcjseQu1SyTko+2idmCw94TgyaEZi9HUSOe3Q==",
+ "version": "0.25.2",
+ "resolved": "https://registry.npmjs.org/@esbuild/freebsd-x64/-/freebsd-x64-0.25.2.tgz",
+ "integrity": "sha512-6qyyn6TjayJSwGpm8J9QYYGQcRgc90nmfdUb0O7pp1s4lTY+9D0H9O02v5JqGApUyiHOtkz6+1hZNvNtEhbwRQ==",
"cpu": [
"x64"
],
"dev": true,
+ "license": "MIT",
"optional": true,
"os": [
"freebsd"
@@ -173,13 +181,14 @@
}
},
"node_modules/@esbuild/linux-arm": {
- "version": "0.24.2",
- "resolved": "https://registry.npmjs.org/@esbuild/linux-arm/-/linux-arm-0.24.2.tgz",
- "integrity": "sha512-n0WRM/gWIdU29J57hJyUdIsk0WarGd6To0s+Y+LwvlC55wt+GT/OgkwoXCXvIue1i1sSNWblHEig00GBWiJgfA==",
+ "version": "0.25.2",
+ "resolved": "https://registry.npmjs.org/@esbuild/linux-arm/-/linux-arm-0.25.2.tgz",
+ "integrity": "sha512-UHBRgJcmjJv5oeQF8EpTRZs/1knq6loLxTsjc3nxO9eXAPDLcWW55flrMVc97qFPbmZP31ta1AZVUKQzKTzb0g==",
"cpu": [
"arm"
],
"dev": true,
+ "license": "MIT",
"optional": true,
"os": [
"linux"
@@ -189,13 +198,14 @@
}
},
"node_modules/@esbuild/linux-arm64": {
- "version": "0.24.2",
- "resolved": "https://registry.npmjs.org/@esbuild/linux-arm64/-/linux-arm64-0.24.2.tgz",
- "integrity": "sha512-7HnAD6074BW43YvvUmE/35Id9/NB7BeX5EoNkK9obndmZBUk8xmJJeU7DwmUeN7tkysslb2eSl6CTrYz6oEMQg==",
+ "version": "0.25.2",
+ "resolved": "https://registry.npmjs.org/@esbuild/linux-arm64/-/linux-arm64-0.25.2.tgz",
+ "integrity": "sha512-gq/sjLsOyMT19I8obBISvhoYiZIAaGF8JpeXu1u8yPv8BE5HlWYobmlsfijFIZ9hIVGYkbdFhEqC0NvM4kNO0g==",
"cpu": [
"arm64"
],
"dev": true,
+ "license": "MIT",
"optional": true,
"os": [
"linux"
@@ -205,13 +215,14 @@
}
},
"node_modules/@esbuild/linux-ia32": {
- "version": "0.24.2",
- "resolved": "https://registry.npmjs.org/@esbuild/linux-ia32/-/linux-ia32-0.24.2.tgz",
- "integrity": "sha512-sfv0tGPQhcZOgTKO3oBE9xpHuUqguHvSo4jl+wjnKwFpapx+vUDcawbwPNuBIAYdRAvIDBfZVvXprIj3HA+Ugw==",
+ "version": "0.25.2",
+ "resolved": "https://registry.npmjs.org/@esbuild/linux-ia32/-/linux-ia32-0.25.2.tgz",
+ "integrity": "sha512-bBYCv9obgW2cBP+2ZWfjYTU+f5cxRoGGQ5SeDbYdFCAZpYWrfjjfYwvUpP8MlKbP0nwZ5gyOU/0aUzZ5HWPuvQ==",
"cpu": [
"ia32"
],
"dev": true,
+ "license": "MIT",
"optional": true,
"os": [
"linux"
@@ -221,13 +232,14 @@
}
},
"node_modules/@esbuild/linux-loong64": {
- "version": "0.24.2",
- "resolved": "https://registry.npmjs.org/@esbuild/linux-loong64/-/linux-loong64-0.24.2.tgz",
- "integrity": "sha512-CN9AZr8kEndGooS35ntToZLTQLHEjtVB5n7dl8ZcTZMonJ7CCfStrYhrzF97eAecqVbVJ7APOEe18RPI4KLhwQ==",
+ "version": "0.25.2",
+ "resolved": "https://registry.npmjs.org/@esbuild/linux-loong64/-/linux-loong64-0.25.2.tgz",
+ "integrity": "sha512-SHNGiKtvnU2dBlM5D8CXRFdd+6etgZ9dXfaPCeJtz+37PIUlixvlIhI23L5khKXs3DIzAn9V8v+qb1TRKrgT5w==",
"cpu": [
"loong64"
],
"dev": true,
+ "license": "MIT",
"optional": true,
"os": [
"linux"
@@ -237,13 +249,14 @@
}
},
"node_modules/@esbuild/linux-mips64el": {
- "version": "0.24.2",
- "resolved": "https://registry.npmjs.org/@esbuild/linux-mips64el/-/linux-mips64el-0.24.2.tgz",
- "integrity": "sha512-iMkk7qr/wl3exJATwkISxI7kTcmHKE+BlymIAbHO8xanq/TjHaaVThFF6ipWzPHryoFsesNQJPE/3wFJw4+huw==",
+ "version": "0.25.2",
+ "resolved": "https://registry.npmjs.org/@esbuild/linux-mips64el/-/linux-mips64el-0.25.2.tgz",
+ "integrity": "sha512-hDDRlzE6rPeoj+5fsADqdUZl1OzqDYow4TB4Y/3PlKBD0ph1e6uPHzIQcv2Z65u2K0kpeByIyAjCmjn1hJgG0Q==",
"cpu": [
"mips64el"
],
"dev": true,
+ "license": "MIT",
"optional": true,
"os": [
"linux"
@@ -253,13 +266,14 @@
}
},
"node_modules/@esbuild/linux-ppc64": {
- "version": "0.24.2",
- "resolved": "https://registry.npmjs.org/@esbuild/linux-ppc64/-/linux-ppc64-0.24.2.tgz",
- "integrity": "sha512-shsVrgCZ57Vr2L8mm39kO5PPIb+843FStGt7sGGoqiiWYconSxwTiuswC1VJZLCjNiMLAMh34jg4VSEQb+iEbw==",
+ "version": "0.25.2",
+ "resolved": "https://registry.npmjs.org/@esbuild/linux-ppc64/-/linux-ppc64-0.25.2.tgz",
+ "integrity": "sha512-tsHu2RRSWzipmUi9UBDEzc0nLc4HtpZEI5Ba+Omms5456x5WaNuiG3u7xh5AO6sipnJ9r4cRWQB2tUjPyIkc6g==",
"cpu": [
"ppc64"
],
"dev": true,
+ "license": "MIT",
"optional": true,
"os": [
"linux"
@@ -269,13 +283,14 @@
}
},
"node_modules/@esbuild/linux-riscv64": {
- "version": "0.24.2",
- "resolved": "https://registry.npmjs.org/@esbuild/linux-riscv64/-/linux-riscv64-0.24.2.tgz",
- "integrity": "sha512-4eSFWnU9Hhd68fW16GD0TINewo1L6dRrB+oLNNbYyMUAeOD2yCK5KXGK1GH4qD/kT+bTEXjsyTCiJGHPZ3eM9Q==",
+ "version": "0.25.2",
+ "resolved": "https://registry.npmjs.org/@esbuild/linux-riscv64/-/linux-riscv64-0.25.2.tgz",
+ "integrity": "sha512-k4LtpgV7NJQOml/10uPU0s4SAXGnowi5qBSjaLWMojNCUICNu7TshqHLAEbkBdAszL5TabfvQ48kK84hyFzjnw==",
"cpu": [
"riscv64"
],
"dev": true,
+ "license": "MIT",
"optional": true,
"os": [
"linux"
@@ -285,13 +300,14 @@
}
},
"node_modules/@esbuild/linux-s390x": {
- "version": "0.24.2",
- "resolved": "https://registry.npmjs.org/@esbuild/linux-s390x/-/linux-s390x-0.24.2.tgz",
- "integrity": "sha512-S0Bh0A53b0YHL2XEXC20bHLuGMOhFDO6GN4b3YjRLK//Ep3ql3erpNcPlEFed93hsQAjAQDNsvcK+hV90FubSw==",
+ "version": "0.25.2",
+ "resolved": "https://registry.npmjs.org/@esbuild/linux-s390x/-/linux-s390x-0.25.2.tgz",
+ "integrity": "sha512-GRa4IshOdvKY7M/rDpRR3gkiTNp34M0eLTaC1a08gNrh4u488aPhuZOCpkF6+2wl3zAN7L7XIpOFBhnaE3/Q8Q==",
"cpu": [
"s390x"
],
"dev": true,
+ "license": "MIT",
"optional": true,
"os": [
"linux"
@@ -301,13 +317,14 @@
}
},
"node_modules/@esbuild/linux-x64": {
- "version": "0.24.2",
- "resolved": "https://registry.npmjs.org/@esbuild/linux-x64/-/linux-x64-0.24.2.tgz",
- "integrity": "sha512-8Qi4nQcCTbLnK9WoMjdC9NiTG6/E38RNICU6sUNqK0QFxCYgoARqVqxdFmWkdonVsvGqWhmm7MO0jyTqLqwj0Q==",
+ "version": "0.25.2",
+ "resolved": "https://registry.npmjs.org/@esbuild/linux-x64/-/linux-x64-0.25.2.tgz",
+ "integrity": "sha512-QInHERlqpTTZ4FRB0fROQWXcYRD64lAoiegezDunLpalZMjcUcld3YzZmVJ2H/Cp0wJRZ8Xtjtj0cEHhYc/uUg==",
"cpu": [
"x64"
],
"dev": true,
+ "license": "MIT",
"optional": true,
"os": [
"linux"
@@ -317,13 +334,14 @@
}
},
"node_modules/@esbuild/netbsd-arm64": {
- "version": "0.24.2",
- "resolved": "https://registry.npmjs.org/@esbuild/netbsd-arm64/-/netbsd-arm64-0.24.2.tgz",
- "integrity": "sha512-wuLK/VztRRpMt9zyHSazyCVdCXlpHkKm34WUyinD2lzK07FAHTq0KQvZZlXikNWkDGoT6x3TD51jKQ7gMVpopw==",
+ "version": "0.25.2",
+ "resolved": "https://registry.npmjs.org/@esbuild/netbsd-arm64/-/netbsd-arm64-0.25.2.tgz",
+ "integrity": "sha512-talAIBoY5M8vHc6EeI2WW9d/CkiO9MQJ0IOWX8hrLhxGbro/vBXJvaQXefW2cP0z0nQVTdQ/eNyGFV1GSKrxfw==",
"cpu": [
"arm64"
],
"dev": true,
+ "license": "MIT",
"optional": true,
"os": [
"netbsd"
@@ -333,13 +351,14 @@
}
},
"node_modules/@esbuild/netbsd-x64": {
- "version": "0.24.2",
- "resolved": "https://registry.npmjs.org/@esbuild/netbsd-x64/-/netbsd-x64-0.24.2.tgz",
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+ "version": "0.25.2",
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+ "license": "MIT",
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],
"dev": true,
+ "license": "MIT",
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@@ -365,13 +385,14 @@
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],
"dev": true,
+ "license": "MIT",
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@@ -381,13 +402,14 @@
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+ "license": "MIT",
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+ "version": "7.8.2",
+ "resolved": "https://registry.npmjs.org/rxjs/-/rxjs-7.8.2.tgz",
+ "integrity": "sha512-dhKf903U/PQZY6boNNtAGdWbG85WAbjT/1xYoZIC7FAY0yWapOBQVsVrDl58W86//e1VpMNBtRV4MaXfdMySFA==",
"license": "Apache-2.0",
"optional": true,
"dependencies": {
@@ -1048,6 +1114,7 @@
"resolved": "https://registry.npmjs.org/source-map-js/-/source-map-js-1.2.1.tgz",
"integrity": "sha512-UXWMKhLOwVKb728IUtQPXxfYU+usdybtUrK/8uGE8CQMvrhOpwvzDBwj0QhSL7MQc7vIsISBG8VQ8+IDQxpfQA==",
"dev": true,
+ "license": "BSD-3-Clause",
"engines": {
"node": ">=0.10.0"
}
@@ -1082,13 +1149,14 @@
}
},
"node_modules/vite": {
- "version": "6.1.0",
- "resolved": "https://registry.npmjs.org/vite/-/vite-6.1.0.tgz",
- "integrity": "sha512-RjjMipCKVoR4hVfPY6GQTgveinjNuyLw+qruksLDvA5ktI1150VmcMBKmQaEWJhg/j6Uaf6dNCNA0AfdzUb/hQ==",
+ "version": "6.2.5",
+ "resolved": "https://registry.npmjs.org/vite/-/vite-6.2.5.tgz",
+ "integrity": "sha512-j023J/hCAa4pRIUH6J9HemwYfjB5llR2Ps0CWeikOtdR8+pAURAk0DoJC5/mm9kd+UgdnIy7d6HE4EAvlYhPhA==",
"dev": true,
+ "license": "MIT",
"dependencies": {
- "esbuild": "^0.24.2",
- "postcss": "^8.5.1",
+ "esbuild": "^0.25.0",
+ "postcss": "^8.5.3",
"rollup": "^4.30.1"
},
"bin": {
diff --git a/examples/news-chatbot/client/javascript/package.json b/examples/news-chatbot/client/javascript/package.json
index 84a4c3fb6..6a112c3ad 100644
--- a/examples/news-chatbot/client/javascript/package.json
+++ b/examples/news-chatbot/client/javascript/package.json
@@ -15,7 +15,7 @@
"vite": "^6.0.9"
},
"dependencies": {
- "@pipecat-ai/client-js": "^0.3.2",
- "@pipecat-ai/daily-transport": "^0.3.4"
+ "@pipecat-ai/client-js": "^0.3.5",
+ "@pipecat-ai/daily-transport": "^0.3.8"
}
}
diff --git a/examples/news-chatbot/client/javascript/src/app.js b/examples/news-chatbot/client/javascript/src/app.js
index e1b0c10a6..45c8d9035 100644
--- a/examples/news-chatbot/client/javascript/src/app.js
+++ b/examples/news-chatbot/client/javascript/src/app.js
@@ -16,30 +16,34 @@
* - Browser with WebRTC support
*/
-import {LogLevel, RTVIClient, RTVIClientHelper, RTVIEvent} from '@pipecat-ai/client-js';
+import {
+ LogLevel,
+ RTVIClient,
+ RTVIClientHelper,
+ RTVIEvent,
+} from '@pipecat-ai/client-js';
import { DailyTransport } from '@pipecat-ai/daily-transport';
class SearchResponseHelper extends RTVIClientHelper {
-
constructor(contentPanel) {
- super()
- this.contentPanel = contentPanel
+ super();
+ this.contentPanel = contentPanel;
}
handleMessage(rtviMessage) {
- console.log("SearchResponseHelper, received message:", rtviMessage)
+ console.log('SearchResponseHelper, received message:', rtviMessage);
if (rtviMessage.data) {
// Clear existing content
- this.contentPanel.innerHTML = "";
+ this.contentPanel.innerHTML = '';
// Create a container for all content
const contentContainer = document.createElement('div');
- contentContainer.className = "content-container";
+ contentContainer.className = 'content-container';
// Add the search_result
if (rtviMessage.data.search_result) {
const searchResultDiv = document.createElement('div');
- searchResultDiv.className = "search-result";
+ searchResultDiv.className = 'search-result';
searchResultDiv.textContent = rtviMessage.data.search_result;
contentContainer.appendChild(searchResultDiv);
}
@@ -47,18 +51,18 @@ class SearchResponseHelper extends RTVIClientHelper {
// Add the sources
if (rtviMessage.data.origins) {
const sourcesDiv = document.createElement('div');
- sourcesDiv.className = "sources";
+ sourcesDiv.className = 'sources';
const sourcesTitle = document.createElement('h3');
- sourcesTitle.className = "sources-title";
- sourcesTitle.textContent = "Sources:";
+ sourcesTitle.className = 'sources-title';
+ sourcesTitle.textContent = 'Sources:';
sourcesDiv.appendChild(sourcesTitle);
- rtviMessage.data.origins.forEach(origin => {
+ rtviMessage.data.origins.forEach((origin) => {
const sourceLink = document.createElement('a');
- sourceLink.className = "source-link";
+ sourceLink.className = 'source-link';
sourceLink.href = origin.site_uri;
- sourceLink.target = "_blank";
+ sourceLink.target = '_blank';
sourceLink.textContent = origin.site_title;
sourcesDiv.appendChild(sourceLink);
});
@@ -69,7 +73,7 @@ class SearchResponseHelper extends RTVIClientHelper {
// Add the rendered_content in an iframe
if (rtviMessage.data.rendered_content) {
const iframe = document.createElement('iframe');
- iframe.className = "iframe-container";
+ iframe.className = 'iframe-container';
iframe.srcdoc = rtviMessage.data.rendered_content;
contentContainer.appendChild(iframe);
}
@@ -80,7 +84,7 @@ class SearchResponseHelper extends RTVIClientHelper {
}
getMessageTypes() {
- return ["bot-llm-search-response"]
+ return ['bot-llm-search-response'];
}
}
@@ -105,7 +109,9 @@ class ChatbotClient {
this.disconnectBtn = document.getElementById('disconnect-btn');
this.statusSpan = document.getElementById('connection-status');
this.debugLog = document.getElementById('debug-log');
- this.searchResultContainer = document.getElementById('search-result-container');
+ this.searchResultContainer = document.getElementById(
+ 'search-result-container'
+ );
// Create an audio element for bot's voice output
this.botAudio = document.createElement('audio');
@@ -211,12 +217,9 @@ class ChatbotClient {
*/
async connect() {
try {
- // Create a new Daily transport for WebRTC communication
- const transport = new DailyTransport();
-
- // Initialize the RTVI client with our configuration
+ // Initialize the RTVI client with a Daily WebRTC transport and our configuration
this.rtviClient = new RTVIClient({
- transport,
+ transport: new DailyTransport(),
params: {
// The baseURL and endpoint of your bot server that the client will connect to
baseUrl: 'http://localhost:7860',
@@ -279,7 +282,10 @@ class ChatbotClient {
},
});
//this.rtviClient.setLogLevel(LogLevel.DEBUG)
- this.rtviClient.registerHelper("llm", new SearchResponseHelper(this.searchResultContainer))
+ this.rtviClient.registerHelper(
+ 'llm',
+ new SearchResponseHelper(this.searchResultContainer)
+ );
// Set up listeners for media track events
this.setupTrackListeners();
diff --git a/examples/news-chatbot/server/news_bot.py b/examples/news-chatbot/server/news_bot.py
index a8f09ba43..80355b43c 100644
--- a/examples/news-chatbot/server/news_bot.py
+++ b/examples/news-chatbot/server/news_bot.py
@@ -86,10 +86,9 @@ async def main():
token,
"Latest news!",
DailyParams(
+ audio_in_enabled=True,
audio_out_enabled=True,
- vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
),
)
@@ -148,10 +147,13 @@ async def main():
@rtvi.event_handler("on_client_ready")
async def on_client_ready(rtvi):
await rtvi.set_bot_ready()
+ # Kick off the conversation
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
@transport.event_handler("on_first_participant_joined")
async def on_first_participant_joined(transport, participant):
- await task.queue_frames([context_aggregator.user().get_context_frame()])
+ logger.debug("First participant joined: {}", participant["id"])
+ await transport.capture_participant_transcription(participant["id"])
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
diff --git a/examples/p2p-webrtc/daily-interop-bridge/README.md b/examples/p2p-webrtc/daily-interop-bridge/README.md
new file mode 100644
index 000000000..9de3f379f
--- /dev/null
+++ b/examples/p2p-webrtc/daily-interop-bridge/README.md
@@ -0,0 +1,61 @@
+# SmallWebRTC and Daily
+
+A Pipecat example demonstrating how to interoperate audio and video between `SmallWebRTCTransport` and `DailyTransport`.
+
+## π Quick Start
+
+### 1οΈβ£ Start the Bot Server
+
+#### π§ Set Up the Environment
+1. Create and activate a virtual environment:
+ ```bash
+ python3 -m venv venv
+ source venv/bin/activate # On Windows: venv\Scripts\activate
+ ```
+
+2. Install dependencies:
+ ```bash
+ pip install -r requirements.txt
+ ```
+
+3. Configure environment variables:
+ - Copy `env.example` to `.env`
+ ```bash
+ cp env.example .env
+ ```
+ - Add your API keys
+
+#### βΆοΈ Run the Server
+```bash
+python server.py
+```
+
+### 1οΈβ£ Connect the first client using Daily Prebuilt
+
+- Open your browser and navigate to the same URL that you configured inside your `.env` file:
+ - `DAILY_SAMPLE_ROOM_URL`
+
+### 2οΈβ£ Connect the second client using SmallWebRTC Prebuilt UI
+
+- Open your browser and navigate to:
+π http://localhost:7860
+ - (Or use your custom port, if configured)
+
+## β οΈ Important Note
+Ensure the bot server is running before using any client implementations.
+
+## π Requirements
+
+- Python **3.10+**
+- Node.js **16+** (for JavaScript components)
+- Google API Key
+- Modern web browser with WebRTC support
+
+---
+
+### π‘ Notes
+- Ensure all dependencies are installed before running the server.
+- Check the `.env` file for missing configurations.
+- WebRTC requires a secure environment (HTTPS) for full functionality in production.
+
+Happy coding! π
\ No newline at end of file
diff --git a/examples/p2p-webrtc/daily-interop-bridge/bot.py b/examples/p2p-webrtc/daily-interop-bridge/bot.py
new file mode 100644
index 000000000..0e859b5a0
--- /dev/null
+++ b/examples/p2p-webrtc/daily-interop-bridge/bot.py
@@ -0,0 +1,123 @@
+#
+# Copyright (c) 2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+import os
+import sys
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.frames.frames import (
+ InputAudioRawFrame,
+ InputImageRawFrame,
+ OutputAudioRawFrame,
+ OutputImageRawFrame,
+)
+from pipecat.pipeline.parallel_pipeline import ParallelPipeline
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.frame_processor import Frame, FrameDirection, FrameProcessor
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+
+class MirrorProcessor(FrameProcessor):
+ async def process_frame(self, frame: Frame, direction: FrameDirection):
+ await super().process_frame(frame, direction)
+
+ if isinstance(frame, InputAudioRawFrame):
+ await self.push_frame(
+ OutputAudioRawFrame(
+ audio=frame.audio,
+ sample_rate=frame.sample_rate,
+ num_channels=frame.num_channels,
+ )
+ )
+ elif isinstance(frame, InputImageRawFrame):
+ await self.push_frame(
+ OutputImageRawFrame(image=frame.image, size=frame.size, format=frame.format)
+ )
+ else:
+ await self.push_frame(frame, direction)
+
+
+async def run_bot(webrtc_connection):
+ pipecat_transport = SmallWebRTCTransport(
+ webrtc_connection=webrtc_connection,
+ params=TransportParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ video_in_enabled=True,
+ video_out_enabled=True,
+ video_out_is_live=True,
+ video_out_width=1280,
+ video_out_height=720,
+ ),
+ )
+
+ room_url = os.getenv("DAILY_SAMPLE_ROOM_URL", "")
+ daily_transport = DailyTransport(
+ room_url,
+ None,
+ "SmallWebRTC",
+ params=DailyParams(
+ audio_in_enabled=True,
+ audio_out_enabled=True,
+ video_in_enabled=True,
+ video_out_enabled=True,
+ video_out_is_live=True,
+ video_out_width=1280,
+ video_out_height=720,
+ ),
+ )
+
+ pipeline = Pipeline(
+ [
+ ParallelPipeline(
+ [
+ daily_transport.input(),
+ MirrorProcessor(),
+ pipecat_transport.output(),
+ ],
+ [
+ pipecat_transport.input(),
+ MirrorProcessor(),
+ daily_transport.output(),
+ ],
+ )
+ ]
+ )
+
+ task = PipelineTask(
+ pipeline,
+ params=PipelineParams(
+ allow_interruptions=False,
+ ),
+ )
+
+ @daily_transport.event_handler("on_participant_joined")
+ async def on_participant_joined(transport, participant):
+ await transport.capture_participant_video(participant["id"])
+
+ @pipecat_transport.event_handler("on_client_connected")
+ async def on_client_connected(transport, client):
+ logger.info("Pipecat Client connected")
+
+ @pipecat_transport.event_handler("on_client_disconnected")
+ async def on_client_disconnected(transport, client):
+ logger.info("Pipecat Client disconnected")
+
+ @pipecat_transport.event_handler("on_client_closed")
+ async def on_client_closed(transport, client):
+ logger.info("Pipecat Client closed")
+ await task.cancel()
+
+ runner = PipelineRunner(handle_sigint=False)
+
+ await runner.run(task)
diff --git a/examples/p2p-webrtc/daily-interop-bridge/env.example b/examples/p2p-webrtc/daily-interop-bridge/env.example
new file mode 100644
index 000000000..8ea5961f2
--- /dev/null
+++ b/examples/p2p-webrtc/daily-interop-bridge/env.example
@@ -0,0 +1,2 @@
+DAILY_API_KEY=
+DAILY_SAMPLE_ROOM_URL=
\ No newline at end of file
diff --git a/examples/p2p-webrtc/daily-interop-bridge/requirements.txt b/examples/p2p-webrtc/daily-interop-bridge/requirements.txt
new file mode 100644
index 000000000..19d9ca501
--- /dev/null
+++ b/examples/p2p-webrtc/daily-interop-bridge/requirements.txt
@@ -0,0 +1,5 @@
+python-dotenv
+fastapi[all]
+uvicorn
+aiortc
+pipecat-ai[silero, webrtc, daily]
\ No newline at end of file
diff --git a/examples/p2p-webrtc/daily-interop-bridge/server.py b/examples/p2p-webrtc/daily-interop-bridge/server.py
new file mode 100644
index 000000000..d65bd3013
--- /dev/null
+++ b/examples/p2p-webrtc/daily-interop-bridge/server.py
@@ -0,0 +1,95 @@
+#
+# Copyright (c) 2024β2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import asyncio
+import sys
+from contextlib import asynccontextmanager
+from typing import Dict
+
+import uvicorn
+from bot import run_bot
+from dotenv import load_dotenv
+from fastapi import BackgroundTasks, FastAPI
+from fastapi.responses import RedirectResponse
+from loguru import logger
+from pipecat_ai_small_webrtc_prebuilt.frontend import SmallWebRTCPrebuiltUI
+
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+
+# Load environment variables
+load_dotenv(override=True)
+
+app = FastAPI()
+
+# Store connections by pc_id
+pcs_map: Dict[str, SmallWebRTCConnection] = {}
+
+ice_servers = ["stun:stun.l.google.com:19302"]
+
+# Mount the frontend at /
+app.mount("/prebuilt", SmallWebRTCPrebuiltUI)
+
+
+@app.get("/", include_in_schema=False)
+async def root_redirect():
+ return RedirectResponse(url="/prebuilt/")
+
+
+@app.post("/api/offer")
+async def offer(request: dict, background_tasks: BackgroundTasks):
+ pc_id = request.get("pc_id")
+
+ if pc_id and pc_id in pcs_map:
+ pipecat_connection = pcs_map[pc_id]
+ logger.info(f"Reusing existing connection for pc_id: {pc_id}")
+ await pipecat_connection.renegotiate(
+ sdp=request["sdp"], type=request["type"], restart_pc=request.get("restart_pc", False)
+ )
+ else:
+ pipecat_connection = SmallWebRTCConnection(ice_servers)
+ await pipecat_connection.initialize(sdp=request["sdp"], type=request["type"])
+
+ @pipecat_connection.event_handler("closed")
+ async def handle_disconnected(webrtc_connection: SmallWebRTCConnection):
+ logger.info(f"Discarding peer connection for pc_id: {webrtc_connection.pc_id}")
+ pcs_map.pop(webrtc_connection.pc_id, None)
+
+ background_tasks.add_task(run_bot, pipecat_connection)
+
+ answer = pipecat_connection.get_answer()
+ # Updating the peer connection inside the map
+ pcs_map[answer["pc_id"]] = pipecat_connection
+
+ return answer
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+ yield # Run app
+ coros = [pc.close() for pc in pcs_map.values()]
+ await asyncio.gather(*coros)
+ pcs_map.clear()
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser(description="WebRTC demo")
+ parser.add_argument(
+ "--host", default="localhost", help="Host for HTTP server (default: localhost)"
+ )
+ parser.add_argument(
+ "--port", type=int, default=7860, help="Port for HTTP server (default: 7860)"
+ )
+ parser.add_argument("--verbose", "-v", action="count")
+ args = parser.parse_args()
+
+ logger.remove(0)
+ if args.verbose:
+ logger.add(sys.stderr, level="TRACE")
+ else:
+ logger.add(sys.stderr, level="DEBUG")
+
+ uvicorn.run(app, host=args.host, port=args.port)
diff --git a/examples/p2p-webrtc/video-transform/README.md b/examples/p2p-webrtc/video-transform/README.md
index 7125e3e68..fdc4a8862 100644
--- a/examples/p2p-webrtc/video-transform/README.md
+++ b/examples/p2p-webrtc/video-transform/README.md
@@ -35,9 +35,19 @@ cd server
python server.py
```
-### 2οΈβ£ Connect Using the Client App
+### 2οΈβ£ Test with SmallWebRTC Prebuilt UI
-For client-side setup, refer to the [JavaScript Guide](client/typescript/README.md).
+You can quickly test your bot using the `SmallWebRTCPrebuiltUI`:
+
+- Open your browser and navigate to:
+π http://localhost:7860
+ - (Or use your custom port, if configured)
+
+### 3οΈβ£ Connect Using a Custom Client App
+
+For client-side setup, refer to the:
+- [Typescript Guide](client/typescript/README.md).
+- [iOS Guide](client/ios/README.md).
## β οΈ Important Note
Ensure the bot server is running before using any client implementations.
diff --git a/examples/p2p-webrtc/video-transform/client/ios/.gitignore b/examples/p2p-webrtc/video-transform/client/ios/.gitignore
new file mode 100644
index 000000000..f89982e48
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/.gitignore
@@ -0,0 +1,2 @@
+/SimpleChatbot.xcodeproj/xcuserdata/
+/SimpleChatbot.xcodeproj/project.xcworkspace/xcuserdata/
diff --git a/examples/p2p-webrtc/video-transform/client/ios/README.md b/examples/p2p-webrtc/video-transform/client/ios/README.md
new file mode 100644
index 000000000..d9cd6539c
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/README.md
@@ -0,0 +1,18 @@
+# iOS implementation
+
+Basic implementation using the [Pipecat iOS SDK](https://docs.pipecat.ai/client/ios/introduction).
+
+## Prerequisites
+
+1. Run the bot server. See the [server README](../../server).
+2. Install [Xcode](https://developer.apple.com/xcode/), and set up your device [to run your own applications](https://developer.apple.com/documentation/xcode/distributing-your-app-to-registered-devices).
+
+## Running locally
+
+1. Clone this repository locally.
+2. Open the SimpleChatbot.xcodeproj in Xcode.
+3. Tell Xcode to update its Package Dependencies by clicking File -> Packages -> Update to Latest Package Versions.
+4. Build the project.
+5. Run the project on your device.
+6. Connect to the URL you are testing.
+
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot.xcodeproj/project.pbxproj b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot.xcodeproj/project.pbxproj
new file mode 100644
index 000000000..b6ea476e4
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot.xcodeproj/project.pbxproj
@@ -0,0 +1,727 @@
+// !$*UTF8*$!
+{
+ archiveVersion = 1;
+ classes = {
+ };
+ objectVersion = 56;
+ objects = {
+
+/* Begin PBXBuildFile section */
+ 90031FA72C616EE700408370 /* SimpleChatbotApp.swift in Sources */ = {isa = PBXBuildFile; fileRef = 90031FA62C616EE700408370 /* SimpleChatbotApp.swift */; };
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+ 90031FAB2C616EE800408370 /* Assets.xcassets in Resources */ = {isa = PBXBuildFile; fileRef = 90031FAA2C616EE800408370 /* Assets.xcassets */; };
+ 90031FAE2C616EE800408370 /* Preview Assets.xcassets in Resources */ = {isa = PBXBuildFile; fileRef = 90031FAD2C616EE800408370 /* Preview Assets.xcassets */; };
+ 90031FB82C616EE900408370 /* SimpleChatbotTests.swift in Sources */ = {isa = PBXBuildFile; fileRef = 90031FB72C616EE900408370 /* SimpleChatbotTests.swift */; };
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+ LOCALIZATION_PREFERS_STRING_CATALOGS = YES;
+ MTL_ENABLE_DEBUG_INFO = NO;
+ MTL_FAST_MATH = YES;
+ SDKROOT = iphoneos;
+ SWIFT_COMPILATION_MODE = wholemodule;
+ VALIDATE_PRODUCT = YES;
+ };
+ name = Release;
+ };
+ 90031FC82C616EE900408370 /* Debug */ = {
+ isa = XCBuildConfiguration;
+ buildSettings = {
+ ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon;
+ ASSETCATALOG_COMPILER_GLOBAL_ACCENT_COLOR_NAME = AccentColor;
+ CODE_SIGN_STYLE = Automatic;
+ CURRENT_PROJECT_VERSION = 1;
+ DEVELOPMENT_ASSET_PATHS = "\"SimpleChatbot/Preview Content\"";
+ DEVELOPMENT_TEAM = EEBGKV9N3N;
+ ENABLE_PREVIEWS = YES;
+ GENERATE_INFOPLIST_FILE = YES;
+ INFOPLIST_FILE = SimpleChatbot/Info.plist;
+ INFOPLIST_KEY_UIApplicationSceneManifest_Generation = YES;
+ INFOPLIST_KEY_UIApplicationSupportsIndirectInputEvents = YES;
+ INFOPLIST_KEY_UILaunchScreen_Generation = YES;
+ INFOPLIST_KEY_UISupportedInterfaceOrientations_iPad = "UIInterfaceOrientationPortrait UIInterfaceOrientationPortraitUpsideDown UIInterfaceOrientationLandscapeLeft UIInterfaceOrientationLandscapeRight";
+ INFOPLIST_KEY_UISupportedInterfaceOrientations_iPhone = "UIInterfaceOrientationPortrait UIInterfaceOrientationLandscapeLeft UIInterfaceOrientationLandscapeRight";
+ LD_RUNPATH_SEARCH_PATHS = (
+ "$(inherited)",
+ "@executable_path/Frameworks",
+ );
+ MARKETING_VERSION = 1.0;
+ PRODUCT_BUNDLE_IDENTIFIER = co.daily.SimpleChatbot;
+ PRODUCT_NAME = "$(TARGET_NAME)";
+ SWIFT_EMIT_LOC_STRINGS = YES;
+ SWIFT_VERSION = 5.0;
+ TARGETED_DEVICE_FAMILY = "1,2";
+ };
+ name = Debug;
+ };
+ 90031FC92C616EE900408370 /* Release */ = {
+ isa = XCBuildConfiguration;
+ buildSettings = {
+ ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon;
+ ASSETCATALOG_COMPILER_GLOBAL_ACCENT_COLOR_NAME = AccentColor;
+ CODE_SIGN_STYLE = Automatic;
+ CURRENT_PROJECT_VERSION = 1;
+ DEVELOPMENT_ASSET_PATHS = "\"SimpleChatbot/Preview Content\"";
+ DEVELOPMENT_TEAM = EEBGKV9N3N;
+ ENABLE_PREVIEWS = YES;
+ GENERATE_INFOPLIST_FILE = YES;
+ INFOPLIST_FILE = SimpleChatbot/Info.plist;
+ INFOPLIST_KEY_UIApplicationSceneManifest_Generation = YES;
+ INFOPLIST_KEY_UIApplicationSupportsIndirectInputEvents = YES;
+ INFOPLIST_KEY_UILaunchScreen_Generation = YES;
+ INFOPLIST_KEY_UISupportedInterfaceOrientations_iPad = "UIInterfaceOrientationPortrait UIInterfaceOrientationPortraitUpsideDown UIInterfaceOrientationLandscapeLeft UIInterfaceOrientationLandscapeRight";
+ INFOPLIST_KEY_UISupportedInterfaceOrientations_iPhone = "UIInterfaceOrientationPortrait UIInterfaceOrientationLandscapeLeft UIInterfaceOrientationLandscapeRight";
+ LD_RUNPATH_SEARCH_PATHS = (
+ "$(inherited)",
+ "@executable_path/Frameworks",
+ );
+ MARKETING_VERSION = 1.0;
+ PRODUCT_BUNDLE_IDENTIFIER = co.daily.SimpleChatbot;
+ PRODUCT_NAME = "$(TARGET_NAME)";
+ SWIFT_EMIT_LOC_STRINGS = YES;
+ SWIFT_VERSION = 5.0;
+ TARGETED_DEVICE_FAMILY = "1,2";
+ };
+ name = Release;
+ };
+ 90031FCB2C616EE900408370 /* Debug */ = {
+ isa = XCBuildConfiguration;
+ buildSettings = {
+ ALWAYS_EMBED_SWIFT_STANDARD_LIBRARIES = YES;
+ BUNDLE_LOADER = "$(TEST_HOST)";
+ CODE_SIGN_STYLE = Automatic;
+ CURRENT_PROJECT_VERSION = 1;
+ DEVELOPMENT_TEAM = EEBGKV9N3N;
+ GENERATE_INFOPLIST_FILE = YES;
+ IPHONEOS_DEPLOYMENT_TARGET = 17.5;
+ MARKETING_VERSION = 1.0;
+ PRODUCT_BUNDLE_IDENTIFIER = co.daily.SimpleChatbotTests;
+ PRODUCT_NAME = "$(TARGET_NAME)";
+ SWIFT_EMIT_LOC_STRINGS = NO;
+ SWIFT_VERSION = 5.0;
+ TARGETED_DEVICE_FAMILY = "1,2";
+ TEST_HOST = "$(BUILT_PRODUCTS_DIR)/SimpleChatbot.app/$(BUNDLE_EXECUTABLE_FOLDER_PATH)/SimpleChatbot";
+ };
+ name = Debug;
+ };
+ 90031FCC2C616EE900408370 /* Release */ = {
+ isa = XCBuildConfiguration;
+ buildSettings = {
+ ALWAYS_EMBED_SWIFT_STANDARD_LIBRARIES = YES;
+ BUNDLE_LOADER = "$(TEST_HOST)";
+ CODE_SIGN_STYLE = Automatic;
+ CURRENT_PROJECT_VERSION = 1;
+ DEVELOPMENT_TEAM = EEBGKV9N3N;
+ GENERATE_INFOPLIST_FILE = YES;
+ IPHONEOS_DEPLOYMENT_TARGET = 17.5;
+ MARKETING_VERSION = 1.0;
+ PRODUCT_BUNDLE_IDENTIFIER = co.daily.SimpleChatbotTests;
+ PRODUCT_NAME = "$(TARGET_NAME)";
+ SWIFT_EMIT_LOC_STRINGS = NO;
+ SWIFT_VERSION = 5.0;
+ TARGETED_DEVICE_FAMILY = "1,2";
+ TEST_HOST = "$(BUILT_PRODUCTS_DIR)/SimpleChatbot.app/$(BUNDLE_EXECUTABLE_FOLDER_PATH)/SimpleChatbot";
+ };
+ name = Release;
+ };
+ 90031FCE2C616EE900408370 /* Debug */ = {
+ isa = XCBuildConfiguration;
+ buildSettings = {
+ ALWAYS_EMBED_SWIFT_STANDARD_LIBRARIES = YES;
+ CODE_SIGN_STYLE = Automatic;
+ CURRENT_PROJECT_VERSION = 1;
+ DEVELOPMENT_TEAM = EEBGKV9N3N;
+ GENERATE_INFOPLIST_FILE = YES;
+ MARKETING_VERSION = 1.0;
+ PRODUCT_BUNDLE_IDENTIFIER = co.daily.SimpleChatbotUITests;
+ PRODUCT_NAME = "$(TARGET_NAME)";
+ SWIFT_EMIT_LOC_STRINGS = NO;
+ SWIFT_VERSION = 5.0;
+ TARGETED_DEVICE_FAMILY = "1,2";
+ TEST_TARGET_NAME = SimpleChatbot;
+ };
+ name = Debug;
+ };
+ 90031FCF2C616EE900408370 /* Release */ = {
+ isa = XCBuildConfiguration;
+ buildSettings = {
+ ALWAYS_EMBED_SWIFT_STANDARD_LIBRARIES = YES;
+ CODE_SIGN_STYLE = Automatic;
+ CURRENT_PROJECT_VERSION = 1;
+ DEVELOPMENT_TEAM = EEBGKV9N3N;
+ GENERATE_INFOPLIST_FILE = YES;
+ MARKETING_VERSION = 1.0;
+ PRODUCT_BUNDLE_IDENTIFIER = co.daily.SimpleChatbotUITests;
+ PRODUCT_NAME = "$(TARGET_NAME)";
+ SWIFT_EMIT_LOC_STRINGS = NO;
+ SWIFT_VERSION = 5.0;
+ TARGETED_DEVICE_FAMILY = "1,2";
+ TEST_TARGET_NAME = SimpleChatbot;
+ };
+ name = Release;
+ };
+/* End XCBuildConfiguration section */
+
+/* Begin XCConfigurationList section */
+ 90031F9E2C616EE700408370 /* Build configuration list for PBXProject "SimpleChatbot" */ = {
+ isa = XCConfigurationList;
+ buildConfigurations = (
+ 90031FC52C616EE900408370 /* Debug */,
+ 90031FC62C616EE900408370 /* Release */,
+ );
+ defaultConfigurationIsVisible = 0;
+ defaultConfigurationName = Release;
+ };
+ 90031FC72C616EE900408370 /* Build configuration list for PBXNativeTarget "SimpleChatbot" */ = {
+ isa = XCConfigurationList;
+ buildConfigurations = (
+ 90031FC82C616EE900408370 /* Debug */,
+ 90031FC92C616EE900408370 /* Release */,
+ );
+ defaultConfigurationIsVisible = 0;
+ defaultConfigurationName = Release;
+ };
+ 90031FCA2C616EE900408370 /* Build configuration list for PBXNativeTarget "SimpleChatbotTests" */ = {
+ isa = XCConfigurationList;
+ buildConfigurations = (
+ 90031FCB2C616EE900408370 /* Debug */,
+ 90031FCC2C616EE900408370 /* Release */,
+ );
+ defaultConfigurationIsVisible = 0;
+ defaultConfigurationName = Release;
+ };
+ 90031FCD2C616EE900408370 /* Build configuration list for PBXNativeTarget "SimpleChatbotUITests" */ = {
+ isa = XCConfigurationList;
+ buildConfigurations = (
+ 90031FCE2C616EE900408370 /* Debug */,
+ 90031FCF2C616EE900408370 /* Release */,
+ );
+ defaultConfigurationIsVisible = 0;
+ defaultConfigurationName = Release;
+ };
+/* End XCConfigurationList section */
+
+/* Begin XCRemoteSwiftPackageReference section */
+ 90383A992DA4620800D0DDA3 /* XCRemoteSwiftPackageReference "pipecat-client-ios-small-webrtc" */ = {
+ isa = XCRemoteSwiftPackageReference;
+ repositoryURL = "https://github.com/pipecat-ai/pipecat-client-ios-small-webrtc";
+ requirement = {
+ kind = upToNextMajorVersion;
+ minimumVersion = 0.0.1;
+ };
+ };
+/* End XCRemoteSwiftPackageReference section */
+
+/* Begin XCSwiftPackageProductDependency section */
+ 90383A902D9C357F00D0DDA3 /* PipecatClientIOSSmallWebrtc */ = {
+ isa = XCSwiftPackageProductDependency;
+ productName = PipecatClientIOSSmallWebrtc;
+ };
+ 90383A9A2DA4620800D0DDA3 /* PipecatClientIOSSmallWebrtc */ = {
+ isa = XCSwiftPackageProductDependency;
+ package = 90383A992DA4620800D0DDA3 /* XCRemoteSwiftPackageReference "pipecat-client-ios-small-webrtc" */;
+ productName = PipecatClientIOSSmallWebrtc;
+ };
+/* End XCSwiftPackageProductDependency section */
+ };
+ rootObject = 90031F9B2C616EE700408370 /* Project object */;
+}
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot.xcodeproj/project.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot.xcodeproj/project.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist
new file mode 100644
index 000000000..18d981003
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot.xcodeproj/project.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist
@@ -0,0 +1,8 @@
+
+
+
+
+ IDEDidComputeMac32BitWarning
+
+
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot.xcodeproj/project.xcworkspace/xcshareddata/WorkspaceSettings.xcsettings b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot.xcodeproj/project.xcworkspace/xcshareddata/WorkspaceSettings.xcsettings
new file mode 100644
index 000000000..08de0be8d
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot.xcodeproj/project.xcworkspace/xcshareddata/WorkspaceSettings.xcsettings
@@ -0,0 +1,8 @@
+
+
+
+
+ IDEWorkspaceSharedSettings_AutocreateContextsIfNeeded
+
+
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot.xcodeproj/project.xcworkspace/xcshareddata/swiftpm/Package.resolved b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot.xcodeproj/project.xcworkspace/xcshareddata/swiftpm/Package.resolved
new file mode 100644
index 000000000..a36695ef7
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot.xcodeproj/project.xcworkspace/xcshareddata/swiftpm/Package.resolved
@@ -0,0 +1,33 @@
+{
+ "originHash" : "77cb3fee4071811f880e69dbcd5a8ba01711a73372960391d6366c4c3a0d36eb",
+ "pins" : [
+ {
+ "identity" : "pipecat-client-ios",
+ "kind" : "remoteSourceControl",
+ "location" : "https://github.com/pipecat-ai/pipecat-client-ios.git",
+ "state" : {
+ "revision" : "992641fb5f7d1a794ecfc33babb5fe36e2a8ffdd",
+ "version" : "0.3.5"
+ }
+ },
+ {
+ "identity" : "pipecat-client-ios-small-webrtc",
+ "kind" : "remoteSourceControl",
+ "location" : "https://github.com/pipecat-ai/pipecat-client-ios-small-webrtc",
+ "state" : {
+ "revision" : "a6e4516b1fcbed772ca97a9616dddc9329097958",
+ "version" : "0.0.1"
+ }
+ },
+ {
+ "identity" : "webrtc",
+ "kind" : "remoteSourceControl",
+ "location" : "https://github.com/stasel/WebRTC",
+ "state" : {
+ "revision" : "5b2eb61cace7d62726b29a38b768b07d6bc55c45",
+ "version" : "134.0.0"
+ }
+ }
+ ],
+ "version" : 3
+}
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot.xcodeproj/xcshareddata/xcschemes/SimpleChatbot.xcscheme b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot.xcodeproj/xcshareddata/xcschemes/SimpleChatbot.xcscheme
new file mode 100644
index 000000000..632ae199d
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot.xcodeproj/xcshareddata/xcschemes/SimpleChatbot.xcscheme
@@ -0,0 +1,78 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/Assets.xcassets/AppIcon.appiconset/Contents.json b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/Assets.xcassets/AppIcon.appiconset/Contents.json
new file mode 100644
index 000000000..44ac9cc28
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/Assets.xcassets/AppIcon.appiconset/Contents.json
@@ -0,0 +1,14 @@
+{
+ "images" : [
+ {
+ "filename" : "appstore.png",
+ "idiom" : "universal",
+ "platform" : "ios",
+ "size" : "1024x1024"
+ }
+ ],
+ "info" : {
+ "author" : "xcode",
+ "version" : 1
+ }
+}
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/Assets.xcassets/AppIcon.appiconset/appstore.png b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/Assets.xcassets/AppIcon.appiconset/appstore.png
new file mode 100644
index 000000000..e6e615ad2
Binary files /dev/null and b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/Assets.xcassets/AppIcon.appiconset/appstore.png differ
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/Assets.xcassets/Contents.json b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/Assets.xcassets/Contents.json
new file mode 100644
index 000000000..73c00596a
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/Assets.xcassets/Contents.json
@@ -0,0 +1,6 @@
+{
+ "info" : {
+ "author" : "xcode",
+ "version" : 1
+ }
+}
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/Assets.xcassets/pipecat.imageset/Contents.json b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/Assets.xcassets/pipecat.imageset/Contents.json
new file mode 100644
index 000000000..6e1102089
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/Assets.xcassets/pipecat.imageset/Contents.json
@@ -0,0 +1,21 @@
+{
+ "images" : [
+ {
+ "filename" : "Square Black.svg",
+ "idiom" : "universal",
+ "scale" : "1x"
+ },
+ {
+ "idiom" : "universal",
+ "scale" : "2x"
+ },
+ {
+ "idiom" : "universal",
+ "scale" : "3x"
+ }
+ ],
+ "info" : {
+ "author" : "xcode",
+ "version" : 1
+ }
+}
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/Assets.xcassets/pipecat.imageset/Square Black.svg b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/Assets.xcassets/pipecat.imageset/Square Black.svg
new file mode 100644
index 000000000..0db9a0f64
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/Assets.xcassets/pipecat.imageset/Square Black.svg
@@ -0,0 +1,8 @@
+
+
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/Assets.xcassets/vision.imageset/Contents.json b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/Assets.xcassets/vision.imageset/Contents.json
new file mode 100644
index 000000000..0c0cddcdc
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/Assets.xcassets/vision.imageset/Contents.json
@@ -0,0 +1,21 @@
+{
+ "images" : [
+ {
+ "filename" : "vision.svg",
+ "idiom" : "universal",
+ "scale" : "1x"
+ },
+ {
+ "idiom" : "universal",
+ "scale" : "2x"
+ },
+ {
+ "idiom" : "universal",
+ "scale" : "3x"
+ }
+ ],
+ "info" : {
+ "author" : "xcode",
+ "version" : 1
+ }
+}
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/Assets.xcassets/vision.imageset/vision.svg b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/Assets.xcassets/vision.imageset/vision.svg
new file mode 100644
index 000000000..8225a460f
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/Assets.xcassets/vision.imageset/vision.svg
@@ -0,0 +1,9 @@
+
+
+
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/Info.plist b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/Info.plist
new file mode 100644
index 000000000..c5a7b5685
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/Info.plist
@@ -0,0 +1,19 @@
+
+
+
+
+ UIBackgroundModes
+
+ voip
+
+ NSCameraUsageDescription
+ Camera is necessary for transmitting video in a call
+ NSMicrophoneUsageDescription
+ Microphone is necessary for transmitting audio in a call
+ NSAppTransportSecurity
+
+ NSAllowsLocalNetworking
+
+
+
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/Preview Content/Preview Assets.xcassets/Contents.json b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/Preview Content/Preview Assets.xcassets/Contents.json
new file mode 100644
index 000000000..73c00596a
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/Preview Content/Preview Assets.xcassets/Contents.json
@@ -0,0 +1,6 @@
+{
+ "info" : {
+ "author" : "xcode",
+ "version" : 1
+ }
+}
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/SimpleChatbotApp.swift b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/SimpleChatbotApp.swift
new file mode 100644
index 000000000..7b1e78703
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/SimpleChatbotApp.swift
@@ -0,0 +1,18 @@
+import SwiftUI
+
+@main
+struct SimpleChatbotApp: App {
+
+ @StateObject var callContainerModel = CallContainerModel()
+
+ var body: some Scene {
+ WindowGroup {
+ if (!callContainerModel.isInCall) {
+ PreJoinView().environmentObject(callContainerModel)
+ } else {
+ MeetingView().environmentObject(callContainerModel)
+ }
+ }
+ }
+
+}
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/model/CallContainerModel.swift b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/model/CallContainerModel.swift
new file mode 100644
index 000000000..0f35f1326
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/model/CallContainerModel.swift
@@ -0,0 +1,284 @@
+import SwiftUI
+
+import PipecatClientIOSSmallWebrtc
+import PipecatClientIOS
+
+class CallContainerModel: ObservableObject {
+
+ @Published var voiceClientStatus: String = TransportState.disconnected.description
+ @Published var isInCall: Bool = false
+ @Published var isBotReady: Bool = false
+
+ @Published var isMicEnabled: Bool = false
+ @Published var isCamEnabled: Bool = false
+ @Published var localCamId: MediaTrackId? = nil
+ @Published var botCamId: MediaTrackId? = nil
+
+ @Published var toastMessage: String? = nil
+ @Published var showToast: Bool = false
+
+ @Published var messages: [LiveMessage] = []
+ @Published var liveBotMessage: LiveMessage?
+ @Published var liveUserMessage: LiveMessage?
+
+ var rtviClientIOS: RTVIClient?
+
+ @Published var selectedMic: MediaDeviceId? = nil {
+ didSet {
+ guard let selectedMic else { return } // don't store nil
+ var settings = SettingsManager.getSettings()
+ settings.selectedMic = selectedMic.id
+ SettingsManager.updateSettings(settings: settings)
+ }
+ }
+ @Published var availableMics: [MediaDeviceInfo] = []
+
+ init() {
+ // Changing the log level
+ PipecatClientIOS.setLogLevel(.warn)
+ PipecatClientIOSSmallWebrtc.setLogLevel(.info)
+ }
+
+ @MainActor
+ func connect(backendURL: String) {
+ self.resetLiveMessages()
+
+ let baseUrl = backendURL.trimmingCharacters(in: .whitespacesAndNewlines)
+ if(baseUrl.trimmingCharacters(in: .whitespacesAndNewlines).isEmpty){
+ self.showError(message: "Need to fill the backendURL")
+ return
+ }
+
+ let currentSettings = SettingsManager.getSettings()
+ let rtviClientOptions = RTVIClientOptions.init(
+ enableMic: currentSettings.enableMic,
+ enableCam: currentSettings.enableCam,
+ params: RTVIClientParams(
+ config: [
+ .init(
+ service: SmallWebRTCTransport.SERVICE_NAME,
+ options: [
+ .init(name: "server_url", value: .string(baseUrl))
+ ]
+ )
+ ]
+ )
+ )
+ self.rtviClientIOS = RTVIClient.init(
+ transport: SmallWebRTCTransport.init(options: rtviClientOptions),
+ options: rtviClientOptions
+ )
+ self.rtviClientIOS?.delegate = self
+
+ // Registering the llm helper, we will need this to handle the function calling
+ let llmHelper = try? self.rtviClientIOS?.registerHelper(service: "llm", helper: LLMHelper.self)
+ llmHelper?.delegate = self
+
+ self.rtviClientIOS?.start() { result in
+ switch result {
+ case .failure(let error):
+ self.showError(message: error.localizedDescription)
+ self.rtviClientIOS = nil
+ case .success():
+ // Apply initial mic preference
+ if let selectedMic = SettingsManager.getSettings().selectedMic {
+ self.selectMic(MediaDeviceId(id: selectedMic))
+ }
+ // Populate available devices list
+ self.availableMics = self.rtviClientIOS?.getAllMics() ?? []
+ }
+ }
+ self.saveCredentials(backendURL: backendURL)
+ }
+
+ @MainActor
+ func disconnect() {
+ self.rtviClientIOS?.disconnect(completion: nil)
+ self.rtviClientIOS?.release()
+ self.rtviClientIOS = nil
+ }
+
+ func showError(message: String) {
+ self.toastMessage = message
+ self.showToast = true
+ // Hide the toast after 5 seconds
+ DispatchQueue.main.asyncAfter(deadline: .now() + 5) {
+ self.showToast = false
+ self.toastMessage = nil
+ }
+ }
+
+ @MainActor
+ func toggleMicInput() {
+ self.rtviClientIOS?.enableMic(enable: !self.isMicEnabled) { result in
+ switch result {
+ case .success():
+ self.isMicEnabled = self.rtviClientIOS?.isMicEnabled ?? false
+ case .failure(let error):
+ self.showError(message: error.localizedDescription)
+ }
+ }
+ }
+
+ @MainActor
+ func toggleCamInput() {
+ self.rtviClientIOS?.enableCam(enable: !self.isCamEnabled) { result in
+ switch result {
+ case .success():
+ self.isCamEnabled = self.rtviClientIOS?.isCamEnabled ?? false
+ case .failure(let error):
+ self.showError(message: error.localizedDescription)
+ }
+ }
+ }
+
+ func saveCredentials(backendURL: String) {
+ var currentSettings = SettingsManager.getSettings()
+ currentSettings.backendURL = backendURL
+ // Saving the settings
+ SettingsManager.updateSettings(settings: currentSettings)
+ }
+
+ @MainActor
+ func selectMic(_ mic: MediaDeviceId) {
+ self.selectedMic = mic
+ self.rtviClientIOS?.updateMic(micId: mic, completion: nil)
+ }
+
+ private func createLiveMessage(content:String = "", type:MessageType) {
+ // Creating a new one
+ DispatchQueue.main.async {
+ let liveMessage = LiveMessage(content: content, type: type, updatedAt: Date())
+ self.messages.append(liveMessage)
+ if type == .bot {
+ self.liveBotMessage = liveMessage
+ } else if type == .user {
+ self.liveUserMessage = liveMessage
+ }
+ }
+ }
+
+ private func appendTextToLiveMessage(fromBot: Bool, content:String) {
+ DispatchQueue.main.async {
+ // Updating the last message with the new content
+ if fromBot {
+ self.liveBotMessage?.content += content
+ } else {
+ self.liveUserMessage?.content += content
+ }
+ }
+ }
+
+ private func resetLiveMessages() {
+ DispatchQueue.main.async {
+ self.messages = []
+ }
+ }
+}
+
+extension CallContainerModel:RTVIClientDelegate, LLMHelperDelegate {
+
+ private func handleEvent(eventName: String, eventValue: Any? = nil) {
+ if let value = eventValue {
+ print("Pipecat Demo, received event: \(eventName), value:\(value)")
+ } else {
+ print("Pipecat Demo, received event: \(eventName)")
+ }
+ }
+
+ func onTransportStateChanged(state: TransportState) {
+ Task { @MainActor in
+ self.handleEvent(eventName: "onTransportStateChanged", eventValue: state)
+ self.voiceClientStatus = state.description
+ self.isInCall = ( state == .connecting || state == .connected || state == .ready || state == .authenticating )
+ self.createLiveMessage(content: state.description, type: .system)
+ }
+ }
+
+ func onBotReady(botReadyData: BotReadyData) {
+ Task { @MainActor in
+ self.handleEvent(eventName: "onBotReady")
+ self.isBotReady = true
+ }
+ }
+
+ func onConnected() {
+ Task { @MainActor in
+ self.handleEvent(eventName: "onConnected")
+ self.isMicEnabled = self.rtviClientIOS?.isMicEnabled ?? false
+ self.isCamEnabled = self.rtviClientIOS?.isCamEnabled ?? false
+ }
+ }
+
+ func onDisconnected() {
+ Task { @MainActor in
+ self.handleEvent(eventName: "onDisconnected")
+ self.isBotReady = false
+ }
+ }
+
+ func onError(message: String) {
+ Task { @MainActor in
+ self.handleEvent(eventName: "onError", eventValue: message)
+ self.showError(message: message)
+ }
+ }
+
+ func onAvailableMicsUpdated(mics: [MediaDeviceInfo]) {
+ Task { @MainActor in
+ self.availableMics = mics
+ }
+ }
+
+ func onMicUpdated(mic: MediaDeviceInfo?) {
+ Task { @MainActor in
+ self.selectedMic = mic?.id
+ }
+ }
+
+ func onBotTranscript(data: String) {
+ self.handleEvent(eventName: "onBotTranscript", eventValue: data)
+ }
+
+ func onTracksUpdated(tracks: Tracks) {
+ self.handleEvent(eventName: "onTracksUpdated", eventValue: tracks)
+ Task { @MainActor in
+ self.localCamId = tracks.local.video
+ self.botCamId = tracks.bot?.video ?? nil
+ }
+ }
+
+ func onUserStartedSpeaking() {
+ self.createLiveMessage(content: "User started speaking", type: .system)
+ self.handleEvent(eventName: "onUserStartedSpeaking")
+ self.createLiveMessage(type: .user)
+ }
+
+ func onUserStoppedSpeaking() {
+ self.createLiveMessage(content: "User stopped speaking", type: .system)
+ self.handleEvent(eventName: "onUserStoppedSpeaking")
+ }
+
+ func onBotStartedSpeaking() {
+ self.createLiveMessage(content: "Bot started speaking", type: .system)
+ self.handleEvent(eventName: "onBotStartedSpeaking")
+ self.createLiveMessage(type: .bot)
+ }
+
+ func onBotStoppedSpeaking() {
+ self.createLiveMessage(content: "Bot stopped speaking", type: .system)
+ self.handleEvent(eventName: "onBotStoppedSpeaking")
+ }
+
+ func onUserTranscript(data: Transcript) {
+ if data.final ?? false {
+ self.handleEvent(eventName: "onUserTranscript", eventValue: data.text)
+ self.appendTextToLiveMessage(fromBot: false, content: data.text)
+ }
+ }
+
+ func onBotTTSText(data: BotTTSText) {
+ self.appendTextToLiveMessage(fromBot: true, content: data.text)
+ }
+
+}
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/model/MockCallContainerModel.swift b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/model/MockCallContainerModel.swift
new file mode 100644
index 000000000..aabc6e2a2
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/model/MockCallContainerModel.swift
@@ -0,0 +1,43 @@
+import SwiftUI
+import PipecatClientIOS
+
+class MockCallContainerModel: CallContainerModel {
+
+ override init() {
+ super.init()
+ let liveMessageFromSystem = LiveMessage(
+ content: "System message",
+ type: .system,
+ updatedAt: Date()
+ )
+ let liveMessageFromUser = LiveMessage(
+ content: "Message from User",
+ type: .user,
+ updatedAt: Date()
+ )
+ let liveMessageFromBot = LiveMessage(
+ content: "Message from bot",
+ type: .bot,
+ updatedAt: Date()
+ )
+ self.messages = [ liveMessageFromSystem, liveMessageFromUser, liveMessageFromBot ]
+ }
+
+ override func connect(backendURL: String) {
+ print("connect")
+ }
+
+ override func disconnect() {
+ print("disconnect")
+ }
+
+ override func showError(message: String) {
+ self.toastMessage = message
+ self.showToast = true
+ // Hide the toast after 5 seconds
+ DispatchQueue.main.asyncAfter(deadline: .now() + 5) {
+ self.showToast = false
+ self.toastMessage = nil
+ }
+ }
+}
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/types/LiveMessage.swift b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/types/LiveMessage.swift
new file mode 100644
index 000000000..6e85214c0
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/types/LiveMessage.swift
@@ -0,0 +1,21 @@
+import Foundation
+
+enum MessageType {
+ case bot, user, system
+}
+
+class LiveMessage: ObservableObject, Identifiable, Equatable {
+ @Published var content: String
+ let type: MessageType
+ let updatedAt: Date
+
+ init(content: String, type: MessageType, updatedAt: Date) {
+ self.content = content
+ self.type = type
+ self.updatedAt = updatedAt
+ }
+
+ static func == (lhs: LiveMessage, rhs: LiveMessage) -> Bool {
+ lhs.updatedAt == rhs.updatedAt
+ }
+}
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/views/MeetingView.swift b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/views/MeetingView.swift
new file mode 100644
index 000000000..8a1a5c361
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/views/MeetingView.swift
@@ -0,0 +1,81 @@
+import SwiftUI
+import PipecatClientIOSSmallWebrtc
+
+struct MeetingView: View {
+
+ @State private var showingSettings = false
+ @EnvironmentObject private var model: CallContainerModel
+
+ var body: some View {
+ VStack {
+ ZStack {
+ SmallWebRTCVideoViewSwiftUI(videoTrack: self.model.botCamId, videoScaleMode: .fill)
+ .edgesIgnoringSafeArea(.all)
+
+ VStack {
+ ChatView()
+ .frame(maxHeight: .infinity)
+
+ HStack {
+ MicrophoneView(audioLevel: 0, isMuted: !self.model.isMicEnabled)
+ .frame(width: 100, height: 100)
+ .onTapGesture {
+ self.model.toggleMicInput()
+ }
+ CameraButtonView(trackId: self.model.localCamId, isMuted: !self.model.isCamEnabled)
+ .frame(width: 120, height: 120)
+ .onTapGesture {
+ self.model.toggleCamInput()
+ }
+ }
+ .padding()
+ }
+ }
+ Button(action: {
+ self.showingSettings = true
+ }) {
+ HStack {
+ Image(systemName: "gearshape")
+ .resizable()
+ .frame(width: 24, height: 24)
+ Text("Settings")
+ }
+ .frame(maxWidth: .infinity)
+ .padding()
+ .sheet(isPresented: $showingSettings) {
+ SettingsView(showingSettings: $showingSettings).environmentObject(self.model)
+ }
+ }
+ .foregroundColor(.black)
+ .background(Color.white)
+ .border(Color.buttonsBorder, width: 1)
+ .cornerRadius(12)
+ .padding([.horizontal])
+
+ Button(action: {
+ self.model.disconnect()
+ }) {
+ HStack {
+ Image(systemName: "rectangle.portrait.and.arrow.right")
+ .resizable()
+ .frame(width: 24, height: 24)
+ Text("End")
+ }
+ .frame(maxWidth: .infinity)
+ .padding()
+ }
+ .foregroundColor(.white)
+ .background(Color.black)
+ .cornerRadius(12)
+ .padding([.bottom, .horizontal])
+ }
+ .background(Color.backgroundApp)
+ .toast(message: model.toastMessage, isShowing: model.showToast)
+ }
+}
+
+#Preview {
+ let mockModel = MockCallContainerModel()
+ let result = MeetingView().environmentObject(mockModel as CallContainerModel)
+ return result
+}
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/views/PreJoinView.swift b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/views/PreJoinView.swift
new file mode 100644
index 000000000..64f9952fc
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/views/PreJoinView.swift
@@ -0,0 +1,44 @@
+import SwiftUI
+
+struct PreJoinView: View {
+
+ @State var backendURL: String
+
+ @EnvironmentObject private var model: CallContainerModel
+
+ init() {
+ let currentSettings = SettingsManager.getSettings()
+ self.backendURL = currentSettings.backendURL
+ }
+
+ var body: some View {
+ VStack(spacing: 20) {
+ Image("pipecat")
+ .resizable()
+ .frame(width: 80, height: 80)
+ Text("Pipecat Client iOS.")
+ .font(.headline)
+ TextField("Server URL", text: $backendURL)
+ .textFieldStyle(RoundedBorderTextFieldStyle())
+ .frame(maxWidth: .infinity)
+ .padding([.bottom, .horizontal])
+ Button("Connect") {
+ Task {
+ await self.model.connect(backendURL: self.backendURL)
+ }
+ }
+ .padding()
+ .background(Color.black)
+ .foregroundColor(.white)
+ .cornerRadius(8)
+ }
+ .padding()
+ .frame(maxHeight: .infinity)
+ .background(Color.backgroundApp)
+ .toast(message: model.toastMessage, isShowing: model.showToast)
+ }
+}
+
+#Preview {
+ PreJoinView().environmentObject(MockCallContainerModel() as CallContainerModel)
+}
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/views/components/CameraButtonView.swift b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/views/components/CameraButtonView.swift
new file mode 100644
index 000000000..c5c50e6cc
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/views/components/CameraButtonView.swift
@@ -0,0 +1,42 @@
+import SwiftUI
+import PipecatClientIOS
+import PipecatClientIOSSmallWebrtc
+
+struct CameraButtonView: View {
+ var trackId: MediaTrackId?
+ var isMuted: Bool
+
+ var body: some View {
+ GeometryReader { geometry in
+ let width = geometry.size.width
+ let circleSize = width * 0.9
+ let innerCircleSize = width * 0.82
+
+ ZStack {
+ Circle()
+ .stroke(Color.gray, lineWidth: 1)
+ .frame(width: circleSize)
+
+ if (!isMuted){
+ SmallWebRTCVideoViewSwiftUI(videoTrack: trackId, videoScaleMode: .fill)
+ .aspectRatio(1, contentMode: .fit)
+ .clipShape(Circle())
+ } else {
+ Circle()
+ .fill(Color.disabledVision)
+ .frame(width: innerCircleSize)
+ Image("vision")
+ .resizable()
+ .scaledToFit()
+ .frame(width: width * 0.3)
+ .foregroundColor(.green)
+ }
+ }
+ .frame(maxWidth: .infinity, maxHeight: .infinity) // Ensures the ZStack is centered
+ }
+ }
+}
+
+#Preview {
+ CameraButtonView(trackId: nil, isMuted: true)
+}
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/views/components/ChatView.swift b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/views/components/ChatView.swift
new file mode 100644
index 000000000..751b4cbc3
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/views/components/ChatView.swift
@@ -0,0 +1,96 @@
+import SwiftUI
+
+struct ChatView: View {
+ @EnvironmentObject private var model: CallContainerModel
+ @State private var timer = Timer.publish(every: 0.5, on: .main, in: .common).autoconnect()
+
+ var body: some View {
+ VStack {
+ ScrollViewReader { scrollViewProxy in
+ ScrollView {
+ VStack(spacing: 10) {
+ ForEach(self.model.messages) { message in
+ MessageView(message: message)
+ .frame(maxWidth: .infinity, alignment: messageAlignment(for: message.type))
+ .padding(.horizontal)
+ .id(message.id)
+ }
+ }
+ .onChange(of: self.model.messages) { _, _ in
+ scrollToLastMessage(scrollViewProxy)
+ }
+ }
+ .onReceive(timer) { _ in
+ scrollToLastMessage(scrollViewProxy)
+ }
+ .onAppear {
+ scrollToLastMessage(scrollViewProxy)
+ }
+ }
+ }
+ .edgesIgnoringSafeArea(.bottom)
+ }
+
+ private func messageAlignment(for type: MessageType) -> Alignment {
+ switch type {
+ case .bot: return .leading
+ case .user: return .trailing
+ case .system: return .center
+ }
+ }
+
+ private func scrollToLastMessage(_ scrollViewProxy: ScrollViewProxy) {
+ if let lastMessageId = self.model.messages.last?.id {
+ withAnimation {
+ scrollViewProxy.scrollTo(lastMessageId, anchor: .bottom)
+ }
+ }
+ }
+}
+
+struct MessageView: View {
+ @ObservedObject var message: LiveMessage
+
+ var body: some View {
+ HStack {
+ if message.type == .bot {
+ Image(systemName: "gearshape")
+ .resizable()
+ .frame(width: 24, height: 24)
+ }
+
+ Text(message.content)
+ .padding(message.type == .system ? 5 : 10)
+ .foregroundColor(.white)
+ .background(messageBackgroundColor(for: message.type))
+ .cornerRadius(15)
+ .overlay(
+ RoundedRectangle(cornerRadius: 15)
+ .stroke(Color.gray.opacity(0.5), lineWidth: 1)
+ )
+ }
+ .padding(messagePadding(for: message.type))
+ }
+
+ private func messageBackgroundColor(for type: MessageType) -> Color {
+ switch type {
+ case .bot: return .black
+ case .user: return .gray
+ case .system: return .blue.opacity(0.6)
+ }
+ }
+
+ private func messagePadding(for type: MessageType) -> EdgeInsets {
+ switch type {
+ case .bot: return EdgeInsets(top: 0, leading: 0, bottom: 0, trailing: 40)
+ case .user: return EdgeInsets(top: 0, leading: 40, bottom: 0, trailing: 0)
+ case .system: return EdgeInsets(top: 0, leading: 0, bottom: 0, trailing: 0)
+ }
+ }
+}
+
+#Preview {
+ let mockModel = MockCallContainerModel()
+ let result = ChatView().environmentObject(mockModel as CallContainerModel)
+ return result
+}
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/views/components/MicrophoneView.swift b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/views/components/MicrophoneView.swift
new file mode 100644
index 000000000..7cdaf1e80
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/views/components/MicrophoneView.swift
@@ -0,0 +1,44 @@
+import SwiftUI
+
+struct MicrophoneView: View {
+ var audioLevel: Float // Current audio level
+ var isMuted: Bool // Muted state
+
+ var body: some View {
+ GeometryReader { geometry in
+ let width = geometry.size.width
+ let circleSize = width * 0.9
+ let innerCircleSize = width * 0.82
+ let audioCircleSize = CGFloat(audioLevel) * (width * 0.95)
+
+ ZStack {
+ Circle()
+ .stroke(Color.gray, lineWidth: 1)
+ .frame(width: circleSize)
+
+ Circle()
+ .fill(isMuted ? Color.disabledMic : Color.backgroundCircle)
+ .frame(width: innerCircleSize)
+
+ if !isMuted {
+ Circle()
+ .fill(Color.micVolume)
+ .opacity(0.5)
+ .frame(width: audioCircleSize)
+ .animation(.easeInOut(duration: 0.2), value: audioLevel)
+ }
+
+ Image(systemName: isMuted ? "mic.slash.fill" : "mic.fill")
+ .resizable()
+ .scaledToFit()
+ .frame(width: width * 0.2)
+ .foregroundColor(.white)
+ }
+ .frame(maxWidth: .infinity, maxHeight: .infinity) // Ensures the ZStack is centered
+ }
+ }
+}
+
+#Preview {
+ MicrophoneView(audioLevel: 1, isMuted: false)
+}
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/views/components/ToastModifier.swift b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/views/components/ToastModifier.swift
new file mode 100644
index 000000000..9e36125eb
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/views/components/ToastModifier.swift
@@ -0,0 +1,31 @@
+import SwiftUI
+
+struct ToastModifier: ViewModifier {
+ var message: String?
+ var isShowing: Bool
+
+ func body(content: Content) -> some View {
+ ZStack {
+ content
+ if isShowing, let message = message {
+ VStack {
+ Text(message)
+ .padding()
+ .background(Color.black.opacity(0.7))
+ .foregroundColor(.white)
+ .cornerRadius(8)
+ .transition(.slide)
+ .padding(.top, 50)
+ Spacer()
+ }
+ .animation(.easeInOut(duration: 0.5), value: isShowing)
+ }
+ }
+ }
+}
+
+extension View {
+ func toast(message: String?, isShowing: Bool) -> some View {
+ self.modifier(ToastModifier(message: message, isShowing: isShowing))
+ }
+}
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/views/extensions/CustomColors.swift b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/views/extensions/CustomColors.swift
new file mode 100644
index 000000000..44faeee50
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/views/extensions/CustomColors.swift
@@ -0,0 +1,26 @@
+import SwiftUI
+
+public extension Color {
+
+ static let backgroundCircle = Color(hex: "#374151")
+ static let backgroundCircleNotConnected = Color(hex: "#D1D5DB")
+ static let backgroundApp = Color(hex: "#F9FAFB")
+ static let buttonsBorder = Color(hex: "#E5E7EB")
+ static let micVolume = Color(hex: "#86EFAC")
+ static let disabledMic = Color(hex: "#ee6b6e")
+ static let disabledVision = Color(hex: "#BBF7D0")
+
+ init(hex: String) {
+ let scanner = Scanner(string: hex)
+ _ = scanner.scanString("#")
+
+ var rgb: UInt64 = 0
+ scanner.scanHexInt64(&rgb)
+
+ let red = Double((rgb >> 16) & 0xFF) / 255.0
+ let green = Double((rgb >> 8) & 0xFF) / 255.0
+ let blue = Double(rgb & 0xFF) / 255.0
+
+ self.init(red: red, green: green, blue: blue)
+ }
+}
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/views/settings/SettingsManager.swift b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/views/settings/SettingsManager.swift
new file mode 100644
index 000000000..495780dfd
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/views/settings/SettingsManager.swift
@@ -0,0 +1,21 @@
+import Foundation
+
+class SettingsManager {
+ private static let preferencesKey = "settingsPreference"
+
+ static func getSettings() -> SettingsPreference {
+ if let data = UserDefaults.standard.data(forKey: preferencesKey),
+ let settings = try? JSONDecoder().decode(SettingsPreference.self, from: data) {
+ return settings
+ } else {
+ // default values in case we don't have any settings
+ return SettingsPreference(enableMic: true, enableCam: true, backendURL: "http://YOUR_IP:7860")
+ }
+ }
+
+ static func updateSettings(settings: SettingsPreference) {
+ if let data = try? JSONEncoder().encode(settings) {
+ UserDefaults.standard.set(data, forKey: preferencesKey)
+ }
+ }
+}
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/views/settings/SettingsPreference.swift b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/views/settings/SettingsPreference.swift
new file mode 100644
index 000000000..5314340b2
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/views/settings/SettingsPreference.swift
@@ -0,0 +1,9 @@
+import Foundation
+
+struct SettingsPreference: Codable {
+ var selectedMic: String?
+ var enableMic: Bool
+ var enableCam: Bool
+ var backendURL: String
+}
+
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/views/settings/SettingsView.swift b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/views/settings/SettingsView.swift
new file mode 100644
index 000000000..22ac55b0c
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbot/views/settings/SettingsView.swift
@@ -0,0 +1,82 @@
+import SwiftUI
+
+struct SettingsView: View {
+
+ @EnvironmentObject private var model: CallContainerModel
+
+ @Binding var showingSettings: Bool
+
+ @State private var isMicEnabled: Bool = true
+ @State private var isCamEnabled: Bool = true
+ @State private var backendURL: String = ""
+
+ var body: some View {
+ NavigationView {
+ Form {
+ Section {
+ List(model.availableMics, id: \.self.id.id) { mic in
+ Button(action: {
+ model.selectMic(mic.id)
+ }) {
+ HStack {
+ Text(mic.name)
+ Spacer()
+ if mic.id == model.selectedMic {
+ Image(systemName: "checkmark")
+ }
+ }
+ }
+ }
+ } header: {
+ VStack(alignment: .leading) {
+ Text("Audio Settings")
+ Text("(No selection = system default)")
+ }
+ }
+ Section(header: Text("Start options")) {
+ Toggle("Enable Microphone", isOn: $isMicEnabled)
+ Toggle("Enable Cam", isOn: $isCamEnabled)
+ }
+ Section(header: Text("Server")) {
+ TextField("Backend URL", text: $backendURL)
+ .keyboardType(.URL)
+ }
+ }
+ .navigationTitle("Settings")
+ .toolbar {
+ ToolbarItem(placement: .cancellationAction) {
+ Button("Close") {
+ self.saveSettings()
+ self.showingSettings = false
+ }
+ }
+ }
+ .onAppear {
+ self.loadSettings()
+ }
+ }
+ }
+
+ private func saveSettings() {
+ let newSettings = SettingsPreference(
+ selectedMic: model.selectedMic?.id,
+ enableMic: isMicEnabled,
+ enableCam: isCamEnabled,
+ backendURL: backendURL
+ )
+ SettingsManager.updateSettings(settings: newSettings)
+ }
+
+ private func loadSettings() {
+ let savedSettings = SettingsManager.getSettings()
+ self.isMicEnabled = savedSettings.enableMic
+ self.isCamEnabled = savedSettings.enableCam
+ self.backendURL = savedSettings.backendURL
+ }
+}
+
+#Preview {
+ let mockModel = MockCallContainerModel()
+ let result = SettingsView(showingSettings: .constant(true)).environmentObject(mockModel as CallContainerModel)
+ return result
+}
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbotTests/SimpleChatbotTests.swift b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbotTests/SimpleChatbotTests.swift
new file mode 100644
index 000000000..8fcb737d7
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbotTests/SimpleChatbotTests.swift
@@ -0,0 +1,29 @@
+import XCTest
+@testable import SimpleChatbot
+
+final class SimpleChatbotTests: XCTestCase {
+
+ override func setUpWithError() throws {
+ // Put setup code here. This method is called before the invocation of each test method in the class.
+ }
+
+ override func tearDownWithError() throws {
+ // Put teardown code here. This method is called after the invocation of each test method in the class.
+ }
+
+ func testExample() throws {
+ // This is an example of a functional test case.
+ // Use XCTAssert and related functions to verify your tests produce the correct results.
+ // Any test you write for XCTest can be annotated as throws and async.
+ // Mark your test throws to produce an unexpected failure when your test encounters an uncaught error.
+ // Mark your test async to allow awaiting for asynchronous code to complete. Check the results with assertions afterwards.
+ }
+
+ func testPerformanceExample() throws {
+ // This is an example of a performance test case.
+ self.measure {
+ // Put the code you want to measure the time of here.
+ }
+ }
+
+}
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbotUITests/SimpleChatbotUITests.swift b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbotUITests/SimpleChatbotUITests.swift
new file mode 100644
index 000000000..3a4d61916
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbotUITests/SimpleChatbotUITests.swift
@@ -0,0 +1,34 @@
+import XCTest
+
+final class SimpleChatbotUITests: XCTestCase {
+
+ override func setUpWithError() throws {
+ // Put setup code here. This method is called before the invocation of each test method in the class.
+
+ // In UI tests it is usually best to stop immediately when a failure occurs.
+ continueAfterFailure = false
+
+ // In UI tests itβs important to set the initial state - such as interface orientation - required for your tests before they run. The setUp method is a good place to do this.
+ }
+
+ override func tearDownWithError() throws {
+ // Put teardown code here. This method is called after the invocation of each test method in the class.
+ }
+
+ func testExample() throws {
+ // UI tests must launch the application that they test.
+ let app = XCUIApplication()
+ app.launch()
+
+ // Use XCTAssert and related functions to verify your tests produce the correct results.
+ }
+
+ func testLaunchPerformance() throws {
+ if #available(macOS 10.15, iOS 13.0, tvOS 13.0, watchOS 7.0, *) {
+ // This measures how long it takes to launch your application.
+ measure(metrics: [XCTApplicationLaunchMetric()]) {
+ XCUIApplication().launch()
+ }
+ }
+ }
+}
diff --git a/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbotUITests/SimpleChatbotUITestsLaunchTests.swift b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbotUITests/SimpleChatbotUITestsLaunchTests.swift
new file mode 100644
index 000000000..87d03195d
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/ios/SimpleChatbotUITests/SimpleChatbotUITestsLaunchTests.swift
@@ -0,0 +1,25 @@
+import XCTest
+
+final class SimpleChatbotUITestsLaunchTests: XCTestCase {
+
+ override class var runsForEachTargetApplicationUIConfiguration: Bool {
+ true
+ }
+
+ override func setUpWithError() throws {
+ continueAfterFailure = false
+ }
+
+ func testLaunch() throws {
+ let app = XCUIApplication()
+ app.launch()
+
+ // Insert steps here to perform after app launch but before taking a screenshot,
+ // such as logging into a test account or navigating somewhere in the app
+
+ let attachment = XCTAttachment(screenshot: app.screenshot())
+ attachment.name = "Launch Screen"
+ attachment.lifetime = .keepAlways
+ add(attachment)
+ }
+}
diff --git a/examples/p2p-webrtc/video-transform/client/typescript/.gitignore b/examples/p2p-webrtc/video-transform/client/typescript/.gitignore
new file mode 100644
index 000000000..a547bf36d
--- /dev/null
+++ b/examples/p2p-webrtc/video-transform/client/typescript/.gitignore
@@ -0,0 +1,24 @@
+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+lerna-debug.log*
+
+node_modules
+dist
+dist-ssr
+*.local
+
+# Editor directories and files
+.vscode/*
+!.vscode/extensions.json
+.idea
+.DS_Store
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?
diff --git a/examples/p2p-webrtc/video-transform/client/typescript/index.html b/examples/p2p-webrtc/video-transform/client/typescript/index.html
index d8d82ff52..95f8f59de 100644
--- a/examples/p2p-webrtc/video-transform/client/typescript/index.html
+++ b/examples/p2p-webrtc/video-transform/client/typescript/index.html
@@ -51,6 +51,7 @@
diff --git a/examples/p2p-webrtc/video-transform/client/typescript/package-lock.json b/examples/p2p-webrtc/video-transform/client/typescript/package-lock.json
index 4fb1ecd50..3e9cee890 100644
--- a/examples/p2p-webrtc/video-transform/client/typescript/package-lock.json
+++ b/examples/p2p-webrtc/video-transform/client/typescript/package-lock.json
@@ -10,7 +10,7 @@
"license": "ISC",
"dependencies": {
"@pipecat-ai/client-js": "^0.3.2",
- "@pipecat-ai/small-webrtc-transport": "^0.0.1"
+ "@pipecat-ai/small-webrtc-transport": "^0.0.2"
},
"devDependencies": {
"@types/node": "^22.13.1",
@@ -32,9 +32,9 @@
}
},
"node_modules/@daily-co/daily-js": {
- "version": "0.73.0",
- "resolved": "https://registry.npmjs.org/@daily-co/daily-js/-/daily-js-0.73.0.tgz",
- "integrity": "sha512-Wz8c60hgmkx8fcEeDAi4L4J0rbafiihWKyXFyhYoFYPsw2OdChHpA4RYwIB+1enRws5IK+/HdmzFDYLQsB4A6w==",
+ "version": "0.77.0",
+ "resolved": "https://registry.npmjs.org/@daily-co/daily-js/-/daily-js-0.77.0.tgz",
+ "integrity": "sha512-icNXKieKAkRR/C5dcPjrCkL1jQGFp5C5WtLHy5uHAdTztm+mo9wlPJuehbWaGOM3TV24mgWHZ/+8jOys1G0I4w==",
"license": "BSD-2-Clause",
"dependencies": {
"@babel/runtime": "^7.12.5",
@@ -78,12 +78,12 @@
}
},
"node_modules/@pipecat-ai/small-webrtc-transport": {
- "version": "0.0.1",
- "resolved": "https://registry.npmjs.org/@pipecat-ai/small-webrtc-transport/-/small-webrtc-transport-0.0.1.tgz",
- "integrity": "sha512-WAOI7lT0V7cYOn0+qwUAryGxcOGe+wPVPEPzkR3qsM5GWIZ73spykZnuOndQGycq4UkcXVawCzERfNhpi+Uv7A==",
+ "version": "0.0.2",
+ "resolved": "https://registry.npmjs.org/@pipecat-ai/small-webrtc-transport/-/small-webrtc-transport-0.0.2.tgz",
+ "integrity": "sha512-9QQBjfAY0yh+ehDt6jX+bX7Ar5GFl+iI6QFS+JPRXeDYCj70bqmUgCYkScbgWzb5uRWZ8ORM+ueVkaLibe+Y4Q==",
"license": "BSD-2-Clause",
"dependencies": {
- "@daily-co/daily-js": "^0.73.0",
+ "@daily-co/daily-js": "^0.77.0",
"dequal": "^2.0.3"
},
"peerDependencies": {
diff --git a/examples/p2p-webrtc/video-transform/client/typescript/package.json b/examples/p2p-webrtc/video-transform/client/typescript/package.json
index e50084020..3f17f9d10 100644
--- a/examples/p2p-webrtc/video-transform/client/typescript/package.json
+++ b/examples/p2p-webrtc/video-transform/client/typescript/package.json
@@ -19,6 +19,6 @@
},
"dependencies": {
"@pipecat-ai/client-js": "^0.3.2",
- "@pipecat-ai/small-webrtc-transport": "^0.0.1"
+ "@pipecat-ai/small-webrtc-transport": "^0.0.2"
}
}
diff --git a/examples/p2p-webrtc/video-transform/client/typescript/src/app.ts b/examples/p2p-webrtc/video-transform/client/typescript/src/app.ts
index 6402d4c9a..577715373 100644
--- a/examples/p2p-webrtc/video-transform/client/typescript/src/app.ts
+++ b/examples/p2p-webrtc/video-transform/client/typescript/src/app.ts
@@ -1,12 +1,13 @@
import {
SmallWebRTCTransport
} from "@pipecat-ai/small-webrtc-transport";
-import {Participant, RTVIClient, RTVIClientOptions} from "@pipecat-ai/client-js";
+import {Participant, RTVIClient, RTVIClientOptions, Transport} from "@pipecat-ai/client-js";
class WebRTCApp {
private declare connectBtn: HTMLButtonElement;
private declare disconnectBtn: HTMLButtonElement;
+ private declare muteBtn: HTMLButtonElement;
private declare audioInput: HTMLSelectElement;
private declare videoInput: HTMLSelectElement;
@@ -32,12 +33,10 @@ class WebRTCApp {
private initializeRTVIClient(): void {
const transport = new SmallWebRTCTransport();
const RTVIConfig: RTVIClientOptions = {
- // need to understand why it is complaining
- // @ts-ignore
- transport,
params: {
baseUrl: "/api/offer"
},
+ transport: transport as Transport,
enableMic: true,
enableCam: true,
callbacks: {
@@ -92,6 +91,7 @@ class WebRTCApp {
private setupDOMElements(): void {
this.connectBtn = document.getElementById('connect-btn') as HTMLButtonElement;
this.disconnectBtn = document.getElementById('disconnect-btn') as HTMLButtonElement;
+ this.muteBtn = document.getElementById('mute-btn') as HTMLButtonElement;
this.audioInput = document.getElementById('audio-input') as HTMLSelectElement;
this.videoInput = document.getElementById('video-input') as HTMLSelectElement;
@@ -118,6 +118,12 @@ class WebRTCApp {
let videoDevice = e.target?.value
this.rtviClient.updateCam(videoDevice)
})
+ this.muteBtn.addEventListener('click', () => {
+ let isCamEnabled = this.rtviClient.isCamEnabled
+ this.rtviClient.enableCam(!isCamEnabled)
+ this.muteBtn.textContent = isCamEnabled ? 'π΅' : 'π·';
+ });
+
}
private log(message: string): void {
diff --git a/examples/p2p-webrtc/video-transform/client/typescript/src/style.css b/examples/p2p-webrtc/video-transform/client/typescript/src/style.css
index 6be42d2ea..7397332ac 100644
--- a/examples/p2p-webrtc/video-transform/client/typescript/src/style.css
+++ b/examples/p2p-webrtc/video-transform/client/typescript/src/style.css
@@ -32,6 +32,7 @@ select {
.status-bar {
display: flex;
+ flex-wrap: wrap;
justify-content: space-between;
align-items: center;
padding: 10px;
@@ -69,6 +70,7 @@ button:disabled {
padding: 20px;
margin-bottom: 20px;
display: flex;
+ flex-wrap: wrap;
}
.bot-container {
@@ -79,14 +81,15 @@ button:disabled {
}
#bot-video-container {
- width: 640px;
- height: 360px;
+ width: 90%;
+ aspect-ratio: 16 / 9;
background-color: #e0e0e0;
border-radius: 8px;
overflow: hidden;
display: flex;
align-items: center;
justify-content: center;
+ position: relative;
}
#bot-video-container video {
@@ -95,13 +98,35 @@ button:disabled {
object-fit: cover;
}
+#mute-btn {
+ position: absolute;
+ bottom: 10px;
+ right: 10px;
+ background-color: rgba(0, 0, 0, 0.6);
+ color: white;
+ border: none;
+ border-radius: 20px;
+ padding: 8px 12px;
+ cursor: pointer;
+ font-size: 16px;
+ z-index: 1;
+}
+
.debug-panel {
background-color: #fff;
border-radius: 8px;
- padding-left: 20px;
width: 50%;
}
+@media (max-width: 768px) {
+ .bot-container {
+ width: 100%;
+ }
+ .debug-panel {
+ width: 100%;
+ }
+}
+
.debug-panel h3 {
margin: 0 0 10px 0;
font-size: 16px;
@@ -109,10 +134,9 @@ button:disabled {
}
#debug-log {
- height: 500px;
+ height: 360px;
overflow-y: auto;
background-color: #f8f8f8;
- padding: 10px;
border-radius: 4px;
font-family: monospace;
font-size: 12px;
diff --git a/examples/p2p-webrtc/video-transform/client/typescript/vite.config.js b/examples/p2p-webrtc/video-transform/client/typescript/vite.config.js
index 58f9cfaf9..bd7b7f9d0 100644
--- a/examples/p2p-webrtc/video-transform/client/typescript/vite.config.js
+++ b/examples/p2p-webrtc/video-transform/client/typescript/vite.config.js
@@ -4,6 +4,7 @@ import react from '@vitejs/plugin-react-swc';
export default defineConfig({
plugins: [react()],
server: {
+ allowedHosts: true, // Allows external connections like ngrok
proxy: {
// Proxy /api requests to the backend server
'/api': {
diff --git a/examples/p2p-webrtc/video-transform/server/bot.py b/examples/p2p-webrtc/video-transform/server/bot.py
index 917a13fb4..a6d885cea 100644
--- a/examples/p2p-webrtc/video-transform/server/bot.py
+++ b/examples/p2p-webrtc/video-transform/server/bot.py
@@ -25,15 +25,12 @@ from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
class EdgeDetectionProcessor(FrameProcessor):
- def __init__(self, camera_out_width, camera_out_height: int):
+ def __init__(self, video_out_width, video_out_height: int):
super().__init__()
- self._camera_out_width = camera_out_width
- self._camera_out_height = camera_out_height
+ self._video_out_width = video_out_width
+ self._video_out_height = video_out_height
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
@@ -48,7 +45,7 @@ class EdgeDetectionProcessor(FrameProcessor):
img = cv2.cvtColor(cv2.Canny(img, 100, 200), cv2.COLOR_GRAY2BGR)
# convert the size if needed
- desired_size = (self._camera_out_width, self._camera_out_height)
+ desired_size = (self._video_out_width, self._video_out_height)
if frame.size != desired_size:
resized_image = cv2.resize(img, desired_size)
frame = OutputImageRawFrame(resized_image.tobytes(), desired_size, frame.format)
@@ -74,14 +71,13 @@ Respond to what the user said in a creative and helpful way. Keep your responses
async def run_bot(webrtc_connection):
transport_params = TransportParams(
- camera_in_enabled=True,
- camera_out_enabled=True,
- camera_out_is_live=True,
audio_in_enabled=True,
audio_out_enabled=True,
- vad_enabled=True,
+ audio_out_10ms_chunks=2,
+ video_in_enabled=True,
+ video_out_enabled=True,
+ video_out_is_live=True,
vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
)
pipecat_transport = SmallWebRTCTransport(
@@ -92,7 +88,6 @@ async def run_bot(webrtc_connection):
api_key=os.getenv("GOOGLE_API_KEY"),
voice_id="Puck", # Aoede, Charon, Fenrir, Kore, Puck
transcribe_user_audio=True,
- transcribe_model_audio=True,
system_instruction=SYSTEM_INSTRUCTION,
)
@@ -116,7 +111,7 @@ async def run_bot(webrtc_connection):
rtvi,
llm, # LLM
EdgeDetectionProcessor(
- transport_params.camera_out_width, transport_params.camera_out_height
+ transport_params.video_out_width, transport_params.video_out_height
), # Sending the video back to the user
pipecat_transport.output(),
context_aggregator.assistant(),
@@ -135,12 +130,12 @@ async def run_bot(webrtc_connection):
async def on_client_ready(rtvi):
logger.info("Pipecat client ready.")
await rtvi.set_bot_ready()
+ # Kick off the conversation.
+ await task.queue_frames([context_aggregator.user().get_context_frame()])
@pipecat_transport.event_handler("on_client_connected")
async def on_client_connected(transport, client):
logger.info("Pipecat Client connected")
- # Kick off the conversation.
- await task.queue_frames([context_aggregator.user().get_context_frame()])
@pipecat_transport.event_handler("on_client_disconnected")
async def on_client_disconnected(transport, client):
diff --git a/examples/p2p-webrtc/video-transform/server/requirements.txt b/examples/p2p-webrtc/video-transform/server/requirements.txt
index 24579bd41..d5aa05ef3 100644
--- a/examples/p2p-webrtc/video-transform/server/requirements.txt
+++ b/examples/p2p-webrtc/video-transform/server/requirements.txt
@@ -3,4 +3,5 @@ fastapi[all]
uvicorn
aiortc
opencv-python
-pipecat-ai[google,silero]
\ No newline at end of file
+pipecat-ai[google,silero,webrtc]
+pipecat-ai-small-webrtc-prebuilt
\ No newline at end of file
diff --git a/examples/p2p-webrtc/video-transform/server/server.py b/examples/p2p-webrtc/video-transform/server/server.py
index 79ee75ea3..d65bd3013 100644
--- a/examples/p2p-webrtc/video-transform/server/server.py
+++ b/examples/p2p-webrtc/video-transform/server/server.py
@@ -1,6 +1,12 @@
+#
+# Copyright (c) 2024β2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
import argparse
import asyncio
-import logging
+import sys
from contextlib import asynccontextmanager
from typing import Dict
@@ -8,14 +14,15 @@ import uvicorn
from bot import run_bot
from dotenv import load_dotenv
from fastapi import BackgroundTasks, FastAPI
+from fastapi.responses import RedirectResponse
+from loguru import logger
+from pipecat_ai_small_webrtc_prebuilt.frontend import SmallWebRTCPrebuiltUI
from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
# Load environment variables
load_dotenv(override=True)
-logger = logging.getLogger("pc")
-
app = FastAPI()
# Store connections by pc_id
@@ -23,6 +30,14 @@ pcs_map: Dict[str, SmallWebRTCConnection] = {}
ice_servers = ["stun:stun.l.google.com:19302"]
+# Mount the frontend at /
+app.mount("/prebuilt", SmallWebRTCPrebuiltUI)
+
+
+@app.get("/", include_in_schema=False)
+async def root_redirect():
+ return RedirectResponse(url="/prebuilt/")
+
@app.post("/api/offer")
async def offer(request: dict, background_tasks: BackgroundTasks):
@@ -71,9 +86,10 @@ if __name__ == "__main__":
parser.add_argument("--verbose", "-v", action="count")
args = parser.parse_args()
+ logger.remove(0)
if args.verbose:
- logging.basicConfig(level=logging.DEBUG)
+ logger.add(sys.stderr, level="TRACE")
else:
- logging.basicConfig(level=logging.INFO)
+ logger.add(sys.stderr, level="DEBUG")
uvicorn.run(app, host=args.host, port=args.port)
diff --git a/examples/p2p-webrtc/voice-agent/bot.py b/examples/p2p-webrtc/voice-agent/bot.py
index 07106022c..4cda32a59 100644
--- a/examples/p2p-webrtc/voice-agent/bot.py
+++ b/examples/p2p-webrtc/voice-agent/bot.py
@@ -20,10 +20,6 @@ from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
SYSTEM_INSTRUCTION = f"""
"You are Gemini Chatbot, a friendly, helpful robot.
@@ -41,9 +37,8 @@ async def run_bot(webrtc_connection):
params=TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
- vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
+ audio_out_10ms_chunks=2,
),
)
diff --git a/examples/p2p-webrtc/voice-agent/index.html b/examples/p2p-webrtc/voice-agent/index.html
index dea1bbccf..0692f5b7c 100644
--- a/examples/p2p-webrtc/voice-agent/index.html
+++ b/examples/p2p-webrtc/voice-agent/index.html
@@ -40,7 +40,9 @@
const createSmallWebRTCConnection = async (audioTrack) => {
const pc = new RTCPeerConnection()
pc.ontrack = e => audioEl.srcObject = e.streams[0]
+ // SmallWebRTCTransport expects to receive both transceivers
pc.addTransceiver(audioTrack, { direction: 'sendrecv' })
+ pc.addTransceiver('video', { direction: 'sendrecv' })
await pc.setLocalDescription(await pc.createOffer())
//await waitForIceGatheringComplete(pc)
const offer = pc.localDescription
diff --git a/examples/p2p-webrtc/voice-agent/requirements.txt b/examples/p2p-webrtc/voice-agent/requirements.txt
index d8ffd53ef..b16fb9ddc 100644
--- a/examples/p2p-webrtc/voice-agent/requirements.txt
+++ b/examples/p2p-webrtc/voice-agent/requirements.txt
@@ -1,5 +1,4 @@
python-dotenv
fastapi[all]
uvicorn
-aiortc
-pipecat-ai[google,silero]
\ No newline at end of file
+pipecat-ai[google,silero, webrtc]
\ No newline at end of file
diff --git a/examples/p2p-webrtc/voice-agent/server.py b/examples/p2p-webrtc/voice-agent/server.py
index e91340a32..3706455a5 100644
--- a/examples/p2p-webrtc/voice-agent/server.py
+++ b/examples/p2p-webrtc/voice-agent/server.py
@@ -1,6 +1,12 @@
+#
+# Copyright (c) 2024β2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
import argparse
import asyncio
-import logging
+import sys
from contextlib import asynccontextmanager
from typing import Dict
@@ -9,14 +15,13 @@ from bot import run_bot
from dotenv import load_dotenv
from fastapi import BackgroundTasks, FastAPI
from fastapi.responses import FileResponse
+from loguru import logger
from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
# Load environment variables
load_dotenv(override=True)
-logger = logging.getLogger("pc")
-
app = FastAPI()
# Store connections by pc_id
@@ -73,9 +78,10 @@ if __name__ == "__main__":
parser.add_argument("--verbose", "-v", action="count")
args = parser.parse_args()
+ logger.remove(0)
if args.verbose:
- logging.basicConfig(level=logging.DEBUG)
+ logger.add(sys.stderr, level="TRACE")
else:
- logging.basicConfig(level=logging.INFO)
+ logger.add(sys.stderr, level="DEBUG")
uvicorn.run(app, host=args.host, port=args.port)
diff --git a/examples/patient-intake/bot.py b/examples/patient-intake/bot.py
index cf2d5ed92..4aac3945d 100644
--- a/examples/patient-intake/bot.py
+++ b/examples/patient-intake/bot.py
@@ -299,8 +299,8 @@ async def main():
token,
"Chatbot",
DailyParams(
+ audio_in_enabled=True,
audio_out_enabled=True,
- vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
transcription_enabled=True,
#
@@ -324,7 +324,7 @@ async def main():
# voice_id="846d6cb0-2301-48b6-9683-48f5618ea2f6", # Spanish-speaking Lady
# )
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
messages = []
context = OpenAILLMContext(messages=messages)
diff --git a/examples/phone-chatbot/.dockerignore b/examples/phone-chatbot/.dockerignore
deleted file mode 100644
index 87fb9af3a..000000000
--- a/examples/phone-chatbot/.dockerignore
+++ /dev/null
@@ -1,3 +0,0 @@
-**/.DS_Store
-.env
-.env.*
\ No newline at end of file
diff --git a/examples/phone-chatbot/Dockerfile b/examples/phone-chatbot/Dockerfile
deleted file mode 100644
index e0838f0d7..000000000
--- a/examples/phone-chatbot/Dockerfile
+++ /dev/null
@@ -1,40 +0,0 @@
-FROM python:3.11-bullseye
-
-ARG DEBIAN_FRONTEND=noninteractive
-ARG USE_PERSISTENT_DATA
-ENV PYTHONUNBUFFERED=1
-# Expose FastAPI port
-ENV FAST_API_PORT=7860
-EXPOSE 7860
-
-# Install system dependencies
-RUN apt-get update && apt-get install --no-install-recommends -y \
- build-essential \
- git \
- ffmpeg \
- google-perftools \
- ca-certificates curl gnupg \
- && apt-get clean && rm -rf /var/lib/apt/lists/*
-
-# Set up a new user named "user" with user ID 1000
-RUN useradd -m -u 1000 user
-
-# Set home to the user's home directory
-ENV HOME=/home/user \
- PATH=/home/user/.local/bin:$PATH \
- PYTHONPATH=$HOME/app \
- PYTHONUNBUFFERED=1
-
-# Switch to the "user" user
-USER user
-
-# Set the working directory to the user's home directory
-WORKDIR $HOME/app
-
-# Install Python dependencies
-COPY *.py .
-COPY ./requirements.txt requirements.txt
-RUN pip3 install --no-cache-dir --upgrade -r requirements.txt
-
-# Start the FastAPI server
-CMD python3 bot_runner.py --host "0.0.0.0" --port ${FAST_API_PORT}
\ No newline at end of file
diff --git a/examples/phone-chatbot/README.md b/examples/phone-chatbot/README.md
index 040fb7038..3d20f504a 100644
--- a/examples/phone-chatbot/README.md
+++ b/examples/phone-chatbot/README.md
@@ -1,209 +1,602 @@
-Β
+
` with your ngrok URL (without `https://`)
- - The final URL should look like: `wss://abc123.ngrok.io/ws`
- - The encoding (`bidirectionalCodec`) should be `PCMU` or `PCMA` depending on your needs. Based on selected encoding, set the outbound_encoding in `server.py` when the bot is initialized.
+ - The final URL should look like: `wss://abc123.ngrok.io/ws`. This needs to be the same URL that you added to your TeXML app above.
+ - The encoding (`bidirectionalCodec`) should be `PCMU` or `PCMA` depending on your needs. Based on selected encoding, set the outbound_encoding in `server.py` when the bot is initialized. (No changes are required by default.)
- The inbound encoding can be controlled from the application configuration for inbound calls and dial/transfer commands for outbound calls.
## Running the Application
diff --git a/examples/telnyx-chatbot/bot.py b/examples/telnyx-chatbot/bot.py
index 4d8584bfb..1abbbd348 100644
--- a/examples/telnyx-chatbot/bot.py
+++ b/examples/telnyx-chatbot/bot.py
@@ -33,22 +33,30 @@ logger.add(sys.stderr, level="DEBUG")
async def run_bot(
websocket_client,
stream_id: str,
+ call_control_id: str,
outbound_encoding: str,
inbound_encoding: str,
):
+ serializer = TelnyxFrameSerializer(
+ stream_id=stream_id,
+ outbound_encoding=outbound_encoding,
+ inbound_encoding=inbound_encoding,
+ call_control_id=call_control_id,
+ api_key=os.getenv("TELNYX_API_KEY"),
+ )
+
transport = FastAPIWebsocketTransport(
websocket=websocket_client,
params=FastAPIWebsocketParams(
+ audio_in_enabled=True,
audio_out_enabled=True,
add_wav_header=False,
- vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
- serializer=TelnyxFrameSerializer(stream_id, outbound_encoding, inbound_encoding),
+ serializer=serializer,
),
)
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
diff --git a/examples/telnyx-chatbot/server.py b/examples/telnyx-chatbot/server.py
index 7bc1c0615..23cb9f065 100644
--- a/examples/telnyx-chatbot/server.py
+++ b/examples/telnyx-chatbot/server.py
@@ -37,9 +37,10 @@ async def websocket_endpoint(websocket: WebSocket):
call_data = json.loads(await start_data.__anext__())
print(call_data, flush=True)
stream_id = call_data["stream_id"]
+ call_control_id = call_data["start"]["call_control_id"]
outbound_encoding = call_data["start"]["media_format"]["encoding"]
print("WebSocket connection accepted")
- await run_bot(websocket, stream_id, outbound_encoding, "PCMU")
+ await run_bot(websocket, stream_id, call_control_id, outbound_encoding, "PCMU")
if __name__ == "__main__":
diff --git a/examples/translation-chatbot/bot.py b/examples/translation-chatbot/bot.py
index 88c9c66b7..d68261cd0 100644
--- a/examples/translation-chatbot/bot.py
+++ b/examples/translation-chatbot/bot.py
@@ -132,10 +132,9 @@ async def main():
token,
"Translator",
DailyParams(
+ audio_in_enabled=True,
audio_out_enabled=True,
- vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
),
)
@@ -150,7 +149,7 @@ async def main():
in_language = "English"
out_language = "Spanish"
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
context = OpenAILLMContext()
context_aggregator = llm.create_context_aggregator(context)
diff --git a/examples/twilio-chatbot/bot.py b/examples/twilio-chatbot/bot.py
index 6821d8139..8aa73a2be 100644
--- a/examples/twilio-chatbot/bot.py
+++ b/examples/twilio-chatbot/bot.py
@@ -54,21 +54,26 @@ async def save_audio(server_name: str, audio: bytes, sample_rate: int, num_chann
logger.info("No audio data to save")
-async def run_bot(websocket_client: WebSocket, stream_sid: str, testing: bool):
+async def run_bot(websocket_client: WebSocket, stream_sid: str, call_sid: str, testing: bool):
+ serializer = TwilioFrameSerializer(
+ stream_sid=stream_sid,
+ call_sid=call_sid,
+ account_sid=os.getenv("TWILIO_ACCOUNT_SID", ""),
+ auth_token=os.getenv("TWILIO_AUTH_TOKEN", ""),
+ )
+
transport = FastAPIWebsocketTransport(
websocket=websocket_client,
params=FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
add_wav_header=False,
- vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
- serializer=TwilioFrameSerializer(stream_sid),
+ serializer=serializer,
),
)
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"), audio_passthrough=True)
diff --git a/examples/twilio-chatbot/client.py b/examples/twilio-chatbot/client.py
index e5826fb19..f52ceaae8 100644
--- a/examples/twilio-chatbot/client.py
+++ b/examples/twilio-chatbot/client.py
@@ -92,13 +92,11 @@ async def run_client(client_name: str, server_url: str, duration_secs: int):
audio_out_enabled=True,
add_wav_header=False,
serializer=TwilioFrameSerializer(stream_sid),
- vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=1.5)),
- vad_audio_passthrough=True,
),
)
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
# We let the audio passthrough so we can record the conversation.
stt = DeepgramSTTService(
diff --git a/examples/twilio-chatbot/server.py b/examples/twilio-chatbot/server.py
index d65015b5c..598cbb9cf 100644
--- a/examples/twilio-chatbot/server.py
+++ b/examples/twilio-chatbot/server.py
@@ -38,8 +38,9 @@ async def websocket_endpoint(websocket: WebSocket):
call_data = json.loads(await start_data.__anext__())
print(call_data, flush=True)
stream_sid = call_data["start"]["streamSid"]
+ call_sid = call_data["start"]["callSid"]
print("WebSocket connection accepted")
- await run_bot(websocket, stream_sid, app.state.testing)
+ await run_bot(websocket, stream_sid, call_sid, app.state.testing)
if __name__ == "__main__":
diff --git a/examples/websocket-server/bot.py b/examples/websocket-server/bot.py
index 0e2f1d4b0..816d7540b 100644
--- a/examples/websocket-server/bot.py
+++ b/examples/websocket-server/bot.py
@@ -82,16 +82,15 @@ async def main():
transport = WebsocketServerTransport(
params=WebsocketServerParams(
serializer=ProtobufFrameSerializer(),
+ audio_in_enabled=True,
audio_out_enabled=True,
add_wav_header=True,
- vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
- vad_audio_passthrough=True,
session_timeout=60 * 3, # 3 minutes
)
)
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
diff --git a/pyproject.toml b/pyproject.toml
index ecf4d5272..58d2097ab 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,21 +6,21 @@ build-backend = "setuptools.build_meta"
name = "pipecat-ai"
dynamic = ["version"]
description = "An open source framework for voice (and multimodal) assistants"
-license = { text = "BSD 2-Clause License" }
+license = "BSD-2-Clause"
+license-files = ["LICENSE"]
readme = "README.md"
requires-python = ">=3.10"
keywords = ["webrtc", "audio", "video", "ai"]
classifiers = [
"Development Status :: 5 - Production/Stable",
"Intended Audience :: Developers",
- "License :: OSI Approved :: BSD License",
"Topic :: Communications :: Conferencing",
"Topic :: Multimedia :: Sound/Audio",
"Topic :: Multimedia :: Video",
"Topic :: Scientific/Engineering :: Artificial Intelligence"
]
dependencies = [
- "aiohttp~=3.11.13",
+ "aiohttp~=3.11.12",
"audioop-lts~=0.2.1; python_version>='3.13'",
"loguru~=0.7.3",
"Markdown~=3.7",
@@ -31,7 +31,7 @@ dependencies = [
"pyloudnorm~=0.1.1",
"resampy~=0.4.3",
"soxr~=0.5.0",
- "openai~=1.67.0"
+ "openai~=1.70.0"
]
[project.urls]
@@ -47,14 +47,14 @@ canonical = [ "aiofiles~=24.1.0" ]
cartesia = [ "cartesia~=1.4.0", "websockets~=13.1" ]
cerebras = []
deepseek = []
-daily = [ "daily-python~=0.16.1" ]
+daily = [ "daily-python~=0.17.0" ]
deepgram = [ "deepgram-sdk~=3.8.0" ]
elevenlabs = [ "websockets~=13.1" ]
fal = [ "fal-client~=0.5.9" ]
fireworks = []
fish = [ "ormsgpack~=1.7.0", "websockets~=13.1" ]
gladia = [ "websockets~=13.1" ]
-google = [ "google-cloud-speech~=2.31.1", "google-cloud-texttospeech~=2.25.1", "google-genai~=1.7.0", "google-generativeai~=0.8.4" ]
+google = [ "google-cloud-speech~=2.31.1", "google-cloud-texttospeech~=2.25.1", "google-genai~=1.7.0", "google-generativeai~=0.8.4", "websockets~=13.1" ]
grok = []
groq = [ "groq~=0.20.0" ]
gstreamer = [ "pygobject~=3.50.0" ]
@@ -64,6 +64,7 @@ langchain = [ "langchain~=0.3.20", "langchain-community~=0.3.20", "langchain-ope
livekit = [ "livekit~=0.22.0", "livekit-api~=0.8.2", "tenacity~=9.0.0" ]
lmnt = [ "websockets~=13.1" ]
local = [ "pyaudio~=0.2.14" ]
+mcp = [ "mcp[cli]~=1.6.0" ]
mem0 = [ "mem0ai~=0.1.76" ]
mlx-whisper = [ "mlx-whisper~=0.4.2" ]
moondream = [ "einops~=0.8.0", "timm~=1.0.13", "transformers~=4.48.0" ]
@@ -79,20 +80,24 @@ qwen = []
rime = [ "websockets~=13.1" ]
riva = [ "nvidia-riva-client~=2.19.0" ]
sentry = [ "sentry-sdk~=2.23.1" ]
+local-smart-turn = [ "coremltools>=8.0", "transformers", "torch==2.5.0", "torchaudio==2.5.0" ]
+remote-smart-turn = []
silero = [ "onnxruntime~=1.20.1" ]
simli = [ "simli-ai~=0.1.10"]
soundfile = [ "soundfile~=0.13.0" ]
tavus=[]
together = []
ultravox = [ "transformers~=4.48.0", "vllm~=0.7.3" ]
-webrtc = [ "aiortc~=1.10.1", "opencv-python~=4.11.0.86" ]
+webrtc = [ "aiortc~=1.11.0", "opencv-python~=4.11.0.86" ]
websocket = [ "websockets~=13.1", "fastapi~=0.115.6" ]
whisper = [ "faster-whisper~=1.1.1" ]
[tool.setuptools.packages.find]
-# All the following settings are optional:
where = ["src"]
+[tool.setuptools.package-data]
+"pipecat" = ["py.typed"]
+
[tool.pytest.ini_options]
addopts = "--verbose"
testpaths = ["tests"]
@@ -115,6 +120,9 @@ select = [
"D", # Docstring rules
"I", # Import rules
]
+# We ignore D107 because class docstrings already document __init__ parameters
+# and our Sphinx configuration uses napoleon_include_init_with_doc=True
+ignore = ["D107"]
[tool.ruff.lint.pydocstyle]
convention = "google"
diff --git a/src/pipecat/adapters/schemas/function_schema.py b/src/pipecat/adapters/schemas/function_schema.py
index f6e59cef1..55a070cf9 100644
--- a/src/pipecat/adapters/schemas/function_schema.py
+++ b/src/pipecat/adapters/schemas/function_schema.py
@@ -8,16 +8,22 @@ from typing import Any, Dict, List
class FunctionSchema:
+ """Standardized function schema representation for tool definition.
+
+ Provides a structured way to define function tools used with AI models like OpenAI.
+ This schema defines the function's name, description, parameter properties, and
+ required parameters, following specifications required by AI service providers.
+
+ Args:
+ name: Name of the function to be called.
+ description: Description of what the function does.
+ properties: Dictionary defining parameter types, descriptions, and constraints.
+ required: List of property names that are required parameters.
+ """
+
def __init__(
self, name: str, description: str, properties: Dict[str, Any], required: List[str]
) -> None:
- """Standardized function schema representation.
-
- :param name: Name of the function.
- :param description: Description of the function.
- :param properties: Dictionary defining properties types and descriptions.
- :param required: List of required parameters.
- """
self._name = name
self._description = description
self._properties = properties
@@ -26,7 +32,8 @@ class FunctionSchema:
def to_default_dict(self) -> Dict[str, Any]:
"""Converts the function schema to a dictionary.
- :return: Dictionary representation of the function schema.
+ Returns:
+ Dictionary representation of the function schema.
"""
return {
"name": self._name,
@@ -40,16 +47,36 @@ class FunctionSchema:
@property
def name(self) -> str:
+ """Get the function name.
+
+ Returns:
+ The function name.
+ """
return self._name
@property
def description(self) -> str:
+ """Get the function description.
+
+ Returns:
+ The function description.
+ """
return self._description
@property
def properties(self) -> Dict[str, Any]:
+ """Get the function properties.
+
+ Returns:
+ Dictionary of parameter specifications.
+ """
return self._properties
@property
def required(self) -> List[str]:
+ """Get the required parameters.
+
+ Returns:
+ List of required parameter names.
+ """
return self._required
diff --git a/src/pipecat/audio/mixers/soundfile_mixer.py b/src/pipecat/audio/mixers/soundfile_mixer.py
index 0c01be7fa..1628c4d8a 100644
--- a/src/pipecat/audio/mixers/soundfile_mixer.py
+++ b/src/pipecat/audio/mixers/soundfile_mixer.py
@@ -38,9 +38,11 @@ class SoundfileMixer(BaseAudioMixer):
def __init__(
self,
+ *,
sound_files: Mapping[str, str],
default_sound: str,
volume: float = 0.4,
+ mixing: bool = True,
loop: bool = True,
**kwargs,
):
@@ -52,7 +54,7 @@ class SoundfileMixer(BaseAudioMixer):
self._sound_pos = 0
self._sounds: Dict[str, Any] = {}
self._current_sound = default_sound
- self._mixing = True
+ self._mixing = mixing
self._loop = loop
async def start(self, sample_rate: int):
diff --git a/src/pipecat/audio/turn/__init__.py b/src/pipecat/audio/turn/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/src/pipecat/audio/turn/base_turn_analyzer.py b/src/pipecat/audio/turn/base_turn_analyzer.py
new file mode 100644
index 000000000..fd4f18d66
--- /dev/null
+++ b/src/pipecat/audio/turn/base_turn_analyzer.py
@@ -0,0 +1,80 @@
+#
+# Copyright (c) 2024β2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+from abc import ABC, abstractmethod
+from enum import Enum
+from typing import Optional, Tuple
+
+from pipecat.metrics.metrics import MetricsData
+
+
+class EndOfTurnState(Enum):
+ COMPLETE = 1
+ INCOMPLETE = 2
+
+
+class BaseTurnAnalyzer(ABC):
+ """Abstract base class for analyzing user end of turn.
+
+ This class inherits from BaseObject to leverage its event handling system
+ while still defining an abstract interface through abstract methods.
+ """
+
+ def __init__(self, *, sample_rate: Optional[int] = None):
+ self._init_sample_rate = sample_rate
+ self._sample_rate = 0
+
+ @property
+ def sample_rate(self) -> int:
+ """Returns the current sample rate.
+
+ Returns:
+ int: The effective sample rate for audio processing.
+ """
+ return self._sample_rate
+
+ def set_sample_rate(self, sample_rate: int):
+ """Sets the sample rate for audio processing.
+
+ If the initial sample rate was provided, it will use that; otherwise, it sets to
+ the provided sample rate.
+
+ Args:
+ sample_rate (int): The sample rate to set.
+ """
+ self._sample_rate = self._init_sample_rate or sample_rate
+
+ @property
+ @abstractmethod
+ def speech_triggered(self) -> bool:
+ """Determines if speech has been detected.
+
+ Returns:
+ bool: True if speech is triggered, otherwise False.
+ """
+ pass
+
+ @abstractmethod
+ def append_audio(self, buffer: bytes, is_speech: bool) -> EndOfTurnState:
+ """Appends audio data for analysis.
+
+ Args:
+ buffer (bytes): The audio data to append.
+ is_speech (bool): Indicates whether the appended audio is speech or not.
+
+ Returns:
+ EndOfTurnState: The resulting state after appending the audio.
+ """
+ pass
+
+ @abstractmethod
+ async def analyze_end_of_turn(self) -> Tuple[EndOfTurnState, Optional[MetricsData]]:
+ """Analyzes if an end of turn has occurred based on the audio input.
+
+ Returns:
+ EndOfTurnState: The result of the end of turn analysis.
+ """
+ pass
diff --git a/src/pipecat/audio/turn/smart_turn/__init__.py b/src/pipecat/audio/turn/smart_turn/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/src/pipecat/audio/turn/smart_turn/base_smart_turn.py b/src/pipecat/audio/turn/smart_turn/base_smart_turn.py
new file mode 100644
index 000000000..e7f30246a
--- /dev/null
+++ b/src/pipecat/audio/turn/smart_turn/base_smart_turn.py
@@ -0,0 +1,198 @@
+#
+# Copyright (c) 2024β2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import time
+from abc import abstractmethod
+from typing import Any, Dict, Optional, Tuple
+
+import numpy as np
+from loguru import logger
+from pydantic import BaseModel
+
+from pipecat.audio.turn.base_turn_analyzer import BaseTurnAnalyzer, EndOfTurnState
+from pipecat.metrics.metrics import MetricsData, SmartTurnMetricsData
+
+# Default timing parameters
+STOP_SECS = 3
+PRE_SPEECH_MS = 0
+MAX_DURATION_SECONDS = 8 # Max allowed segment duration
+USE_ONLY_LAST_VAD_SEGMENT = True
+
+
+class SmartTurnParams(BaseModel):
+ stop_secs: float = STOP_SECS
+ pre_speech_ms: float = PRE_SPEECH_MS
+ max_duration_secs: float = MAX_DURATION_SECONDS
+ # not exposing this for now yet until the model can handle it.
+ # use_only_last_vad_segment: bool = USE_ONLY_LAST_VAD_SEGMENT
+
+
+class SmartTurnTimeoutException(Exception):
+ pass
+
+
+class BaseSmartTurn(BaseTurnAnalyzer):
+ def __init__(
+ self, *, sample_rate: Optional[int] = None, params: SmartTurnParams = SmartTurnParams()
+ ):
+ super().__init__(sample_rate=sample_rate)
+ self._params = params
+ # Configuration
+ self._stop_ms = self._params.stop_secs * 1000 # silence threshold in ms
+ # Inference state
+ self._audio_buffer = []
+ self._speech_triggered = False
+ self._silence_ms = 0
+ self._speech_start_time = 0
+
+ @property
+ def speech_triggered(self) -> bool:
+ return self._speech_triggered
+
+ def append_audio(self, buffer: bytes, is_speech: bool) -> EndOfTurnState:
+ # Convert raw audio to float32 format and append to the buffer
+ audio_int16 = np.frombuffer(buffer, dtype=np.int16)
+ audio_float32 = np.frombuffer(audio_int16, dtype=np.int16).astype(np.float32) / 32768.0
+ self._audio_buffer.append((time.time(), audio_float32))
+
+ state = EndOfTurnState.INCOMPLETE
+
+ if is_speech:
+ # Reset silence tracking on speech
+ self._silence_ms = 0
+ self._speech_triggered = True
+ if self._speech_start_time == 0:
+ self._speech_start_time = time.time()
+ else:
+ if self._speech_triggered:
+ chunk_duration_ms = len(audio_int16) / (self._sample_rate / 1000)
+ self._silence_ms += chunk_duration_ms
+ # If silence exceeds threshold, mark end of turn
+ if self._silence_ms >= self._stop_ms:
+ logger.debug(
+ f"End of Turn complete due to stop_secs. Silence in ms: {self._silence_ms}"
+ )
+ state = EndOfTurnState.COMPLETE
+ self._clear(state)
+ else:
+ # Trim buffer to prevent unbounded growth before speech
+ max_buffer_time = (
+ (self._params.pre_speech_ms / 1000)
+ + self._params.stop_secs
+ + self._params.max_duration_secs
+ )
+ while (
+ self._audio_buffer and self._audio_buffer[0][0] < time.time() - max_buffer_time
+ ):
+ self._audio_buffer.pop(0)
+
+ return state
+
+ async def analyze_end_of_turn(self) -> Tuple[EndOfTurnState, Optional[MetricsData]]:
+ state, result = await self._process_speech_segment(self._audio_buffer)
+ if state == EndOfTurnState.COMPLETE or USE_ONLY_LAST_VAD_SEGMENT:
+ self._clear(state)
+ logger.debug(f"End of Turn result: {state}")
+ return state, result
+
+ def _clear(self, turn_state: EndOfTurnState):
+ # If the state is still incomplete, keep the _speech_triggered as True
+ self._speech_triggered = turn_state == EndOfTurnState.INCOMPLETE
+ self._audio_buffer = []
+ self._speech_start_time = 0
+ self._silence_ms = 0
+
+ async def _process_speech_segment(
+ self, audio_buffer
+ ) -> Tuple[EndOfTurnState, Optional[MetricsData]]:
+ state = EndOfTurnState.INCOMPLETE
+
+ if not audio_buffer:
+ return state, None
+
+ # Extract recent audio segment for prediction
+ start_time = self._speech_start_time - (self._params.pre_speech_ms / 1000)
+ start_index = 0
+ for i, (t, _) in enumerate(audio_buffer):
+ if t >= start_time:
+ start_index = i
+ break
+
+ end_index = len(audio_buffer) - 1
+
+ # Extract the audio segment
+ segment_audio_chunks = [chunk for _, chunk in audio_buffer[start_index : end_index + 1]]
+ segment_audio = np.concatenate(segment_audio_chunks)
+
+ # Limit maximum duration
+ max_samples = int(self._params.max_duration_secs * self.sample_rate)
+ if len(segment_audio) > max_samples:
+ # slices the array to keep the last max_samples samples, discarding the earlier part.
+ segment_audio = segment_audio[-max_samples:]
+
+ result_data = None
+
+ if len(segment_audio) > 0:
+ start_time = time.perf_counter()
+ try:
+ result = await self._predict_endpoint(segment_audio)
+ state = (
+ EndOfTurnState.COMPLETE
+ if result["prediction"] == 1
+ else EndOfTurnState.INCOMPLETE
+ )
+ end_time = time.perf_counter()
+
+ # Calculate processing time
+ e2e_processing_time_ms = (end_time - start_time) * 1000
+
+ # Extract metrics from the nested structure
+ metrics = result.get("metrics", {})
+ inference_time = metrics.get("inference_time", 0)
+ total_time = metrics.get("total_time", 0)
+
+ # Prepare the result data
+ result_data = SmartTurnMetricsData(
+ processor="BaseSmartTurn",
+ is_complete=result["prediction"] == 1,
+ probability=result["probability"],
+ inference_time_ms=inference_time * 1000,
+ server_total_time_ms=total_time * 1000,
+ e2e_processing_time_ms=e2e_processing_time_ms,
+ )
+
+ logger.trace(
+ f"Prediction: {'Complete' if result_data.is_complete else 'Incomplete'}"
+ )
+ logger.trace(f"Probability of complete: {result_data.probability:.4f}")
+ logger.trace(f"Inference time: {result_data.inference_time_ms:.2f}ms")
+ logger.trace(f"Server total time: {result_data.server_total_time_ms:.2f}ms")
+ logger.trace(f"E2E processing time: {result_data.e2e_processing_time_ms:.2f}ms")
+ except SmartTurnTimeoutException:
+ logger.debug(
+ f"End of Turn complete due to stop_secs. Silence in ms: {self._silence_ms}"
+ )
+ state = EndOfTurnState.COMPLETE
+
+ else:
+ logger.trace(f"params: {self._params}, stop_ms: {self._stop_ms}")
+ logger.trace("Captured empty audio segment, skipping prediction.")
+
+ return state, result_data
+
+ @abstractmethod
+ async def _predict_endpoint(self, audio_array: np.ndarray) -> Dict[str, Any]:
+ """Abstract method to predict if a turn has ended based on audio.
+
+ Args:
+ audio_array: Float32 numpy array of audio samples at 16kHz.
+
+ Returns:
+ Dictionary with:
+ - prediction: 1 if turn is complete, else 0
+ - probability: Confidence of the prediction
+ """
+ pass
diff --git a/src/pipecat/audio/turn/smart_turn/fal_smart_turn.py b/src/pipecat/audio/turn/smart_turn/fal_smart_turn.py
new file mode 100644
index 000000000..9e3a85b56
--- /dev/null
+++ b/src/pipecat/audio/turn/smart_turn/fal_smart_turn.py
@@ -0,0 +1,26 @@
+#
+# Copyright (c) 2024β2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+from typing import Optional
+
+import aiohttp
+
+from pipecat.audio.turn.smart_turn.http_smart_turn import HttpSmartTurnAnalyzer
+
+
+class FalSmartTurnAnalyzer(HttpSmartTurnAnalyzer):
+ def __init__(
+ self,
+ *,
+ aiohttp_session: aiohttp.ClientSession,
+ url: str = "https://fal.run/fal-ai/smart-turn/raw",
+ api_key: Optional[str] = None,
+ **kwargs,
+ ):
+ headers = {}
+ if api_key:
+ headers = {"Authorization": f"Key {api_key}"}
+ super().__init__(url=url, aiohttp_session=aiohttp_session, headers=headers, **kwargs)
diff --git a/src/pipecat/audio/turn/smart_turn/http_smart_turn.py b/src/pipecat/audio/turn/smart_turn/http_smart_turn.py
new file mode 100644
index 000000000..4f542f81d
--- /dev/null
+++ b/src/pipecat/audio/turn/smart_turn/http_smart_turn.py
@@ -0,0 +1,80 @@
+#
+# Copyright (c) 2024β2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import io
+from typing import Any, Dict
+
+import aiohttp
+import numpy as np
+from loguru import logger
+
+from pipecat.audio.turn.smart_turn.base_smart_turn import BaseSmartTurn, SmartTurnTimeoutException
+
+
+class HttpSmartTurnAnalyzer(BaseSmartTurn):
+ def __init__(
+ self,
+ *,
+ url: str,
+ aiohttp_session: aiohttp.ClientSession,
+ headers: Dict[str, str] = {},
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+ self._url = url
+ self._headers = headers
+ self._aiohttp_session = aiohttp_session
+
+ def _serialize_array(self, audio_array: np.ndarray) -> bytes:
+ logger.trace("Serializing NumPy array to bytes...")
+ buffer = io.BytesIO()
+ np.save(buffer, audio_array)
+ serialized_bytes = buffer.getvalue()
+ logger.trace(f"Serialized size: {len(serialized_bytes)} bytes")
+ return serialized_bytes
+
+ async def _send_raw_request(self, data_bytes: bytes) -> Dict[str, Any]:
+ headers = {"Content-Type": "application/octet-stream"}
+ headers.update(self._headers)
+ logger.trace(f"Sending {len(data_bytes)} bytes as raw body to {self._url}...")
+ try:
+ timeout = aiohttp.ClientTimeout(total=self._params.stop_secs)
+
+ async with self._aiohttp_session.post(
+ self._url, data=data_bytes, headers=headers, timeout=timeout
+ ) as response:
+ logger.trace("\n--- Response ---")
+ logger.trace(f"Status Code: {response.status}")
+
+ if response.status == 200:
+ try:
+ json_data = await response.json()
+ logger.trace("Response JSON:")
+ logger.trace(json_data)
+ return json_data
+ except aiohttp.ContentTypeError:
+ # Non-JSON response
+ text = await response.text()
+ logger.trace("Response Content (non-JSON):")
+ logger.trace(text)
+ raise Exception(f"Non-JSON response: {text}")
+ else:
+ error_text = await response.text()
+ logger.trace("Response Content (Error):")
+ logger.trace(error_text)
+ response.raise_for_status()
+
+ except asyncio.TimeoutError:
+ logger.error(f"Request timed out after {self._params.stop_secs} seconds")
+ raise SmartTurnTimeoutException(f"Request exceeded {self._params.stop_secs} seconds.")
+ except aiohttp.ClientError as e:
+ logger.error(f"Failed to send raw request to Daily Smart Turn: {e}")
+ raise Exception("Failed to send raw request to Daily Smart Turn.")
+
+ async def _predict_endpoint(self, audio_array: np.ndarray) -> Dict[str, Any]:
+ serialized_array = self._serialize_array(audio_array)
+ return await self._send_raw_request(serialized_array)
diff --git a/src/pipecat/audio/turn/smart_turn/local_coreml_smart_turn.py b/src/pipecat/audio/turn/smart_turn/local_coreml_smart_turn.py
new file mode 100644
index 000000000..88d6530bd
--- /dev/null
+++ b/src/pipecat/audio/turn/smart_turn/local_coreml_smart_turn.py
@@ -0,0 +1,64 @@
+#
+# Copyright (c) 2024β2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+
+from typing import Any, Dict
+
+import numpy as np
+from loguru import logger
+
+from pipecat.audio.turn.smart_turn.base_smart_turn import BaseSmartTurn
+
+try:
+ import coremltools as ct
+ import torch
+ from transformers import AutoFeatureExtractor
+except ModuleNotFoundError as e:
+ logger.error(f"Exception: {e}")
+ logger.error(
+ "In order to use the LocalSmartTurnAnalyzer, you need to `pip install pipecat-ai[local-smart-turn]`."
+ )
+ raise Exception(f"Missing module: {e}")
+
+
+class LocalCoreMLSmartTurnAnalyzer(BaseSmartTurn):
+ def __init__(self, *, smart_turn_model_path: str, **kwargs):
+ super().__init__(**kwargs)
+
+ if not smart_turn_model_path:
+ logger.error("smart_turn_model_path is not set.")
+ raise Exception("smart_turn_model_path must be provided.")
+
+ core_ml_model_path = f"{smart_turn_model_path}/coreml/smart_turn_classifier.mlpackage"
+
+ logger.debug("Loading Local Smart Turn model...")
+ # Only load the processor, not the torch model
+ self._turn_processor = AutoFeatureExtractor.from_pretrained(smart_turn_model_path)
+ self._turn_model = ct.models.MLModel(core_ml_model_path)
+ logger.debug("Loaded Local Smart Turn")
+
+ async def _predict_endpoint(self, audio_array: np.ndarray) -> Dict[str, Any]:
+ inputs = self._turn_processor(
+ audio_array,
+ sampling_rate=16000,
+ padding="max_length",
+ truncation=True,
+ max_length=800, # Maximum length as specified in training
+ return_attention_mask=True,
+ return_tensors="pt",
+ )
+
+ output = self._turn_model.predict(dict(inputs))
+ logits = output["logits"] # Core ML returns numpy array
+ logits_tensor = torch.tensor(logits)
+ probabilities = torch.nn.functional.softmax(logits_tensor, dim=1)
+ completion_prob = probabilities[0, 1].item() # Probability of class 1 (Complete)
+ prediction = 1 if completion_prob > 0.5 else 0
+
+ return {
+ "prediction": prediction,
+ "probability": completion_prob,
+ }
diff --git a/src/pipecat/frames/frames.py b/src/pipecat/frames/frames.py
index 30d8622d9..72acf1a2a 100644
--- a/src/pipecat/frames/frames.py
+++ b/src/pipecat/frames/frames.py
@@ -377,25 +377,6 @@ class LLMEnablePromptCachingFrame(DataFrame):
enable: bool
-@dataclass
-class FunctionCallResultProperties:
- """Properties for a function call result frame."""
-
- run_llm: Optional[bool] = None
- on_context_updated: Optional[Callable[[], Awaitable[None]]] = None
-
-
-@dataclass
-class FunctionCallResultFrame(DataFrame):
- """A frame containing the result of an LLM function (tool) call."""
-
- function_name: str
- tool_call_id: str
- arguments: Any
- result: Any
- properties: Optional[FunctionCallResultProperties] = None
-
-
@dataclass
class TTSSpeakFrame(DataFrame):
"""A frame that contains a text that should be spoken by the TTS in the
@@ -652,6 +633,25 @@ class FunctionCallCancelFrame(SystemFrame):
tool_call_id: str
+@dataclass
+class FunctionCallResultProperties:
+ """Properties for a function call result frame."""
+
+ run_llm: Optional[bool] = None
+ on_context_updated: Optional[Callable[[], Awaitable[None]]] = None
+
+
+@dataclass
+class FunctionCallResultFrame(SystemFrame):
+ """A frame containing the result of an LLM function (tool) call."""
+
+ function_name: str
+ tool_call_id: str
+ arguments: Any
+ result: Any
+ properties: Optional[FunctionCallResultProperties] = None
+
+
@dataclass
class STTMuteFrame(SystemFrame):
"""System frame to mute/unmute the STT service."""
diff --git a/src/pipecat/metrics/metrics.py b/src/pipecat/metrics/metrics.py
index c40f68590..262254ffd 100644
--- a/src/pipecat/metrics/metrics.py
+++ b/src/pipecat/metrics/metrics.py
@@ -30,3 +30,13 @@ class LLMUsageMetricsData(MetricsData):
class TTSUsageMetricsData(MetricsData):
value: int
+
+
+class SmartTurnMetricsData(MetricsData):
+ """Metrics data for smart turn predictions."""
+
+ is_complete: bool
+ probability: float
+ inference_time_ms: float
+ server_total_time_ms: float
+ e2e_processing_time_ms: float
diff --git a/src/pipecat/observers/loggers/llm_log_observer.py b/src/pipecat/observers/loggers/llm_log_observer.py
index 907dce70b..dd270abf5 100644
--- a/src/pipecat/observers/loggers/llm_log_observer.py
+++ b/src/pipecat/observers/loggers/llm_log_observer.py
@@ -18,7 +18,7 @@ from pipecat.frames.frames import (
from pipecat.observers.base_observer import BaseObserver
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContextFrame
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.ai_services import LLMService
+from pipecat.services.llm_service import LLMService
class LLMLogObserver(BaseObserver):
diff --git a/src/pipecat/observers/loggers/transcription_log_observer.py b/src/pipecat/observers/loggers/transcription_log_observer.py
index 630f7ab33..4547ee54f 100644
--- a/src/pipecat/observers/loggers/transcription_log_observer.py
+++ b/src/pipecat/observers/loggers/transcription_log_observer.py
@@ -13,7 +13,7 @@ from pipecat.frames.frames import (
)
from pipecat.observers.base_observer import BaseObserver
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.ai_services import STTService
+from pipecat.services.stt_service import STTService
class TranscriptionLogObserver(BaseObserver):
diff --git a/src/pipecat/processors/aggregators/llm_response.py b/src/pipecat/processors/aggregators/llm_response.py
index e40ad266b..1f7a33ed1 100644
--- a/src/pipecat/processors/aggregators/llm_response.py
+++ b/src/pipecat/processors/aggregators/llm_response.py
@@ -6,6 +6,7 @@
import asyncio
from abc import abstractmethod
+from dataclasses import dataclass
from typing import Dict, List, Literal, Set
from loguru import logger
@@ -46,6 +47,16 @@ from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.utils.time import time_now_iso8601
+@dataclass
+class LLMUserAggregatorParams:
+ aggregation_timeout: float = 1.0
+
+
+@dataclass
+class LLMAssistantAggregatorParams:
+ expect_stripped_words: bool = True
+
+
class LLMFullResponseAggregator(FrameProcessor):
"""This is an LLM aggregator that aggregates a full LLM completion. It
aggregates LLM text frames (tokens) received between
@@ -149,7 +160,8 @@ class BaseLLMResponseAggregator(FrameProcessor):
@abstractmethod
def reset(self):
"""Reset the internals of this aggregator. This should not modify the
- internal messages."""
+ internal messages.
+ """
pass
@abstractmethod
@@ -229,11 +241,23 @@ class LLMUserContextAggregator(LLMContextResponseAggregator):
def __init__(
self,
context: OpenAILLMContext,
- aggregation_timeout: float = 1.0,
+ *,
+ params: LLMUserAggregatorParams = LLMUserAggregatorParams(),
**kwargs,
):
super().__init__(context=context, role="user", **kwargs)
- self._aggregation_timeout = aggregation_timeout
+ self._params = params
+ if "aggregation_timeout" in kwargs:
+ import warnings
+
+ with warnings.catch_warnings():
+ warnings.simplefilter("always")
+ warnings.warn(
+ "Parameter 'aggregation_timeout' is deprecated, use 'params' instead.",
+ DeprecationWarning,
+ )
+
+ self._params.aggregation_timeout = kwargs["aggregation_timeout"]
self._seen_interim_results = False
self._user_speaking = False
@@ -356,7 +380,9 @@ class LLMUserContextAggregator(LLMContextResponseAggregator):
async def _aggregation_task_handler(self):
while True:
try:
- await asyncio.wait_for(self._aggregation_event.wait(), self._aggregation_timeout)
+ await asyncio.wait_for(
+ self._aggregation_event.wait(), self._params.aggregation_timeout
+ )
await self._maybe_push_bot_interruption()
except asyncio.TimeoutError:
if not self._user_speaking:
@@ -393,9 +419,27 @@ class LLMAssistantContextAggregator(LLMContextResponseAggregator):
"""
- def __init__(self, context: OpenAILLMContext, *, expect_stripped_words: bool = True, **kwargs):
+ def __init__(
+ self,
+ context: OpenAILLMContext,
+ *,
+ params: LLMAssistantAggregatorParams = LLMAssistantAggregatorParams(),
+ **kwargs,
+ ):
super().__init__(context=context, role="assistant", **kwargs)
- self._expect_stripped_words = expect_stripped_words
+ self._params = params
+
+ if "expect_stripped_words" in kwargs:
+ import warnings
+
+ with warnings.catch_warnings():
+ warnings.simplefilter("always")
+ warnings.warn(
+ "Parameter 'expect_stripped_words' is deprecated, use 'params' instead.",
+ DeprecationWarning,
+ )
+
+ self._params.expect_stripped_words = kwargs["expect_stripped_words"]
self._started = 0
self._function_calls_in_progress: Dict[str, FunctionCallInProgressFrame] = {}
@@ -446,6 +490,7 @@ class LLMAssistantContextAggregator(LLMContextResponseAggregator):
await self._handle_user_image_frame(frame)
elif isinstance(frame, BotStoppedSpeakingFrame):
await self.push_aggregation()
+ await self.push_frame(frame, direction)
else:
await self.push_frame(frame, direction)
@@ -556,7 +601,7 @@ class LLMAssistantContextAggregator(LLMContextResponseAggregator):
if not self._started:
return
- if self._expect_stripped_words:
+ if self._params.expect_stripped_words:
self._aggregation += f" {frame.text}" if self._aggregation else frame.text
else:
self._aggregation += frame.text
@@ -570,8 +615,14 @@ class LLMAssistantContextAggregator(LLMContextResponseAggregator):
class LLMUserResponseAggregator(LLMUserContextAggregator):
- def __init__(self, messages: List[dict] = [], **kwargs):
- super().__init__(context=OpenAILLMContext(messages), **kwargs)
+ def __init__(
+ self,
+ messages: List[dict] = [],
+ *,
+ params: LLMUserAggregatorParams = LLMUserAggregatorParams(),
+ **kwargs,
+ ):
+ super().__init__(context=OpenAILLMContext(messages), params=params, **kwargs)
async def push_aggregation(self):
if len(self._aggregation) > 0:
@@ -586,8 +637,14 @@ class LLMUserResponseAggregator(LLMUserContextAggregator):
class LLMAssistantResponseAggregator(LLMAssistantContextAggregator):
- def __init__(self, messages: List[dict] = [], **kwargs):
- super().__init__(context=OpenAILLMContext(messages), **kwargs)
+ def __init__(
+ self,
+ messages: List[dict] = [],
+ *,
+ params: LLMAssistantAggregatorParams = LLMAssistantAggregatorParams(),
+ **kwargs,
+ ):
+ super().__init__(context=OpenAILLMContext(messages), params=params, **kwargs)
async def push_aggregation(self):
if len(self._aggregation) > 0:
diff --git a/src/pipecat/processors/consumer_processor.py b/src/pipecat/processors/consumer_processor.py
new file mode 100644
index 000000000..dbdfc97e5
--- /dev/null
+++ b/src/pipecat/processors/consumer_processor.py
@@ -0,0 +1,65 @@
+#
+# Copyright (c) 2024β2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+from typing import Awaitable, Callable, Optional
+
+from pipecat.frames.frames import CancelFrame, EndFrame, Frame, StartFrame
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.processors.producer_processor import ProducerProcessor, identity_transformer
+
+
+class ConsumerProcessor(FrameProcessor):
+ """This class passes-through frames and also consumes frames from a
+ producer's queue. When a frame from a producer queue is received it will be
+ pushed to the specified direction. The frames can be transformed into a
+ different type of frame before being pushed.
+
+ """
+
+ def __init__(
+ self,
+ *,
+ producer: ProducerProcessor,
+ transformer: Callable[[Frame], Awaitable[Frame]] = identity_transformer,
+ direction: FrameDirection = FrameDirection.DOWNSTREAM,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+ self._transformer = transformer
+ self._direction = direction
+ self._queue: asyncio.Queue = producer.add_consumer()
+ self._consumer_task: Optional[asyncio.Task] = None
+
+ async def process_frame(self, frame: Frame, direction: FrameDirection):
+ await super().process_frame(frame, direction)
+
+ if isinstance(frame, StartFrame):
+ await self._start(frame)
+ elif isinstance(frame, EndFrame):
+ await self._stop(frame)
+ elif isinstance(frame, CancelFrame):
+ await self._cancel(frame)
+
+ await self.push_frame(frame, direction)
+
+ async def _start(self, _: StartFrame):
+ if not self._consumer_task:
+ self._consumer_task = self.create_task(self._consumer_task_handler())
+
+ async def _stop(self, _: EndFrame):
+ if self._consumer_task:
+ await self.cancel_task(self._consumer_task)
+
+ async def _cancel(self, _: CancelFrame):
+ if self._consumer_task:
+ await self.cancel_task(self._consumer_task)
+
+ async def _consumer_task_handler(self):
+ while True:
+ frame = await self._queue.get()
+ new_frame = await self._transformer(frame)
+ await self.push_frame(new_frame, self._direction)
diff --git a/src/pipecat/processors/filters/stt_mute_filter.py b/src/pipecat/processors/filters/stt_mute_filter.py
index cce087f22..ae81acc1e 100644
--- a/src/pipecat/processors/filters/stt_mute_filter.py
+++ b/src/pipecat/processors/filters/stt_mute_filter.py
@@ -108,7 +108,7 @@ class STTMuteFilter(FrameProcessor):
async def _handle_mute_state(self, should_mute: bool):
"""Handles both STT muting and interruption control."""
if should_mute != self.is_muted:
- logger.debug(f"STT {'muting' if should_mute else 'unmuting'}")
+ logger.debug(f"STTMuteFilter {'muting' if should_mute else 'unmuting'}")
self._is_muted = should_mute
await self.push_frame(STTMuteFrame(mute=should_mute))
diff --git a/src/pipecat/processors/producer_processor.py b/src/pipecat/processors/producer_processor.py
new file mode 100644
index 000000000..6ada2ed83
--- /dev/null
+++ b/src/pipecat/processors/producer_processor.py
@@ -0,0 +1,73 @@
+#
+# Copyright (c) 2024β2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+from typing import Awaitable, Callable, List
+
+from pipecat.frames.frames import Frame
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+
+
+async def identity_transformer(frame: Frame):
+ return frame
+
+
+class ProducerProcessor(FrameProcessor):
+ """This class optionally passes-through received frames and decides if those
+ frames should be sent to consumers based on a user-defined filter. The
+ frames can be transformed into a different type of frame before being
+ sending them to the consumers. More than one consumer can be added.
+
+ """
+
+ def __init__(
+ self,
+ *,
+ filter: Callable[[Frame], Awaitable[bool]],
+ transformer: Callable[[Frame], Awaitable[Frame]] = identity_transformer,
+ passthrough: bool = True,
+ ):
+ super().__init__()
+ self._filter = filter
+ self._transformer = transformer
+ self._passthrough = passthrough
+ self._consumers: List[asyncio.Queue] = []
+
+ def add_consumer(self):
+ """
+ Adds a new consumer and returns its associated queue.
+
+ Returns:
+ asyncio.Queue: The queue for the newly added consumer.
+ """
+ queue = asyncio.Queue()
+ self._consumers.append(queue)
+ return queue
+
+ async def process_frame(self, frame: Frame, direction: FrameDirection):
+ """
+ Processes an incoming frame and determines whether to produce it as a ProducerItem.
+
+ If the frame meets the produce criteria, it will be added to the consumer queues.
+ If passthrough is enabled, the frame will also be sent to consumers.
+
+ Args:
+ frame (Frame): The frame to process.
+ direction (FrameDirection): The direction of the frame.
+ """
+ await super().process_frame(frame, direction)
+
+ if await self._filter(frame):
+ await self._produce(frame)
+ if self._passthrough:
+ await self.push_frame(frame, direction)
+ else:
+ await self.push_frame(frame, direction)
+
+ async def _produce(self, frame: Frame):
+ for consumer in self._consumers:
+ new_frame = await self._transformer(frame)
+ await consumer.put(new_frame)
diff --git a/src/pipecat/processors/transcript_processor.py b/src/pipecat/processors/transcript_processor.py
index 3eaff66ca..a2ad22223 100644
--- a/src/pipecat/processors/transcript_processor.py
+++ b/src/pipecat/processors/transcript_processor.py
@@ -175,22 +175,28 @@ class AssistantTranscriptProcessor(BaseTranscriptProcessor):
"""
await super().process_frame(frame, direction)
- if isinstance(frame, TTSTextFrame):
+ if isinstance(frame, (StartInterruptionFrame, CancelFrame)):
+ # Push frame first otherwise our emitted transcription update frame
+ # might get cleaned up.
+ await self.push_frame(frame, direction)
+ # Emit accumulated text with interruptions
+ await self._emit_aggregated_text()
+ elif isinstance(frame, TTSTextFrame):
# Start timestamp on first text part
if not self._aggregation_start_time:
self._aggregation_start_time = time_now_iso8601()
self._current_text_parts.append(frame.text)
- elif isinstance(frame, (BotStoppedSpeakingFrame, StartInterruptionFrame, CancelFrame)):
- # Emit accumulated text when bot finishes speaking or is interrupted
+ # Push frame.
+ await self.push_frame(frame, direction)
+ elif isinstance(frame, (BotStoppedSpeakingFrame, EndFrame)):
+ # Emit accumulated text when bot finishes speaking or pipeline ends.
await self._emit_aggregated_text()
-
- elif isinstance(frame, EndFrame):
- # Emit any remaining text when pipeline ends
- await self._emit_aggregated_text()
-
- await self.push_frame(frame, direction)
+ # Push frame.
+ await self.push_frame(frame, direction)
+ else:
+ await self.push_frame(frame, direction)
class TranscriptProcessor:
diff --git a/src/pipecat/processors/user_idle_processor.py b/src/pipecat/processors/user_idle_processor.py
index e88df3540..e7b08f4a3 100644
--- a/src/pipecat/processors/user_idle_processor.py
+++ b/src/pipecat/processors/user_idle_processor.py
@@ -127,9 +127,10 @@ class UserIdleProcessor(FrameProcessor):
# Check for end frames before processing
if isinstance(frame, (EndFrame, CancelFrame)):
- await self.push_frame(frame, direction) # Push the frame down the pipeline
- if self._idle_task:
- await self._stop() # Stop the idle task, if it exists
+ # Stop the idle task, if it exists
+ await self._stop()
+ # Push the frame down the pipeline
+ await self.push_frame(frame, direction)
return
await self.push_frame(frame, direction)
diff --git a/src/pipecat/py.typed b/src/pipecat/py.typed
new file mode 100644
index 000000000..e69de29bb
diff --git a/src/pipecat/serializers/telnyx.py b/src/pipecat/serializers/telnyx.py
index cbf16a156..61a89c7f0 100644
--- a/src/pipecat/serializers/telnyx.py
+++ b/src/pipecat/serializers/telnyx.py
@@ -8,6 +8,8 @@ import base64
import json
from typing import Optional
+import aiohttp
+from loguru import logger
from pydantic import BaseModel
from pipecat.audio.utils import (
@@ -19,6 +21,8 @@ from pipecat.audio.utils import (
)
from pipecat.frames.frames import (
AudioRawFrame,
+ CancelFrame,
+ EndFrame,
Frame,
InputAudioRawFrame,
InputDTMFFrame,
@@ -30,38 +34,120 @@ from pipecat.serializers.base_serializer import FrameSerializer, FrameSerializer
class TelnyxFrameSerializer(FrameSerializer):
+ """Serializer for Telnyx WebSocket protocol.
+
+ This serializer handles converting between Pipecat frames and Telnyx's WebSocket
+ media streams protocol. It supports audio conversion, DTMF events, and automatic
+ call termination.
+
+ When auto_hang_up is enabled (default), the serializer will automatically terminate
+ the Telnyx call when an EndFrame or CancelFrame is processed, but requires Telnyx
+ credentials to be provided.
+
+ Attributes:
+ _stream_id: The Telnyx Stream ID.
+ _call_control_id: The associated Telnyx Call Control ID.
+ _api_key: Telnyx API key for API access.
+ _params: Configuration parameters.
+ _telnyx_sample_rate: Sample rate used by Telnyx (typically 8kHz).
+ _sample_rate: Input sample rate for the pipeline.
+ _resampler: Audio resampler for format conversion.
+ _hangup_attempted: Flag to track if hang-up has been attempted.
+ """
+
class InputParams(BaseModel):
- telnyx_sample_rate: int = 8000 # Default Telnyx rate (8kHz)
- sample_rate: Optional[int] = None # Pipeline input rate
+ """Configuration parameters for TelnyxFrameSerializer.
+
+ Attributes:
+ telnyx_sample_rate: Sample rate used by Telnyx, defaults to 8000 Hz.
+ sample_rate: Optional override for pipeline input sample rate.
+ inbound_encoding: Audio encoding for data sent to Telnyx (e.g., "PCMU").
+ outbound_encoding: Audio encoding for data received from Telnyx (e.g., "PCMU").
+ auto_hang_up: Whether to automatically terminate call on EndFrame.
+ """
+
+ telnyx_sample_rate: int = 8000
+ sample_rate: Optional[int] = None
inbound_encoding: str = "PCMU"
outbound_encoding: str = "PCMU"
+ auto_hang_up: bool = True
def __init__(
self,
stream_id: str,
outbound_encoding: str,
inbound_encoding: str,
+ call_control_id: Optional[str] = None,
+ api_key: Optional[str] = None,
params: InputParams = InputParams(),
):
+ """Initialize the TelnyxFrameSerializer.
+
+ Args:
+ stream_id: The Stream ID for Telnyx.
+ outbound_encoding: The encoding type for outbound audio (e.g., "PCMU").
+ inbound_encoding: The encoding type for inbound audio (e.g., "PCMU").
+ call_control_id: The Call Control ID for the Telnyx call (optional, but required for auto hang-up).
+ api_key: Your Telnyx API key (required for auto hang-up).
+ params: Configuration parameters.
+ """
self._stream_id = stream_id
params.outbound_encoding = outbound_encoding
params.inbound_encoding = inbound_encoding
+ self._call_control_id = call_control_id
+ self._api_key = api_key
self._params = params
self._telnyx_sample_rate = self._params.telnyx_sample_rate
self._sample_rate = 0 # Pipeline input rate
self._resampler = create_default_resampler()
+ self._hangup_attempted = False
@property
def type(self) -> FrameSerializerType:
+ """Gets the serializer type.
+
+ Returns:
+ The serializer type, either TEXT or BINARY.
+ """
return FrameSerializerType.TEXT
async def setup(self, frame: StartFrame):
+ """Sets up the serializer with pipeline configuration.
+
+ Args:
+ frame: The StartFrame containing pipeline configuration.
+ """
self._sample_rate = self._params.sample_rate or frame.audio_in_sample_rate
async def serialize(self, frame: Frame) -> str | bytes | None:
- if isinstance(frame, AudioRawFrame):
+ """Serializes a Pipecat frame to Telnyx WebSocket format.
+
+ Handles conversion of various frame types to Telnyx WebSocket messages.
+ For EndFrames and CancelFrames, initiates call termination if auto_hang_up is enabled.
+
+ Args:
+ frame: The Pipecat frame to serialize.
+
+ Returns:
+ Serialized data as string or bytes, or None if the frame isn't handled.
+
+ Raises:
+ ValueError: If an unsupported encoding is specified.
+ """
+ if (
+ self._params.auto_hang_up
+ and not self._hangup_attempted
+ and isinstance(frame, (EndFrame, CancelFrame))
+ ):
+ self._hangup_attempted = True
+ await self._hang_up_call()
+ return None
+ elif isinstance(frame, StartInterruptionFrame):
+ answer = {"event": "clear"}
+ return json.dumps(answer)
+ elif isinstance(frame, AudioRawFrame):
data = frame.audio
# Output: Convert PCM at frame's rate to 8kHz encoded for Telnyx
@@ -84,11 +170,58 @@ class TelnyxFrameSerializer(FrameSerializer):
return json.dumps(answer)
- if isinstance(frame, StartInterruptionFrame):
- answer = {"event": "clear"}
- return json.dumps(answer)
+ # Return None for unhandled frames
+ return None
+
+ async def _hang_up_call(self):
+ """Hang up the Telnyx call using Telnyx's REST API."""
+ try:
+ call_control_id = self._call_control_id
+ api_key = self._api_key
+
+ if not call_control_id or not api_key:
+ logger.warning(
+ "Cannot hang up Telnyx call: call_control_id and api_key must be provided"
+ )
+ return
+
+ # Telnyx API endpoint for hanging up a call
+ endpoint = f"https://api.telnyx.com/v2/calls/{call_control_id}/actions/hangup"
+
+ # Set headers with API key
+ headers = {"Content-Type": "application/json", "Authorization": f"Bearer {api_key}"}
+
+ # Make the POST request to hang up the call
+ async with aiohttp.ClientSession() as session:
+ async with session.post(endpoint, headers=headers) as response:
+ if response.status == 200:
+ logger.info(f"Successfully terminated Telnyx call {call_control_id}")
+ else:
+ # Get the error details for better debugging
+ error_text = await response.text()
+ logger.error(
+ f"Failed to terminate Telnyx call {call_control_id}: "
+ f"Status {response.status}, Response: {error_text}"
+ )
+
+ except Exception as e:
+ logger.exception(f"Failed to hang up Telnyx call: {e}")
async def deserialize(self, data: str | bytes) -> Frame | None:
+ """Deserializes Telnyx WebSocket data to Pipecat frames.
+
+ Handles conversion of Telnyx media events to appropriate Pipecat frames,
+ including audio data and DTMF keypresses.
+
+ Args:
+ data: The raw WebSocket data from Telnyx.
+
+ Returns:
+ A Pipecat frame corresponding to the Telnyx event, or None if unhandled.
+
+ Raises:
+ ValueError: If an unsupported encoding is specified.
+ """
message = json.loads(data)
if message["event"] == "media":
diff --git a/src/pipecat/serializers/twilio.py b/src/pipecat/serializers/twilio.py
index e08920132..b169955ce 100644
--- a/src/pipecat/serializers/twilio.py
+++ b/src/pipecat/serializers/twilio.py
@@ -8,11 +8,14 @@ import base64
import json
from typing import Optional
+from loguru import logger
from pydantic import BaseModel
from pipecat.audio.utils import create_default_resampler, pcm_to_ulaw, ulaw_to_pcm
from pipecat.frames.frames import (
AudioRawFrame,
+ CancelFrame,
+ EndFrame,
Frame,
InputAudioRawFrame,
InputDTMFFrame,
@@ -26,28 +29,107 @@ from pipecat.serializers.base_serializer import FrameSerializer, FrameSerializer
class TwilioFrameSerializer(FrameSerializer):
- class InputParams(BaseModel):
- twilio_sample_rate: int = 8000 # Default Twilio rate (8kHz)
- sample_rate: Optional[int] = None # Pipeline input rate
+ """Serializer for Twilio Media Streams WebSocket protocol.
- def __init__(self, stream_sid: str, params: InputParams = InputParams()):
+ This serializer handles converting between Pipecat frames and Twilio's WebSocket
+ media streams protocol. It supports audio conversion, DTMF events, and automatic
+ call termination.
+
+ When auto_hang_up is enabled (default), the serializer will automatically terminate
+ the Twilio call when an EndFrame or CancelFrame is processed, but requires Twilio
+ credentials to be provided.
+
+ Attributes:
+ _stream_sid: The Twilio Media Stream SID.
+ _call_sid: The associated Twilio Call SID.
+ _account_sid: Twilio account SID for API access.
+ _auth_token: Twilio authentication token for API access.
+ _params: Configuration parameters.
+ _twilio_sample_rate: Sample rate used by Twilio (typically 8kHz).
+ _sample_rate: Input sample rate for the pipeline.
+ _resampler: Audio resampler for format conversion.
+ """
+
+ class InputParams(BaseModel):
+ """Configuration parameters for TwilioFrameSerializer.
+
+ Attributes:
+ twilio_sample_rate: Sample rate used by Twilio, defaults to 8000 Hz.
+ sample_rate: Optional override for pipeline input sample rate.
+ auto_hang_up: Whether to automatically terminate call on EndFrame.
+ """
+
+ twilio_sample_rate: int = 8000
+ sample_rate: Optional[int] = None
+ auto_hang_up: bool = True
+
+ def __init__(
+ self,
+ stream_sid: str,
+ call_sid: Optional[str] = None,
+ account_sid: Optional[str] = None,
+ auth_token: Optional[str] = None,
+ params: InputParams = InputParams(),
+ ):
+ """Initialize the TwilioFrameSerializer.
+
+ Args:
+ stream_sid: The Twilio Media Stream SID.
+ call_sid: The associated Twilio Call SID (optional, but required for auto hang-up).
+ account_sid: Twilio account SID (required for auto hang-up).
+ auth_token: Twilio auth token (required for auto hang-up).
+ params: Configuration parameters.
+ """
self._stream_sid = stream_sid
+ self._call_sid = call_sid
+ self._account_sid = account_sid
+ self._auth_token = auth_token
self._params = params
self._twilio_sample_rate = self._params.twilio_sample_rate
self._sample_rate = 0 # Pipeline input rate
self._resampler = create_default_resampler()
+ self._hangup_attempted = False
@property
def type(self) -> FrameSerializerType:
+ """Gets the serializer type.
+
+ Returns:
+ The serializer type, either TEXT or BINARY.
+ """
return FrameSerializerType.TEXT
async def setup(self, frame: StartFrame):
+ """Sets up the serializer with pipeline configuration.
+
+ Args:
+ frame: The StartFrame containing pipeline configuration.
+ """
self._sample_rate = self._params.sample_rate or frame.audio_in_sample_rate
async def serialize(self, frame: Frame) -> str | bytes | None:
- if isinstance(frame, StartInterruptionFrame):
+ """Serializes a Pipecat frame to Twilio WebSocket format.
+
+ Handles conversion of various frame types to Twilio WebSocket messages.
+ For EndFrames, initiates call termination if auto_hang_up is enabled.
+
+ Args:
+ frame: The Pipecat frame to serialize.
+
+ Returns:
+ Serialized data as string or bytes, or None if the frame isn't handled.
+ """
+ if (
+ self._params.auto_hang_up
+ and not self._hangup_attempted
+ and isinstance(frame, (EndFrame, CancelFrame))
+ ):
+ self._hangup_attempted = True
+ await self._hang_up_call()
+ return None
+ elif isinstance(frame, StartInterruptionFrame):
answer = {"event": "clear", "streamSid": self._stream_sid}
return json.dumps(answer)
elif isinstance(frame, AudioRawFrame):
@@ -68,7 +150,70 @@ class TwilioFrameSerializer(FrameSerializer):
elif isinstance(frame, (TransportMessageFrame, TransportMessageUrgentFrame)):
return json.dumps(frame.message)
+ # Return None for unhandled frames
+ return None
+
+ async def _hang_up_call(self):
+ """Hang up the Twilio call using Twilio's REST API."""
+ try:
+ import aiohttp
+
+ account_sid = self._account_sid
+ auth_token = self._auth_token
+ call_sid = self._call_sid
+
+ if not call_sid or not account_sid or not auth_token:
+ missing = []
+ if not call_sid:
+ missing.append("call_sid")
+ if not account_sid:
+ missing.append("account_sid")
+ if not auth_token:
+ missing.append("auth_token")
+
+ logger.warning(
+ f"Cannot hang up Twilio call: missing required parameters: {', '.join(missing)}"
+ )
+ return
+
+ # Twilio API endpoint for updating calls
+ endpoint = (
+ f"https://api.twilio.com/2010-04-01/Accounts/{account_sid}/Calls/{call_sid}.json"
+ )
+
+ # Create basic auth from account_sid and auth_token
+ auth = aiohttp.BasicAuth(account_sid, auth_token)
+
+ # Parameters to set the call status to "completed" (hang up)
+ params = {"Status": "completed"}
+
+ # Make the POST request to update the call
+ async with aiohttp.ClientSession() as session:
+ async with session.post(endpoint, auth=auth, data=params) as response:
+ if response.status == 200:
+ logger.info(f"Successfully terminated Twilio call {call_sid}")
+ else:
+ # Get the error details for better debugging
+ error_text = await response.text()
+ logger.error(
+ f"Failed to terminate Twilio call {call_sid}: "
+ f"Status {response.status}, Response: {error_text}"
+ )
+
+ except Exception as e:
+ logger.exception(f"Failed to hang up Twilio call: {e}")
+
async def deserialize(self, data: str | bytes) -> Frame | None:
+ """Deserializes Twilio WebSocket data to Pipecat frames.
+
+ Handles conversion of Twilio media events to appropriate Pipecat frames.
+
+ Args:
+ data: The raw WebSocket data from Twilio.
+
+ Returns:
+ A Pipecat frame corresponding to the Twilio event, or None if unhandled.
+ """
message = json.loads(data)
if message["event"] == "media":
diff --git a/src/pipecat/services/__init__.py b/src/pipecat/services/__init__.py
index a541ecfe7..0df8d028f 100644
--- a/src/pipecat/services/__init__.py
+++ b/src/pipecat/services/__init__.py
@@ -37,4 +37,4 @@ class DeprecatedModuleProxy:
def __getattr__(self, attr):
if attr in self._globals:
return _warn_deprecated_access(self._globals, attr, self._old, self._new)
- raise AttributeError(f"module 'pipecat.{self._old}' has no attribute '{attr}'")
+ raise AttributeError(f"module 'pipecat.services.{self._old}' has no attribute '{attr}'")
diff --git a/src/pipecat/services/ai_service.py b/src/pipecat/services/ai_service.py
new file mode 100644
index 000000000..985b61c8e
--- /dev/null
+++ b/src/pipecat/services/ai_service.py
@@ -0,0 +1,105 @@
+#
+# Copyright (c) 2024β2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+from typing import Any, AsyncGenerator, Dict, Mapping
+
+from loguru import logger
+
+from pipecat.frames.frames import (
+ CancelFrame,
+ EndFrame,
+ ErrorFrame,
+ Frame,
+ StartFrame,
+)
+from pipecat.metrics.metrics import MetricsData
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+
+
+class AIService(FrameProcessor):
+ def __init__(self, **kwargs):
+ super().__init__(**kwargs)
+ self._model_name: str = ""
+ self._settings: Dict[str, Any] = {}
+ self._session_properties: Dict[str, Any] = {}
+
+ @property
+ def model_name(self) -> str:
+ return self._model_name
+
+ def set_model_name(self, model: str):
+ self._model_name = model
+ self.set_core_metrics_data(MetricsData(processor=self.name, model=self._model_name))
+
+ async def start(self, frame: StartFrame):
+ pass
+
+ async def stop(self, frame: EndFrame):
+ pass
+
+ async def cancel(self, frame: CancelFrame):
+ pass
+
+ async def _update_settings(self, settings: Mapping[str, Any]):
+ from pipecat.services.openai_realtime_beta.events import (
+ SessionProperties,
+ )
+
+ for key, value in settings.items():
+ logger.debug("Update request for:", key, value)
+
+ if key in self._settings:
+ logger.info(f"Updating LLM setting {key} to: [{value}]")
+ self._settings[key] = value
+ elif key in SessionProperties.model_fields:
+ logger.debug("Attempting to update", key, value)
+
+ try:
+ from pipecat.services.openai_realtime_beta.events import (
+ TurnDetection,
+ )
+
+ if isinstance(self._session_properties, SessionProperties):
+ current_properties = self._session_properties
+ else:
+ current_properties = SessionProperties(**self._session_properties)
+
+ if key == "turn_detection" and isinstance(value, dict):
+ turn_detection = TurnDetection(**value)
+ setattr(current_properties, key, turn_detection)
+ else:
+ setattr(current_properties, key, value)
+
+ validated_properties = SessionProperties.model_validate(
+ current_properties.model_dump()
+ )
+ logger.info(f"Updating LLM setting {key} to: [{value}]")
+ self._session_properties = validated_properties.model_dump()
+ except Exception as e:
+ logger.warning(f"Unexpected error updating session property {key}: {e}")
+ elif key == "model":
+ logger.info(f"Updating LLM setting {key} to: [{value}]")
+ self.set_model_name(value)
+ else:
+ logger.warning(f"Unknown setting for {self.name} service: {key}")
+
+ async def process_frame(self, frame: Frame, direction: FrameDirection):
+ await super().process_frame(frame, direction)
+
+ if isinstance(frame, StartFrame):
+ await self.start(frame)
+ elif isinstance(frame, CancelFrame):
+ await self.cancel(frame)
+ elif isinstance(frame, EndFrame):
+ await self.stop(frame)
+
+ async def process_generator(self, generator: AsyncGenerator[Frame | None, None]):
+ async for f in generator:
+ if f:
+ if isinstance(f, ErrorFrame):
+ await self.push_error(f)
+ else:
+ await self.push_frame(f)
diff --git a/src/pipecat/services/ai_services.py b/src/pipecat/services/ai_services.py
index 97aad0d40..cda43c016 100644
--- a/src/pipecat/services/ai_services.py
+++ b/src/pipecat/services/ai_services.py
@@ -4,1122 +4,19 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
-import io
-import wave
-from abc import abstractmethod
-from dataclasses import dataclass
-from typing import Any, AsyncGenerator, Dict, List, Mapping, Optional, Sequence, Set, Tuple, Type
+import sys
-from loguru import logger
+from pipecat.services import DeprecatedModuleProxy
-from pipecat.adapters.base_llm_adapter import BaseLLMAdapter
-from pipecat.adapters.services.open_ai_adapter import OpenAILLMAdapter
-from pipecat.frames.frames import (
- AudioRawFrame,
- BotStartedSpeakingFrame,
- BotStoppedSpeakingFrame,
- CancelFrame,
- EndFrame,
- ErrorFrame,
- Frame,
- FunctionCallCancelFrame,
- FunctionCallInProgressFrame,
- FunctionCallResultFrame,
- InterimTranscriptionFrame,
- LLMFullResponseEndFrame,
- StartFrame,
- StartInterruptionFrame,
- STTMuteFrame,
- STTUpdateSettingsFrame,
- TextFrame,
- TranscriptionFrame,
- TTSAudioRawFrame,
- TTSSpeakFrame,
- TTSStartedFrame,
- TTSStoppedFrame,
- TTSTextFrame,
- TTSUpdateSettingsFrame,
- UserImageRequestFrame,
- UserStartedSpeakingFrame,
- UserStoppedSpeakingFrame,
- VisionImageRawFrame,
+from .ai_service import *
+from .image_service import *
+from .llm_service import *
+from .stt_service import *
+from .tts_service import *
+from .vision_service import *
+
+sys.modules[__name__] = DeprecatedModuleProxy(
+ globals(),
+ "ai_services",
+ "ai_service.[image_service,llm_service,stt_service,tts_service,vision_service]",
)
-from pipecat.metrics.metrics import MetricsData
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.websocket_service import WebsocketService
-from pipecat.transcriptions.language import Language
-from pipecat.utils.text.base_text_aggregator import BaseTextAggregator
-from pipecat.utils.text.base_text_filter import BaseTextFilter
-from pipecat.utils.text.simple_text_aggregator import SimpleTextAggregator
-from pipecat.utils.time import seconds_to_nanoseconds
-
-
-class AIService(FrameProcessor):
- def __init__(self, **kwargs):
- super().__init__(**kwargs)
- self._model_name: str = ""
- self._settings: Dict[str, Any] = {}
- self._session_properties: Dict[str, Any] = {}
-
- @property
- def model_name(self) -> str:
- return self._model_name
-
- def set_model_name(self, model: str):
- self._model_name = model
- self.set_core_metrics_data(MetricsData(processor=self.name, model=self._model_name))
-
- async def start(self, frame: StartFrame):
- pass
-
- async def stop(self, frame: EndFrame):
- pass
-
- async def cancel(self, frame: CancelFrame):
- pass
-
- async def _update_settings(self, settings: Mapping[str, Any]):
- from pipecat.services.openai_realtime_beta.events import (
- SessionProperties,
- )
-
- for key, value in settings.items():
- logger.debug("Update request for:", key, value)
-
- if key in self._settings:
- logger.info(f"Updating LLM setting {key} to: [{value}]")
- self._settings[key] = value
- elif key in SessionProperties.model_fields:
- logger.debug("Attempting to update", key, value)
-
- try:
- from pipecat.services.openai_realtime_beta.events import (
- TurnDetection,
- )
-
- if isinstance(self._session_properties, SessionProperties):
- current_properties = self._session_properties
- else:
- current_properties = SessionProperties(**self._session_properties)
-
- if key == "turn_detection" and isinstance(value, dict):
- turn_detection = TurnDetection(**value)
- setattr(current_properties, key, turn_detection)
- else:
- setattr(current_properties, key, value)
-
- validated_properties = SessionProperties.model_validate(
- current_properties.model_dump()
- )
- logger.info(f"Updating LLM setting {key} to: [{value}]")
- self._session_properties = validated_properties.model_dump()
- except Exception as e:
- logger.warning(f"Unexpected error updating session property {key}: {e}")
- elif key == "model":
- logger.info(f"Updating LLM setting {key} to: [{value}]")
- self.set_model_name(value)
- else:
- logger.warning(f"Unknown setting for {self.name} service: {key}")
-
- async def process_frame(self, frame: Frame, direction: FrameDirection):
- await super().process_frame(frame, direction)
-
- if isinstance(frame, StartFrame):
- await self.start(frame)
- elif isinstance(frame, CancelFrame):
- await self.cancel(frame)
- elif isinstance(frame, EndFrame):
- await self.stop(frame)
-
- async def process_generator(self, generator: AsyncGenerator[Frame | None, None]):
- async for f in generator:
- if f:
- if isinstance(f, ErrorFrame):
- await self.push_error(f)
- else:
- await self.push_frame(f)
-
-
-@dataclass
-class FunctionEntry:
- function_name: Optional[str]
- callback: Any # TODO(aleix): add proper typing.
- cancel_on_interruption: bool
-
-
-class LLMService(AIService):
- """This class is a no-op but serves as a base class for LLM services."""
-
- # OpenAILLMAdapter is used as the default adapter since it aligns with most LLM implementations.
- # However, subclasses should override this with a more specific adapter when necessary.
- adapter_class: Type[BaseLLMAdapter] = OpenAILLMAdapter
-
- def __init__(self, **kwargs):
- super().__init__(**kwargs)
- self._functions = {}
- self._start_callbacks = {}
- self._adapter = self.adapter_class()
- self._function_call_tasks: Set[Tuple[asyncio.Task, str, str]] = set()
-
- self._register_event_handler("on_completion_timeout")
-
- def get_llm_adapter(self) -> BaseLLMAdapter:
- return self._adapter
-
- def create_context_aggregator(
- self,
- context: OpenAILLMContext,
- *,
- user_kwargs: Mapping[str, Any] = {},
- assistant_kwargs: Mapping[str, Any] = {},
- ) -> Any:
- pass
-
- async def process_frame(self, frame: Frame, direction: FrameDirection):
- await super().process_frame(frame, direction)
-
- if isinstance(frame, StartInterruptionFrame):
- await self._handle_interruptions(frame)
-
- async def _handle_interruptions(self, frame: StartInterruptionFrame):
- for function_name, entry in self._functions.items():
- if entry.cancel_on_interruption:
- await self._cancel_function_call(function_name)
-
- def register_function(
- self,
- function_name: Optional[str],
- callback: Any,
- start_callback=None,
- *,
- cancel_on_interruption: bool = False,
- ):
- # Registering a function with the function_name set to None will run that callback
- # for all functions
- self._functions[function_name] = FunctionEntry(
- function_name=function_name,
- callback=callback,
- cancel_on_interruption=cancel_on_interruption,
- )
-
- # Start callbacks are now deprecated.
- if start_callback:
- import warnings
-
- with warnings.catch_warnings():
- warnings.simplefilter("always")
- warnings.warn(
- "Parameter 'start_callback' is deprecated, just put your code on top of the actual function call instead.",
- DeprecationWarning,
- )
-
- self._start_callbacks[function_name] = start_callback
-
- def unregister_function(self, function_name: Optional[str]):
- del self._functions[function_name]
- if self._start_callbacks[function_name]:
- del self._start_callbacks[function_name]
-
- def has_function(self, function_name: str):
- if None in self._functions.keys():
- return True
- return function_name in self._functions.keys()
-
- async def call_function(
- self,
- *,
- context: OpenAILLMContext,
- tool_call_id: str,
- function_name: str,
- arguments: str,
- run_llm: bool = True,
- ):
- if not function_name in self._functions.keys() and not None in self._functions.keys():
- return
-
- task = self.create_task(
- self._run_function_call(context, tool_call_id, function_name, arguments, run_llm)
- )
-
- self._function_call_tasks.add((task, tool_call_id, function_name))
-
- task.add_done_callback(self._function_call_task_finished)
-
- async def call_start_function(self, context: OpenAILLMContext, function_name: str):
- if function_name in self._start_callbacks.keys():
- await self._start_callbacks[function_name](function_name, self, context)
- elif None in self._start_callbacks.keys():
- return await self._start_callbacks[None](function_name, self, context)
-
- async def request_image_frame(
- self,
- user_id: str,
- *,
- function_name: Optional[str] = None,
- tool_call_id: Optional[str] = None,
- text_content: Optional[str] = None,
- ):
- await self.push_frame(
- UserImageRequestFrame(
- user_id=user_id,
- function_name=function_name,
- tool_call_id=tool_call_id,
- context=text_content,
- ),
- FrameDirection.UPSTREAM,
- )
-
- async def _run_function_call(
- self,
- context: OpenAILLMContext,
- tool_call_id: str,
- function_name: str,
- arguments: str,
- run_llm: bool = True,
- ):
- if function_name in self._functions.keys():
- entry = self._functions[function_name]
- elif None in self._functions.keys():
- entry = self._functions[None]
- else:
- return
-
- logger.debug(
- f"{self} Calling function [{function_name}:{tool_call_id}] with arguments {arguments}"
- )
-
- # NOTE(aleix): This needs to be removed after we remove the deprecation.
- await self.call_start_function(context, function_name)
-
- # Push a SystemFrame downstream. This frame will let our assistant context aggregator
- # know that we are in the middle of a function call. Some contexts/aggregators may
- # not need this. But some definitely do (Anthropic, for example).
- # Also push a SystemFrame upstream for use by other processors, like STTMuteFilter.
- progress_frame_downstream = FunctionCallInProgressFrame(
- function_name=function_name,
- tool_call_id=tool_call_id,
- arguments=arguments,
- cancel_on_interruption=entry.cancel_on_interruption,
- )
- progress_frame_upstream = FunctionCallInProgressFrame(
- function_name=function_name,
- tool_call_id=tool_call_id,
- arguments=arguments,
- cancel_on_interruption=entry.cancel_on_interruption,
- )
-
- # Push frame both downstream and upstream
- await self.push_frame(progress_frame_downstream, FrameDirection.DOWNSTREAM)
- await self.push_frame(progress_frame_upstream, FrameDirection.UPSTREAM)
-
- # Define a callback function that pushes a FunctionCallResultFrame upstream & downstream.
- async def function_call_result_callback(result, *, properties=None):
- result_frame_downstream = FunctionCallResultFrame(
- function_name=function_name,
- tool_call_id=tool_call_id,
- arguments=arguments,
- result=result,
- properties=properties,
- )
- result_frame_upstream = FunctionCallResultFrame(
- function_name=function_name,
- tool_call_id=tool_call_id,
- arguments=arguments,
- result=result,
- properties=properties,
- )
-
- await self.push_frame(result_frame_downstream, FrameDirection.DOWNSTREAM)
- await self.push_frame(result_frame_upstream, FrameDirection.UPSTREAM)
-
- await entry.callback(
- function_name, tool_call_id, arguments, self, context, function_call_result_callback
- )
-
- async def _cancel_function_call(self, function_name: str):
- cancelled_tasks = set()
- for task, tool_call_id, name in self._function_call_tasks:
- if name == function_name:
- # We remove the callback because we are going to cancel the task
- # now, otherwise we will be removing it from the set while we
- # are iterating.
- task.remove_done_callback(self._function_call_task_finished)
-
- logger.debug(f"{self} Cancelling function call [{name}:{tool_call_id}]...")
-
- await self.cancel_task(task)
-
- frame = FunctionCallCancelFrame(
- function_name=function_name, tool_call_id=tool_call_id
- )
- await self.push_frame(frame)
-
- logger.debug(f"{self} Function call [{name}:{tool_call_id}] has been cancelled")
-
- cancelled_tasks.add(task)
-
- # Remove all cancelled tasks from our set.
- for task in cancelled_tasks:
- self._function_call_task_finished(task)
-
- def _function_call_task_finished(self, task: asyncio.Task):
- tuple_to_remove = next((t for t in self._function_call_tasks if t[0] == task), None)
- if tuple_to_remove:
- self._function_call_tasks.discard(tuple_to_remove)
- # The task is finished so this should exit immediately. We need to
- # do this because otherwise the task manager would report a dangling
- # task if we don't remove it.
- asyncio.run_coroutine_threadsafe(self.wait_for_task(task), self.get_event_loop())
-
-
-class TTSService(AIService):
- def __init__(
- self,
- *,
- aggregate_sentences: bool = True,
- # if True, TTSService will push TextFrames and LLMFullResponseEndFrames,
- # otherwise subclass must do it
- push_text_frames: bool = True,
- # if True, TTSService will push TTSStoppedFrames, otherwise subclass must do it
- push_stop_frames: bool = False,
- # if push_stop_frames is True, wait for this idle period before pushing TTSStoppedFrame
- stop_frame_timeout_s: float = 2.0,
- # if True, TTSService will push silence audio frames after TTSStoppedFrame
- push_silence_after_stop: bool = False,
- # if push_silence_after_stop is True, send this amount of audio silence
- silence_time_s: float = 2.0,
- # if True, we will pause processing frames while we are receiving audio
- pause_frame_processing: bool = False,
- # TTS output sample rate
- sample_rate: Optional[int] = None,
- # Text aggregator to aggregate incoming tokens and decide when to push to the TTS.
- text_aggregator: Optional[BaseTextAggregator] = None,
- # Text filter executed after text has been aggregated.
- text_filters: Sequence[BaseTextFilter] = [],
- text_filter: Optional[BaseTextFilter] = None,
- **kwargs,
- ):
- super().__init__(**kwargs)
- self._aggregate_sentences: bool = aggregate_sentences
- self._push_text_frames: bool = push_text_frames
- self._push_stop_frames: bool = push_stop_frames
- self._stop_frame_timeout_s: float = stop_frame_timeout_s
- self._push_silence_after_stop: bool = push_silence_after_stop
- self._silence_time_s: float = silence_time_s
- self._pause_frame_processing: bool = pause_frame_processing
- self._init_sample_rate = sample_rate
- self._sample_rate = 0
- self._voice_id: str = ""
- self._settings: Dict[str, Any] = {}
- self._text_aggregator: BaseTextAggregator = text_aggregator or SimpleTextAggregator()
- self._text_filters: Sequence[BaseTextFilter] = text_filters
- if text_filter:
- import warnings
-
- with warnings.catch_warnings():
- warnings.simplefilter("always")
- warnings.warn(
- "Parameter 'text_filter' is deprecated, use 'text_filters' instead.",
- DeprecationWarning,
- )
- self._text_filters = [text_filter]
-
- self._stop_frame_task: Optional[asyncio.Task] = None
- self._stop_frame_queue: asyncio.Queue = asyncio.Queue()
-
- self._processing_text: bool = False
-
- @property
- def sample_rate(self) -> int:
- return self._sample_rate
-
- async def set_model(self, model: str):
- self.set_model_name(model)
-
- def set_voice(self, voice: str):
- self._voice_id = voice
-
- # Converts the text to audio.
- @abstractmethod
- async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
- pass
-
- def language_to_service_language(self, language: Language) -> Optional[str]:
- return Language(language)
-
- async def update_setting(self, key: str, value: Any):
- pass
-
- async def flush_audio(self):
- pass
-
- async def start(self, frame: StartFrame):
- await super().start(frame)
- self._sample_rate = self._init_sample_rate or frame.audio_out_sample_rate
- if self._push_stop_frames and not self._stop_frame_task:
- self._stop_frame_task = self.create_task(self._stop_frame_handler())
-
- async def stop(self, frame: EndFrame):
- await super().stop(frame)
- if self._stop_frame_task:
- await self.cancel_task(self._stop_frame_task)
- self._stop_frame_task = None
-
- async def cancel(self, frame: CancelFrame):
- await super().cancel(frame)
- if self._stop_frame_task:
- await self.cancel_task(self._stop_frame_task)
- self._stop_frame_task = None
-
- async def _update_settings(self, settings: Mapping[str, Any]):
- for key, value in settings.items():
- if key in self._settings:
- logger.info(f"Updating TTS setting {key} to: [{value}]")
- self._settings[key] = value
- if key == "language":
- self._settings[key] = self.language_to_service_language(value)
- elif key == "model":
- self.set_model_name(value)
- elif key == "voice":
- self.set_voice(value)
- elif key == "text_filter":
- for filter in self._text_filters:
- filter.update_settings(value)
- else:
- logger.warning(f"Unknown setting for TTS service: {key}")
-
- async def say(self, text: str):
- await self.queue_frame(TTSSpeakFrame(text))
-
- async def process_frame(self, frame: Frame, direction: FrameDirection):
- await super().process_frame(frame, direction)
-
- if (
- isinstance(frame, TextFrame)
- and not isinstance(frame, InterimTranscriptionFrame)
- and not isinstance(frame, TranscriptionFrame)
- ):
- await self._process_text_frame(frame)
- elif isinstance(frame, StartInterruptionFrame):
- await self._handle_interruption(frame, direction)
- await self.push_frame(frame, direction)
- elif isinstance(frame, (LLMFullResponseEndFrame, EndFrame)):
- # We pause processing incoming frames if the LLM response included
- # text (it might be that it's only a function calling response). We
- # pause to avoid audio overlapping.
- await self._maybe_pause_frame_processing()
-
- sentence = self._text_aggregator.text
- self._text_aggregator.reset()
- self._processing_text = False
- await self._push_tts_frames(sentence)
- if isinstance(frame, LLMFullResponseEndFrame):
- if self._push_text_frames:
- await self.push_frame(frame, direction)
- else:
- await self.push_frame(frame, direction)
- elif isinstance(frame, TTSSpeakFrame):
- # Store if we were processing text or not so we can set it back.
- processing_text = self._processing_text
- await self._push_tts_frames(frame.text)
- # We pause processing incoming frames because we are sending data to
- # the TTS. We pause to avoid audio overlapping.
- await self._maybe_pause_frame_processing()
- await self.flush_audio()
- self._processing_text = processing_text
- elif isinstance(frame, TTSUpdateSettingsFrame):
- await self._update_settings(frame.settings)
- elif isinstance(frame, BotStoppedSpeakingFrame):
- await self._maybe_resume_frame_processing()
- await self.push_frame(frame, direction)
- else:
- await self.push_frame(frame, direction)
-
- async def push_frame(self, frame: Frame, direction: FrameDirection = FrameDirection.DOWNSTREAM):
- if self._push_silence_after_stop and isinstance(frame, TTSStoppedFrame):
- silence_num_bytes = int(self._silence_time_s * self.sample_rate * 2) # 16-bit
- await self.push_frame(
- TTSAudioRawFrame(
- audio=b"\x00" * silence_num_bytes,
- sample_rate=self.sample_rate,
- num_channels=1,
- )
- )
-
- await super().push_frame(frame, direction)
-
- if self._push_stop_frames and (
- isinstance(frame, StartInterruptionFrame)
- or isinstance(frame, TTSStartedFrame)
- or isinstance(frame, TTSAudioRawFrame)
- or isinstance(frame, TTSStoppedFrame)
- ):
- await self._stop_frame_queue.put(frame)
-
- async def _handle_interruption(self, frame: StartInterruptionFrame, direction: FrameDirection):
- self._processing_text = False
- self._text_aggregator.handle_interruption()
- for filter in self._text_filters:
- filter.handle_interruption()
-
- async def _maybe_pause_frame_processing(self):
- if self._processing_text and self._pause_frame_processing:
- await self.pause_processing_frames()
-
- async def _maybe_resume_frame_processing(self):
- if self._pause_frame_processing:
- await self.resume_processing_frames()
-
- async def _process_text_frame(self, frame: TextFrame):
- text: Optional[str] = None
- if not self._aggregate_sentences:
- text = frame.text
- else:
- text = self._text_aggregator.aggregate(frame.text)
-
- if text:
- await self._push_tts_frames(text)
-
- async def _push_tts_frames(self, text: str):
- # Remove leading newlines only
- text = text.lstrip("\n")
-
- # Don't send only whitespace. This causes problems for some TTS models. But also don't
- # strip all whitespace, as whitespace can influence prosody.
- if not text.strip():
- return
-
- # This is just a flag that indicates if we sent something to the TTS
- # service. It will be cleared if we sent text because of a TTSSpeakFrame
- # or when we received an LLMFullResponseEndFrame
- self._processing_text = True
-
- await self.start_processing_metrics()
-
- # Process all filter.
- for filter in self._text_filters:
- filter.reset_interruption()
- text = filter.filter(text)
-
- await self.process_generator(self.run_tts(text))
-
- await self.stop_processing_metrics()
-
- if self._push_text_frames:
- # We send the original text after the audio. This way, if we are
- # interrupted, the text is not added to the assistant context.
- await self.push_frame(TTSTextFrame(text))
-
- async def _stop_frame_handler(self):
- has_started = False
- while True:
- try:
- frame = await asyncio.wait_for(
- self._stop_frame_queue.get(), self._stop_frame_timeout_s
- )
- if isinstance(frame, TTSStartedFrame):
- has_started = True
- elif isinstance(frame, (TTSStoppedFrame, StartInterruptionFrame)):
- has_started = False
- except asyncio.TimeoutError:
- if has_started:
- await self.push_frame(TTSStoppedFrame())
- has_started = False
-
-
-class WordTTSService(TTSService):
- """This is a base class for TTS services that support word timestamps. Word
- timestamps are useful to synchronize audio with text of the spoken
- words. This way only the spoken words are added to the conversation context.
-
- """
-
- def __init__(self, **kwargs):
- super().__init__(**kwargs)
- self._initial_word_timestamp = -1
- self._words_queue = asyncio.Queue()
- self._words_task = None
-
- def start_word_timestamps(self):
- if self._initial_word_timestamp == -1:
- self._initial_word_timestamp = self.get_clock().get_time()
-
- def reset_word_timestamps(self):
- self._initial_word_timestamp = -1
-
- async def add_word_timestamps(self, word_times: List[Tuple[str, float]]):
- for word, timestamp in word_times:
- await self._words_queue.put((word, seconds_to_nanoseconds(timestamp)))
-
- async def start(self, frame: StartFrame):
- await super().start(frame)
- self._create_words_task()
-
- async def stop(self, frame: EndFrame):
- await super().stop(frame)
- await self._stop_words_task()
-
- async def cancel(self, frame: CancelFrame):
- await super().cancel(frame)
- await self._stop_words_task()
-
- async def process_frame(self, frame: Frame, direction: FrameDirection):
- await super().process_frame(frame, direction)
-
- if isinstance(frame, (LLMFullResponseEndFrame, EndFrame)):
- await self.flush_audio()
-
- async def _handle_interruption(self, frame: StartInterruptionFrame, direction: FrameDirection):
- await super()._handle_interruption(frame, direction)
- self.reset_word_timestamps()
-
- def _create_words_task(self):
- if not self._words_task:
- self._words_task = self.create_task(self._words_task_handler())
-
- async def _stop_words_task(self):
- if self._words_task:
- await self.cancel_task(self._words_task)
- self._words_task = None
-
- async def _words_task_handler(self):
- last_pts = 0
- while True:
- (word, timestamp) = await self._words_queue.get()
- if word == "Reset" and timestamp == 0:
- self.reset_word_timestamps()
- frame = None
- elif word == "LLMFullResponseEndFrame" and timestamp == 0:
- frame = LLMFullResponseEndFrame()
- frame.pts = last_pts
- elif word == "TTSStoppedFrame" and timestamp == 0:
- frame = TTSStoppedFrame()
- frame.pts = last_pts
- else:
- frame = TTSTextFrame(word)
- frame.pts = self._initial_word_timestamp + timestamp
- if frame:
- last_pts = frame.pts
- await self.push_frame(frame)
- self._words_queue.task_done()
-
-
-class WebsocketTTSService(TTSService, WebsocketService):
- """This is a base class for websocket-based TTS services.
-
- If an error occurs with the websocket, an "on_connection_error" event will
- be triggered:
-
- @tts.event_handler("on_connection_error")
- async def on_connection_error(tts: TTSService, error: str):
- ...
-
- """
-
- def __init__(self, *, reconnect_on_error: bool = True, **kwargs):
- TTSService.__init__(self, **kwargs)
- WebsocketService.__init__(self, reconnect_on_error=reconnect_on_error, **kwargs)
- self._register_event_handler("on_connection_error")
-
- async def _report_error(self, error: ErrorFrame):
- await self._call_event_handler("on_connection_error", error.error)
- await self.push_error(error)
-
-
-class InterruptibleTTSService(WebsocketTTSService):
- """This is a base class for websocket-based TTS services that don't support
- word timestamps and that don't offer a way to correlate the generated audio
- to the requested text.
-
- """
-
- def __init__(self, **kwargs):
- super().__init__(**kwargs)
-
- # Indicates if the bot is speaking. If the bot is not speaking we don't
- # need to reconnect when the user speaks. If the bot is speaking and the
- # user interrupts we need to reconnect.
- self._bot_speaking = False
-
- async def _handle_interruption(self, frame: StartInterruptionFrame, direction: FrameDirection):
- await super()._handle_interruption(frame, direction)
- if self._bot_speaking:
- await self._disconnect()
- await self._connect()
-
- async def process_frame(self, frame: Frame, direction: FrameDirection):
- await super().process_frame(frame, direction)
-
- if isinstance(frame, BotStartedSpeakingFrame):
- self._bot_speaking = True
- elif isinstance(frame, BotStoppedSpeakingFrame):
- self._bot_speaking = False
-
-
-class WebsocketWordTTSService(WordTTSService, WebsocketService):
- """This is a base class for websocket-based TTS services that support word
- timestamps.
-
- If an error occurs with the websocket a "on_connection_error" event will be
- triggered:
-
- @tts.event_handler("on_connection_error")
- async def on_connection_error(tts: TTSService, error: str):
- ...
-
- """
-
- def __init__(self, *, reconnect_on_error: bool = True, **kwargs):
- WordTTSService.__init__(self, **kwargs)
- WebsocketService.__init__(self, reconnect_on_error=reconnect_on_error, **kwargs)
- self._register_event_handler("on_connection_error")
-
- async def _report_error(self, error: ErrorFrame):
- await self._call_event_handler("on_connection_error", error.error)
- await self.push_error(error)
-
-
-class InterruptibleWordTTSService(WebsocketWordTTSService):
- """This is a base class for websocket-based TTS services that support word
- timestamps but don't offer a way to correlate the generated audio to the
- requested text.
-
- """
-
- def __init__(self, **kwargs):
- super().__init__(**kwargs)
-
- # Indicates if the bot is speaking. If the bot is not speaking we don't
- # need to reconnect when the user speaks. If the bot is speaking and the
- # user interrupts we need to reconnect.
- self._bot_speaking = False
-
- async def _handle_interruption(self, frame: StartInterruptionFrame, direction: FrameDirection):
- await super()._handle_interruption(frame, direction)
- if self._bot_speaking:
- await self._disconnect()
- await self._connect()
-
- async def process_frame(self, frame: Frame, direction: FrameDirection):
- await super().process_frame(frame, direction)
-
- if isinstance(frame, BotStartedSpeakingFrame):
- self._bot_speaking = True
- elif isinstance(frame, BotStoppedSpeakingFrame):
- self._bot_speaking = False
-
-
-class AudioContextWordTTSService(WebsocketWordTTSService):
- """This is a base class for websocket-based TTS services that support word
- timestamps and also allow correlating the generated audio with the requested
- text.
-
- Each request could be multiple sentences long which are grouped by
- context. For this to work, the TTS service needs to support handling
- multiple requests at once (i.e. multiple simultaneous contexts).
-
- The audio received from the TTS will be played in context order. That is, if
- we requested audio for a context "A" and then audio for context "B", the
- audio from context ID "A" will be played first.
-
- """
-
- def __init__(self, **kwargs):
- super().__init__(**kwargs)
- self._contexts_queue = asyncio.Queue()
- self._contexts: Dict[str, asyncio.Queue] = {}
- self._audio_context_task = None
-
- async def create_audio_context(self, context_id: str):
- """Create a new audio context."""
- await self._contexts_queue.put(context_id)
- self._contexts[context_id] = asyncio.Queue()
- logger.trace(f"{self} created audio context {context_id}")
-
- async def append_to_audio_context(self, context_id: str, frame: TTSAudioRawFrame):
- """Append audio to an existing context."""
- if self.audio_context_available(context_id):
- logger.trace(f"{self} appending audio {frame} to audio context {context_id}")
- await self._contexts[context_id].put(frame)
- else:
- logger.warning(f"{self} unable to append audio to context {context_id}")
-
- async def remove_audio_context(self, context_id: str):
- """Remove an existing audio context."""
- if self.audio_context_available(context_id):
- # We just mark the audio context for deletion by appending
- # None. Once we reach None while handling audio we know we can
- # safely remove the context.
- logger.trace(f"{self} marking audio context {context_id} for deletion")
- await self._contexts[context_id].put(None)
- else:
- logger.warning(f"{self} unable to remove context {context_id}")
-
- def audio_context_available(self, context_id: str) -> bool:
- """Checks whether the given audio context is registered."""
- return context_id in self._contexts
-
- async def start(self, frame: StartFrame):
- await super().start(frame)
- self._create_audio_context_task()
-
- async def stop(self, frame: EndFrame):
- await super().stop(frame)
- if self._audio_context_task:
- # Indicate no more audio contexts are available. this will end the
- # task cleanly after all contexts have been processed.
- await self._contexts_queue.put(None)
- await self.wait_for_task(self._audio_context_task)
- self._audio_context_task = None
-
- async def cancel(self, frame: CancelFrame):
- await super().cancel(frame)
- await self._stop_audio_context_task()
-
- async def _handle_interruption(self, frame: StartInterruptionFrame, direction: FrameDirection):
- await super()._handle_interruption(frame, direction)
- await self._stop_audio_context_task()
- self._create_audio_context_task()
-
- def _create_audio_context_task(self):
- if not self._audio_context_task:
- self._contexts_queue = asyncio.Queue()
- self._contexts: Dict[str, asyncio.Queue] = {}
- self._audio_context_task = self.create_task(self._audio_context_task_handler())
-
- async def _stop_audio_context_task(self):
- if self._audio_context_task:
- await self.cancel_task(self._audio_context_task)
- self._audio_context_task = None
-
- async def _audio_context_task_handler(self):
- """In this task we process audio contexts in order."""
- running = True
- while running:
- context_id = await self._contexts_queue.get()
-
- if context_id:
- # Process the audio context until the context doesn't have more
- # audio available (i.e. we find None).
- await self._handle_audio_context(context_id)
-
- # We just finished processing the context, so we can safely remove it.
- del self._contexts[context_id]
-
- # Append some silence between sentences.
- silence = b"\x00" * self.sample_rate
- frame = TTSAudioRawFrame(
- audio=silence, sample_rate=self.sample_rate, num_channels=1
- )
- await self.push_frame(frame)
- else:
- running = False
-
- self._contexts_queue.task_done()
-
- async def _handle_audio_context(self, context_id: str):
- # If we don't receive any audio during this time, we consider the context finished.
- AUDIO_CONTEXT_TIMEOUT = 3.0
- queue = self._contexts[context_id]
- running = True
- while running:
- try:
- frame = await asyncio.wait_for(queue.get(), timeout=AUDIO_CONTEXT_TIMEOUT)
- if frame:
- await self.push_frame(frame)
- running = frame is not None
- except asyncio.TimeoutError:
- # We didn't get audio, so let's consider this context finished.
- logger.trace(f"{self} time out on audio context {context_id}")
- break
-
-
-class STTService(AIService):
- """STTService is a base class for speech-to-text services."""
-
- def __init__(
- self,
- audio_passthrough=False,
- # STT input sample rate
- sample_rate: Optional[int] = None,
- **kwargs,
- ):
- super().__init__(**kwargs)
- self._audio_passthrough = audio_passthrough
- self._init_sample_rate = sample_rate
- self._sample_rate = 0
- self._settings: Dict[str, Any] = {}
- self._muted: bool = False
-
- @property
- def is_muted(self) -> bool:
- """Returns whether the STT service is currently muted."""
- return self._muted
-
- @property
- def sample_rate(self) -> int:
- return self._sample_rate
-
- async def set_model(self, model: str):
- self.set_model_name(model)
-
- async def set_language(self, language: Language):
- pass
-
- @abstractmethod
- async def run_stt(self, audio: bytes) -> AsyncGenerator[Frame, None]:
- """Returns transcript as a string"""
- pass
-
- async def start(self, frame: StartFrame):
- await super().start(frame)
- self._sample_rate = self._init_sample_rate or frame.audio_in_sample_rate
-
- async def _update_settings(self, settings: Mapping[str, Any]):
- logger.info(f"Updating STT settings: {self._settings}")
- for key, value in settings.items():
- if key in self._settings:
- logger.info(f"Updating STT setting {key} to: [{value}]")
- self._settings[key] = value
- if key == "language":
- await self.set_language(value)
- elif key == "model":
- self.set_model_name(value)
- else:
- logger.warning(f"Unknown setting for STT service: {key}")
-
- async def process_audio_frame(self, frame: AudioRawFrame, direction: FrameDirection):
- if self._muted:
- return
-
- await self.process_generator(self.run_stt(frame.audio))
-
- async def process_frame(self, frame: Frame, direction: FrameDirection):
- """Processes a frame of audio data, either buffering or transcribing it."""
- await super().process_frame(frame, direction)
-
- if isinstance(frame, AudioRawFrame):
- # In this service we accumulate audio internally and at the end we
- # push a TextFrame. We also push audio downstream in case someone
- # else needs it.
- await self.process_audio_frame(frame, direction)
- if self._audio_passthrough:
- await self.push_frame(frame, direction)
- elif isinstance(frame, STTUpdateSettingsFrame):
- await self._update_settings(frame.settings)
- elif isinstance(frame, STTMuteFrame):
- self._muted = frame.mute
- logger.debug(f"STT service {'muted' if frame.mute else 'unmuted'}")
- else:
- await self.push_frame(frame, direction)
-
-
-class SegmentedSTTService(STTService):
- """SegmentedSTTService is an STTService that uses VAD events to detect
- speech and will run speech-to-text on speech segments only, instead of a
- continous stream. Since it uses VAD it means that VAD needs to be enabled in
- the pipeline.
-
- This service always keeps a small audio buffer to take into account that VAD
- events are delayed from when the user speech really starts.
-
- """
-
- def __init__(self, *, sample_rate: Optional[int] = None, **kwargs):
- super().__init__(sample_rate=sample_rate, **kwargs)
- self._content = None
- self._wave = None
- self._audio_buffer = bytearray()
- self._audio_buffer_size_1s = 0
- self._user_speaking = False
-
- async def start(self, frame: StartFrame):
- await super().start(frame)
- self._audio_buffer_size_1s = self.sample_rate * 2
-
- async def process_frame(self, frame: Frame, direction: FrameDirection):
- await super().process_frame(frame, direction)
-
- if isinstance(frame, UserStartedSpeakingFrame):
- await self._handle_user_started_speaking(frame)
- elif isinstance(frame, UserStoppedSpeakingFrame):
- await self._handle_user_stopped_speaking(frame)
-
- async def _handle_user_started_speaking(self, frame: UserStartedSpeakingFrame):
- if frame.emulated:
- return
- self._user_speaking = True
-
- async def _handle_user_stopped_speaking(self, frame: UserStoppedSpeakingFrame):
- if frame.emulated:
- return
-
- self._user_speaking = False
-
- content = io.BytesIO()
- wav = wave.open(content, "wb")
- wav.setsampwidth(2)
- wav.setnchannels(1)
- wav.setframerate(self.sample_rate)
- wav.writeframes(self._audio_buffer)
- wav.close()
- content.seek(0)
-
- await self.process_generator(self.run_stt(content.read()))
-
- # Start clean.
- self._audio_buffer.clear()
-
- async def process_audio_frame(self, frame: AudioRawFrame, direction: FrameDirection):
- # If the user is speaking the audio buffer will keep growing.
- self._audio_buffer += frame.audio
-
- # If the user is not speaking we keep just a little bit of audio.
- if not self._user_speaking and len(self._audio_buffer) > self._audio_buffer_size_1s:
- discarded = len(self._audio_buffer) - self._audio_buffer_size_1s
- self._audio_buffer = self._audio_buffer[discarded:]
-
-
-class ImageGenService(AIService):
- def __init__(self, **kwargs):
- super().__init__(**kwargs)
-
- # Renders the image. Returns an Image object.
- @abstractmethod
- async def run_image_gen(self, prompt: str) -> AsyncGenerator[Frame, None]:
- pass
-
- async def process_frame(self, frame: Frame, direction: FrameDirection):
- await super().process_frame(frame, direction)
-
- if isinstance(frame, TextFrame):
- await self.push_frame(frame, direction)
- await self.start_processing_metrics()
- await self.process_generator(self.run_image_gen(frame.text))
- await self.stop_processing_metrics()
- else:
- await self.push_frame(frame, direction)
-
-
-class VisionService(AIService):
- """VisionService is a base class for vision services."""
-
- def __init__(self, **kwargs):
- super().__init__(**kwargs)
- self._describe_text = None
-
- @abstractmethod
- async def run_vision(self, frame: VisionImageRawFrame) -> AsyncGenerator[Frame, None]:
- pass
-
- async def process_frame(self, frame: Frame, direction: FrameDirection):
- await super().process_frame(frame, direction)
-
- if isinstance(frame, VisionImageRawFrame):
- await self.start_processing_metrics()
- await self.process_generator(self.run_vision(frame))
- await self.stop_processing_metrics()
- else:
- await self.push_frame(frame, direction)
diff --git a/src/pipecat/services/anthropic/llm.py b/src/pipecat/services/anthropic/llm.py
index 3e369075a..277e29f83 100644
--- a/src/pipecat/services/anthropic/llm.py
+++ b/src/pipecat/services/anthropic/llm.py
@@ -11,7 +11,7 @@ import io
import json
import re
from dataclasses import dataclass
-from typing import Any, Dict, List, Mapping, Optional, Union
+from typing import Any, Dict, List, Optional, Union
import httpx
from loguru import logger
@@ -35,7 +35,9 @@ from pipecat.frames.frames import (
)
from pipecat.metrics.metrics import LLMTokenUsage
from pipecat.processors.aggregators.llm_response import (
+ LLMAssistantAggregatorParams,
LLMAssistantContextAggregator,
+ LLMUserAggregatorParams,
LLMUserContextAggregator,
)
from pipecat.processors.aggregators.openai_llm_context import (
@@ -43,16 +45,13 @@ from pipecat.processors.aggregators.openai_llm_context import (
OpenAILLMContextFrame,
)
from pipecat.processors.frame_processor import FrameDirection
-from pipecat.services.ai_services import LLMService
+from pipecat.services.llm_service import LLMService
try:
from anthropic import NOT_GIVEN, AsyncAnthropic, NotGiven
except ModuleNotFoundError as e:
logger.error(f"Exception: {e}")
- logger.error(
- "In order to use Anthropic, you need to `pip install pipecat-ai[anthropic]`. "
- + "Also, set `ANTHROPIC_API_KEY` environment variable."
- )
+ logger.error("In order to use Anthropic, you need to `pip install pipecat-ai[anthropic]`.")
raise Exception(f"Missing module: {e}")
@@ -120,8 +119,8 @@ class AnthropicLLMService(LLMService):
self,
context: OpenAILLMContext,
*,
- user_kwargs: Mapping[str, Any] = {},
- assistant_kwargs: Mapping[str, Any] = {},
+ user_params: LLMUserAggregatorParams = LLMUserAggregatorParams(),
+ assistant_params: LLMAssistantAggregatorParams = LLMAssistantAggregatorParams(),
) -> AnthropicContextAggregatorPair:
"""Create an instance of AnthropicContextAggregatorPair from an
OpenAILLMContext. Constructor keyword arguments for both the user and
@@ -129,12 +128,10 @@ class AnthropicLLMService(LLMService):
Args:
context (OpenAILLMContext): The LLM context.
- user_kwargs (Mapping[str, Any], optional): Additional keyword
- arguments for the user context aggregator constructor. Defaults
- to an empty mapping.
- assistant_kwargs (Mapping[str, Any], optional): Additional keyword
- arguments for the assistant context aggregator
- constructor. Defaults to an empty mapping.
+ user_params (LLMUserAggregatorParams, optional): User aggregator
+ parameters.
+ assistant_params (LLMAssistantAggregatorParams, optional): User
+ aggregator parameters.
Returns:
AnthropicContextAggregatorPair: A pair of context aggregators, one
@@ -146,8 +143,8 @@ class AnthropicLLMService(LLMService):
if isinstance(context, OpenAILLMContext):
context = AnthropicLLMContext.from_openai_context(context)
- user = AnthropicUserContextAggregator(context, **user_kwargs)
- assistant = AnthropicAssistantContextAggregator(context, **assistant_kwargs)
+ user = AnthropicUserContextAggregator(context, params=user_params)
+ assistant = AnthropicAssistantContextAggregator(context, params=assistant_params)
return AnthropicContextAggregatorPair(_user=user, _assistant=assistant)
async def _process_context(self, context: OpenAILLMContext):
diff --git a/src/pipecat/services/assemblyai/stt.py b/src/pipecat/services/assemblyai/stt.py
index 87dd18bf4..e6705a4a7 100644
--- a/src/pipecat/services/assemblyai/stt.py
+++ b/src/pipecat/services/assemblyai/stt.py
@@ -18,7 +18,7 @@ from pipecat.frames.frames import (
StartFrame,
TranscriptionFrame,
)
-from pipecat.services.ai_services import STTService
+from pipecat.services.stt_service import STTService
from pipecat.transcriptions.language import Language
from pipecat.utils.time import time_now_iso8601
diff --git a/src/pipecat/services/aws/tts.py b/src/pipecat/services/aws/tts.py
index 4c0417d72..db6e168ab 100644
--- a/src/pipecat/services/aws/tts.py
+++ b/src/pipecat/services/aws/tts.py
@@ -18,7 +18,7 @@ from pipecat.frames.frames import (
TTSStartedFrame,
TTSStoppedFrame,
)
-from pipecat.services.ai_services import TTSService
+from pipecat.services.tts_service import TTSService
from pipecat.transcriptions.language import Language
try:
@@ -231,9 +231,9 @@ class PollyTTSService(TTSService):
yield TTSStartedFrame()
- chunk_size = 8192
- for i in range(0, len(audio_data), chunk_size):
- chunk = audio_data[i : i + chunk_size]
+ CHUNK_SIZE = 1024
+ for i in range(0, len(audio_data), CHUNK_SIZE):
+ chunk = audio_data[i : i + CHUNK_SIZE]
if len(chunk) > 0:
await self.stop_ttfb_metrics()
frame = TTSAudioRawFrame(chunk, self.sample_rate, 1)
diff --git a/src/pipecat/services/azure/image.py b/src/pipecat/services/azure/image.py
index d86c6075b..a1bae3af6 100644
--- a/src/pipecat/services/azure/image.py
+++ b/src/pipecat/services/azure/image.py
@@ -13,7 +13,7 @@ from loguru import logger
from PIL import Image
from pipecat.frames.frames import ErrorFrame, Frame, URLImageRawFrame
-from pipecat.services.ai_services import ImageGenService
+from pipecat.services.image_service import ImageGenService
class AzureImageGenServiceREST(ImageGenService):
diff --git a/src/pipecat/services/azure/stt.py b/src/pipecat/services/azure/stt.py
index 3e1302029..95f3dcae1 100644
--- a/src/pipecat/services/azure/stt.py
+++ b/src/pipecat/services/azure/stt.py
@@ -16,8 +16,8 @@ from pipecat.frames.frames import (
StartFrame,
TranscriptionFrame,
)
-from pipecat.services.ai_services import STTService
from pipecat.services.azure.common import language_to_azure_language
+from pipecat.services.stt_service import STTService
from pipecat.transcriptions.language import Language
from pipecat.utils.time import time_now_iso8601
diff --git a/src/pipecat/services/azure/tts.py b/src/pipecat/services/azure/tts.py
index 1227a4e96..cf50c9fba 100644
--- a/src/pipecat/services/azure/tts.py
+++ b/src/pipecat/services/azure/tts.py
@@ -18,8 +18,8 @@ from pipecat.frames.frames import (
TTSStartedFrame,
TTSStoppedFrame,
)
-from pipecat.services.ai_services import TTSService
from pipecat.services.azure.common import language_to_azure_language
+from pipecat.services.tts_service import TTSService
from pipecat.transcriptions.language import Language
try:
@@ -159,8 +159,8 @@ class AzureTTSService(AzureBaseTTSService):
self._speech_config = SpeechConfig(
subscription=self._api_key,
region=self._region,
- speech_recognition_language=self._settings["language"],
)
+ self._speech_config.speech_synthesis_language = self._settings["language"]
self._speech_config.set_speech_synthesis_output_format(
sample_rate_to_output_format(self.sample_rate)
)
@@ -254,8 +254,8 @@ class AzureHttpTTSService(AzureBaseTTSService):
self._speech_config = SpeechConfig(
subscription=self._api_key,
region=self._region,
- speech_recognition_language=self._settings["language"],
)
+ self._speech_config.speech_synthesis_language = self._settings["language"]
self._speech_config.set_speech_synthesis_output_format(
sample_rate_to_output_format(self.sample_rate)
)
diff --git a/src/pipecat/services/canonical/metrics.py b/src/pipecat/services/canonical/metrics.py
index 7b62273d1..012cd4ab7 100644
--- a/src/pipecat/services/canonical/metrics.py
+++ b/src/pipecat/services/canonical/metrics.py
@@ -18,7 +18,7 @@ from pipecat.frames.frames import CancelFrame, EndFrame, Frame
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
from pipecat.processors.frame_processor import FrameDirection
-from pipecat.services.ai_services import AIService
+from pipecat.services.ai_service import AIService
try:
import aiofiles
diff --git a/src/pipecat/services/cartesia/tts.py b/src/pipecat/services/cartesia/tts.py
index 45c2fcaf8..2acabd541 100644
--- a/src/pipecat/services/cartesia/tts.py
+++ b/src/pipecat/services/cartesia/tts.py
@@ -24,7 +24,7 @@ from pipecat.frames.frames import (
TTSStoppedFrame,
)
from pipecat.processors.frame_processor import FrameDirection
-from pipecat.services.ai_services import AudioContextWordTTSService, TTSService
+from pipecat.services.tts_service import AudioContextWordTTSService, TTSService
from pipecat.transcriptions.language import Language
from pipecat.utils.text.base_text_aggregator import BaseTextAggregator
from pipecat.utils.text.skip_tags_aggregator import SkipTagsAggregator
@@ -158,7 +158,7 @@ class CartesiaTTSService(AudioContextWordTTSService):
voice_config["__experimental_controls"]["emotion"] = self._settings["emotion"]
msg = {
- "transcript": text or " ", # Text must contain at least one character
+ "transcript": text,
"continue": continue_transcript,
"context_id": self._context_id,
"model_id": self.model_name,
@@ -166,6 +166,7 @@ class CartesiaTTSService(AudioContextWordTTSService):
"output_format": self._settings["output_format"],
"language": self._settings["language"],
"add_timestamps": add_timestamps,
+ "use_original_timestamps": True,
}
return json.dumps(msg)
@@ -184,7 +185,8 @@ class CartesiaTTSService(AudioContextWordTTSService):
async def _connect(self):
await self._connect_websocket()
- if not self._receive_task:
+
+ if self._websocket and not self._receive_task:
self._receive_task = self.create_task(self._receive_task_handler(self._report_error))
async def _disconnect(self):
@@ -196,7 +198,7 @@ class CartesiaTTSService(AudioContextWordTTSService):
async def _connect_websocket(self):
try:
- if self._websocket:
+ if self._websocket and self._websocket.open:
return
logger.debug("Connecting to Cartesia")
self._websocket = await websockets.connect(
@@ -214,11 +216,11 @@ class CartesiaTTSService(AudioContextWordTTSService):
if self._websocket:
logger.debug("Disconnecting from Cartesia")
await self._websocket.close()
- self._websocket = None
-
- self._context_id = None
except Exception as e:
logger.error(f"{self} error closing websocket: {e}")
+ finally:
+ self._context_id = None
+ self._websocket = None
def _get_websocket(self):
if self._websocket:
@@ -278,7 +280,7 @@ class CartesiaTTSService(AudioContextWordTTSService):
logger.debug(f"{self}: Generating TTS [{text}]")
try:
- if not self._websocket:
+ if not self._websocket or self._websocket.closed:
await self._connect()
if not self._context_id:
@@ -287,7 +289,7 @@ class CartesiaTTSService(AudioContextWordTTSService):
self._context_id = str(uuid.uuid4())
await self.create_audio_context(self._context_id)
- msg = self._build_msg(text=text or " ") # Text must contain at least one character
+ msg = self._build_msg(text=text)
try:
await self._get_websocket().send(msg)
diff --git a/src/pipecat/services/deepgram/stt.py b/src/pipecat/services/deepgram/stt.py
index ae8b2318a..7b5209e0b 100644
--- a/src/pipecat/services/deepgram/stt.py
+++ b/src/pipecat/services/deepgram/stt.py
@@ -19,7 +19,7 @@ from pipecat.frames.frames import (
UserStoppedSpeakingFrame,
)
from pipecat.processors.frame_processor import FrameDirection
-from pipecat.services.ai_services import STTService
+from pipecat.services.stt_service import STTService
from pipecat.transcriptions.language import Language
from pipecat.utils.time import time_now_iso8601
@@ -45,6 +45,7 @@ class DeepgramSTTService(STTService):
*,
api_key: str,
url: str = "",
+ base_url: str = "",
sample_rate: Optional[int] = None,
live_options: Optional[LiveOptions] = None,
addons: Optional[Dict] = None,
@@ -53,6 +54,17 @@ class DeepgramSTTService(STTService):
sample_rate = sample_rate or (live_options.sample_rate if live_options else None)
super().__init__(sample_rate=sample_rate, **kwargs)
+ if url:
+ import warnings
+
+ with warnings.catch_warnings():
+ warnings.simplefilter("always")
+ warnings.warn(
+ "Parameter 'url' is deprecated, use 'base_url' instead.",
+ DeprecationWarning,
+ )
+ base_url = url
+
default_options = LiveOptions(
encoding="linear16",
language=Language.EN,
@@ -81,7 +93,7 @@ class DeepgramSTTService(STTService):
self._client = DeepgramClient(
api_key,
config=DeepgramClientOptions(
- url=url,
+ url=base_url,
options={"keepalive": "true"}, # verbose=logging.DEBUG
),
)
diff --git a/src/pipecat/services/deepgram/tts.py b/src/pipecat/services/deepgram/tts.py
index 5e05292d9..ec8a755a0 100644
--- a/src/pipecat/services/deepgram/tts.py
+++ b/src/pipecat/services/deepgram/tts.py
@@ -4,7 +4,6 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
-import asyncio
from typing import AsyncGenerator, Optional
from loguru import logger
@@ -16,10 +15,10 @@ from pipecat.frames.frames import (
TTSStartedFrame,
TTSStoppedFrame,
)
-from pipecat.services.ai_services import TTSService
+from pipecat.services.tts_service import TTSService
try:
- from deepgram import DeepgramClient, SpeakOptions
+ from deepgram import DeepgramClient, DeepgramClientOptions, SpeakOptions
except ModuleNotFoundError as e:
logger.error(f"Exception: {e}")
logger.error("In order to use Deepgram, you need to `pip install pipecat-ai[deepgram]`.")
@@ -32,6 +31,7 @@ class DeepgramTTSService(TTSService):
*,
api_key: str,
voice: str = "aura-helios-en",
+ base_url: str = "",
sample_rate: Optional[int] = None,
encoding: str = "linear16",
**kwargs,
@@ -42,7 +42,9 @@ class DeepgramTTSService(TTSService):
"encoding": encoding,
}
self.set_voice(voice)
- self._deepgram_client = DeepgramClient(api_key=api_key)
+
+ client_options = DeepgramClientOptions(url=base_url)
+ self._deepgram_client = DeepgramClient(api_key, config=client_options)
def can_generate_metrics(self) -> bool:
return True
@@ -60,8 +62,8 @@ class DeepgramTTSService(TTSService):
try:
await self.start_ttfb_metrics()
- response = await asyncio.to_thread(
- self._deepgram_client.speak.v("1").stream, {"text": text}, options
+ response = await self._deepgram_client.speak.asyncrest.v("1").stream_memory(
+ {"text": text}, options
)
await self.start_tts_usage_metrics(text)
diff --git a/src/pipecat/services/elevenlabs/tts.py b/src/pipecat/services/elevenlabs/tts.py
index 7b4e4f0dc..b9a22b388 100644
--- a/src/pipecat/services/elevenlabs/tts.py
+++ b/src/pipecat/services/elevenlabs/tts.py
@@ -18,6 +18,7 @@ from pipecat.frames.frames import (
EndFrame,
ErrorFrame,
Frame,
+ LLMFullResponseEndFrame,
StartFrame,
StartInterruptionFrame,
TTSAudioRawFrame,
@@ -25,7 +26,7 @@ from pipecat.frames.frames import (
TTSStoppedFrame,
)
from pipecat.processors.frame_processor import FrameDirection
-from pipecat.services.ai_services import InterruptibleWordTTSService, TTSService
+from pipecat.services.tts_service import InterruptibleWordTTSService, WordTTSService
from pipecat.transcriptions.language import Language
# See .env.example for ElevenLabs configuration needed
@@ -125,31 +126,14 @@ def build_elevenlabs_voice_settings(
settings: Dictionary containing voice settings parameters
Returns:
- Dictionary of voice settings or None if required parameters are missing
+ Dictionary of voice settings or None if no valid settings are provided
"""
+ voice_setting_keys = ["stability", "similarity_boost", "style", "use_speaker_boost", "speed"]
+
voice_settings = {}
- if settings["stability"] is not None and settings["similarity_boost"] is not None:
- voice_settings["stability"] = settings["stability"]
- voice_settings["similarity_boost"] = settings["similarity_boost"]
- if settings["style"] is not None:
- voice_settings["style"] = settings["style"]
- if settings["use_speaker_boost"] is not None:
- voice_settings["use_speaker_boost"] = settings["use_speaker_boost"]
- if settings["speed"] is not None:
- voice_settings["speed"] = settings["speed"]
- else:
- if settings["style"] is not None:
- logger.warning(
- "'style' is set but will not be applied because 'stability' and 'similarity_boost' are not both set."
- )
- if settings["use_speaker_boost"] is not None:
- logger.warning(
- "'use_speaker_boost' is set but will not be applied because 'stability' and 'similarity_boost' are not both set."
- )
- if settings["speed"] is not None:
- logger.warning(
- "'speed' is set but will not be applied because 'stability' and 'similarity_boost' are not both set."
- )
+ for key in voice_setting_keys:
+ if key in settings and settings[key] is not None:
+ voice_settings[key] = settings[key]
return voice_settings or None
@@ -308,10 +292,10 @@ class ElevenLabsTTSService(InterruptibleWordTTSService):
async def _connect(self):
await self._connect_websocket()
- if not self._receive_task:
+ if self._websocket and not self._receive_task:
self._receive_task = self.create_task(self._receive_task_handler(self._report_error))
- if not self._keepalive_task:
+ if self._websocket and not self._keepalive_task:
self._keepalive_task = self.create_task(self._keepalive_task_handler())
async def _disconnect(self):
@@ -327,7 +311,7 @@ class ElevenLabsTTSService(InterruptibleWordTTSService):
async def _connect_websocket(self):
try:
- if self._websocket:
+ if self._websocket and self._websocket.open:
return
logger.debug("Connecting to ElevenLabs")
@@ -374,11 +358,11 @@ class ElevenLabsTTSService(InterruptibleWordTTSService):
logger.debug("Disconnecting from ElevenLabs")
await self._websocket.send(json.dumps({"text": ""}))
await self._websocket.close()
- self._websocket = None
-
- self._started = False
except Exception as e:
logger.error(f"{self} error closing websocket: {e}")
+ finally:
+ self._started = False
+ self._websocket = None
def _get_websocket(self):
if self._websocket:
@@ -418,7 +402,7 @@ class ElevenLabsTTSService(InterruptibleWordTTSService):
logger.debug(f"{self}: Generating TTS [{text}]")
try:
- if not self._websocket:
+ if not self._websocket or self._websocket.closed:
await self._connect()
try:
@@ -441,8 +425,8 @@ class ElevenLabsTTSService(InterruptibleWordTTSService):
logger.error(f"{self} exception: {e}")
-class ElevenLabsHttpTTSService(TTSService):
- """ElevenLabs Text-to-Speech service using HTTP streaming.
+class ElevenLabsHttpTTSService(WordTTSService):
+ """ElevenLabs Text-to-Speech service using HTTP streaming with word timestamps.
Args:
api_key: ElevenLabs API key
@@ -475,7 +459,13 @@ class ElevenLabsHttpTTSService(TTSService):
params: InputParams = InputParams(),
**kwargs,
):
- super().__init__(sample_rate=sample_rate, **kwargs)
+ super().__init__(
+ aggregate_sentences=True,
+ push_text_frames=False,
+ push_stop_frames=True,
+ sample_rate=sample_rate,
+ **kwargs,
+ )
self._api_key = api_key
self._base_url = base_url
@@ -498,34 +488,136 @@ class ElevenLabsHttpTTSService(TTSService):
self._output_format = "" # initialized in start()
self._voice_settings = self._set_voice_settings()
+ # Track cumulative time to properly sequence word timestamps across utterances
+ self._cumulative_time = 0
+ self._started = False
+
+ # Store previous text for context within a turn
+ self._previous_text = ""
+
+ def language_to_service_language(self, language: Language) -> Optional[str]:
+ """Convert pipecat Language to ElevenLabs language code."""
+ return language_to_elevenlabs_language(language)
+
def can_generate_metrics(self) -> bool:
+ """Indicate that this service can generate usage metrics."""
return True
def _set_voice_settings(self):
return build_elevenlabs_voice_settings(self._settings)
+ def _reset_state(self):
+ """Reset internal state variables."""
+ self._cumulative_time = 0
+ self._started = False
+ self._previous_text = ""
+ logger.debug(f"{self}: Reset internal state")
+
async def start(self, frame: StartFrame):
+ """Initialize the service upon receiving a StartFrame."""
await super().start(frame)
self._output_format = output_format_from_sample_rate(self.sample_rate)
+ self._reset_state()
- async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
- """Generate speech from text using ElevenLabs streaming API.
+ async def push_frame(self, frame: Frame, direction: FrameDirection = FrameDirection.DOWNSTREAM):
+ await super().push_frame(frame, direction)
+ if isinstance(frame, (StartInterruptionFrame, TTSStoppedFrame)):
+ # Reset timing on interruption or stop
+ self._reset_state()
+
+ if isinstance(frame, TTSStoppedFrame):
+ await self.add_word_timestamps([("LLMFullResponseEndFrame", 0), ("Reset", 0)])
+
+ elif isinstance(frame, LLMFullResponseEndFrame):
+ # End of turn - reset previous text
+ self._previous_text = ""
+
+ def calculate_word_times(self, alignment_info: Mapping[str, Any]) -> List[Tuple[str, float]]:
+ """Calculate word timing from character alignment data.
+
+ Example input data:
+ {
+ "characters": [" ", "H", "e", "l", "l", "o", " ", "w", "o", "r", "l", "d"],
+ "character_start_times_seconds": [0.0, 0.1, 0.15, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
+ "character_end_times_seconds": [0.1, 0.15, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
+ }
+
+ Would produce word times (with cumulative_time=0):
+ [("Hello", 0.1), ("world", 0.5)]
Args:
- text: The text to convert to speech
+ alignment_info: Character timing data from ElevenLabs
+
+ Returns:
+ List of (word, timestamp) pairs
+ """
+ chars = alignment_info.get("characters", [])
+ char_start_times = alignment_info.get("character_start_times_seconds", [])
+
+ if not chars or not char_start_times or len(chars) != len(char_start_times):
+ logger.warning(
+ f"Invalid alignment data: chars={len(chars)}, times={len(char_start_times)}"
+ )
+ return []
+
+ # Build the words and find their start times
+ words = []
+ word_start_times = []
+ current_word = ""
+ first_char_idx = -1
+
+ for i, char in enumerate(chars):
+ if char == " ":
+ if current_word: # Only add non-empty words
+ words.append(current_word)
+ # Use time of the first character of the word, offset by cumulative time
+ word_start_times.append(
+ self._cumulative_time + char_start_times[first_char_idx]
+ )
+ current_word = ""
+ first_char_idx = -1
+ else:
+ if not current_word: # This is the first character of a new word
+ first_char_idx = i
+ current_word += char
+
+ # Don't forget the last word if there's no trailing space
+ if current_word and first_char_idx >= 0:
+ words.append(current_word)
+ word_start_times.append(self._cumulative_time + char_start_times[first_char_idx])
+
+ # Create word-time pairs
+ word_times = list(zip(words, word_start_times))
+
+ return word_times
+
+ async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
+ """Generate speech from text using ElevenLabs streaming API with timestamps.
+
+ Makes a request to the ElevenLabs API to generate audio and timing data.
+ Tracks the duration of each utterance to ensure correct sequencing.
+ Includes previous text as context for better prosody continuity.
+
+ Args:
+ text: Text to convert to speech
Yields:
- Frames containing audio data and status information
+ Audio and control frames
"""
logger.debug(f"{self}: Generating TTS [{text}]")
- url = f"{self._base_url}/v1/text-to-speech/{self._voice_id}/stream"
+ # Use the with-timestamps endpoint
+ url = f"{self._base_url}/v1/text-to-speech/{self._voice_id}/stream/with-timestamps"
payload: Dict[str, Union[str, Dict[str, Union[float, bool]]]] = {
"text": text,
"model_id": self._model_name,
}
+ # Include previous text as context if available
+ if self._previous_text:
+ payload["previous_text"] = self._previous_text
+
if self._voice_settings:
payload["voice_settings"] = self._voice_settings
@@ -550,8 +642,6 @@ class ElevenLabsHttpTTSService(TTSService):
if self._settings["optimize_streaming_latency"] is not None:
params["optimize_streaming_latency"] = self._settings["optimize_streaming_latency"]
- logger.debug(f"ElevenLabs request - payload: {payload}, params: {params}")
-
try:
await self.start_ttfb_metrics()
@@ -566,17 +656,66 @@ class ElevenLabsHttpTTSService(TTSService):
await self.start_tts_usage_metrics(text)
- # Process the streaming response
- CHUNK_SIZE = 1024
+ # Start TTS sequence if not already started
+ if not self._started:
+ self.start_word_timestamps()
+ yield TTSStartedFrame()
+ self._started = True
+
+ # Track the duration of this utterance based on the last character's end time
+ utterance_duration = 0
+ async for line in response.content:
+ line_str = line.decode("utf-8").strip()
+ if not line_str:
+ continue
+
+ try:
+ # Parse the JSON object
+ data = json.loads(line_str)
+
+ # Process audio if present
+ if data and "audio_base64" in data:
+ await self.stop_ttfb_metrics()
+ audio = base64.b64decode(data["audio_base64"])
+ yield TTSAudioRawFrame(audio, self.sample_rate, 1)
+
+ # Process alignment if present
+ if data and "alignment" in data:
+ alignment = data["alignment"]
+ if alignment: # Ensure alignment is not None
+ # Get end time of the last character in this chunk
+ char_end_times = alignment.get("character_end_times_seconds", [])
+ if char_end_times:
+ chunk_end_time = char_end_times[-1]
+ # Update to the longest end time seen so far
+ utterance_duration = max(utterance_duration, chunk_end_time)
+
+ # Calculate word timestamps
+ word_times = self.calculate_word_times(alignment)
+ if word_times:
+ await self.add_word_timestamps(word_times)
+ except json.JSONDecodeError as e:
+ logger.warning(f"Failed to parse JSON from stream: {e}")
+ continue
+ except Exception as e:
+ logger.error(f"Error processing response: {e}", exc_info=True)
+ continue
+
+ # After processing all chunks, add the total utterance duration
+ # to the cumulative time to ensure next utterance starts after this one
+ if utterance_duration > 0:
+ self._cumulative_time += utterance_duration
+
+ # Append the current text to previous_text for context continuity
+ # Only add a space if there's already text
+ if self._previous_text:
+ self._previous_text += " " + text
+ else:
+ self._previous_text = text
- yield TTSStartedFrame()
- async for chunk in response.content.iter_chunked(CHUNK_SIZE):
- if len(chunk) > 0:
- await self.stop_ttfb_metrics()
- yield TTSAudioRawFrame(chunk, self.sample_rate, 1)
except Exception as e:
logger.error(f"Error in run_tts: {e}")
yield ErrorFrame(error=str(e))
finally:
await self.stop_ttfb_metrics()
- yield TTSStoppedFrame()
+ # Let the parent class handle TTSStoppedFrame
diff --git a/src/pipecat/services/fal/image.py b/src/pipecat/services/fal/image.py
index 6ba14caf9..78439486e 100644
--- a/src/pipecat/services/fal/image.py
+++ b/src/pipecat/services/fal/image.py
@@ -15,7 +15,7 @@ from PIL import Image
from pydantic import BaseModel
from pipecat.frames.frames import ErrorFrame, Frame, URLImageRawFrame
-from pipecat.services.ai_services import ImageGenService
+from pipecat.services.image_service import ImageGenService
try:
import fal_client
diff --git a/src/pipecat/services/fal/stt.py b/src/pipecat/services/fal/stt.py
index 4926e4718..477d50f3b 100644
--- a/src/pipecat/services/fal/stt.py
+++ b/src/pipecat/services/fal/stt.py
@@ -11,7 +11,7 @@ from loguru import logger
from pydantic import BaseModel
from pipecat.frames.frames import ErrorFrame, Frame, TranscriptionFrame
-from pipecat.services.ai_services import SegmentedSTTService
+from pipecat.services.stt_service import SegmentedSTTService
from pipecat.transcriptions.language import Language
from pipecat.utils.time import time_now_iso8601
diff --git a/src/pipecat/services/fish/tts.py b/src/pipecat/services/fish/tts.py
index d4fe59635..abf609177 100644
--- a/src/pipecat/services/fish/tts.py
+++ b/src/pipecat/services/fish/tts.py
@@ -22,7 +22,7 @@ from pipecat.frames.frames import (
TTSStoppedFrame,
)
from pipecat.processors.frame_processor import FrameDirection
-from pipecat.services.ai_services import InterruptibleTTSService
+from pipecat.services.tts_service import InterruptibleTTSService
from pipecat.transcriptions.language import Language
try:
@@ -104,7 +104,8 @@ class FishAudioTTSService(InterruptibleTTSService):
async def _connect(self):
await self._connect_websocket()
- if not self._receive_task:
+
+ if self._websocket and not self._receive_task:
self._receive_task = self.create_task(self._receive_task_handler(self._report_error))
async def _disconnect(self):
@@ -116,7 +117,7 @@ class FishAudioTTSService(InterruptibleTTSService):
async def _connect_websocket(self):
try:
- if self._websocket:
+ if self._websocket and self._websocket.open:
return
logger.debug("Connecting to Fish Audio")
@@ -141,16 +142,17 @@ class FishAudioTTSService(InterruptibleTTSService):
stop_message = {"event": "stop"}
await self._websocket.send(ormsgpack.packb(stop_message))
await self._websocket.close()
- self._websocket = None
- self._request_id = None
- self._started = False
except Exception as e:
logger.error(f"Error closing websocket: {e}")
+ finally:
+ self._request_id = None
+ self._started = False
+ self._websocket = None
async def flush_audio(self):
"""Flush any buffered audio by sending a flush event to Fish Audio."""
logger.trace(f"{self}: Flushing audio buffers")
- if not self._websocket:
+ if not self._websocket or self._websocket.closed:
return
flush_message = {"event": "flush"}
await self._get_websocket().send(ormsgpack.packb(flush_message))
diff --git a/src/pipecat/services/gemini_multimodal_live/events.py b/src/pipecat/services/gemini_multimodal_live/events.py
index 60f688997..22cc7b5f4 100644
--- a/src/pipecat/services/gemini_multimodal_live/events.py
+++ b/src/pipecat/services/gemini_multimodal_live/events.py
@@ -8,6 +8,7 @@
import base64
import io
import json
+from enum import Enum
from typing import List, Literal, Optional
from PIL import Image
@@ -35,6 +36,38 @@ class Turn(BaseModel):
parts: List[ContentPart]
+class StartSensitivity(str, Enum):
+ """Determines how start of speech is detected."""
+
+ UNSPECIFIED = "START_SENSITIVITY_UNSPECIFIED" # Default is HIGH
+ HIGH = "START_SENSITIVITY_HIGH" # Detect start of speech more often
+ LOW = "START_SENSITIVITY_LOW" # Detect start of speech less often
+
+
+class EndSensitivity(str, Enum):
+ """Determines how end of speech is detected."""
+
+ UNSPECIFIED = "END_SENSITIVITY_UNSPECIFIED" # Default is HIGH
+ HIGH = "END_SENSITIVITY_HIGH" # End speech more often
+ LOW = "END_SENSITIVITY_LOW" # End speech less often
+
+
+class AutomaticActivityDetection(BaseModel):
+ """Configures automatic detection of activity."""
+
+ disabled: Optional[bool] = None
+ start_of_speech_sensitivity: Optional[StartSensitivity] = None
+ prefix_padding_ms: Optional[int] = None
+ end_of_speech_sensitivity: Optional[EndSensitivity] = None
+ silence_duration_ms: Optional[int] = None
+
+
+class RealtimeInputConfig(BaseModel):
+ """Configures the realtime input behavior."""
+
+ automatic_activity_detection: Optional[AutomaticActivityDetection] = None
+
+
class RealtimeInput(BaseModel):
mediaChunks: List[MediaChunk]
@@ -78,11 +111,17 @@ class SystemInstruction(BaseModel):
parts: List[ContentPart]
+class AudioTranscriptionConfig(BaseModel):
+ pass
+
+
class Setup(BaseModel):
model: str
system_instruction: Optional[SystemInstruction] = None
tools: Optional[List[dict]] = None
generation_config: Optional[dict] = None
+ output_audio_transcription: Optional[AudioTranscriptionConfig] = None
+ realtime_input_config: Optional[RealtimeInputConfig] = None
class Config(BaseModel):
@@ -120,10 +159,15 @@ class ServerContentTurnComplete(BaseModel):
turnComplete: bool
+class BidiGenerateContentTranscription(BaseModel):
+ text: str
+
+
class ServerContent(BaseModel):
modelTurn: Optional[ModelTurn] = None
interrupted: Optional[bool] = None
turnComplete: Optional[bool] = None
+ outputTranscription: Optional[BidiGenerateContentTranscription] = None
class FunctionCall(BaseModel):
diff --git a/src/pipecat/services/gemini_multimodal_live/gemini.py b/src/pipecat/services/gemini_multimodal_live/gemini.py
index 3ecab0186..d953e5065 100644
--- a/src/pipecat/services/gemini_multimodal_live/gemini.py
+++ b/src/pipecat/services/gemini_multimodal_live/gemini.py
@@ -10,9 +10,8 @@ import json
import time
from dataclasses import dataclass
from enum import Enum
-from typing import Any, Dict, List, Mapping, Optional, Union
+from typing import Any, Dict, List, Optional, Union
-import websockets
from loguru import logger
from pydantic import BaseModel, Field
@@ -45,21 +44,125 @@ from pipecat.frames.frames import (
UserStoppedSpeakingFrame,
)
from pipecat.metrics.metrics import LLMTokenUsage
+from pipecat.processors.aggregators.llm_response import (
+ LLMAssistantAggregatorParams,
+ LLMUserAggregatorParams,
+)
from pipecat.processors.aggregators.openai_llm_context import (
OpenAILLMContext,
OpenAILLMContextFrame,
)
from pipecat.processors.frame_processor import FrameDirection
-from pipecat.services.ai_services import LLMService
+from pipecat.services.llm_service import LLMService
from pipecat.services.openai.llm import (
OpenAIAssistantContextAggregator,
OpenAIUserContextAggregator,
)
+from pipecat.transcriptions.language import Language
from pipecat.utils.time import time_now_iso8601
from . import events
from .audio_transcriber import AudioTranscriber
+try:
+ import websockets
+except ModuleNotFoundError as e:
+ logger.error(f"Exception: {e}")
+ logger.error("In order to use Google AI, you need to `pip install pipecat-ai[google]`.")
+ raise Exception(f"Missing module: {e}")
+
+
+def language_to_gemini_language(language: Language) -> Optional[str]:
+ """Maps a Language enum value to a Gemini Live supported language code.
+
+ Source:
+ https://ai.google.dev/api/generate-content#MediaResolution
+
+ Returns None if the language is not supported by Gemini Live.
+ """
+ language_map = {
+ # Arabic
+ Language.AR: "ar-XA",
+ # Bengali
+ Language.BN_IN: "bn-IN",
+ # Chinese (Mandarin)
+ Language.CMN: "cmn-CN",
+ Language.CMN_CN: "cmn-CN",
+ Language.ZH: "cmn-CN", # Map general Chinese to Mandarin for Gemini
+ Language.ZH_CN: "cmn-CN", # Map Simplified Chinese to Mandarin for Gemini
+ # German
+ Language.DE: "de-DE",
+ Language.DE_DE: "de-DE",
+ # English
+ Language.EN: "en-US", # Default to US English (though not explicitly listed in supported codes)
+ Language.EN_US: "en-US",
+ Language.EN_AU: "en-AU",
+ Language.EN_GB: "en-GB",
+ Language.EN_IN: "en-IN",
+ # Spanish
+ Language.ES: "es-ES", # Default to Spain Spanish
+ Language.ES_ES: "es-ES",
+ Language.ES_US: "es-US",
+ # French
+ Language.FR: "fr-FR", # Default to France French
+ Language.FR_FR: "fr-FR",
+ Language.FR_CA: "fr-CA",
+ # Gujarati
+ Language.GU: "gu-IN",
+ Language.GU_IN: "gu-IN",
+ # Hindi
+ Language.HI: "hi-IN",
+ Language.HI_IN: "hi-IN",
+ # Indonesian
+ Language.ID: "id-ID",
+ Language.ID_ID: "id-ID",
+ # Italian
+ Language.IT: "it-IT",
+ Language.IT_IT: "it-IT",
+ # Japanese
+ Language.JA: "ja-JP",
+ Language.JA_JP: "ja-JP",
+ # Kannada
+ Language.KN: "kn-IN",
+ Language.KN_IN: "kn-IN",
+ # Korean
+ Language.KO: "ko-KR",
+ Language.KO_KR: "ko-KR",
+ # Malayalam
+ Language.ML: "ml-IN",
+ Language.ML_IN: "ml-IN",
+ # Marathi
+ Language.MR: "mr-IN",
+ Language.MR_IN: "mr-IN",
+ # Dutch
+ Language.NL: "nl-NL",
+ Language.NL_NL: "nl-NL",
+ # Polish
+ Language.PL: "pl-PL",
+ Language.PL_PL: "pl-PL",
+ # Portuguese (Brazil)
+ Language.PT_BR: "pt-BR",
+ # Russian
+ Language.RU: "ru-RU",
+ Language.RU_RU: "ru-RU",
+ # Tamil
+ Language.TA: "ta-IN",
+ Language.TA_IN: "ta-IN",
+ # Telugu
+ Language.TE: "te-IN",
+ Language.TE_IN: "te-IN",
+ # Thai
+ Language.TH: "th-TH",
+ Language.TH_TH: "th-TH",
+ # Turkish
+ Language.TR: "tr-TR",
+ Language.TR_TR: "tr-TR",
+ # Vietnamese
+ Language.VI: "vi-VN",
+ Language.VI_VN: "vi-VN",
+ }
+ return language_map.get(language)
+
class GeminiMultimodalLiveContext(OpenAILLMContext):
@staticmethod
@@ -143,6 +246,25 @@ class GeminiMultimodalModalities(Enum):
AUDIO = "AUDIO"
+class GeminiMediaResolution(str, Enum):
+ """Media resolution options for Gemini Multimodal Live."""
+
+ UNSPECIFIED = "MEDIA_RESOLUTION_UNSPECIFIED" # Use default
+ LOW = "MEDIA_RESOLUTION_LOW" # 64 tokens
+ MEDIUM = "MEDIA_RESOLUTION_MEDIUM" # 256 tokens
+ HIGH = "MEDIA_RESOLUTION_HIGH" # Zoomed reframing with 256 tokens
+
+
+class GeminiVADParams(BaseModel):
+ """Voice Activity Detection parameters."""
+
+ disabled: Optional[bool] = Field(default=None)
+ start_sensitivity: Optional[events.StartSensitivity] = Field(default=None)
+ end_sensitivity: Optional[events.EndSensitivity] = Field(default=None)
+ prefix_padding_ms: Optional[int] = Field(default=None)
+ silence_duration_ms: Optional[int] = Field(default=None)
+
+
class InputParams(BaseModel):
frequency_penalty: Optional[float] = Field(default=None, ge=0.0, le=2.0)
max_tokens: Optional[int] = Field(default=4096, ge=1)
@@ -153,6 +275,11 @@ class InputParams(BaseModel):
modalities: Optional[GeminiMultimodalModalities] = Field(
default=GeminiMultimodalModalities.AUDIO
)
+ language: Optional[Language] = Field(default=Language.EN_US)
+ media_resolution: Optional[GeminiMediaResolution] = Field(
+ default=GeminiMediaResolution.UNSPECIFIED
+ )
+ vad: Optional[GeminiVADParams] = Field(default=None)
extra: Optional[Dict[str, Any]] = Field(default_factory=dict)
@@ -164,25 +291,25 @@ class GeminiMultimodalLiveLLMService(LLMService):
self,
*,
api_key: str,
- base_url="generativelanguage.googleapis.com",
- model="models/gemini-2.0-flash-exp",
+ base_url: str = "generativelanguage.googleapis.com/ws/google.ai.generativelanguage.v1beta.GenerativeService.BidiGenerateContent",
+ model="models/gemini-2.0-flash-live-001",
voice_id: str = "Charon",
start_audio_paused: bool = False,
start_video_paused: bool = False,
system_instruction: Optional[str] = None,
tools: Optional[Union[List[dict], ToolsSchema]] = None,
transcribe_user_audio: bool = False,
- transcribe_model_audio: bool = False,
params: InputParams = InputParams(),
inference_on_context_initialization: bool = True,
**kwargs,
):
super().__init__(base_url=base_url, **kwargs)
self._last_sent_time = 0
- self.api_key = api_key
- self.base_url = base_url
+ self._api_key = api_key
+ self._base_url = base_url
self.set_model_name(model)
self._voice_id = voice_id
+ self._language_code = params.language
self._system_instruction = system_instruction
self._tools = tools
@@ -195,9 +322,7 @@ class GeminiMultimodalLiveLLMService(LLMService):
self._websocket = None
self._receive_task = None
self._transcribe_audio_task = None
- self._transcribe_model_audio_task = None
self._transcribe_audio_queue = asyncio.Queue()
- self._transcribe_model_audio_queue = asyncio.Queue()
self._disconnecting = False
self._api_session_ready = False
@@ -205,7 +330,6 @@ class GeminiMultimodalLiveLLMService(LLMService):
self._transcriber = AudioTranscriber(api_key)
self._transcribe_user_audio = transcribe_user_audio
- self._transcribe_model_audio = transcribe_model_audio
self._user_is_speaking = False
self._bot_is_speaking = False
self._user_audio_buffer = bytearray()
@@ -214,6 +338,12 @@ class GeminiMultimodalLiveLLMService(LLMService):
self._sample_rate = 24000
+ self._language = params.language
+ self._language_code = (
+ language_to_gemini_language(params.language) if params.language else "en-US"
+ )
+ self._vad_params = params.vad
+
self._settings = {
"frequency_penalty": params.frequency_penalty,
"max_tokens": params.max_tokens,
@@ -222,6 +352,9 @@ class GeminiMultimodalLiveLLMService(LLMService):
"top_k": params.top_k,
"top_p": params.top_p,
"modalities": params.modalities,
+ "language": self._language_code,
+ "media_resolution": params.media_resolution,
+ "vad": params.vad,
"extra": params.extra if isinstance(params.extra, dict) else {},
}
@@ -237,6 +370,13 @@ class GeminiMultimodalLiveLLMService(LLMService):
def set_model_modalities(self, modalities: GeminiMultimodalModalities):
self._settings["modalities"] = modalities
+ def set_language(self, language: Language):
+ """Set the language for generation."""
+ self._language = language
+ self._language_code = language_to_gemini_language(language) or "en-US"
+ self._settings["language"] = self._language_code
+ logger.info(f"Set Gemini language to: {self._language_code}")
+
async def set_context(self, context: OpenAILLMContext):
"""Set the context explicitly from outside the pipeline.
@@ -303,22 +443,6 @@ class GeminiMultimodalLiveLLMService(LLMService):
TranscriptionFrame(text=text, user_id="user", timestamp=time_now_iso8601())
)
- async def _handle_transcribe_model_audio(self, audio, context):
- # Early return if modalities are not set to audio.
- if self._settings["modalities"] != GeminiMultimodalModalities.AUDIO:
- return
-
- text = await self._transcribe_audio(audio, context)
- logger.debug(f"[Transcription:model] {text}")
- # We add user messages directly to the context. We don't do that for assistant messages,
- # because we assume the frames we emit will work normally in this downstream case. This
- # definitely feels like a hack. Need to revisit when the API evolves.
- # context.add_message({"role": "assistant", "content": [{"type": "text", "text": text}]})
- await self.push_frame(LLMFullResponseStartFrame())
- await self.push_frame(LLMTextFrame(text=text))
- await self.push_frame(TTSTextFrame(text=text))
- await self.push_frame(LLMFullResponseEndFrame())
-
async def _transcribe_audio(self, audio, context):
(text, prompt_tokens, completion_tokens, total_tokens) = await self._transcriber.transcribe(
audio, context
@@ -407,36 +531,66 @@ class GeminiMultimodalLiveLLMService(LLMService):
logger.info("Connecting to Gemini service")
try:
- uri = f"wss://{self.base_url}/ws/google.ai.generativelanguage.v1alpha.GenerativeService.BidiGenerateContent?key={self.api_key}"
- logger.info(f"Connecting to {uri}")
+ logger.info(f"Connecting to wss://{self._base_url}")
+ uri = f"wss://{self._base_url}?key={self._api_key}"
self._websocket = await websockets.connect(uri=uri)
self._receive_task = self.create_task(self._receive_task_handler())
self._transcribe_audio_task = self.create_task(self._transcribe_audio_handler())
- self._transcribe_model_audio_task = self.create_task(
- self._transcribe_model_audio_handler()
- )
- config = events.Config.model_validate(
- {
- "setup": {
- "model": self._model_name,
- "generation_config": {
- "frequency_penalty": self._settings["frequency_penalty"],
- "max_output_tokens": self._settings["max_tokens"], # Not supported yet
- "presence_penalty": self._settings["presence_penalty"],
- "temperature": self._settings["temperature"],
- "top_k": self._settings["top_k"],
- "top_p": self._settings["top_p"],
- "response_modalities": self._settings["modalities"].value,
- "speech_config": {
- "voice_config": {
- "prebuilt_voice_config": {"voice_name": self._voice_id}
- },
- },
- },
- },
- }
- )
+ # Create the basic configuration
+ config_data = {
+ "setup": {
+ "model": self._model_name,
+ "generation_config": {
+ "frequency_penalty": self._settings["frequency_penalty"],
+ "max_output_tokens": self._settings["max_tokens"],
+ "presence_penalty": self._settings["presence_penalty"],
+ "temperature": self._settings["temperature"],
+ "top_k": self._settings["top_k"],
+ "top_p": self._settings["top_p"],
+ "response_modalities": self._settings["modalities"].value,
+ "speech_config": {
+ "voice_config": {
+ "prebuilt_voice_config": {"voice_name": self._voice_id}
+ },
+ "language_code": self._settings["language"],
+ },
+ "media_resolution": self._settings["media_resolution"].value,
+ },
+ "output_audio_transcription": {},
+ }
+ }
+
+ # Add VAD configuration if provided
+ if self._settings.get("vad"):
+ vad_config = {}
+ vad_params = self._settings["vad"]
+
+ # Only add parameters that are explicitly set
+ if vad_params.disabled is not None:
+ vad_config["disabled"] = vad_params.disabled
+
+ if vad_params.start_sensitivity:
+ vad_config["start_of_speech_sensitivity"] = vad_params.start_sensitivity.value
+
+ if vad_params.end_sensitivity:
+ vad_config["end_of_speech_sensitivity"] = vad_params.end_sensitivity.value
+
+ if vad_params.prefix_padding_ms is not None:
+ vad_config["prefix_padding_ms"] = vad_params.prefix_padding_ms
+
+ if vad_params.silence_duration_ms is not None:
+ vad_config["silence_duration_ms"] = vad_params.silence_duration_ms
+
+ # Only add automatic_activity_detection if we have VAD settings
+ if vad_config:
+ realtime_config = {"automatic_activity_detection": vad_config}
+
+ config_data["setup"]["realtime_input_config"] = realtime_config
+
+ config = events.Config.model_validate(config_data)
+
+ # Add system instruction if available
system_instruction = self._system_instruction or ""
if self._context and hasattr(self._context, "extract_system_instructions"):
system_instruction += "\n" + self._context.extract_system_instructions()
@@ -445,9 +599,13 @@ class GeminiMultimodalLiveLLMService(LLMService):
config.setup.system_instruction = events.SystemInstruction(
parts=[events.ContentPart(text=system_instruction)]
)
+
+ # Add tools if available
if self._tools:
logger.debug(f"Gemini is configuring to use tools{self._tools}")
config.setup.tools = self.get_llm_adapter().from_standard_tools(self._tools)
+
+ # Send the configuration
await self.send_client_event(config)
except Exception as e:
@@ -469,9 +627,6 @@ class GeminiMultimodalLiveLLMService(LLMService):
if self._transcribe_audio_task:
await self.cancel_task(self._transcribe_audio_task)
self._transcribe_audio_task = None
- if self._transcribe_model_audio_task:
- await self.cancel_task(self._transcribe_model_audio_task)
- self._transcribe_model_audio_task = None
self._disconnecting = False
except Exception as e:
logger.error(f"{self} error disconnecting: {e}")
@@ -508,6 +663,8 @@ class GeminiMultimodalLiveLLMService(LLMService):
await self._handle_evt_model_turn(evt)
elif evt.serverContent and evt.serverContent.turnComplete:
await self._handle_evt_turn_complete(evt)
+ elif evt.serverContent and evt.serverContent.outputTranscription:
+ await self._handle_evt_output_transcription(evt)
elif evt.toolCall:
await self._handle_evt_tool_call(evt)
elif False: # !!! todo: error events?
@@ -522,11 +679,6 @@ class GeminiMultimodalLiveLLMService(LLMService):
audio = await self._transcribe_audio_queue.get()
await self._handle_transcribe_user_audio(audio, self._context)
- async def _transcribe_model_audio_handler(self):
- while True:
- audio = await self._transcribe_model_audio_queue.get()
- await self._handle_transcribe_model_audio(audio, self._context)
-
#
#
#
@@ -706,24 +858,31 @@ class GeminiMultimodalLiveLLMService(LLMService):
async def _handle_evt_turn_complete(self, evt):
self._bot_is_speaking = False
- audio = self._bot_audio_buffer
text = self._bot_text_buffer
- self._bot_audio_buffer = bytearray()
self._bot_text_buffer = ""
- if audio and self._transcribe_model_audio and self._context:
- await self._transcribe_model_audio_queue.put(audio)
- elif text:
+ if text:
await self.push_frame(LLMFullResponseEndFrame())
await self.push_frame(TTSStoppedFrame())
+ async def _handle_evt_output_transcription(self, evt):
+ if not evt.serverContent.outputTranscription:
+ return
+
+ text = evt.serverContent.outputTranscription.text
+ if text:
+ await self.push_frame(LLMFullResponseStartFrame())
+ await self.push_frame(LLMTextFrame(text=text))
+ await self.push_frame(TTSTextFrame(text=text))
+ await self.push_frame(LLMFullResponseEndFrame())
+
def create_context_aggregator(
self,
context: OpenAILLMContext,
*,
- user_kwargs: Mapping[str, Any] = {},
- assistant_kwargs: Mapping[str, Any] = {},
+ user_params: LLMUserAggregatorParams = LLMUserAggregatorParams(),
+ assistant_params: LLMAssistantAggregatorParams = LLMAssistantAggregatorParams(),
) -> GeminiMultimodalLiveContextAggregatorPair:
"""Create an instance of GeminiMultimodalLiveContextAggregatorPair from
an OpenAILLMContext. Constructor keyword arguments for both the user and
@@ -731,12 +890,10 @@ class GeminiMultimodalLiveLLMService(LLMService):
Args:
context (OpenAILLMContext): The LLM context.
- user_kwargs (Mapping[str, Any], optional): Additional keyword
- arguments for the user context aggregator constructor. Defaults
- to an empty mapping.
- assistant_kwargs (Mapping[str, Any], optional): Additional keyword
- arguments for the assistant context aggregator
- constructor. Defaults to an empty mapping.
+ user_params (LLMUserAggregatorParams, optional): User aggregator
+ parameters.
+ assistant_params (LLMAssistantAggregatorParams, optional): User
+ aggregator parameters.
Returns:
GeminiMultimodalLiveContextAggregatorPair: A pair of context
@@ -747,11 +904,8 @@ class GeminiMultimodalLiveLLMService(LLMService):
context.set_llm_adapter(self.get_llm_adapter())
GeminiMultimodalLiveContext.upgrade(context)
- user = GeminiMultimodalLiveUserContextAggregator(context, **user_kwargs)
+ user = GeminiMultimodalLiveUserContextAggregator(context, params=user_params)
- default_assistant_kwargs = {"expect_stripped_words": False}
- default_assistant_kwargs.update(assistant_kwargs)
- assistant = GeminiMultimodalLiveAssistantContextAggregator(
- context, **default_assistant_kwargs
- )
+ assistant_params.expect_stripped_words = True
+ assistant = GeminiMultimodalLiveAssistantContextAggregator(context, params=assistant_params)
return GeminiMultimodalLiveContextAggregatorPair(_user=user, _assistant=assistant)
diff --git a/src/pipecat/services/gladia.py b/src/pipecat/services/gladia.py
deleted file mode 100644
index 09bcd7aa3..000000000
--- a/src/pipecat/services/gladia.py
+++ /dev/null
@@ -1,253 +0,0 @@
-#
-# Copyright (c) 2024β2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import base64
-import json
-from typing import AsyncGenerator, Optional
-
-import aiohttp
-from loguru import logger
-from pydantic import BaseModel
-
-from pipecat.frames.frames import (
- CancelFrame,
- EndFrame,
- Frame,
- InterimTranscriptionFrame,
- StartFrame,
- TranscriptionFrame,
-)
-from pipecat.services.ai_services import STTService
-from pipecat.transcriptions.language import Language
-from pipecat.utils.time import time_now_iso8601
-
-try:
- import websockets
-except ModuleNotFoundError as e:
- logger.error(f"Exception: {e}")
- logger.error(
- "In order to use Gladia, you need to `pip install pipecat-ai[gladia]`. Also, set `GLADIA_API_KEY` environment variable."
- )
- raise Exception(f"Missing module: {e}")
-
-
-def language_to_gladia_language(language: Language) -> Optional[str]:
- BASE_LANGUAGES = {
- Language.AF: "af",
- Language.AM: "am",
- Language.AR: "ar",
- Language.AS: "as",
- Language.AZ: "az",
- Language.BG: "bg",
- Language.BN: "bn",
- Language.BS: "bs",
- Language.CA: "ca",
- Language.CS: "cs",
- Language.CY: "cy",
- Language.DA: "da",
- Language.DE: "de",
- Language.EL: "el",
- Language.EN: "en",
- Language.ES: "es",
- Language.ET: "et",
- Language.EU: "eu",
- Language.FA: "fa",
- Language.FI: "fi",
- Language.FR: "fr",
- Language.GA: "ga",
- Language.GL: "gl",
- Language.GU: "gu",
- Language.HE: "he",
- Language.HI: "hi",
- Language.HR: "hr",
- Language.HU: "hu",
- Language.HY: "hy",
- Language.ID: "id",
- Language.IS: "is",
- Language.IT: "it",
- Language.JA: "ja",
- Language.JV: "jv",
- Language.KA: "ka",
- Language.KK: "kk",
- Language.KM: "km",
- Language.KN: "kn",
- Language.KO: "ko",
- Language.LO: "lo",
- Language.LT: "lt",
- Language.LV: "lv",
- Language.MK: "mk",
- Language.ML: "ml",
- Language.MN: "mn",
- Language.MR: "mr",
- Language.MS: "ms",
- Language.MT: "mt",
- Language.MY: "my",
- Language.NE: "ne",
- Language.NL: "nl",
- Language.NO: "no",
- Language.OR: "or",
- Language.PA: "pa",
- Language.PL: "pl",
- Language.PS: "ps",
- Language.PT: "pt",
- Language.RO: "ro",
- Language.RU: "ru",
- Language.SI: "si",
- Language.SK: "sk",
- Language.SL: "sl",
- Language.SO: "so",
- Language.SQ: "sq",
- Language.SR: "sr",
- Language.SU: "su",
- Language.SV: "sv",
- Language.SW: "sw",
- Language.TA: "ta",
- Language.TE: "te",
- Language.TH: "th",
- Language.TR: "tr",
- Language.UK: "uk",
- Language.UR: "ur",
- Language.UZ: "uz",
- Language.VI: "vi",
- Language.ZH: "zh",
- Language.ZU: "zu",
- }
-
- result = BASE_LANGUAGES.get(language)
-
- # If not found in base languages, try to find the base language from a variant
- if not result:
- # Convert enum value to string and get the base language part (e.g. es-ES -> es)
- lang_str = str(language.value)
- base_code = lang_str.split("-")[0].lower()
- # Look up the base code in our supported languages
- result = base_code if base_code in BASE_LANGUAGES.values() else None
-
- return result
-
-
-class GladiaSTTService(STTService):
- class InputParams(BaseModel):
- language: Optional[Language] = Language.EN
- endpointing: Optional[float] = 0.2
- maximum_duration_without_endpointing: Optional[int] = 10
- audio_enhancer: Optional[bool] = None
- words_accurate_timestamps: Optional[bool] = None
- speech_threshold: Optional[float] = 0.99
-
- def __init__(
- self,
- *,
- api_key: str,
- url: str = "https://api.gladia.io/v2/live",
- confidence: float = 0.5,
- sample_rate: Optional[int] = None,
- params: InputParams = InputParams(),
- **kwargs,
- ):
- super().__init__(sample_rate=sample_rate, **kwargs)
- self._api_key = api_key
- self._url = url
- self._settings = {
- "encoding": "wav/pcm",
- "bit_depth": 16,
- "sample_rate": 0,
- "channels": 1,
- "language_config": {
- "languages": [self.language_to_service_language(params.language)]
- if params.language
- else [],
- "code_switching": False,
- },
- "endpointing": params.endpointing,
- "maximum_duration_without_endpointing": params.maximum_duration_without_endpointing,
- "pre_processing": {
- "audio_enhancer": params.audio_enhancer,
- "speech_threshold": params.speech_threshold,
- },
- "realtime_processing": {
- "words_accurate_timestamps": params.words_accurate_timestamps,
- },
- }
- self._confidence = confidence
- self._websocket = None
- self._receive_task = None
-
- def language_to_service_language(self, language: Language) -> Optional[str]:
- return language_to_gladia_language(language)
-
- async def start(self, frame: StartFrame):
- await super().start(frame)
- if self._websocket:
- return
- self._settings["sample_rate"] = self.sample_rate
- response = await self._setup_gladia()
- self._websocket = await websockets.connect(response["url"])
- if not self._receive_task:
- self._receive_task = self.create_task(self._receive_task_handler())
-
- async def stop(self, frame: EndFrame):
- await super().stop(frame)
- await self._send_stop_recording()
- if self._websocket:
- await self._websocket.close()
- self._websocket = None
- if self._receive_task:
- await self.wait_for_task(self._receive_task)
- self._receive_task = None
-
- async def cancel(self, frame: CancelFrame):
- await super().cancel(frame)
- await self._websocket.close()
- if self._receive_task:
- await self.cancel_task(self._receive_task)
- self._receive_task = None
-
- async def run_stt(self, audio: bytes) -> AsyncGenerator[Frame, None]:
- await self.start_processing_metrics()
- await self._send_audio(audio)
- await self.stop_processing_metrics()
- yield None
-
- async def _setup_gladia(self):
- async with aiohttp.ClientSession() as session:
- async with session.post(
- self._url,
- headers={"X-Gladia-Key": self._api_key, "Content-Type": "application/json"},
- json=self._settings,
- ) as response:
- if response.ok:
- return await response.json()
- else:
- logger.error(
- f"Gladia error: {response.status}: {response.text or response.reason}"
- )
- raise Exception(f"Failed to initialize Gladia session: {response.status}")
-
- async def _send_audio(self, audio: bytes):
- data = base64.b64encode(audio).decode("utf-8")
- message = {"type": "audio_chunk", "data": {"chunk": data}}
- await self._websocket.send(json.dumps(message))
-
- async def _send_stop_recording(self):
- await self._websocket.send(json.dumps({"type": "stop_recording"}))
-
- async def _receive_task_handler(self):
- async for message in self._websocket:
- content = json.loads(message)
- if content["type"] == "transcript":
- utterance = content["data"]["utterance"]
- confidence = utterance.get("confidence", 0)
- transcript = utterance["text"]
- if confidence >= self._confidence:
- if content["data"]["is_final"]:
- await self.push_frame(
- TranscriptionFrame(transcript, "", time_now_iso8601())
- )
- else:
- await self.push_frame(
- InterimTranscriptionFrame(transcript, "", time_now_iso8601())
- )
diff --git a/src/pipecat/services/gladia/__init__.py b/src/pipecat/services/gladia/__init__.py
new file mode 100644
index 000000000..916988e42
--- /dev/null
+++ b/src/pipecat/services/gladia/__init__.py
@@ -0,0 +1,13 @@
+#
+# Copyright (c) 2024β2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import sys
+
+from pipecat.services import DeprecatedModuleProxy
+
+from .stt import *
+
+sys.modules[__name__] = DeprecatedModuleProxy(globals(), "gladia", "gladia.stt")
diff --git a/src/pipecat/services/gladia/config.py b/src/pipecat/services/gladia/config.py
new file mode 100644
index 000000000..275554418
--- /dev/null
+++ b/src/pipecat/services/gladia/config.py
@@ -0,0 +1,163 @@
+#
+# Copyright (c) 2024β2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+from typing import Any, Dict, List, Optional, Union
+
+from pydantic import BaseModel
+
+from pipecat.transcriptions.language import Language
+
+
+class LanguageConfig(BaseModel):
+ """Configuration for language detection and handling.
+
+ Attributes:
+ languages: List of language codes to use for transcription
+ code_switching: Whether to auto-detect language changes during transcription
+ """
+
+ languages: Optional[List[str]] = None
+ code_switching: Optional[bool] = None
+
+
+class PreProcessingConfig(BaseModel):
+ """Configuration for audio pre-processing options.
+
+ Attributes:
+ speech_threshold: Sensitivity for speech detection (0-1)
+ """
+
+ speech_threshold: Optional[float] = None
+
+
+class CustomVocabularyItem(BaseModel):
+ """Represents a custom vocabulary item with an intensity value.
+
+ Attributes:
+ value: The vocabulary word or phrase
+ intensity: The bias intensity for this vocabulary item (0-1)
+ """
+
+ value: str
+ intensity: float
+
+
+class CustomVocabularyConfig(BaseModel):
+ """Configuration for custom vocabulary.
+
+ Attributes:
+ vocabulary: List of words/phrases or CustomVocabularyItem objects
+ default_intensity: Default intensity for simple string vocabulary items
+ """
+
+ vocabulary: Optional[List[Union[str, CustomVocabularyItem]]] = None
+ default_intensity: Optional[float] = None
+
+
+class CustomSpellingConfig(BaseModel):
+ """Configuration for custom spelling rules.
+
+ Attributes:
+ spelling_dictionary: Mapping of correct spellings to phonetic variations
+ """
+
+ spelling_dictionary: Optional[Dict[str, List[str]]] = None
+
+
+class TranslationConfig(BaseModel):
+ """Configuration for real-time translation.
+
+ Attributes:
+ target_languages: List of target language codes for translation
+ model: Translation model to use ("base" or "enhanced")
+ match_original_utterances: Whether to align translations with original utterances
+ """
+
+ target_languages: Optional[List[str]] = None
+ model: Optional[str] = None
+ match_original_utterances: Optional[bool] = None
+
+
+class RealtimeProcessingConfig(BaseModel):
+ """Configuration for real-time processing features.
+
+ Attributes:
+ words_accurate_timestamps: Whether to provide per-word timestamps
+ custom_vocabulary: Whether to enable custom vocabulary
+ custom_vocabulary_config: Custom vocabulary configuration
+ custom_spelling: Whether to enable custom spelling
+ custom_spelling_config: Custom spelling configuration
+ translation: Whether to enable translation
+ translation_config: Translation configuration
+ named_entity_recognition: Whether to enable named entity recognition
+ sentiment_analysis: Whether to enable sentiment analysis
+ """
+
+ words_accurate_timestamps: Optional[bool] = None
+ custom_vocabulary: Optional[bool] = None
+ custom_vocabulary_config: Optional[CustomVocabularyConfig] = None
+ custom_spelling: Optional[bool] = None
+ custom_spelling_config: Optional[CustomSpellingConfig] = None
+ translation: Optional[bool] = None
+ translation_config: Optional[TranslationConfig] = None
+ named_entity_recognition: Optional[bool] = None
+ sentiment_analysis: Optional[bool] = None
+
+
+class MessagesConfig(BaseModel):
+ """Configuration for controlling which message types are sent via WebSocket.
+
+ Attributes:
+ receive_partial_transcripts: Whether to receive intermediate transcription results
+ receive_final_transcripts: Whether to receive final transcription results
+ receive_speech_events: Whether to receive speech begin/end events
+ receive_pre_processing_events: Whether to receive pre-processing events
+ receive_realtime_processing_events: Whether to receive real-time processing events
+ receive_post_processing_events: Whether to receive post-processing events
+ receive_acknowledgments: Whether to receive acknowledgment messages
+ receive_errors: Whether to receive error messages
+ receive_lifecycle_events: Whether to receive lifecycle events
+ """
+
+ receive_partial_transcripts: Optional[bool] = None
+ receive_final_transcripts: Optional[bool] = None
+ receive_speech_events: Optional[bool] = None
+ receive_pre_processing_events: Optional[bool] = None
+ receive_realtime_processing_events: Optional[bool] = None
+ receive_post_processing_events: Optional[bool] = None
+ receive_acknowledgments: Optional[bool] = None
+ receive_errors: Optional[bool] = None
+ receive_lifecycle_events: Optional[bool] = None
+
+
+class GladiaInputParams(BaseModel):
+ """Configuration parameters for the Gladia STT service.
+
+ Attributes:
+ encoding: Audio encoding format
+ bit_depth: Audio bit depth
+ channels: Number of audio channels
+ custom_metadata: Additional metadata to include with requests
+ endpointing: Silence duration in seconds to mark end of speech
+ maximum_duration_without_endpointing: Maximum utterance duration without silence
+ language: DEPRECATED - Use language_config instead
+ language_config: Detailed language configuration
+ pre_processing: Audio pre-processing options
+ realtime_processing: Real-time processing features
+ messages_config: WebSocket message filtering options
+ """
+
+ encoding: Optional[str] = "wav/pcm"
+ bit_depth: Optional[int] = 16
+ channels: Optional[int] = 1
+ custom_metadata: Optional[Dict[str, Any]] = None
+ endpointing: Optional[float] = None
+ maximum_duration_without_endpointing: Optional[int] = 10
+ language: Optional[Language] = None # Deprecated
+ language_config: Optional[LanguageConfig] = None
+ pre_processing: Optional[PreProcessingConfig] = None
+ realtime_processing: Optional[RealtimeProcessingConfig] = None
+ messages_config: Optional[MessagesConfig] = None
diff --git a/src/pipecat/services/gladia/stt.py b/src/pipecat/services/gladia/stt.py
new file mode 100644
index 000000000..df8038bd3
--- /dev/null
+++ b/src/pipecat/services/gladia/stt.py
@@ -0,0 +1,401 @@
+#
+# Copyright (c) 2024β2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import base64
+import json
+import warnings
+from typing import Any, AsyncGenerator, Dict, Optional
+
+import aiohttp
+from loguru import logger
+
+from pipecat.frames.frames import (
+ CancelFrame,
+ EndFrame,
+ Frame,
+ InterimTranscriptionFrame,
+ StartFrame,
+ TranscriptionFrame,
+)
+from pipecat.services.gladia.config import GladiaInputParams
+from pipecat.services.stt_service import STTService
+from pipecat.transcriptions.language import Language
+from pipecat.utils.time import time_now_iso8601
+
+try:
+ import websockets
+except ModuleNotFoundError as e:
+ logger.error(f"Exception: {e}")
+ logger.error("In order to use Gladia, you need to `pip install pipecat-ai[gladia]`.")
+ raise Exception(f"Missing module: {e}")
+
+
+def language_to_gladia_language(language: Language) -> Optional[str]:
+ """Convert a Language enum to Gladia's language code format.
+
+ Args:
+ language: The Language enum value to convert
+
+ Returns:
+ The Gladia language code string or None if not supported
+ """
+ BASE_LANGUAGES = {
+ Language.AF: "af",
+ Language.AM: "am",
+ Language.AR: "ar",
+ Language.AS: "as",
+ Language.AZ: "az",
+ Language.BA: "ba",
+ Language.BE: "be",
+ Language.BG: "bg",
+ Language.BN: "bn",
+ Language.BO: "bo",
+ Language.BR: "br",
+ Language.BS: "bs",
+ Language.CA: "ca",
+ Language.CS: "cs",
+ Language.CY: "cy",
+ Language.DA: "da",
+ Language.DE: "de",
+ Language.EL: "el",
+ Language.EN: "en",
+ Language.ES: "es",
+ Language.ET: "et",
+ Language.EU: "eu",
+ Language.FA: "fa",
+ Language.FI: "fi",
+ Language.FO: "fo",
+ Language.FR: "fr",
+ Language.GL: "gl",
+ Language.GU: "gu",
+ Language.HA: "ha",
+ Language.HAW: "haw",
+ Language.HE: "he",
+ Language.HI: "hi",
+ Language.HR: "hr",
+ Language.HT: "ht",
+ Language.HU: "hu",
+ Language.HY: "hy",
+ Language.ID: "id",
+ Language.IS: "is",
+ Language.IT: "it",
+ Language.JA: "ja",
+ Language.JV: "jv",
+ Language.KA: "ka",
+ Language.KK: "kk",
+ Language.KM: "km",
+ Language.KN: "kn",
+ Language.KO: "ko",
+ Language.LA: "la",
+ Language.LB: "lb",
+ Language.LN: "ln",
+ Language.LO: "lo",
+ Language.LT: "lt",
+ Language.LV: "lv",
+ Language.MG: "mg",
+ Language.MI: "mi",
+ Language.MK: "mk",
+ Language.ML: "ml",
+ Language.MN: "mn",
+ Language.MR: "mr",
+ Language.MS: "ms",
+ Language.MT: "mt",
+ Language.MY_MR: "mymr",
+ Language.NE: "ne",
+ Language.NL: "nl",
+ Language.NN: "nn",
+ Language.NO: "no",
+ Language.OC: "oc",
+ Language.PA: "pa",
+ Language.PL: "pl",
+ Language.PS: "ps",
+ Language.PT: "pt",
+ Language.RO: "ro",
+ Language.RU: "ru",
+ Language.SA: "sa",
+ Language.SD: "sd",
+ Language.SI: "si",
+ Language.SK: "sk",
+ Language.SL: "sl",
+ Language.SN: "sn",
+ Language.SO: "so",
+ Language.SQ: "sq",
+ Language.SR: "sr",
+ Language.SU: "su",
+ Language.SV: "sv",
+ Language.SW: "sw",
+ Language.TA: "ta",
+ Language.TE: "te",
+ Language.TG: "tg",
+ Language.TH: "th",
+ Language.TK: "tk",
+ Language.TL: "tl",
+ Language.TR: "tr",
+ Language.TT: "tt",
+ Language.UK: "uk",
+ Language.UR: "ur",
+ Language.UZ: "uz",
+ Language.VI: "vi",
+ Language.YI: "yi",
+ Language.YO: "yo",
+ Language.ZH: "zh",
+ }
+
+ result = BASE_LANGUAGES.get(language)
+
+ # If not found in base languages, try to find the base language from a variant
+ if not result:
+ # Convert enum value to string and get the base language part (e.g. es-ES -> es)
+ lang_str = str(language.value)
+ base_code = lang_str.split("-")[0].lower()
+ # Look up the base code in our supported languages
+ result = base_code if base_code in BASE_LANGUAGES.values() else None
+
+ return result
+
+
+# Deprecation warning for nested InputParams
+class _InputParamsDescriptor:
+ """Descriptor for backward compatibility with deprecation warning."""
+
+ def __get__(self, obj, objtype=None):
+ warnings.warn(
+ "GladiaSTTService.InputParams is deprecated and will be removed in a future version. "
+ "Import and use GladiaInputParams directly instead.",
+ DeprecationWarning,
+ stacklevel=2,
+ )
+ return GladiaInputParams
+
+
+class GladiaSTTService(STTService):
+ """Speech-to-Text service using Gladia's API.
+
+ This service connects to Gladia's WebSocket API for real-time transcription
+ with support for multiple languages, custom vocabulary, and various processing options.
+
+ For complete API documentation, see: https://docs.gladia.io/api-reference/v2/live/init
+ """
+
+ # Maintain backward compatibility
+ InputParams = _InputParamsDescriptor()
+
+ def __init__(
+ self,
+ *,
+ api_key: str,
+ url: str = "https://api.gladia.io/v2/live",
+ confidence: float = 0.5,
+ sample_rate: Optional[int] = None,
+ model: str = "solaria-1",
+ params: GladiaInputParams = GladiaInputParams(),
+ **kwargs,
+ ):
+ """Initialize the Gladia STT service.
+
+ Args:
+ api_key: Gladia API key
+ url: Gladia API URL
+ confidence: Minimum confidence threshold for transcriptions
+ sample_rate: Audio sample rate in Hz
+ model: Model to use ("solaria-1", "solaria-mini-1", "fast",
+ or "accurate")
+ params: Additional configuration parameters
+ **kwargs: Additional arguments passed to the STTService
+ """
+ super().__init__(sample_rate=sample_rate, **kwargs)
+
+ # Warn about deprecated language parameter if it's used
+ if params.language is not None:
+ warnings.warn(
+ "The 'language' parameter is deprecated and will be removed in a future version. "
+ "Use 'language_config' instead.",
+ DeprecationWarning,
+ stacklevel=2,
+ )
+
+ self._api_key = api_key
+ self._url = url
+ self.set_model_name(model)
+ self._confidence = confidence
+ self._params = params
+ self._websocket = None
+ self._receive_task = None
+ self._keepalive_task = None
+
+ def language_to_service_language(self, language: Language) -> Optional[str]:
+ """Convert pipecat Language enum to Gladia's language code."""
+ return language_to_gladia_language(language)
+
+ def _prepare_settings(self) -> Dict[str, Any]:
+ settings = {
+ "encoding": self._params.encoding or "wav/pcm",
+ "bit_depth": self._params.bit_depth or 16,
+ "sample_rate": self.sample_rate,
+ "channels": self._params.channels or 1,
+ "model": self._model_name,
+ }
+
+ # Add custom_metadata if provided
+ if self._params.custom_metadata:
+ settings["custom_metadata"] = self._params.custom_metadata
+
+ # Add endpointing parameters if provided
+ if self._params.endpointing is not None:
+ settings["endpointing"] = self._params.endpointing
+ if self._params.maximum_duration_without_endpointing is not None:
+ settings["maximum_duration_without_endpointing"] = (
+ self._params.maximum_duration_without_endpointing
+ )
+
+ # Add language configuration (prioritize language_config over deprecated language)
+ if self._params.language_config:
+ settings["language_config"] = self._params.language_config.model_dump(exclude_none=True)
+ elif self._params.language: # Backward compatibility for deprecated parameter
+ language_code = self.language_to_service_language(self._params.language)
+ if language_code:
+ settings["language_config"] = {
+ "languages": [language_code],
+ "code_switching": False,
+ }
+
+ # Add pre_processing configuration if provided
+ if self._params.pre_processing:
+ settings["pre_processing"] = self._params.pre_processing.model_dump(exclude_none=True)
+
+ # Add realtime_processing configuration if provided
+ if self._params.realtime_processing:
+ settings["realtime_processing"] = self._params.realtime_processing.model_dump(
+ exclude_none=True
+ )
+
+ # Add messages_config if provided
+ if self._params.messages_config:
+ settings["messages_config"] = self._params.messages_config.model_dump(exclude_none=True)
+
+ return settings
+
+ async def start(self, frame: StartFrame):
+ """Start the Gladia STT websocket connection."""
+ await super().start(frame)
+ if self._websocket:
+ return
+ settings = self._prepare_settings()
+ response = await self._setup_gladia(settings)
+ self._websocket = await websockets.connect(response["url"])
+ if self._websocket and not self._receive_task:
+ self._receive_task = self.create_task(self._receive_task_handler())
+ if self._websocket and not self._keepalive_task:
+ self._keepalive_task = self.create_task(self._keepalive_task_handler())
+
+ async def stop(self, frame: EndFrame):
+ """Stop the Gladia STT websocket connection."""
+ await super().stop(frame)
+ await self._send_stop_recording()
+
+ if self._keepalive_task:
+ await self.cancel_task(self._keepalive_task)
+ self._keepalive_task = None
+
+ if self._websocket:
+ await self._websocket.close()
+ self._websocket = None
+
+ if self._receive_task:
+ await self.wait_for_task(self._receive_task)
+ self._receive_task = None
+
+ async def cancel(self, frame: CancelFrame):
+ """Cancel the Gladia STT websocket connection."""
+ await super().cancel(frame)
+
+ if self._keepalive_task:
+ await self.cancel_task(self._keepalive_task)
+ self._keepalive_task = None
+
+ if self._websocket:
+ await self._websocket.close()
+ self._websocket = None
+
+ if self._receive_task:
+ await self.cancel_task(self._receive_task)
+ self._receive_task = None
+
+ async def run_stt(self, audio: bytes) -> AsyncGenerator[Frame, None]:
+ """Run speech-to-text on audio data."""
+ await self.start_processing_metrics()
+ await self._send_audio(audio)
+ await self.stop_processing_metrics()
+ yield None
+
+ async def _setup_gladia(self, settings: Dict[str, Any]):
+ async with aiohttp.ClientSession() as session:
+ async with session.post(
+ self._url,
+ headers={"X-Gladia-Key": self._api_key, "Content-Type": "application/json"},
+ json=settings,
+ ) as response:
+ if response.ok:
+ return await response.json()
+ else:
+ error_text = await response.text()
+ logger.error(
+ f"Gladia error: {response.status}: {error_text or response.reason}"
+ )
+ raise Exception(
+ f"Failed to initialize Gladia session: {response.status} - {error_text}"
+ )
+
+ async def _send_audio(self, audio: bytes):
+ data = base64.b64encode(audio).decode("utf-8")
+ message = {"type": "audio_chunk", "data": {"chunk": data}}
+ await self._websocket.send(json.dumps(message))
+
+ async def _send_stop_recording(self):
+ if self._websocket and not self._websocket.closed:
+ await self._websocket.send(json.dumps({"type": "stop_recording"}))
+
+ async def _keepalive_task_handler(self):
+ """Send periodic empty audio chunks to keep the connection alive."""
+ try:
+ while True:
+ # Send keepalive every 20 seconds (Gladia times out after 30 seconds)
+ await asyncio.sleep(20)
+ if self._websocket and not self._websocket.closed:
+ # Send an empty audio chunk as keepalive
+ empty_audio = b""
+ await self._send_audio(empty_audio)
+ else:
+ logger.debug("Websocket closed, stopping keepalive")
+ break
+ except websockets.exceptions.ConnectionClosed:
+ logger.debug("Connection closed during keepalive")
+ except Exception as e:
+ logger.error(f"Error in Gladia keepalive task: {e}")
+
+ async def _receive_task_handler(self):
+ try:
+ async for message in self._websocket:
+ content = json.loads(message)
+ if content["type"] == "transcript":
+ utterance = content["data"]["utterance"]
+ confidence = utterance.get("confidence", 0)
+ transcript = utterance["text"]
+ if confidence >= self._confidence:
+ if content["data"]["is_final"]:
+ await self.push_frame(
+ TranscriptionFrame(transcript, "", time_now_iso8601())
+ )
+ else:
+ await self.push_frame(
+ InterimTranscriptionFrame(transcript, "", time_now_iso8601())
+ )
+ except websockets.exceptions.ConnectionClosed:
+ # Expected when closing the connection
+ pass
+ except Exception as e:
+ logger.error(f"Error in Gladia WebSocket handler: {e}")
diff --git a/src/pipecat/services/google/image.py b/src/pipecat/services/google/image.py
index f7a7764f2..5a73168dc 100644
--- a/src/pipecat/services/google/image.py
+++ b/src/pipecat/services/google/image.py
@@ -17,7 +17,7 @@ from PIL import Image
from pydantic import BaseModel, Field
from pipecat.frames.frames import ErrorFrame, Frame, URLImageRawFrame
-from pipecat.services.ai_services import ImageGenService
+from pipecat.services.image_service import ImageGenService
try:
from google import genai
diff --git a/src/pipecat/services/google/llm.py b/src/pipecat/services/google/llm.py
index e78e3949b..bf9714817 100644
--- a/src/pipecat/services/google/llm.py
+++ b/src/pipecat/services/google/llm.py
@@ -9,21 +9,14 @@ import io
import json
import os
import uuid
-
-from google.api_core.exceptions import DeadlineExceeded
-
-from pipecat.adapters.services.gemini_adapter import GeminiLLMAdapter
-
-# Suppress gRPC fork warnings
-os.environ["GRPC_ENABLE_FORK_SUPPORT"] = "false"
-
from dataclasses import dataclass
-from typing import Any, Dict, List, Mapping, Optional, Union
+from typing import Any, Dict, List, Optional
from loguru import logger
from PIL import Image
from pydantic import BaseModel, Field
+from pipecat.adapters.services.gemini_adapter import GeminiLLMAdapter
from pipecat.frames.frames import (
AudioRawFrame,
Frame,
@@ -39,23 +32,30 @@ from pipecat.frames.frames import (
VisionImageRawFrame,
)
from pipecat.metrics.metrics import LLMTokenUsage
+from pipecat.processors.aggregators.llm_response import (
+ LLMAssistantAggregatorParams,
+ LLMUserAggregatorParams,
+)
from pipecat.processors.aggregators.openai_llm_context import (
OpenAILLMContext,
OpenAILLMContextFrame,
)
from pipecat.processors.frame_processor import FrameDirection
-from pipecat.services.ai_services import LLMService
from pipecat.services.google.frames import LLMSearchResponseFrame
+from pipecat.services.llm_service import LLMService
from pipecat.services.openai.llm import (
OpenAIAssistantContextAggregator,
OpenAIUserContextAggregator,
)
+# Suppress gRPC fork warnings
+os.environ["GRPC_ENABLE_FORK_SUPPORT"] = "false"
+
try:
import google.ai.generativelanguage as glm
import google.generativeai as gai
+ from google.api_core.exceptions import DeadlineExceeded
from google.generativeai.types import GenerationConfig
-
except ModuleNotFoundError as e:
logger.error(f"Exception: {e}")
logger.error("In order to use Google AI, you need to `pip install pipecat-ai[google]`.")
@@ -686,8 +686,8 @@ class GoogleLLMService(LLMService):
self,
context: OpenAILLMContext,
*,
- user_kwargs: Mapping[str, Any] = {},
- assistant_kwargs: Mapping[str, Any] = {},
+ user_params: LLMUserAggregatorParams = LLMUserAggregatorParams(),
+ assistant_params: LLMAssistantAggregatorParams = LLMAssistantAggregatorParams(),
) -> GoogleContextAggregatorPair:
"""Create an instance of GoogleContextAggregatorPair from an
OpenAILLMContext. Constructor keyword arguments for both the user and
@@ -695,12 +695,10 @@ class GoogleLLMService(LLMService):
Args:
context (OpenAILLMContext): The LLM context.
- user_kwargs (Mapping[str, Any], optional): Additional keyword
- arguments for the user context aggregator constructor. Defaults
- to an empty mapping.
- assistant_kwargs (Mapping[str, Any], optional): Additional keyword
- arguments for the assistant context aggregator
- constructor. Defaults to an empty mapping.
+ user_params (LLMUserAggregatorParams, optional): User aggregator
+ parameters.
+ assistant_params (LLMAssistantAggregatorParams, optional): User
+ aggregator parameters.
Returns:
GoogleContextAggregatorPair: A pair of context aggregators, one for
@@ -712,6 +710,6 @@ class GoogleLLMService(LLMService):
if isinstance(context, OpenAILLMContext):
context = GoogleLLMContext.upgrade_to_google(context)
- user = GoogleUserContextAggregator(context, **user_kwargs)
- assistant = GoogleAssistantContextAggregator(context, **assistant_kwargs)
+ user = GoogleUserContextAggregator(context, params=user_params)
+ assistant = GoogleAssistantContextAggregator(context, params=assistant_params)
return GoogleContextAggregatorPair(_user=user, _assistant=assistant)
diff --git a/src/pipecat/services/google/llm_vertex.py b/src/pipecat/services/google/llm_vertex.py
index 1a23fe7d5..a2a485ccd 100644
--- a/src/pipecat/services/google/llm_vertex.py
+++ b/src/pipecat/services/google/llm_vertex.py
@@ -17,6 +17,8 @@ from loguru import logger
from pipecat.services.openai.llm import OpenAILLMService
try:
+ from google.auth import default
+ from google.auth.exceptions import GoogleAuthError
from google.auth.transport.requests import Request
from google.oauth2 import service_account
@@ -65,7 +67,9 @@ class GoogleVertexLLMService(OpenAILLMService):
base_url = self._get_base_url(params)
self._api_key = self._get_api_token(credentials, credentials_path)
- super().__init__(api_key=self._api_key, base_url=base_url, model=model, **kwargs)
+ super().__init__(
+ api_key=self._api_key, base_url=base_url, model=model, params=params, **kwargs
+ )
@staticmethod
def _get_base_url(params: InputParams) -> str:
@@ -98,6 +102,13 @@ class GoogleVertexLLMService(OpenAILLMService):
creds = service_account.Credentials.from_service_account_file(
credentials_path, scopes=["https://www.googleapis.com/auth/cloud-platform"]
)
+ else:
+ try:
+ creds, project_id = default(
+ scopes=["https://www.googleapis.com/auth/cloud-platform"]
+ )
+ except GoogleAuthError:
+ pass
if not creds:
raise ValueError("No valid credentials provided.")
diff --git a/src/pipecat/services/google/stt.py b/src/pipecat/services/google/stt.py
index 7af994bb2..cf0385857 100644
--- a/src/pipecat/services/google/stt.py
+++ b/src/pipecat/services/google/stt.py
@@ -26,12 +26,14 @@ from pipecat.frames.frames import (
StartFrame,
TranscriptionFrame,
)
-from pipecat.services.ai_services import STTService
+from pipecat.services.stt_service import STTService
from pipecat.transcriptions.language import Language
from pipecat.utils.time import time_now_iso8601
try:
from google.api_core.client_options import ClientOptions
+ from google.auth import default
+ from google.auth.exceptions import GoogleAuthError
from google.cloud import speech_v2
from google.cloud.speech_v2.types import cloud_speech
from google.oauth2 import service_account
@@ -451,6 +453,7 @@ class GoogleSTTService(STTService):
client_options = ClientOptions(api_endpoint=f"{self._location}-speech.googleapis.com")
# Extract project ID and create client
+ creds: Optional[service_account.Credentials] = None
if credentials:
json_account_info = json.loads(credentials)
self._project_id = json_account_info.get("project_id")
@@ -461,7 +464,16 @@ class GoogleSTTService(STTService):
self._project_id = json_account_info.get("project_id")
creds = service_account.Credentials.from_service_account_file(credentials_path)
else:
- raise ValueError("Either credentials or credentials_path must be provided")
+ try:
+ creds, project_id = default(
+ scopes=["https://www.googleapis.com/auth/cloud-platform"]
+ )
+ self._project_id = project_id
+ except GoogleAuthError:
+ pass
+
+ if not creds:
+ raise ValueError("No valid credentials provided.")
if not self._project_id:
raise ValueError("Project ID not found in credentials")
diff --git a/src/pipecat/services/google/tts.py b/src/pipecat/services/google/tts.py
index 36bd27a51..6ecf6d762 100644
--- a/src/pipecat/services/google/tts.py
+++ b/src/pipecat/services/google/tts.py
@@ -23,10 +23,12 @@ from pipecat.frames.frames import (
TTSStartedFrame,
TTSStoppedFrame,
)
-from pipecat.services.ai_services import TTSService
+from pipecat.services.tts_service import TTSService
from pipecat.transcriptions.language import Language
try:
+ from google.auth import default
+ from google.auth.exceptions import GoogleAuthError
from google.cloud import texttospeech_v1
from google.oauth2 import service_account
@@ -251,6 +253,16 @@ class GoogleTTSService(TTSService):
elif credentials_path:
# Use service account JSON file if provided
creds = service_account.Credentials.from_service_account_file(credentials_path)
+ else:
+ try:
+ creds, project_id = default(
+ scopes=["https://www.googleapis.com/auth/cloud-platform"]
+ )
+ except GoogleAuthError:
+ pass
+
+ if not creds:
+ raise ValueError("No valid credentials provided.")
return texttospeech_v1.TextToSpeechAsyncClient(credentials=creds)
@@ -346,9 +358,9 @@ class GoogleTTSService(TTSService):
audio_content = response.audio_content[44:]
# Read and yield audio data in chunks
- chunk_size = 8192
- for i in range(0, len(audio_content), chunk_size):
- chunk = audio_content[i : i + chunk_size]
+ CHUNK_SIZE = 1024
+ for i in range(0, len(audio_content), CHUNK_SIZE):
+ chunk = audio_content[i : i + CHUNK_SIZE]
if not chunk:
break
await self.stop_ttfb_metrics()
diff --git a/src/pipecat/services/grok/llm.py b/src/pipecat/services/grok/llm.py
index 57517eb3e..a57434986 100644
--- a/src/pipecat/services/grok/llm.py
+++ b/src/pipecat/services/grok/llm.py
@@ -5,11 +5,14 @@
#
from dataclasses import dataclass
-from typing import Any, Mapping
from loguru import logger
from pipecat.metrics.metrics import LLMTokenUsage
+from pipecat.processors.aggregators.llm_response import (
+ LLMAssistantAggregatorParams,
+ LLMUserAggregatorParams,
+)
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.openai.llm import (
OpenAIAssistantContextAggregator,
@@ -39,7 +42,7 @@ class GrokLLMService(OpenAILLMService):
Args:
api_key (str): The API key for accessing Grok's API
base_url (str, optional): The base URL for Grok API. Defaults to "https://api.x.ai/v1"
- model (str, optional): The model identifier to use. Defaults to "grok-2"
+ model (str, optional): The model identifier to use. Defaults to "grok-3-beta"
**kwargs: Additional keyword arguments passed to OpenAILLMService
"""
@@ -48,7 +51,7 @@ class GrokLLMService(OpenAILLMService):
*,
api_key: str,
base_url: str = "https://api.x.ai/v1",
- model: str = "grok-2",
+ model: str = "grok-3-beta",
**kwargs,
):
super().__init__(api_key=api_key, base_url=base_url, model=model, **kwargs)
@@ -124,8 +127,8 @@ class GrokLLMService(OpenAILLMService):
self,
context: OpenAILLMContext,
*,
- user_kwargs: Mapping[str, Any] = {},
- assistant_kwargs: Mapping[str, Any] = {},
+ user_params: LLMUserAggregatorParams = LLMUserAggregatorParams(),
+ assistant_params: LLMAssistantAggregatorParams = LLMAssistantAggregatorParams(),
) -> GrokContextAggregatorPair:
"""Create an instance of GrokContextAggregatorPair from an
OpenAILLMContext. Constructor keyword arguments for both the user and
@@ -133,12 +136,10 @@ class GrokLLMService(OpenAILLMService):
Args:
context (OpenAILLMContext): The LLM context.
- user_kwargs (Mapping[str, Any], optional): Additional keyword
- arguments for the user context aggregator constructor. Defaults
- to an empty mapping.
- assistant_kwargs (Mapping[str, Any], optional): Additional keyword
- arguments for the assistant context aggregator
- constructor. Defaults to an empty mapping.
+ user_params (LLMUserAggregatorParams, optional): User aggregator
+ parameters.
+ assistant_params (LLMAssistantAggregatorParams, optional): User
+ aggregator parameters.
Returns:
GrokContextAggregatorPair: A pair of context aggregators, one for
@@ -148,6 +149,6 @@ class GrokLLMService(OpenAILLMService):
"""
context.set_llm_adapter(self.get_llm_adapter())
- user = OpenAIUserContextAggregator(context, **user_kwargs)
- assistant = OpenAIAssistantContextAggregator(context, **assistant_kwargs)
+ user = OpenAIUserContextAggregator(context, params=user_params)
+ assistant = OpenAIAssistantContextAggregator(context, params=assistant_params)
return GrokContextAggregatorPair(_user=user, _assistant=assistant)
diff --git a/src/pipecat/services/groq/tts.py b/src/pipecat/services/groq/tts.py
index 69429424a..f4f0f308b 100644
--- a/src/pipecat/services/groq/tts.py
+++ b/src/pipecat/services/groq/tts.py
@@ -10,7 +10,7 @@ from loguru import logger
from pydantic import BaseModel
from pipecat.frames.frames import Frame, TTSAudioRawFrame, TTSStartedFrame, TTSStoppedFrame
-from pipecat.services.ai_services import TTSService
+from pipecat.services.tts_service import TTSService
from pipecat.transcriptions.language import Language
try:
diff --git a/src/pipecat/services/image_service.py b/src/pipecat/services/image_service.py
new file mode 100644
index 000000000..43dbd0bb5
--- /dev/null
+++ b/src/pipecat/services/image_service.py
@@ -0,0 +1,33 @@
+#
+# Copyright (c) 2024β2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+from abc import abstractmethod
+from typing import AsyncGenerator
+
+from pipecat.frames.frames import Frame, TextFrame
+from pipecat.processors.frame_processor import FrameDirection
+from pipecat.services.ai_service import AIService
+
+
+class ImageGenService(AIService):
+ def __init__(self, **kwargs):
+ super().__init__(**kwargs)
+
+ # Renders the image. Returns an Image object.
+ @abstractmethod
+ async def run_image_gen(self, prompt: str) -> AsyncGenerator[Frame, None]:
+ pass
+
+ async def process_frame(self, frame: Frame, direction: FrameDirection):
+ await super().process_frame(frame, direction)
+
+ if isinstance(frame, TextFrame):
+ await self.push_frame(frame, direction)
+ await self.start_processing_metrics()
+ await self.process_generator(self.run_image_gen(frame.text))
+ await self.stop_processing_metrics()
+ else:
+ await self.push_frame(frame, direction)
diff --git a/src/pipecat/services/llm_service.py b/src/pipecat/services/llm_service.py
new file mode 100644
index 000000000..6ac841f25
--- /dev/null
+++ b/src/pipecat/services/llm_service.py
@@ -0,0 +1,261 @@
+#
+# Copyright (c) 2024β2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+from dataclasses import dataclass
+from typing import Any, Optional, Set, Tuple, Type
+
+from loguru import logger
+
+from pipecat.adapters.base_llm_adapter import BaseLLMAdapter
+from pipecat.adapters.services.open_ai_adapter import OpenAILLMAdapter
+from pipecat.frames.frames import (
+ Frame,
+ FunctionCallCancelFrame,
+ FunctionCallInProgressFrame,
+ FunctionCallResultFrame,
+ StartInterruptionFrame,
+ UserImageRequestFrame,
+)
+from pipecat.processors.aggregators.llm_response import (
+ LLMAssistantAggregatorParams,
+ LLMUserAggregatorParams,
+)
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.frame_processor import FrameDirection
+from pipecat.services.ai_service import AIService
+
+
+@dataclass
+class FunctionEntry:
+ function_name: Optional[str]
+ callback: Any # TODO(aleix): add proper typing.
+ cancel_on_interruption: bool
+
+
+class LLMService(AIService):
+ """This class is a no-op but serves as a base class for LLM services."""
+
+ # OpenAILLMAdapter is used as the default adapter since it aligns with most LLM implementations.
+ # However, subclasses should override this with a more specific adapter when necessary.
+ adapter_class: Type[BaseLLMAdapter] = OpenAILLMAdapter
+
+ def __init__(self, **kwargs):
+ super().__init__(**kwargs)
+ self._functions = {}
+ self._start_callbacks = {}
+ self._adapter = self.adapter_class()
+ self._function_call_tasks: Set[Tuple[asyncio.Task, str, str]] = set()
+
+ self._register_event_handler("on_completion_timeout")
+
+ def get_llm_adapter(self) -> BaseLLMAdapter:
+ return self._adapter
+
+ def create_context_aggregator(
+ self,
+ context: OpenAILLMContext,
+ *,
+ user_params: LLMUserAggregatorParams = LLMUserAggregatorParams(),
+ assistant_params: LLMAssistantAggregatorParams = LLMAssistantAggregatorParams(),
+ ) -> Any:
+ pass
+
+ async def process_frame(self, frame: Frame, direction: FrameDirection):
+ await super().process_frame(frame, direction)
+
+ if isinstance(frame, StartInterruptionFrame):
+ await self._handle_interruptions(frame)
+
+ async def _handle_interruptions(self, frame: StartInterruptionFrame):
+ for function_name, entry in self._functions.items():
+ if entry.cancel_on_interruption:
+ await self._cancel_function_call(function_name)
+
+ def register_function(
+ self,
+ function_name: Optional[str],
+ callback: Any,
+ start_callback=None,
+ *,
+ cancel_on_interruption: bool = False,
+ ):
+ # Registering a function with the function_name set to None will run that callback
+ # for all functions
+ self._functions[function_name] = FunctionEntry(
+ function_name=function_name,
+ callback=callback,
+ cancel_on_interruption=cancel_on_interruption,
+ )
+
+ # Start callbacks are now deprecated.
+ if start_callback:
+ import warnings
+
+ with warnings.catch_warnings():
+ warnings.simplefilter("always")
+ warnings.warn(
+ "Parameter 'start_callback' is deprecated, just put your code on top of the actual function call instead.",
+ DeprecationWarning,
+ )
+
+ self._start_callbacks[function_name] = start_callback
+
+ def unregister_function(self, function_name: Optional[str]):
+ del self._functions[function_name]
+ if self._start_callbacks[function_name]:
+ del self._start_callbacks[function_name]
+
+ def has_function(self, function_name: str):
+ if None in self._functions.keys():
+ return True
+ return function_name in self._functions.keys()
+
+ async def call_function(
+ self,
+ *,
+ context: OpenAILLMContext,
+ tool_call_id: str,
+ function_name: str,
+ arguments: str,
+ run_llm: bool = True,
+ ):
+ if not function_name in self._functions.keys() and not None in self._functions.keys():
+ return
+
+ task = self.create_task(
+ self._run_function_call(context, tool_call_id, function_name, arguments, run_llm)
+ )
+
+ self._function_call_tasks.add((task, tool_call_id, function_name))
+
+ task.add_done_callback(self._function_call_task_finished)
+
+ async def call_start_function(self, context: OpenAILLMContext, function_name: str):
+ if function_name in self._start_callbacks.keys():
+ await self._start_callbacks[function_name](function_name, self, context)
+ elif None in self._start_callbacks.keys():
+ return await self._start_callbacks[None](function_name, self, context)
+
+ async def request_image_frame(
+ self,
+ user_id: str,
+ *,
+ function_name: Optional[str] = None,
+ tool_call_id: Optional[str] = None,
+ text_content: Optional[str] = None,
+ ):
+ await self.push_frame(
+ UserImageRequestFrame(
+ user_id=user_id,
+ function_name=function_name,
+ tool_call_id=tool_call_id,
+ context=text_content,
+ ),
+ FrameDirection.UPSTREAM,
+ )
+
+ async def _run_function_call(
+ self,
+ context: OpenAILLMContext,
+ tool_call_id: str,
+ function_name: str,
+ arguments: str,
+ run_llm: bool = True,
+ ):
+ if function_name in self._functions.keys():
+ entry = self._functions[function_name]
+ elif None in self._functions.keys():
+ entry = self._functions[None]
+ else:
+ return
+
+ logger.debug(
+ f"{self} Calling function [{function_name}:{tool_call_id}] with arguments {arguments}"
+ )
+
+ # NOTE(aleix): This needs to be removed after we remove the deprecation.
+ await self.call_start_function(context, function_name)
+
+ # Push a SystemFrame downstream. This frame will let our assistant context aggregator
+ # know that we are in the middle of a function call. Some contexts/aggregators may
+ # not need this. But some definitely do (Anthropic, for example).
+ # Also push a SystemFrame upstream for use by other processors, like STTMuteFilter.
+ progress_frame_downstream = FunctionCallInProgressFrame(
+ function_name=function_name,
+ tool_call_id=tool_call_id,
+ arguments=arguments,
+ cancel_on_interruption=entry.cancel_on_interruption,
+ )
+ progress_frame_upstream = FunctionCallInProgressFrame(
+ function_name=function_name,
+ tool_call_id=tool_call_id,
+ arguments=arguments,
+ cancel_on_interruption=entry.cancel_on_interruption,
+ )
+
+ # Push frame both downstream and upstream
+ await self.push_frame(progress_frame_downstream, FrameDirection.DOWNSTREAM)
+ await self.push_frame(progress_frame_upstream, FrameDirection.UPSTREAM)
+
+ # Define a callback function that pushes a FunctionCallResultFrame upstream & downstream.
+ async def function_call_result_callback(result, *, properties=None):
+ result_frame_downstream = FunctionCallResultFrame(
+ function_name=function_name,
+ tool_call_id=tool_call_id,
+ arguments=arguments,
+ result=result,
+ properties=properties,
+ )
+ result_frame_upstream = FunctionCallResultFrame(
+ function_name=function_name,
+ tool_call_id=tool_call_id,
+ arguments=arguments,
+ result=result,
+ properties=properties,
+ )
+
+ await self.push_frame(result_frame_downstream, FrameDirection.DOWNSTREAM)
+ await self.push_frame(result_frame_upstream, FrameDirection.UPSTREAM)
+
+ await entry.callback(
+ function_name, tool_call_id, arguments, self, context, function_call_result_callback
+ )
+
+ async def _cancel_function_call(self, function_name: str):
+ cancelled_tasks = set()
+ for task, tool_call_id, name in self._function_call_tasks:
+ if name == function_name:
+ # We remove the callback because we are going to cancel the task
+ # now, otherwise we will be removing it from the set while we
+ # are iterating.
+ task.remove_done_callback(self._function_call_task_finished)
+
+ logger.debug(f"{self} Cancelling function call [{name}:{tool_call_id}]...")
+
+ await self.cancel_task(task)
+
+ frame = FunctionCallCancelFrame(
+ function_name=function_name, tool_call_id=tool_call_id
+ )
+ await self.push_frame(frame)
+
+ logger.debug(f"{self} Function call [{name}:{tool_call_id}] has been cancelled")
+
+ cancelled_tasks.add(task)
+
+ # Remove all cancelled tasks from our set.
+ for task in cancelled_tasks:
+ self._function_call_task_finished(task)
+
+ def _function_call_task_finished(self, task: asyncio.Task):
+ tuple_to_remove = next((t for t in self._function_call_tasks if t[0] == task), None)
+ if tuple_to_remove:
+ self._function_call_tasks.discard(tuple_to_remove)
+ # The task is finished so this should exit immediately. We need to
+ # do this because otherwise the task manager would report a dangling
+ # task if we don't remove it.
+ asyncio.run_coroutine_threadsafe(self.wait_for_task(task), self.get_event_loop())
diff --git a/src/pipecat/services/lmnt/tts.py b/src/pipecat/services/lmnt/tts.py
index 040d526f9..b9e0bc79f 100644
--- a/src/pipecat/services/lmnt/tts.py
+++ b/src/pipecat/services/lmnt/tts.py
@@ -21,7 +21,7 @@ from pipecat.frames.frames import (
TTSStoppedFrame,
)
from pipecat.processors.frame_processor import FrameDirection
-from pipecat.services.ai_services import InterruptibleTTSService
+from pipecat.services.tts_service import InterruptibleTTSService
from pipecat.transcriptions.language import Language
# See .env.example for LMNT configuration needed
@@ -109,7 +109,7 @@ class LmntTTSService(InterruptibleTTSService):
async def _connect(self):
await self._connect_websocket()
- if not self._receive_task:
+ if self._websocket and not self._receive_task:
self._receive_task = self.create_task(self._receive_task_handler(self._report_error))
async def _disconnect(self):
@@ -122,7 +122,7 @@ class LmntTTSService(InterruptibleTTSService):
async def _connect_websocket(self):
"""Connect to LMNT websocket."""
try:
- if self._websocket:
+ if self._websocket and self._websocket.open:
return
logger.debug("Connecting to LMNT")
@@ -158,11 +158,11 @@ class LmntTTSService(InterruptibleTTSService):
# errors on the websocket, so we just skip it for now.
# await self._websocket.send(json.dumps({"eof": True}))
await self._websocket.close()
- self._websocket = None
-
- self._started = False
except Exception as e:
logger.error(f"{self} error closing websocket: {e}")
+ finally:
+ self._started = False
+ self._websocket = None
def _get_websocket(self):
if self._websocket:
@@ -170,7 +170,7 @@ class LmntTTSService(InterruptibleTTSService):
raise Exception("Websocket not connected")
async def flush_audio(self):
- if not self._websocket:
+ if not self._websocket or self._websocket.closed:
return
await self._get_websocket().send(json.dumps({"flush": True}))
@@ -203,7 +203,7 @@ class LmntTTSService(InterruptibleTTSService):
logger.debug(f"{self}: Generating TTS [{text}]")
try:
- if not self._websocket:
+ if not self._websocket or self._websocket.closed:
await self._connect()
try:
diff --git a/src/pipecat/services/mcp_service.py b/src/pipecat/services/mcp_service.py
new file mode 100644
index 000000000..637170d39
--- /dev/null
+++ b/src/pipecat/services/mcp_service.py
@@ -0,0 +1,213 @@
+import json
+from typing import Any, Dict, List, Optional, Union
+
+from loguru import logger
+
+from pipecat.adapters.schemas.function_schema import FunctionSchema
+from pipecat.adapters.schemas.tools_schema import ToolsSchema
+from pipecat.utils.base_object import BaseObject
+
+try:
+ from mcp import ClientSession, StdioServerParameters, types
+ from mcp.client.session import ClientSession
+ from mcp.client.sse import sse_client
+ from mcp.client.stdio import stdio_client
+except ModuleNotFoundError as e:
+ logger.error(f"Exception: {e}")
+ logger.error("In order to use an MCP client, you need to `pip install pipecat-ai[mcp]`.")
+ raise Exception(f"Missing module: {e}")
+
+
+class MCPClient(BaseObject):
+ def __init__(
+ self,
+ server_params: Union[StdioServerParameters, str],
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+ self._server_params = server_params
+ self._session = ClientSession
+ if isinstance(server_params, StdioServerParameters):
+ self._client = stdio_client
+ self._register_tools = self._stdio_register_tools
+ elif isinstance(server_params, str):
+ self._client = sse_client
+ self._register_tools = self._sse_register_tools
+ else:
+ raise TypeError(
+ f"{self} invalid argument type: `server_params` must be either StdioServerParameters or an SSE server url string."
+ )
+
+ async def register_tools(self, llm) -> ToolsSchema:
+ tools_schema = await self._register_tools(llm)
+ return tools_schema
+
+ def _convert_mcp_schema_to_pipecat(
+ self, tool_name: str, tool_schema: Dict[str, Any]
+ ) -> FunctionSchema:
+ """Convert an mcp tool schema to Pipecat's FunctionSchema format.
+ Args:
+ tool_name: The name of the tool
+ tool_schema: The mcp tool schema
+ Returns:
+ A FunctionSchema instance
+ """
+
+ logger.debug(f"Converting schema for tool '{tool_name}'")
+ logger.trace(f"Original schema: {json.dumps(tool_schema, indent=2)}")
+
+ properties = tool_schema["input_schema"].get("properties", {})
+ required = tool_schema["input_schema"].get("required", [])
+
+ schema = FunctionSchema(
+ name=tool_name,
+ description=tool_schema["description"],
+ properties=properties,
+ required=required,
+ )
+
+ logger.trace(f"Converted schema: {json.dumps(schema.to_default_dict(), indent=2)}")
+
+ return schema
+
+ async def _sse_register_tools(self, llm) -> ToolsSchema:
+ """Register all available mcp.run tools with the LLM service.
+ Args:
+ llm: The Pipecat LLM service to register tools with
+ Returns:
+ A ToolsSchema containing all registered tools
+ """
+
+ async def mcp_tool_wrapper(
+ function_name: str,
+ tool_call_id: str,
+ arguments: Dict[str, Any],
+ llm: any,
+ context: any,
+ result_callback: any,
+ ) -> None:
+ """Wrapper for mcp.run tool calls to match Pipecat's function call interface."""
+ logger.debug(f"Executing tool '{function_name}' with call ID: {tool_call_id}")
+ logger.trace(f"Tool arguments: {json.dumps(arguments, indent=2)}")
+ try:
+ async with self._client(self._server_params) as (read, write):
+ async with self._session(read, write) as session:
+ await session.initialize()
+ await self._call_tool(session, function_name, arguments, result_callback)
+ except Exception as e:
+ error_msg = f"Error calling mcp tool {function_name}: {str(e)}"
+ logger.error(error_msg)
+ logger.exception("Full exception details:")
+ await result_callback(error_msg)
+
+ logger.debug("Starting registration of mcp.run tools")
+ tool_schemas: List[FunctionSchema] = []
+
+ async with self._client(self._server_params) as (read, write):
+ async with self._session(read, write) as session:
+ await session.initialize()
+ tools_schema = await self._list_tools(session, mcp_tool_wrapper, llm)
+ return tools_schema
+
+ async def _stdio_register_tools(self, llm) -> ToolsSchema:
+ """Register all available mcp.run tools with the LLM service.
+ Args:
+ llm: The Pipecat LLM service to register tools with
+ Returns:
+ A ToolsSchema containing all registered tools
+ """
+
+ async def mcp_tool_wrapper(
+ function_name: str,
+ tool_call_id: str,
+ arguments: Dict[str, Any],
+ llm: any,
+ context: any,
+ result_callback: any,
+ ) -> None:
+ """Wrapper for mcp.run tool calls to match Pipecat's function call interface."""
+ logger.debug(f"Executing tool '{function_name}' with call ID: {tool_call_id}")
+ logger.trace(f"Tool arguments: {json.dumps(arguments, indent=2)}")
+ try:
+ async with self._client(self._server_params) as streams:
+ async with self._session(streams[0], streams[1]) as session:
+ await session.initialize()
+ await self._call_tool(session, function_name, arguments, result_callback)
+ except Exception as e:
+ error_msg = f"Error calling mcp tool {function_name}: {str(e)}"
+ logger.error(error_msg)
+ logger.exception("Full exception details:")
+ await result_callback(error_msg)
+
+ logger.debug("Starting registration of mcp.run tools")
+
+ async with self._client(self._server_params) as streams:
+ async with self._session(streams[0], streams[1]) as session:
+ await session.initialize()
+ tools_schema = await self._list_tools(session, mcp_tool_wrapper, llm)
+ return tools_schema
+
+ async def _call_tool(self, session, function_name, arguments, result_callback):
+ logger.debug(f"Calling mcp tool '{function_name}'")
+ try:
+ results = await session.call_tool(function_name, arguments=arguments)
+ except Exception as e:
+ error_msg = f"Error calling mcp tool {function_name}: {str(e)}"
+ logger.error(error_msg)
+
+ response = "Sorry, could not call the mcp tool"
+ image_url = None
+ if results:
+ if hasattr(results, "content") and results.content:
+ for i, content in enumerate(results.content):
+ if hasattr(content, "text") and content.text:
+ logger.debug(f"Tool response chunk {i}: {content.text}")
+ response += content.text
+ else:
+ # logger.debug(f"Non-text result content: '{content}'")
+ pass
+ logger.info(f"Tool '{function_name}' completed successfully")
+ logger.debug(f"Final response: {response}")
+ else:
+ logger.error(f"Error getting content from {function_name} results.")
+
+ await result_callback(response)
+
+ async def _list_tools(self, session, mcp_tool_wrapper, llm):
+ available_tools = await session.list_tools()
+ tool_schemas: List[FunctionSchema] = []
+
+ try:
+ logger.debug(f"Found {len(available_tools)} available tools")
+ except:
+ pass
+
+ for tool in available_tools.tools:
+ tool_name = tool.name
+ logger.debug(f"Processing tool: {tool_name}")
+ logger.debug(f"Tool description: {tool.description}")
+
+ try:
+ # Convert the schema
+ function_schema = self._convert_mcp_schema_to_pipecat(
+ tool_name,
+ {"description": tool.description, "input_schema": tool.inputSchema},
+ )
+
+ # Register the wrapped function
+ logger.debug(f"Registering function handler for '{tool_name}'")
+ llm.register_function(tool_name, mcp_tool_wrapper)
+
+ # Add to list of schemas
+ tool_schemas.append(function_schema)
+ logger.debug(f"Successfully registered tool '{tool_name}'")
+
+ except Exception as e:
+ logger.error(f"Failed to register tool '{tool_name}': {str(e)}")
+ logger.exception("Full exception details:")
+ continue
+
+ logger.debug(f"Completed registration of {len(tool_schemas)} tools")
+ tools_schema = ToolsSchema(standard_tools=tool_schemas)
+
+ return tools_schema
diff --git a/src/pipecat/services/moondream/vision.py b/src/pipecat/services/moondream/vision.py
index 55b5405f4..6fe44a057 100644
--- a/src/pipecat/services/moondream/vision.py
+++ b/src/pipecat/services/moondream/vision.py
@@ -11,7 +11,7 @@ from loguru import logger
from PIL import Image
from pipecat.frames.frames import ErrorFrame, Frame, TextFrame, VisionImageRawFrame
-from pipecat.services.ai_services import VisionService
+from pipecat.services.vision_service import VisionService
try:
import torch
diff --git a/src/pipecat/services/neuphonic/tts.py b/src/pipecat/services/neuphonic/tts.py
index 7ac005afc..1819482b8 100644
--- a/src/pipecat/services/neuphonic/tts.py
+++ b/src/pipecat/services/neuphonic/tts.py
@@ -27,7 +27,7 @@ from pipecat.frames.frames import (
TTSStoppedFrame,
)
from pipecat.processors.frame_processor import FrameDirection
-from pipecat.services.ai_services import InterruptibleTTSService, TTSService
+from pipecat.services.tts_service import InterruptibleTTSService, TTSService
from pipecat.transcriptions.language import Language
try:
@@ -106,6 +106,9 @@ class NeuphonicTTSService(InterruptibleTTSService):
self._started = False
self._cumulative_time = 0
+ self._receive_task = None
+ self._keepalive_task = None
+
def can_generate_metrics(self) -> bool:
return True
@@ -159,8 +162,11 @@ class NeuphonicTTSService(InterruptibleTTSService):
async def _connect(self):
await self._connect_websocket()
- self._receive_task = self.create_task(self._receive_task_handler(self._report_error))
- self._keepalive_task = self.create_task(self._keepalive_task_handler())
+ if self._websocket and not self._receive_task:
+ self._receive_task = self.create_task(self._receive_task_handler(self._report_error))
+
+ if self._websocket and not self._keepalive_task:
+ self._keepalive_task = self.create_task(self._keepalive_task_handler())
async def _disconnect(self):
if self._receive_task:
@@ -175,6 +181,9 @@ class NeuphonicTTSService(InterruptibleTTSService):
async def _connect_websocket(self):
try:
+ if self._websocket and self._websocket.open:
+ return
+
logger.debug("Connecting to Neuphonic")
tts_config = {
@@ -190,7 +199,6 @@ class NeuphonicTTSService(InterruptibleTTSService):
url = f"{self._url}/speak/{self._settings['lang_code']}?{'&'.join(query_params)}"
self._websocket = await websockets.connect(url)
-
except Exception as e:
logger.error(f"{self} initialization error: {e}")
self._websocket = None
@@ -203,11 +211,11 @@ class NeuphonicTTSService(InterruptibleTTSService):
if self._websocket:
logger.debug("Disconnecting from Neuphonic")
await self._websocket.close()
- self._websocket = None
-
- self._started = False
except Exception as e:
logger.error(f"{self} error closing websocket: {e}")
+ finally:
+ self._started = False
+ self._websocket = None
async def _receive_messages(self):
async for message in self._websocket:
@@ -235,7 +243,7 @@ class NeuphonicTTSService(InterruptibleTTSService):
logger.debug(f"Generating TTS: [{text}]")
try:
- if not self._websocket:
+ if not self._websocket or self._websocket.closed:
await self._connect()
try:
diff --git a/src/pipecat/services/openai/base_llm.py b/src/pipecat/services/openai/base_llm.py
index 5343c1eb7..1aba1b159 100644
--- a/src/pipecat/services/openai/base_llm.py
+++ b/src/pipecat/services/openai/base_llm.py
@@ -34,7 +34,7 @@ from pipecat.processors.aggregators.openai_llm_context import (
OpenAILLMContextFrame,
)
from pipecat.processors.frame_processor import FrameDirection
-from pipecat.services.ai_services import LLMService
+from pipecat.services.llm_service import LLMService
class OpenAIUnhandledFunctionException(Exception):
diff --git a/src/pipecat/services/openai/image.py b/src/pipecat/services/openai/image.py
index fc0c475f9..3d7f6cb70 100644
--- a/src/pipecat/services/openai/image.py
+++ b/src/pipecat/services/openai/image.py
@@ -17,7 +17,7 @@ from pipecat.frames.frames import (
Frame,
URLImageRawFrame,
)
-from pipecat.services.ai_services import ImageGenService
+from pipecat.services.image_service import ImageGenService
class OpenAIImageGenService(ImageGenService):
diff --git a/src/pipecat/services/openai/llm.py b/src/pipecat/services/openai/llm.py
index 5ca51a479..07b564fe1 100644
--- a/src/pipecat/services/openai/llm.py
+++ b/src/pipecat/services/openai/llm.py
@@ -6,7 +6,7 @@
import json
from dataclasses import dataclass
-from typing import Any, Mapping
+from typing import Any
from pipecat.frames.frames import (
FunctionCallCancelFrame,
@@ -15,7 +15,9 @@ from pipecat.frames.frames import (
UserImageRawFrame,
)
from pipecat.processors.aggregators.llm_response import (
+ LLMAssistantAggregatorParams,
LLMAssistantContextAggregator,
+ LLMUserAggregatorParams,
LLMUserContextAggregator,
)
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
@@ -38,7 +40,7 @@ class OpenAILLMService(BaseOpenAILLMService):
def __init__(
self,
*,
- model: str = "gpt-4o",
+ model: str = "gpt-4.1",
params: BaseOpenAILLMService.InputParams = BaseOpenAILLMService.InputParams(),
**kwargs,
):
@@ -48,8 +50,8 @@ class OpenAILLMService(BaseOpenAILLMService):
self,
context: OpenAILLMContext,
*,
- user_kwargs: Mapping[str, Any] = {},
- assistant_kwargs: Mapping[str, Any] = {},
+ user_params: LLMUserAggregatorParams = LLMUserAggregatorParams(),
+ assistant_params: LLMAssistantAggregatorParams = LLMAssistantAggregatorParams(),
) -> OpenAIContextAggregatorPair:
"""Create an instance of OpenAIContextAggregatorPair from an
OpenAILLMContext. Constructor keyword arguments for both the user and
@@ -57,12 +59,8 @@ class OpenAILLMService(BaseOpenAILLMService):
Args:
context (OpenAILLMContext): The LLM context.
- user_kwargs (Mapping[str, Any], optional): Additional keyword
- arguments for the user context aggregator constructor. Defaults
- to an empty mapping.
- assistant_kwargs (Mapping[str, Any], optional): Additional keyword
- arguments for the assistant context aggregator
- constructor. Defaults to an empty mapping.
+ user_params (LLMUserAggregatorParams, optional): User aggregator parameters.
+ assistant_params (LLMAssistantAggregatorParams, optional): User aggregator parameters.
Returns:
OpenAIContextAggregatorPair: A pair of context aggregators, one for
@@ -71,8 +69,8 @@ class OpenAILLMService(BaseOpenAILLMService):
"""
context.set_llm_adapter(self.get_llm_adapter())
- user = OpenAIUserContextAggregator(context, **user_kwargs)
- assistant = OpenAIAssistantContextAggregator(context, **assistant_kwargs)
+ user = OpenAIUserContextAggregator(context, params=user_params)
+ assistant = OpenAIAssistantContextAggregator(context, params=assistant_params)
return OpenAIContextAggregatorPair(_user=user, _assistant=assistant)
diff --git a/src/pipecat/services/openai/tts.py b/src/pipecat/services/openai/tts.py
index 68644f147..634b0a22e 100644
--- a/src/pipecat/services/openai/tts.py
+++ b/src/pipecat/services/openai/tts.py
@@ -17,17 +17,24 @@ from pipecat.frames.frames import (
TTSStartedFrame,
TTSStoppedFrame,
)
-from pipecat.services.ai_services import TTSService
+from pipecat.services.tts_service import TTSService
-ValidVoice = Literal["alloy", "echo", "fable", "onyx", "nova", "shimmer"]
+ValidVoice = Literal[
+ "alloy", "ash", "ballad", "coral", "echo", "fable", "onyx", "nova", "sage", "shimmer", "verse"
+]
VALID_VOICES: Dict[str, ValidVoice] = {
"alloy": "alloy",
+ "ash": "ash",
+ "ballad": "ballad",
+ "coral": "coral",
"echo": "echo",
"fable": "fable",
"onyx": "onyx",
"nova": "nova",
+ "sage": "sage",
"shimmer": "shimmer",
+ "verse": "verse",
}
@@ -63,7 +70,7 @@ class OpenAITTSService(TTSService):
if sample_rate and sample_rate != self.OPENAI_SAMPLE_RATE:
logger.warning(
f"OpenAI TTS only supports {self.OPENAI_SAMPLE_RATE}Hz sample rate. "
- f"Current rate of {self.sample_rate}Hz may cause issues."
+ f"Current rate of {sample_rate}Hz may cause issues."
)
super().__init__(sample_rate=sample_rate, **kwargs)
@@ -98,7 +105,7 @@ class OpenAITTSService(TTSService):
extra_body["instructions"] = self._instructions
async with self._client.audio.speech.with_streaming_response.create(
- input=text or " ", # Text must contain at least one character
+ input=text,
model=self.model_name,
voice=VALID_VOICES[self._voice_id],
response_format="pcm",
diff --git a/src/pipecat/services/openai_realtime_beta/openai.py b/src/pipecat/services/openai_realtime_beta/openai.py
index 7b10b83fc..94d848b77 100644
--- a/src/pipecat/services/openai_realtime_beta/openai.py
+++ b/src/pipecat/services/openai_realtime_beta/openai.py
@@ -8,19 +8,9 @@ import base64
import json
import time
from dataclasses import dataclass
-from typing import Any, Mapping
from loguru import logger
-try:
- import websockets
-except ModuleNotFoundError as e:
- logger.error(f"Exception: {e}")
- logger.error(
- "In order to use OpenAI, you need to `pip install pipecat-ai[openai]`. Also, set `OPENAI_API_KEY` environment variable."
- )
- raise Exception(f"Missing module: {e}")
-
from pipecat.adapters.services.open_ai_realtime_adapter import OpenAIRealtimeLLMAdapter
from pipecat.frames.frames import (
BotStoppedSpeakingFrame,
@@ -48,12 +38,16 @@ from pipecat.frames.frames import (
UserStoppedSpeakingFrame,
)
from pipecat.metrics.metrics import LLMTokenUsage
+from pipecat.processors.aggregators.llm_response import (
+ LLMAssistantAggregatorParams,
+ LLMUserAggregatorParams,
+)
from pipecat.processors.aggregators.openai_llm_context import (
OpenAILLMContext,
OpenAILLMContextFrame,
)
from pipecat.processors.frame_processor import FrameDirection
-from pipecat.services.ai_services import LLMService
+from pipecat.services.llm_service import LLMService
from pipecat.services.openai.llm import OpenAIContextAggregatorPair
from pipecat.utils.time import time_now_iso8601
@@ -65,6 +59,13 @@ from .context import (
)
from .frames import RealtimeFunctionCallResultFrame, RealtimeMessagesUpdateFrame
+try:
+ import websockets
+except ModuleNotFoundError as e:
+ logger.error(f"Exception: {e}")
+ logger.error("In order to use OpenAI, you need to `pip install pipecat-ai[openai]`.")
+ raise Exception(f"Missing module: {e}")
+
@dataclass
class CurrentAudioResponse:
@@ -650,8 +651,8 @@ class OpenAIRealtimeBetaLLMService(LLMService):
self,
context: OpenAILLMContext,
*,
- user_kwargs: Mapping[str, Any] = {},
- assistant_kwargs: Mapping[str, Any] = {},
+ user_params: LLMUserAggregatorParams = LLMUserAggregatorParams(),
+ assistant_params: LLMAssistantAggregatorParams = LLMAssistantAggregatorParams(),
) -> OpenAIContextAggregatorPair:
"""Create an instance of OpenAIContextAggregatorPair from an
OpenAILLMContext. Constructor keyword arguments for both the user and
@@ -659,12 +660,10 @@ class OpenAIRealtimeBetaLLMService(LLMService):
Args:
context (OpenAILLMContext): The LLM context.
- user_kwargs (Mapping[str, Any], optional): Additional keyword
- arguments for the user context aggregator constructor. Defaults
- to an empty mapping.
- assistant_kwargs (Mapping[str, Any], optional): Additional keyword
- arguments for the assistant context aggregator
- constructor. Defaults to an empty mapping.
+ user_params (LLMUserAggregatorParams, optional): User aggregator
+ parameters.
+ assistant_params (LLMAssistantAggregatorParams, optional): User
+ aggregator parameters.
Returns:
OpenAIContextAggregatorPair: A pair of context aggregators, one for
@@ -675,9 +674,8 @@ class OpenAIRealtimeBetaLLMService(LLMService):
context.set_llm_adapter(self.get_llm_adapter())
OpenAIRealtimeLLMContext.upgrade_to_realtime(context)
- user = OpenAIRealtimeUserContextAggregator(context, **user_kwargs)
+ user = OpenAIRealtimeUserContextAggregator(context, params=user_params)
- default_assistant_kwargs = {"expect_stripped_words": False}
- default_assistant_kwargs.update(assistant_kwargs)
- assistant = OpenAIRealtimeAssistantContextAggregator(context, **default_assistant_kwargs)
+ assistant_params.expect_stripped_words = False
+ assistant = OpenAIRealtimeAssistantContextAggregator(context, params=assistant_params)
return OpenAIContextAggregatorPair(_user=user, _assistant=assistant)
diff --git a/src/pipecat/services/openpipe/llm.py b/src/pipecat/services/openpipe/llm.py
index 63e499714..2a2dd1d26 100644
--- a/src/pipecat/services/openpipe/llm.py
+++ b/src/pipecat/services/openpipe/llm.py
@@ -25,7 +25,7 @@ class OpenPipeLLMService(OpenAILLMService):
def __init__(
self,
*,
- model: str = "gpt-4o",
+ model: str = "gpt-4.1",
api_key: Optional[str] = None,
base_url: Optional[str] = None,
openpipe_api_key: Optional[str] = None,
diff --git a/src/pipecat/services/piper/tts.py b/src/pipecat/services/piper/tts.py
index 12a936889..3b5d0fa06 100644
--- a/src/pipecat/services/piper/tts.py
+++ b/src/pipecat/services/piper/tts.py
@@ -16,7 +16,7 @@ from pipecat.frames.frames import (
TTSStartedFrame,
TTSStoppedFrame,
)
-from pipecat.services.ai_services import TTSService
+from pipecat.services.tts_service import TTSService
# This assumes a running TTS service running: https://github.com/rhasspy/piper/blob/master/src/python_run/README_http.md
diff --git a/src/pipecat/services/playht/tts.py b/src/pipecat/services/playht/tts.py
index f7157a950..eb1b8d3f1 100644
--- a/src/pipecat/services/playht/tts.py
+++ b/src/pipecat/services/playht/tts.py
@@ -27,7 +27,7 @@ from pipecat.frames.frames import (
TTSStoppedFrame,
)
from pipecat.processors.frame_processor import FrameDirection
-from pipecat.services.ai_services import InterruptibleTTSService, TTSService
+from pipecat.services.tts_service import InterruptibleTTSService, TTSService
from pipecat.transcriptions.language import Language
try:
@@ -157,7 +157,7 @@ class PlayHTTTSService(InterruptibleTTSService):
async def _connect(self):
await self._connect_websocket()
- if not self._receive_task:
+ if self._websocket and not self._receive_task:
self._receive_task = self.create_task(self._receive_task_handler(self._report_error))
async def _disconnect(self):
@@ -169,7 +169,7 @@ class PlayHTTTSService(InterruptibleTTSService):
async def _connect_websocket(self):
try:
- if self._websocket:
+ if self._websocket and self._websocket.open:
return
logger.debug("Connecting to PlayHT")
@@ -197,11 +197,11 @@ class PlayHTTTSService(InterruptibleTTSService):
if self._websocket:
logger.debug("Disconnecting from PlayHT")
await self._websocket.close()
- self._websocket = None
-
- self._request_id = None
except Exception as e:
logger.error(f"{self} error closing websocket: {e}")
+ finally:
+ self._request_id = None
+ self._websocket = None
async def _get_websocket_url(self):
async with aiohttp.ClientSession() as session:
diff --git a/src/pipecat/services/rime/tts.py b/src/pipecat/services/rime/tts.py
index f5b5da2a6..d3e8e5adb 100644
--- a/src/pipecat/services/rime/tts.py
+++ b/src/pipecat/services/rime/tts.py
@@ -25,7 +25,7 @@ from pipecat.frames.frames import (
TTSStoppedFrame,
)
from pipecat.processors.frame_processor import FrameDirection
-from pipecat.services.ai_services import AudioContextWordTTSService, TTSService
+from pipecat.services.tts_service import AudioContextWordTTSService, TTSService
from pipecat.transcriptions.language import Language
from pipecat.utils.text.base_text_aggregator import BaseTextAggregator
from pipecat.utils.text.skip_tags_aggregator import SkipTagsAggregator
@@ -168,7 +168,7 @@ class RimeTTSService(AudioContextWordTTSService):
"""Establish websocket connection and start receive task."""
await self._connect_websocket()
- if not self._receive_task:
+ if self._websocket and not self._receive_task:
self._receive_task = self.create_task(self._receive_task_handler(self._report_error))
async def _disconnect(self):
@@ -182,7 +182,7 @@ class RimeTTSService(AudioContextWordTTSService):
async def _connect_websocket(self):
"""Connect to Rime websocket API with configured settings."""
try:
- if self._websocket:
+ if self._websocket and self._websocket.open:
return
params = "&".join(f"{k}={v}" for k, v in self._settings.items())
@@ -201,10 +201,11 @@ class RimeTTSService(AudioContextWordTTSService):
if self._websocket:
await self._websocket.send(json.dumps(self._build_eos_msg()))
await self._websocket.close()
- self._websocket = None
- self._context_id = None
except Exception as e:
logger.error(f"{self} error closing websocket: {e}")
+ finally:
+ self._context_id = None
+ self._websocket = None
def _get_websocket(self):
"""Get active websocket connection or raise exception."""
@@ -316,7 +317,7 @@ class RimeTTSService(AudioContextWordTTSService):
"""
logger.debug(f"{self}: Generating TTS [{text}]")
try:
- if not self._websocket:
+ if not self._websocket or self._websocket.closed:
await self._connect()
try:
diff --git a/src/pipecat/services/riva/stt.py b/src/pipecat/services/riva/stt.py
index 63eea8230..6328bcb65 100644
--- a/src/pipecat/services/riva/stt.py
+++ b/src/pipecat/services/riva/stt.py
@@ -18,7 +18,7 @@ from pipecat.frames.frames import (
StartFrame,
TranscriptionFrame,
)
-from pipecat.services.ai_services import STTService
+from pipecat.services.stt_service import STTService
from pipecat.transcriptions.language import Language
from pipecat.utils.time import time_now_iso8601
diff --git a/src/pipecat/services/riva/tts.py b/src/pipecat/services/riva/tts.py
index e0da3ab98..ac123de4f 100644
--- a/src/pipecat/services/riva/tts.py
+++ b/src/pipecat/services/riva/tts.py
@@ -16,7 +16,7 @@ from pipecat.frames.frames import (
TTSStartedFrame,
TTSStoppedFrame,
)
-from pipecat.services.ai_services import TTSService
+from pipecat.services.tts_service import TTSService
from pipecat.transcriptions.language import Language
try:
diff --git a/src/pipecat/services/stt_service.py b/src/pipecat/services/stt_service.py
new file mode 100644
index 000000000..5e57b3104
--- /dev/null
+++ b/src/pipecat/services/stt_service.py
@@ -0,0 +1,171 @@
+#
+# Copyright (c) 2024β2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import io
+import wave
+from abc import abstractmethod
+from typing import Any, AsyncGenerator, Dict, Mapping, Optional
+
+from loguru import logger
+
+from pipecat.frames.frames import (
+ AudioRawFrame,
+ Frame,
+ StartFrame,
+ STTMuteFrame,
+ STTUpdateSettingsFrame,
+ UserStartedSpeakingFrame,
+ UserStoppedSpeakingFrame,
+)
+from pipecat.processors.frame_processor import FrameDirection
+from pipecat.services.ai_service import AIService
+from pipecat.transcriptions.language import Language
+
+
+class STTService(AIService):
+ """STTService is a base class for speech-to-text services."""
+
+ def __init__(
+ self,
+ audio_passthrough=True,
+ # STT input sample rate
+ sample_rate: Optional[int] = None,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+ self._audio_passthrough = audio_passthrough
+ self._init_sample_rate = sample_rate
+ self._sample_rate = 0
+ self._settings: Dict[str, Any] = {}
+ self._muted: bool = False
+
+ @property
+ def is_muted(self) -> bool:
+ """Returns whether the STT service is currently muted."""
+ return self._muted
+
+ @property
+ def sample_rate(self) -> int:
+ return self._sample_rate
+
+ async def set_model(self, model: str):
+ self.set_model_name(model)
+
+ async def set_language(self, language: Language):
+ pass
+
+ @abstractmethod
+ async def run_stt(self, audio: bytes) -> AsyncGenerator[Frame, None]:
+ """Returns transcript as a string"""
+ pass
+
+ async def start(self, frame: StartFrame):
+ await super().start(frame)
+ self._sample_rate = self._init_sample_rate or frame.audio_in_sample_rate
+
+ async def _update_settings(self, settings: Mapping[str, Any]):
+ logger.info(f"Updating STT settings: {self._settings}")
+ for key, value in settings.items():
+ if key in self._settings:
+ logger.info(f"Updating STT setting {key} to: [{value}]")
+ self._settings[key] = value
+ if key == "language":
+ await self.set_language(value)
+ elif key == "model":
+ self.set_model_name(value)
+ else:
+ logger.warning(f"Unknown setting for STT service: {key}")
+
+ async def process_audio_frame(self, frame: AudioRawFrame, direction: FrameDirection):
+ if self._muted:
+ return
+
+ await self.process_generator(self.run_stt(frame.audio))
+
+ async def process_frame(self, frame: Frame, direction: FrameDirection):
+ """Processes a frame of audio data, either buffering or transcribing it."""
+ await super().process_frame(frame, direction)
+
+ if isinstance(frame, AudioRawFrame):
+ # In this service we accumulate audio internally and at the end we
+ # push a TextFrame. We also push audio downstream in case someone
+ # else needs it.
+ await self.process_audio_frame(frame, direction)
+ if self._audio_passthrough:
+ await self.push_frame(frame, direction)
+ elif isinstance(frame, STTUpdateSettingsFrame):
+ await self._update_settings(frame.settings)
+ elif isinstance(frame, STTMuteFrame):
+ self._muted = frame.mute
+ logger.debug(f"STT service {'muted' if frame.mute else 'unmuted'}")
+ else:
+ await self.push_frame(frame, direction)
+
+
+class SegmentedSTTService(STTService):
+ """SegmentedSTTService is an STTService that uses VAD events to detect
+ speech and will run speech-to-text on speech segments only, instead of a
+ continous stream. Since it uses VAD it means that VAD needs to be enabled in
+ the pipeline.
+
+ This service always keeps a small audio buffer to take into account that VAD
+ events are delayed from when the user speech really starts.
+
+ """
+
+ def __init__(self, *, sample_rate: Optional[int] = None, **kwargs):
+ super().__init__(sample_rate=sample_rate, **kwargs)
+ self._content = None
+ self._wave = None
+ self._audio_buffer = bytearray()
+ self._audio_buffer_size_1s = 0
+ self._user_speaking = False
+
+ async def start(self, frame: StartFrame):
+ await super().start(frame)
+ self._audio_buffer_size_1s = self.sample_rate * 2
+
+ async def process_frame(self, frame: Frame, direction: FrameDirection):
+ await super().process_frame(frame, direction)
+
+ if isinstance(frame, UserStartedSpeakingFrame):
+ await self._handle_user_started_speaking(frame)
+ elif isinstance(frame, UserStoppedSpeakingFrame):
+ await self._handle_user_stopped_speaking(frame)
+
+ async def _handle_user_started_speaking(self, frame: UserStartedSpeakingFrame):
+ if frame.emulated:
+ return
+ self._user_speaking = True
+
+ async def _handle_user_stopped_speaking(self, frame: UserStoppedSpeakingFrame):
+ if frame.emulated:
+ return
+
+ self._user_speaking = False
+
+ content = io.BytesIO()
+ wav = wave.open(content, "wb")
+ wav.setsampwidth(2)
+ wav.setnchannels(1)
+ wav.setframerate(self.sample_rate)
+ wav.writeframes(self._audio_buffer)
+ wav.close()
+ content.seek(0)
+
+ await self.process_generator(self.run_stt(content.read()))
+
+ # Start clean.
+ self._audio_buffer.clear()
+
+ async def process_audio_frame(self, frame: AudioRawFrame, direction: FrameDirection):
+ # If the user is speaking the audio buffer will keep growing.
+ self._audio_buffer += frame.audio
+
+ # If the user is not speaking we keep just a little bit of audio.
+ if not self._user_speaking and len(self._audio_buffer) > self._audio_buffer_size_1s:
+ discarded = len(self._audio_buffer) - self._audio_buffer_size_1s
+ self._audio_buffer = self._audio_buffer[discarded:]
diff --git a/src/pipecat/services/tavus/video.py b/src/pipecat/services/tavus/video.py
index 27b1d7155..3699ba512 100644
--- a/src/pipecat/services/tavus/video.py
+++ b/src/pipecat/services/tavus/video.py
@@ -6,7 +6,9 @@
"""This module implements Tavus as a sink transport layer"""
+import asyncio
import base64
+from typing import Optional
import aiohttp
from loguru import logger
@@ -16,6 +18,7 @@ from pipecat.frames.frames import (
CancelFrame,
EndFrame,
Frame,
+ StartFrame,
StartInterruptionFrame,
TransportMessageUrgentFrame,
TTSAudioRawFrame,
@@ -23,7 +26,7 @@ from pipecat.frames.frames import (
TTSStoppedFrame,
)
from pipecat.processors.frame_processor import FrameDirection
-from pipecat.services.ai_services import AIService
+from pipecat.services.ai_service import AIService
class TavusVideoService(AIService):
@@ -50,6 +53,10 @@ class TavusVideoService(AIService):
self._resampler = create_default_resampler()
+ self._audio_buffer = bytearray()
+ self._queue = asyncio.Queue()
+ self._send_task: Optional[asyncio.Task] = None
+
async def initialize(self) -> str:
url = "https://tavusapi.com/v2/conversations"
headers = {"Content-Type": "application/json", "x-api-key": self._api_key}
@@ -78,45 +85,98 @@ class TavusVideoService(AIService):
logger.debug(f"TavusVideoService persona grabbed {response_json}")
return response_json["persona_name"]
+ async def start(self, frame: StartFrame):
+ await super().start(frame)
+ await self._create_send_task()
+
async def stop(self, frame: EndFrame):
await super().stop(frame)
await self._end_conversation()
+ await self._cancel_send_task()
async def cancel(self, frame: CancelFrame):
await super().cancel(frame)
await self._end_conversation()
+ await self._cancel_send_task()
- async def _end_conversation(self) -> None:
+ async def process_frame(self, frame: Frame, direction: FrameDirection):
+ await super().process_frame(frame, direction)
+
+ if isinstance(frame, StartInterruptionFrame):
+ await self._handle_interruptions()
+ await self.push_frame(frame, direction)
+ elif isinstance(frame, TTSStartedFrame):
+ await self.start_processing_metrics()
+ await self.start_ttfb_metrics()
+ self._current_idx_str = str(frame.id)
+ elif isinstance(frame, TTSAudioRawFrame):
+ await self._queue_audio(frame.audio, frame.sample_rate, done=False)
+ elif isinstance(frame, TTSStoppedFrame):
+ await self._queue_audio(b"\x00\x00", self._sample_rate, done=True)
+ await self.stop_ttfb_metrics()
+ await self.stop_processing_metrics()
+ else:
+ await self.push_frame(frame, direction)
+
+ async def _handle_interruptions(self):
+ await self._cancel_send_task()
+ await self._create_send_task()
+ await self._send_interrupt_message()
+
+ async def _end_conversation(self):
url = f"https://tavusapi.com/v2/conversations/{self._conversation_id}/end"
headers = {"Content-Type": "application/json", "x-api-key": self._api_key}
async with self._session.post(url, headers=headers) as r:
r.raise_for_status()
- async def _encode_audio_and_send(self, audio: bytes, in_rate: int, done: bool) -> None:
+ async def _queue_audio(self, audio: bytes, in_rate: int, done: bool):
+ await self._queue.put((audio, in_rate, done))
+
+ async def _create_send_task(self):
+ if not self._send_task:
+ self._queue = asyncio.Queue()
+ self._send_task = self.create_task(self._send_task_handler())
+
+ async def _cancel_send_task(self):
+ if self._send_task:
+ await self.cancel_task(self._send_task)
+ self._send_task = None
+
+ async def _send_task_handler(self):
+ # Daily app-messages have a 4kb limit and also a rate limit of 20
+ # messages per second. Below, we only consider the rate limit because 1
+ # second of a 24000 sample rate would be 48000 bytes (16-bit samples and
+ # 1 channel). So, that is 48000 / 20 = 2400, which is below the 4kb
+ # limit (even including base64 encoding). For a sample rate of 16000,
+ # that would be 32000 / 20 = 1600.
+ MAX_CHUNK_SIZE = int((self._sample_rate * 2) / 20)
+ SLEEP_TIME = 1 / 20
+
+ audio_buffer = bytearray()
+ while True:
+ (audio, in_rate, done) = await self._queue.get()
+
+ if done:
+ # Send any remaining audio.
+ if len(audio_buffer) > 0:
+ await self._encode_audio_and_send(bytes(audio_buffer), done)
+ await self._encode_audio_and_send(audio, done)
+ audio_buffer.clear()
+ else:
+ audio = await self._resampler.resample(audio, in_rate, self._sample_rate)
+ audio_buffer.extend(audio)
+ while len(audio_buffer) >= MAX_CHUNK_SIZE:
+ chunk = audio_buffer[:MAX_CHUNK_SIZE]
+ audio_buffer = audio_buffer[MAX_CHUNK_SIZE:]
+ await self._encode_audio_and_send(bytes(chunk), done)
+ await asyncio.sleep(SLEEP_TIME)
+
+ async def _encode_audio_and_send(self, audio: bytes, done: bool):
"""Encodes audio to base64 and sends it to Tavus"""
- if not done:
- audio = await self._resampler.resample(audio, in_rate, self._sample_rate)
audio_base64 = base64.b64encode(audio).decode("utf-8")
logger.trace(f"{self}: sending {len(audio)} bytes")
await self._send_audio_message(audio_base64, done=done)
- async def process_frame(self, frame: Frame, direction: FrameDirection):
- await super().process_frame(frame, direction)
- if isinstance(frame, TTSStartedFrame):
- await self.start_processing_metrics()
- await self.start_ttfb_metrics()
- self._current_idx_str = str(frame.id)
- elif isinstance(frame, TTSAudioRawFrame):
- await self._encode_audio_and_send(frame.audio, frame.sample_rate, done=False)
- elif isinstance(frame, TTSStoppedFrame):
- await self._encode_audio_and_send(b"\x00", self._sample_rate, done=True)
- await self.stop_ttfb_metrics()
- await self.stop_processing_metrics()
- elif isinstance(frame, StartInterruptionFrame):
- await self._send_interrupt_message()
- else:
- await self.push_frame(frame, direction)
-
async def _send_interrupt_message(self) -> None:
transport_frame = TransportMessageUrgentFrame(
message={
@@ -127,7 +187,7 @@ class TavusVideoService(AIService):
)
await self.push_frame(transport_frame)
- async def _send_audio_message(self, audio_base64: str, done: bool) -> None:
+ async def _send_audio_message(self, audio_base64: str, done: bool):
transport_frame = TransportMessageUrgentFrame(
message={
"message_type": "conversation",
diff --git a/src/pipecat/services/tts_service.py b/src/pipecat/services/tts_service.py
new file mode 100644
index 000000000..1da3c6814
--- /dev/null
+++ b/src/pipecat/services/tts_service.py
@@ -0,0 +1,603 @@
+#
+# Copyright (c) 2024β2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+from abc import abstractmethod
+from typing import Any, AsyncGenerator, Dict, List, Mapping, Optional, Sequence, Tuple
+
+from loguru import logger
+
+from pipecat.frames.frames import (
+ BotStartedSpeakingFrame,
+ BotStoppedSpeakingFrame,
+ CancelFrame,
+ EndFrame,
+ ErrorFrame,
+ Frame,
+ InterimTranscriptionFrame,
+ LLMFullResponseEndFrame,
+ StartFrame,
+ StartInterruptionFrame,
+ TextFrame,
+ TranscriptionFrame,
+ TTSAudioRawFrame,
+ TTSSpeakFrame,
+ TTSStartedFrame,
+ TTSStoppedFrame,
+ TTSTextFrame,
+ TTSUpdateSettingsFrame,
+)
+from pipecat.processors.frame_processor import FrameDirection
+from pipecat.services.ai_service import AIService
+from pipecat.services.websocket_service import WebsocketService
+from pipecat.transcriptions.language import Language
+from pipecat.utils.text.base_text_aggregator import BaseTextAggregator
+from pipecat.utils.text.base_text_filter import BaseTextFilter
+from pipecat.utils.text.simple_text_aggregator import SimpleTextAggregator
+from pipecat.utils.time import seconds_to_nanoseconds
+
+
+class TTSService(AIService):
+ def __init__(
+ self,
+ *,
+ aggregate_sentences: bool = True,
+ # if True, TTSService will push TextFrames and LLMFullResponseEndFrames,
+ # otherwise subclass must do it
+ push_text_frames: bool = True,
+ # if True, TTSService will push TTSStoppedFrames, otherwise subclass must do it
+ push_stop_frames: bool = False,
+ # if push_stop_frames is True, wait for this idle period before pushing TTSStoppedFrame
+ stop_frame_timeout_s: float = 2.0,
+ # if True, TTSService will push silence audio frames after TTSStoppedFrame
+ push_silence_after_stop: bool = False,
+ # if push_silence_after_stop is True, send this amount of audio silence
+ silence_time_s: float = 2.0,
+ # if True, we will pause processing frames while we are receiving audio
+ pause_frame_processing: bool = False,
+ # TTS output sample rate
+ sample_rate: Optional[int] = None,
+ # Text aggregator to aggregate incoming tokens and decide when to push to the TTS.
+ text_aggregator: Optional[BaseTextAggregator] = None,
+ # Text filter executed after text has been aggregated.
+ text_filters: Sequence[BaseTextFilter] = [],
+ text_filter: Optional[BaseTextFilter] = None,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+ self._aggregate_sentences: bool = aggregate_sentences
+ self._push_text_frames: bool = push_text_frames
+ self._push_stop_frames: bool = push_stop_frames
+ self._stop_frame_timeout_s: float = stop_frame_timeout_s
+ self._push_silence_after_stop: bool = push_silence_after_stop
+ self._silence_time_s: float = silence_time_s
+ self._pause_frame_processing: bool = pause_frame_processing
+ self._init_sample_rate = sample_rate
+ self._sample_rate = 0
+ self._voice_id: str = ""
+ self._settings: Dict[str, Any] = {}
+ self._text_aggregator: BaseTextAggregator = text_aggregator or SimpleTextAggregator()
+ self._text_filters: Sequence[BaseTextFilter] = text_filters
+ if text_filter:
+ import warnings
+
+ with warnings.catch_warnings():
+ warnings.simplefilter("always")
+ warnings.warn(
+ "Parameter 'text_filter' is deprecated, use 'text_filters' instead.",
+ DeprecationWarning,
+ )
+ self._text_filters = [text_filter]
+
+ self._stop_frame_task: Optional[asyncio.Task] = None
+ self._stop_frame_queue: asyncio.Queue = asyncio.Queue()
+
+ self._processing_text: bool = False
+
+ @property
+ def sample_rate(self) -> int:
+ return self._sample_rate
+
+ async def set_model(self, model: str):
+ self.set_model_name(model)
+
+ def set_voice(self, voice: str):
+ self._voice_id = voice
+
+ # Converts the text to audio.
+ @abstractmethod
+ async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
+ pass
+
+ def language_to_service_language(self, language: Language) -> Optional[str]:
+ return Language(language)
+
+ async def update_setting(self, key: str, value: Any):
+ pass
+
+ async def flush_audio(self):
+ pass
+
+ async def start(self, frame: StartFrame):
+ await super().start(frame)
+ self._sample_rate = self._init_sample_rate or frame.audio_out_sample_rate
+ if self._push_stop_frames and not self._stop_frame_task:
+ self._stop_frame_task = self.create_task(self._stop_frame_handler())
+
+ async def stop(self, frame: EndFrame):
+ await super().stop(frame)
+ if self._stop_frame_task:
+ await self.cancel_task(self._stop_frame_task)
+ self._stop_frame_task = None
+
+ async def cancel(self, frame: CancelFrame):
+ await super().cancel(frame)
+ if self._stop_frame_task:
+ await self.cancel_task(self._stop_frame_task)
+ self._stop_frame_task = None
+
+ async def _update_settings(self, settings: Mapping[str, Any]):
+ for key, value in settings.items():
+ if key in self._settings:
+ logger.info(f"Updating TTS setting {key} to: [{value}]")
+ self._settings[key] = value
+ if key == "language":
+ self._settings[key] = self.language_to_service_language(value)
+ elif key == "model":
+ self.set_model_name(value)
+ elif key == "voice":
+ self.set_voice(value)
+ elif key == "text_filter":
+ for filter in self._text_filters:
+ filter.update_settings(value)
+ else:
+ logger.warning(f"Unknown setting for TTS service: {key}")
+
+ async def say(self, text: str):
+ await self.queue_frame(TTSSpeakFrame(text))
+
+ async def process_frame(self, frame: Frame, direction: FrameDirection):
+ await super().process_frame(frame, direction)
+
+ if (
+ isinstance(frame, TextFrame)
+ and not isinstance(frame, InterimTranscriptionFrame)
+ and not isinstance(frame, TranscriptionFrame)
+ ):
+ await self._process_text_frame(frame)
+ elif isinstance(frame, StartInterruptionFrame):
+ await self._handle_interruption(frame, direction)
+ await self.push_frame(frame, direction)
+ elif isinstance(frame, (LLMFullResponseEndFrame, EndFrame)):
+ # We pause processing incoming frames if the LLM response included
+ # text (it might be that it's only a function calling response). We
+ # pause to avoid audio overlapping.
+ await self._maybe_pause_frame_processing()
+
+ sentence = self._text_aggregator.text
+ self._text_aggregator.reset()
+ self._processing_text = False
+ await self._push_tts_frames(sentence)
+ if isinstance(frame, LLMFullResponseEndFrame):
+ if self._push_text_frames:
+ await self.push_frame(frame, direction)
+ else:
+ await self.push_frame(frame, direction)
+ elif isinstance(frame, TTSSpeakFrame):
+ # Store if we were processing text or not so we can set it back.
+ processing_text = self._processing_text
+ await self._push_tts_frames(frame.text)
+ # We pause processing incoming frames because we are sending data to
+ # the TTS. We pause to avoid audio overlapping.
+ await self._maybe_pause_frame_processing()
+ await self.flush_audio()
+ self._processing_text = processing_text
+ elif isinstance(frame, TTSUpdateSettingsFrame):
+ await self._update_settings(frame.settings)
+ elif isinstance(frame, BotStoppedSpeakingFrame):
+ await self._maybe_resume_frame_processing()
+ await self.push_frame(frame, direction)
+ else:
+ await self.push_frame(frame, direction)
+
+ async def push_frame(self, frame: Frame, direction: FrameDirection = FrameDirection.DOWNSTREAM):
+ if self._push_silence_after_stop and isinstance(frame, TTSStoppedFrame):
+ silence_num_bytes = int(self._silence_time_s * self.sample_rate * 2) # 16-bit
+ await self.push_frame(
+ TTSAudioRawFrame(
+ audio=b"\x00" * silence_num_bytes,
+ sample_rate=self.sample_rate,
+ num_channels=1,
+ )
+ )
+
+ await super().push_frame(frame, direction)
+
+ if self._push_stop_frames and (
+ isinstance(frame, StartInterruptionFrame)
+ or isinstance(frame, TTSStartedFrame)
+ or isinstance(frame, TTSAudioRawFrame)
+ or isinstance(frame, TTSStoppedFrame)
+ ):
+ await self._stop_frame_queue.put(frame)
+
+ async def _handle_interruption(self, frame: StartInterruptionFrame, direction: FrameDirection):
+ self._processing_text = False
+ self._text_aggregator.handle_interruption()
+ for filter in self._text_filters:
+ filter.handle_interruption()
+
+ async def _maybe_pause_frame_processing(self):
+ if self._processing_text and self._pause_frame_processing:
+ await self.pause_processing_frames()
+
+ async def _maybe_resume_frame_processing(self):
+ if self._pause_frame_processing:
+ await self.resume_processing_frames()
+
+ async def _process_text_frame(self, frame: TextFrame):
+ text: Optional[str] = None
+ if not self._aggregate_sentences:
+ text = frame.text
+ else:
+ text = self._text_aggregator.aggregate(frame.text)
+
+ if text:
+ await self._push_tts_frames(text)
+
+ async def _push_tts_frames(self, text: str):
+ # Remove leading newlines only
+ text = text.lstrip("\n")
+
+ # Don't send only whitespace. This causes problems for some TTS models. But also don't
+ # strip all whitespace, as whitespace can influence prosody.
+ if not text.strip():
+ return
+
+ # This is just a flag that indicates if we sent something to the TTS
+ # service. It will be cleared if we sent text because of a TTSSpeakFrame
+ # or when we received an LLMFullResponseEndFrame
+ self._processing_text = True
+
+ await self.start_processing_metrics()
+
+ # Process all filter.
+ for filter in self._text_filters:
+ filter.reset_interruption()
+ text = filter.filter(text)
+
+ if text:
+ await self.process_generator(self.run_tts(text))
+
+ await self.stop_processing_metrics()
+
+ if self._push_text_frames:
+ # We send the original text after the audio. This way, if we are
+ # interrupted, the text is not added to the assistant context.
+ await self.push_frame(TTSTextFrame(text))
+
+ async def _stop_frame_handler(self):
+ has_started = False
+ while True:
+ try:
+ frame = await asyncio.wait_for(
+ self._stop_frame_queue.get(), self._stop_frame_timeout_s
+ )
+ if isinstance(frame, TTSStartedFrame):
+ has_started = True
+ elif isinstance(frame, (TTSStoppedFrame, StartInterruptionFrame)):
+ has_started = False
+ except asyncio.TimeoutError:
+ if has_started:
+ await self.push_frame(TTSStoppedFrame())
+ has_started = False
+
+
+class WordTTSService(TTSService):
+ """This is a base class for TTS services that support word timestamps. Word
+ timestamps are useful to synchronize audio with text of the spoken
+ words. This way only the spoken words are added to the conversation context.
+
+ """
+
+ def __init__(self, **kwargs):
+ super().__init__(**kwargs)
+ self._initial_word_timestamp = -1
+ self._words_queue = asyncio.Queue()
+ self._words_task = None
+
+ def start_word_timestamps(self):
+ if self._initial_word_timestamp == -1:
+ self._initial_word_timestamp = self.get_clock().get_time()
+
+ def reset_word_timestamps(self):
+ self._initial_word_timestamp = -1
+
+ async def add_word_timestamps(self, word_times: List[Tuple[str, float]]):
+ for word, timestamp in word_times:
+ await self._words_queue.put((word, seconds_to_nanoseconds(timestamp)))
+
+ async def start(self, frame: StartFrame):
+ await super().start(frame)
+ self._create_words_task()
+
+ async def stop(self, frame: EndFrame):
+ await super().stop(frame)
+ await self._stop_words_task()
+
+ async def cancel(self, frame: CancelFrame):
+ await super().cancel(frame)
+ await self._stop_words_task()
+
+ async def process_frame(self, frame: Frame, direction: FrameDirection):
+ await super().process_frame(frame, direction)
+
+ if isinstance(frame, (LLMFullResponseEndFrame, EndFrame)):
+ await self.flush_audio()
+
+ async def _handle_interruption(self, frame: StartInterruptionFrame, direction: FrameDirection):
+ await super()._handle_interruption(frame, direction)
+ self.reset_word_timestamps()
+
+ def _create_words_task(self):
+ if not self._words_task:
+ self._words_task = self.create_task(self._words_task_handler())
+
+ async def _stop_words_task(self):
+ if self._words_task:
+ await self.cancel_task(self._words_task)
+ self._words_task = None
+
+ async def _words_task_handler(self):
+ last_pts = 0
+ while True:
+ (word, timestamp) = await self._words_queue.get()
+ if word == "Reset" and timestamp == 0:
+ self.reset_word_timestamps()
+ frame = None
+ elif word == "LLMFullResponseEndFrame" and timestamp == 0:
+ frame = LLMFullResponseEndFrame()
+ frame.pts = last_pts
+ elif word == "TTSStoppedFrame" and timestamp == 0:
+ frame = TTSStoppedFrame()
+ frame.pts = last_pts
+ else:
+ frame = TTSTextFrame(word)
+ frame.pts = self._initial_word_timestamp + timestamp
+ if frame:
+ last_pts = frame.pts
+ await self.push_frame(frame)
+ self._words_queue.task_done()
+
+
+class WebsocketTTSService(TTSService, WebsocketService):
+ """This is a base class for websocket-based TTS services.
+
+ If an error occurs with the websocket, an "on_connection_error" event will
+ be triggered:
+
+ @tts.event_handler("on_connection_error")
+ async def on_connection_error(tts: TTSService, error: str):
+ ...
+
+ """
+
+ def __init__(self, *, reconnect_on_error: bool = True, **kwargs):
+ TTSService.__init__(self, **kwargs)
+ WebsocketService.__init__(self, reconnect_on_error=reconnect_on_error, **kwargs)
+ self._register_event_handler("on_connection_error")
+
+ async def _report_error(self, error: ErrorFrame):
+ await self._call_event_handler("on_connection_error", error.error)
+ await self.push_error(error)
+
+
+class InterruptibleTTSService(WebsocketTTSService):
+ """This is a base class for websocket-based TTS services that don't support
+ word timestamps and that don't offer a way to correlate the generated audio
+ to the requested text.
+
+ """
+
+ def __init__(self, **kwargs):
+ super().__init__(**kwargs)
+
+ # Indicates if the bot is speaking. If the bot is not speaking we don't
+ # need to reconnect when the user speaks. If the bot is speaking and the
+ # user interrupts we need to reconnect.
+ self._bot_speaking = False
+
+ async def _handle_interruption(self, frame: StartInterruptionFrame, direction: FrameDirection):
+ await super()._handle_interruption(frame, direction)
+ if self._bot_speaking:
+ await self._disconnect()
+ await self._connect()
+
+ async def process_frame(self, frame: Frame, direction: FrameDirection):
+ await super().process_frame(frame, direction)
+
+ if isinstance(frame, BotStartedSpeakingFrame):
+ self._bot_speaking = True
+ elif isinstance(frame, BotStoppedSpeakingFrame):
+ self._bot_speaking = False
+
+
+class WebsocketWordTTSService(WordTTSService, WebsocketService):
+ """This is a base class for websocket-based TTS services that support word
+ timestamps.
+
+ If an error occurs with the websocket a "on_connection_error" event will be
+ triggered:
+
+ @tts.event_handler("on_connection_error")
+ async def on_connection_error(tts: TTSService, error: str):
+ ...
+
+ """
+
+ def __init__(self, *, reconnect_on_error: bool = True, **kwargs):
+ WordTTSService.__init__(self, **kwargs)
+ WebsocketService.__init__(self, reconnect_on_error=reconnect_on_error, **kwargs)
+ self._register_event_handler("on_connection_error")
+
+ async def _report_error(self, error: ErrorFrame):
+ await self._call_event_handler("on_connection_error", error.error)
+ await self.push_error(error)
+
+
+class InterruptibleWordTTSService(WebsocketWordTTSService):
+ """This is a base class for websocket-based TTS services that support word
+ timestamps but don't offer a way to correlate the generated audio to the
+ requested text.
+
+ """
+
+ def __init__(self, **kwargs):
+ super().__init__(**kwargs)
+
+ # Indicates if the bot is speaking. If the bot is not speaking we don't
+ # need to reconnect when the user speaks. If the bot is speaking and the
+ # user interrupts we need to reconnect.
+ self._bot_speaking = False
+
+ async def _handle_interruption(self, frame: StartInterruptionFrame, direction: FrameDirection):
+ await super()._handle_interruption(frame, direction)
+ if self._bot_speaking:
+ await self._disconnect()
+ await self._connect()
+
+ async def process_frame(self, frame: Frame, direction: FrameDirection):
+ await super().process_frame(frame, direction)
+
+ if isinstance(frame, BotStartedSpeakingFrame):
+ self._bot_speaking = True
+ elif isinstance(frame, BotStoppedSpeakingFrame):
+ self._bot_speaking = False
+
+
+class AudioContextWordTTSService(WebsocketWordTTSService):
+ """This is a base class for websocket-based TTS services that support word
+ timestamps and also allow correlating the generated audio with the requested
+ text.
+
+ Each request could be multiple sentences long which are grouped by
+ context. For this to work, the TTS service needs to support handling
+ multiple requests at once (i.e. multiple simultaneous contexts).
+
+ The audio received from the TTS will be played in context order. That is, if
+ we requested audio for a context "A" and then audio for context "B", the
+ audio from context ID "A" will be played first.
+
+ """
+
+ def __init__(self, **kwargs):
+ super().__init__(**kwargs)
+ self._contexts_queue = asyncio.Queue()
+ self._contexts: Dict[str, asyncio.Queue] = {}
+ self._audio_context_task = None
+
+ async def create_audio_context(self, context_id: str):
+ """Create a new audio context."""
+ await self._contexts_queue.put(context_id)
+ self._contexts[context_id] = asyncio.Queue()
+ logger.trace(f"{self} created audio context {context_id}")
+
+ async def append_to_audio_context(self, context_id: str, frame: TTSAudioRawFrame):
+ """Append audio to an existing context."""
+ if self.audio_context_available(context_id):
+ logger.trace(f"{self} appending audio {frame} to audio context {context_id}")
+ await self._contexts[context_id].put(frame)
+ else:
+ logger.warning(f"{self} unable to append audio to context {context_id}")
+
+ async def remove_audio_context(self, context_id: str):
+ """Remove an existing audio context."""
+ if self.audio_context_available(context_id):
+ # We just mark the audio context for deletion by appending
+ # None. Once we reach None while handling audio we know we can
+ # safely remove the context.
+ logger.trace(f"{self} marking audio context {context_id} for deletion")
+ await self._contexts[context_id].put(None)
+ else:
+ logger.warning(f"{self} unable to remove context {context_id}")
+
+ def audio_context_available(self, context_id: str) -> bool:
+ """Checks whether the given audio context is registered."""
+ return context_id in self._contexts
+
+ async def start(self, frame: StartFrame):
+ await super().start(frame)
+ self._create_audio_context_task()
+
+ async def stop(self, frame: EndFrame):
+ await super().stop(frame)
+ if self._audio_context_task:
+ # Indicate no more audio contexts are available. this will end the
+ # task cleanly after all contexts have been processed.
+ await self._contexts_queue.put(None)
+ await self.wait_for_task(self._audio_context_task)
+ self._audio_context_task = None
+
+ async def cancel(self, frame: CancelFrame):
+ await super().cancel(frame)
+ await self._stop_audio_context_task()
+
+ async def _handle_interruption(self, frame: StartInterruptionFrame, direction: FrameDirection):
+ await super()._handle_interruption(frame, direction)
+ await self._stop_audio_context_task()
+ self._create_audio_context_task()
+
+ def _create_audio_context_task(self):
+ if not self._audio_context_task:
+ self._contexts_queue = asyncio.Queue()
+ self._contexts: Dict[str, asyncio.Queue] = {}
+ self._audio_context_task = self.create_task(self._audio_context_task_handler())
+
+ async def _stop_audio_context_task(self):
+ if self._audio_context_task:
+ await self.cancel_task(self._audio_context_task)
+ self._audio_context_task = None
+
+ async def _audio_context_task_handler(self):
+ """In this task we process audio contexts in order."""
+ running = True
+ while running:
+ context_id = await self._contexts_queue.get()
+
+ if context_id:
+ # Process the audio context until the context doesn't have more
+ # audio available (i.e. we find None).
+ await self._handle_audio_context(context_id)
+
+ # We just finished processing the context, so we can safely remove it.
+ del self._contexts[context_id]
+
+ # Append some silence between sentences.
+ silence = b"\x00" * self.sample_rate
+ frame = TTSAudioRawFrame(
+ audio=silence, sample_rate=self.sample_rate, num_channels=1
+ )
+ await self.push_frame(frame)
+ else:
+ running = False
+
+ self._contexts_queue.task_done()
+
+ async def _handle_audio_context(self, context_id: str):
+ # If we don't receive any audio during this time, we consider the context finished.
+ AUDIO_CONTEXT_TIMEOUT = 3.0
+ queue = self._contexts[context_id]
+ running = True
+ while running:
+ try:
+ frame = await asyncio.wait_for(queue.get(), timeout=AUDIO_CONTEXT_TIMEOUT)
+ if frame:
+ await self.push_frame(frame)
+ running = frame is not None
+ except asyncio.TimeoutError:
+ # We didn't get audio, so let's consider this context finished.
+ logger.trace(f"{self} time out on audio context {context_id}")
+ break
diff --git a/src/pipecat/services/ultravox/stt.py b/src/pipecat/services/ultravox/stt.py
index 48bc3a69f..6d09404db 100644
--- a/src/pipecat/services/ultravox/stt.py
+++ b/src/pipecat/services/ultravox/stt.py
@@ -29,7 +29,7 @@ from pipecat.frames.frames import (
UserStoppedSpeakingFrame,
)
from pipecat.processors.frame_processor import FrameDirection
-from pipecat.services.ai_services import AIService
+from pipecat.services.ai_service import AIService
try:
from transformers import AutoTokenizer
diff --git a/src/pipecat/services/vision_service.py b/src/pipecat/services/vision_service.py
new file mode 100644
index 000000000..23eb79c4e
--- /dev/null
+++ b/src/pipecat/services/vision_service.py
@@ -0,0 +1,34 @@
+#
+# Copyright (c) 2024β2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+from abc import abstractmethod
+from typing import AsyncGenerator
+
+from pipecat.frames.frames import Frame, VisionImageRawFrame
+from pipecat.processors.frame_processor import FrameDirection
+from pipecat.services.ai_service import AIService
+
+
+class VisionService(AIService):
+ """VisionService is a base class for vision services."""
+
+ def __init__(self, **kwargs):
+ super().__init__(**kwargs)
+ self._describe_text = None
+
+ @abstractmethod
+ async def run_vision(self, frame: VisionImageRawFrame) -> AsyncGenerator[Frame, None]:
+ pass
+
+ async def process_frame(self, frame: Frame, direction: FrameDirection):
+ await super().process_frame(frame, direction)
+
+ if isinstance(frame, VisionImageRawFrame):
+ await self.start_processing_metrics()
+ await self.process_generator(self.run_vision(frame))
+ await self.stop_processing_metrics()
+ else:
+ await self.push_frame(frame, direction)
diff --git a/src/pipecat/services/websocket_service.py b/src/pipecat/services/websocket_service.py
index 2e82ddaba..d757946f9 100644
--- a/src/pipecat/services/websocket_service.py
+++ b/src/pipecat/services/websocket_service.py
@@ -31,7 +31,7 @@ class WebsocketService(ABC):
bool: True if connection is verified working, False otherwise
"""
try:
- if not self._websocket:
+ if not self._websocket or self._websocket.closed:
return False
await self._websocket.ping()
return True
diff --git a/src/pipecat/services/whisper/base_stt.py b/src/pipecat/services/whisper/base_stt.py
index 146fd4f39..95d14bbe5 100644
--- a/src/pipecat/services/whisper/base_stt.py
+++ b/src/pipecat/services/whisper/base_stt.py
@@ -11,7 +11,7 @@ from openai import AsyncOpenAI
from openai.types.audio import Transcription
from pipecat.frames.frames import ErrorFrame, Frame, TranscriptionFrame
-from pipecat.services.ai_services import SegmentedSTTService
+from pipecat.services.stt_service import SegmentedSTTService
from pipecat.transcriptions.language import Language
from pipecat.utils.time import time_now_iso8601
diff --git a/src/pipecat/services/whisper/stt.py b/src/pipecat/services/whisper/stt.py
index 473a53406..4026dbea1 100644
--- a/src/pipecat/services/whisper/stt.py
+++ b/src/pipecat/services/whisper/stt.py
@@ -15,7 +15,7 @@ from loguru import logger
from typing_extensions import TYPE_CHECKING, override
from pipecat.frames.frames import ErrorFrame, Frame, TranscriptionFrame
-from pipecat.services.ai_services import SegmentedSTTService
+from pipecat.services.stt_service import SegmentedSTTService
from pipecat.transcriptions.language import Language
from pipecat.utils.time import time_now_iso8601
diff --git a/src/pipecat/services/xtts/tts.py b/src/pipecat/services/xtts/tts.py
index d2702d520..5e08732a9 100644
--- a/src/pipecat/services/xtts/tts.py
+++ b/src/pipecat/services/xtts/tts.py
@@ -18,7 +18,7 @@ from pipecat.frames.frames import (
TTSStartedFrame,
TTSStoppedFrame,
)
-from pipecat.services.ai_services import TTSService
+from pipecat.services.tts_service import TTSService
from pipecat.transcriptions.language import Language
# The server below can connect to XTTS through a local running docker
diff --git a/src/pipecat/transcriptions/language.py b/src/pipecat/transcriptions/language.py
index 75f714a72..a6de4f46e 100644
--- a/src/pipecat/transcriptions/language.py
+++ b/src/pipecat/transcriptions/language.py
@@ -83,6 +83,10 @@ class Language(StrEnum):
CA = "ca"
CA_ES = "ca-ES"
+ # Mandarin Chinese
+ CMN = "cmn"
+ CMN_CN = "cmn-CN"
+
# Czech
CS = "cs"
CS_CZ = "cs-CZ"
@@ -182,6 +186,9 @@ class Language(StrEnum):
GA = "ga"
GA_IE = "ga-IE"
+ # Gaelic
+ GD = "gd"
+
# Galician
GL = "gl"
GL_ES = "gl-ES"
@@ -193,6 +200,9 @@ class Language(StrEnum):
# Hausa
HA = "ha"
+ # Hawaiian
+ HAW = "haw"
+
# Hebrew
HE = "he"
HE_IL = "he-IL"
@@ -288,6 +298,9 @@ class Language(StrEnum):
# Malagasy
MG = "mg"
+ # Maori
+ MI = "mi"
+
# Macedonian
MK = "mk"
MK_MK = "mk-MK"
@@ -300,9 +313,6 @@ class Language(StrEnum):
MN = "mn"
MN_MN = "mn-MN"
- # Maori
- MI = "mi"
-
# Marathi
MR = "mr"
MR_IN = "mr-IN"
@@ -318,6 +328,7 @@ class Language(StrEnum):
# Burmese
MY = "my"
MY_MM = "my-MM"
+ MY_MR = "mymr"
# Norwegian
NB = "nb" # Norwegian BokmΓ₯l
@@ -414,9 +425,6 @@ class Language(StrEnum):
SW_KE = "sw-KE"
SW_TZ = "sw-TZ"
- # Tagalog
- TL = "tl"
-
# Tamil
TA = "ta"
TA_IN = "ta-IN"
@@ -438,6 +446,9 @@ class Language(StrEnum):
# Turkmen
TK = "tk"
+ # Tagalog
+ TL = "tl"
+
# Turkish
TR = "tr"
TR_TR = "tr-TR"
@@ -489,7 +500,7 @@ class Language(StrEnum):
ZH_TW = "zh-TW"
# Xhosa
- XH = "xh"
+ XH = "xh-ZA"
# Zulu
ZU = "zu"
diff --git a/src/pipecat/transports/base_input.py b/src/pipecat/transports/base_input.py
index 26f386576..497b21cda 100644
--- a/src/pipecat/transports/base_input.py
+++ b/src/pipecat/transports/base_input.py
@@ -10,6 +10,10 @@ from typing import Optional
from loguru import logger
+from pipecat.audio.turn.base_turn_analyzer import (
+ BaseTurnAnalyzer,
+ EndOfTurnState,
+)
from pipecat.audio.vad.vad_analyzer import VADAnalyzer, VADState
from pipecat.frames.frames import (
BotInterruptionFrame,
@@ -20,6 +24,7 @@ from pipecat.frames.frames import (
FilterUpdateSettingsFrame,
Frame,
InputAudioRawFrame,
+ MetricsFrame,
StartFrame,
StartInterruptionFrame,
StopInterruptionFrame,
@@ -28,6 +33,7 @@ from pipecat.frames.frames import (
UserStoppedSpeakingFrame,
VADParamsUpdateFrame,
)
+from pipecat.metrics.metrics import MetricsData
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.transports.base_transport import TransportParams
@@ -49,6 +55,46 @@ class BaseInputTransport(FrameProcessor):
# if passthrough is enabled.
self._audio_task = None
+ if self._params.vad_enabled:
+ import warnings
+
+ with warnings.catch_warnings():
+ warnings.simplefilter("always")
+ warnings.warn(
+ "Parameter 'vad_enabled' is deprecated, use 'audio_in_enabled' and 'vad_analyzer' instead.",
+ DeprecationWarning,
+ )
+ self._params.audio_in_enabled = True
+
+ if self._params.vad_audio_passthrough:
+ import warnings
+
+ with warnings.catch_warnings():
+ warnings.simplefilter("always")
+ warnings.warn(
+ "Parameter 'vad_audio_passthrough' is deprecated, audio passthrough is now always enabled. Use 'audio_in_passthrough' to disable.",
+ DeprecationWarning,
+ )
+ self._params.audio_in_passthrough = True
+
+ if self._params.camera_in_enabled or self._params.camera_out_enabled:
+ import warnings
+
+ with warnings.catch_warnings():
+ warnings.simplefilter("always")
+ warnings.warn(
+ "Parameters 'camera_*' are deprecated, use 'video_*' instead.",
+ DeprecationWarning,
+ )
+ self._params.video_in_enabled = self._params.camera_in_enabled
+ self._params.video_out_enabled = self._params.camera_out_enabled
+ self._params.video_out_is_live = self._params.camera_out_is_live
+ self._params.video_out_width = self._params.camera_out_width
+ self._params.video_out_height = self._params.camera_out_height
+ self._params.video_out_bitrate = self._params.camera_out_bitrate
+ self._params.video_out_framerate = self._params.camera_out_framerate
+ self._params.video_out_color_format = self._params.camera_out_color_format
+
def enable_audio_in_stream_on_start(self, enabled: bool) -> None:
logger.debug(f"Enabling audio on start. {enabled}")
self._params.audio_in_stream_on_start = enabled
@@ -64,23 +110,31 @@ class BaseInputTransport(FrameProcessor):
def vad_analyzer(self) -> Optional[VADAnalyzer]:
return self._params.vad_analyzer
+ @property
+ def turn_analyzer(self) -> Optional[BaseTurnAnalyzer]:
+ return self._params.turn_analyzer
+
async def start(self, frame: StartFrame):
self._sample_rate = self._params.audio_in_sample_rate or frame.audio_in_sample_rate
# Configure VAD analyzer.
- if self._params.vad_enabled and self._params.vad_analyzer:
+ if self._params.vad_analyzer:
self._params.vad_analyzer.set_sample_rate(self._sample_rate)
+ # Configure End of turn analyzer.
+ if self._params.turn_analyzer:
+ self._params.turn_analyzer.set_sample_rate(self._sample_rate)
+
# Start audio filter.
if self._params.audio_in_filter:
await self._params.audio_in_filter.start(self._sample_rate)
# Create audio input queue and task if needed.
- if not self._audio_task and (self._params.audio_in_enabled or self._params.vad_enabled):
+ if not self._audio_task and self._params.audio_in_enabled:
self._audio_in_queue = asyncio.Queue()
self._audio_task = self.create_task(self._audio_task_handler())
async def stop(self, frame: EndFrame):
# Cancel and wait for the audio input task to finish.
- if self._audio_task and (self._params.audio_in_enabled or self._params.vad_enabled):
+ if self._audio_task and self._params.audio_in_enabled:
await self.cancel_task(self._audio_task)
self._audio_task = None
# Stop audio filter.
@@ -89,12 +143,12 @@ class BaseInputTransport(FrameProcessor):
async def cancel(self, frame: CancelFrame):
# Cancel and wait for the audio input task to finish.
- if self._audio_task and (self._params.audio_in_enabled or self._params.vad_enabled):
+ if self._audio_task and self._params.audio_in_enabled:
await self.cancel_task(self._audio_task)
self._audio_task = None
async def push_audio_frame(self, frame: InputAudioRawFrame):
- if self._params.audio_in_enabled or self._params.vad_enabled:
+ if self._params.audio_in_enabled:
await self._audio_in_queue.put(frame)
#
@@ -187,10 +241,18 @@ class BaseInputTransport(FrameProcessor):
and new_vad_state != VADState.STOPPING
):
frame = None
- if new_vad_state == VADState.SPEAKING:
- frame = UserStartedSpeakingFrame()
- elif new_vad_state == VADState.QUIET:
- frame = UserStoppedSpeakingFrame()
+ # If the turn analyser is enabled, this will prevent:
+ # - Creating the UserStoppedSpeakingFrame
+ # - Creating the UserStartedSpeakingFrame multiple times
+ can_create_user_frames = (
+ self._params.turn_analyzer is None
+ or not self._params.turn_analyzer.speech_triggered
+ )
+ if can_create_user_frames:
+ if new_vad_state == VADState.SPEAKING:
+ frame = UserStartedSpeakingFrame()
+ elif new_vad_state == VADState.QUIET:
+ frame = UserStoppedSpeakingFrame()
if frame:
await self._handle_user_interruption(frame)
@@ -198,25 +260,56 @@ class BaseInputTransport(FrameProcessor):
vad_state = new_vad_state
return vad_state
+ async def _handle_end_of_turn(self):
+ if self.turn_analyzer:
+ state, prediction = await self.turn_analyzer.analyze_end_of_turn()
+ await self._handle_prediction_result(prediction)
+ await self._handle_end_of_turn_complete(state)
+
+ async def _handle_end_of_turn_complete(self, state: EndOfTurnState):
+ if state == EndOfTurnState.COMPLETE:
+ await self._handle_user_interruption(UserStoppedSpeakingFrame())
+
+ async def _run_turn_analyzer(
+ self, frame: InputAudioRawFrame, vad_state: VADState, previous_vad_state: VADState
+ ):
+ is_speech = vad_state == VADState.SPEAKING or vad_state == VADState.STARTING
+ # If silence exceeds threshold, we are going to receive EndOfTurnState.COMPLETE
+ end_of_turn_state = self._params.turn_analyzer.append_audio(frame.audio, is_speech)
+ if end_of_turn_state == EndOfTurnState.COMPLETE:
+ await self._handle_end_of_turn_complete(end_of_turn_state)
+ # Otherwise we are going to trigger to check if the turn is completed based on the VAD
+ elif vad_state == VADState.QUIET and vad_state != previous_vad_state:
+ await self._handle_end_of_turn()
+
async def _audio_task_handler(self):
vad_state: VADState = VADState.QUIET
while True:
frame: InputAudioRawFrame = await self._audio_in_queue.get()
- audio_passthrough = True
-
# If an audio filter is available, run it before VAD.
if self._params.audio_in_filter:
frame.audio = await self._params.audio_in_filter.filter(frame.audio)
# Check VAD and push event if necessary. We just care about
# changes from QUIET to SPEAKING and vice versa.
- if self._params.vad_enabled:
+ previous_vad_state = vad_state
+ if self._params.vad_analyzer:
vad_state = await self._handle_vad(frame, vad_state)
- audio_passthrough = self._params.vad_audio_passthrough
- # Push audio downstream if passthrough.
- if audio_passthrough:
+ if self._params.turn_analyzer:
+ await self._run_turn_analyzer(frame, vad_state, previous_vad_state)
+
+ # Push audio downstream if passthrough is set.
+ if self._params.audio_in_passthrough:
await self.push_frame(frame)
self._audio_in_queue.task_done()
+
+ async def _handle_prediction_result(self, result: MetricsData):
+ """Handle a prediction result event from the turn analyzer.
+
+ Args:
+ result: The prediction result MetricsData.
+ """
+ await self.push_frame(MetricsFrame(data=[result]))
diff --git a/src/pipecat/transports/base_output.py b/src/pipecat/transports/base_output.py
index a2ee5aa92..de53fac60 100644
--- a/src/pipecat/transports/base_output.py
+++ b/src/pipecat/transports/base_output.py
@@ -37,7 +37,7 @@ from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.transports.base_transport import TransportParams
from pipecat.utils.time import nanoseconds_to_seconds
-BOT_VAD_STOP_SECS = 0.3
+BOT_VAD_STOP_SECS = 0.35
class BaseOutputTransport(FrameProcessor):
@@ -53,11 +53,10 @@ class BaseOutputTransport(FrameProcessor):
self._sink_clock_task = None
# Task to write/send audio and image frames.
- self._camera_out_task = None
+ self._video_out_task = None
- # These are the images that we should send to the camera at our desired
- # framerate.
- self._camera_images = None
+ # These are the images that we should send at our desired framerate.
+ self._video_images = None
# Output sample rate. It will be initialized on StartFrame.
self._sample_rate = 0
@@ -79,15 +78,16 @@ class BaseOutputTransport(FrameProcessor):
async def start(self, frame: StartFrame):
self._sample_rate = self._params.audio_out_sample_rate or frame.audio_out_sample_rate
- # We will write 20ms audio at a time. If we receive long audio frames we
+ # We will write 10ms*CHUNKS of audio at a time (where CHUNKS is the
+ # `audio_out_10ms_chunks` parameter). If we receive long audio frames we
# will chunk them. This will help with interruption handling.
audio_bytes_10ms = int(self._sample_rate / 100) * self._params.audio_out_channels * 2
- self._audio_chunk_size = audio_bytes_10ms * 2
+ self._audio_chunk_size = audio_bytes_10ms * self._params.audio_out_10ms_chunks
# Start audio mixer.
if self._params.audio_out_mixer:
await self._params.audio_out_mixer.start(self._sample_rate)
- self._create_camera_task()
+ self._create_video_task()
self._create_sink_tasks()
async def stop(self, frame: EndFrame):
@@ -97,26 +97,26 @@ class BaseOutputTransport(FrameProcessor):
# At this point we have enqueued an EndFrame and we need to wait for
# that EndFrame to be processed by the sink tasks. We also need to wait
- # for these tasks before cancelling the camera and audio tasks below
+ # for these tasks before cancelling the video and audio tasks below
# because they might be still rendering.
if self._sink_task:
await self.wait_for_task(self._sink_task)
if self._sink_clock_task:
await self.wait_for_task(self._sink_clock_task)
- # We can now cancel the camera task.
- await self._cancel_camera_task()
+ # We can now cancel the video task.
+ await self._cancel_video_task()
async def cancel(self, frame: CancelFrame):
# Since we are cancelling everything it doesn't matter if we cancel sink
# tasks first or not.
await self._cancel_sink_tasks()
- await self._cancel_camera_task()
+ await self._cancel_video_task()
async def send_message(self, frame: TransportMessageFrame | TransportMessageUrgentFrame):
pass
- async def write_frame_to_camera(self, frame: OutputImageRawFrame):
+ async def write_raw_video_frame(self, frame: OutputImageRawFrame):
pass
async def write_raw_audio_frames(self, frames: bytes):
@@ -180,11 +180,11 @@ class BaseOutputTransport(FrameProcessor):
return
if isinstance(frame, StartInterruptionFrame):
- # Cancel sink and camera tasks.
+ # Cancel sink and video tasks.
await self._cancel_sink_tasks()
- await self._cancel_camera_task()
- # Create sink and camera tasks.
- self._create_camera_task()
+ await self._cancel_video_task()
+ # Create sink and video tasks.
+ self._create_video_task()
self._create_sink_tasks()
# Let's send a bot stopped speaking if we have to.
await self._bot_stopped_speaking()
@@ -211,11 +211,11 @@ class BaseOutputTransport(FrameProcessor):
self._audio_buffer = self._audio_buffer[self._audio_chunk_size :]
async def _handle_image(self, frame: OutputImageRawFrame | SpriteFrame):
- if not self._params.camera_out_enabled:
+ if not self._params.video_out_enabled:
return
- if self._params.camera_out_is_live:
- await self._camera_out_queue.put(frame)
+ if self._params.video_out_is_live:
+ await self._video_out_queue.put(frame)
else:
await self._sink_queue.put(frame)
@@ -260,9 +260,9 @@ class BaseOutputTransport(FrameProcessor):
async def _sink_frame_handler(self, frame: Frame):
if isinstance(frame, OutputImageRawFrame):
- await self._set_camera_image(frame)
+ await self._set_video_image(frame)
elif isinstance(frame, SpriteFrame):
- await self._set_camera_images(frame.images)
+ await self._set_video_images(frame.images)
elif isinstance(frame, TransportMessageFrame):
await self.send_message(frame)
@@ -335,13 +335,22 @@ class BaseOutputTransport(FrameProcessor):
return without_mixer(BOT_VAD_STOP_SECS)
async def _sink_task_handler(self):
+ # Push a BotSpeakingFrame every 200ms, we don't really need to push it
+ # at every audio chunk. If the audio chunk is bigger than 200ms, push at
+ # every audio chunk.
+ TOTAL_CHUNK_MS = self._params.audio_out_10ms_chunks * 10
+ BOT_SPEAKING_CHUNK_PERIOD = max(int(200 / TOTAL_CHUNK_MS), 1)
+ bot_speaking_counter = 0
async for frame in self._next_frame():
# Notify the bot started speaking upstream if necessary and that
# it's actually speaking.
if isinstance(frame, TTSAudioRawFrame):
await self._bot_started_speaking()
- await self.push_frame(BotSpeakingFrame())
- await self.push_frame(BotSpeakingFrame(), FrameDirection.UPSTREAM)
+ if bot_speaking_counter % BOT_SPEAKING_CHUNK_PERIOD == 0:
+ await self.push_frame(BotSpeakingFrame())
+ await self.push_frame(BotSpeakingFrame(), FrameDirection.UPSTREAM)
+ bot_speaking_counter = 0
+ bot_speaking_counter += 1
# No need to push EndFrame, it's pushed from process_frame().
if isinstance(frame, EndFrame):
@@ -358,76 +367,79 @@ class BaseOutputTransport(FrameProcessor):
await self.write_raw_audio_frames(frame.audio)
#
- # Camera task
+ # Video task
#
- def _create_camera_task(self):
- # Create camera output queue and task if needed.
- if not self._camera_out_task and self._params.camera_out_enabled:
- self._camera_out_queue = asyncio.Queue()
- self._camera_out_task = self.create_task(self._camera_out_task_handler())
+ def _create_video_task(self):
+ # Create video output queue and task if needed.
+ if not self._video_out_task and self._params.video_out_enabled:
+ self._video_out_queue = asyncio.Queue()
+ self._video_out_task = self.create_task(self._video_out_task_handler())
- async def _cancel_camera_task(self):
- # Stop camera output task.
- if self._camera_out_task and self._params.camera_out_enabled:
- await self.cancel_task(self._camera_out_task)
- self._camera_out_task = None
+ async def _cancel_video_task(self):
+ # Stop video output task.
+ if self._video_out_task and self._params.video_out_enabled:
+ await self.cancel_task(self._video_out_task)
+ self._video_out_task = None
async def _draw_image(self, frame: OutputImageRawFrame):
- desired_size = (self._params.camera_out_width, self._params.camera_out_height)
+ desired_size = (self._params.video_out_width, self._params.video_out_height)
+ # TODO: we should refactor in the future to support dynamic resolutions
+ # which is kind of what happens in P2P connections.
+ # We need to add support for that inside the DailyTransport
if frame.size != desired_size:
image = Image.frombytes(frame.format, frame.size, frame.image)
resized_image = image.resize(desired_size)
- logger.warning(f"{frame} does not have the expected size {desired_size}, resizing")
+ # logger.warning(f"{frame} does not have the expected size {desired_size}, resizing")
frame = OutputImageRawFrame(
resized_image.tobytes(), resized_image.size, resized_image.format
)
- await self.write_frame_to_camera(frame)
+ await self.write_raw_video_frame(frame)
- async def _set_camera_image(self, image: OutputImageRawFrame):
- self._camera_images = itertools.cycle([image])
+ async def _set_video_image(self, image: OutputImageRawFrame):
+ self._video_images = itertools.cycle([image])
- async def _set_camera_images(self, images: List[OutputImageRawFrame]):
- self._camera_images = itertools.cycle(images)
+ async def _set_video_images(self, images: List[OutputImageRawFrame]):
+ self._video_images = itertools.cycle(images)
- async def _camera_out_task_handler(self):
- self._camera_out_start_time = None
- self._camera_out_frame_index = 0
- self._camera_out_frame_duration = 1 / self._params.camera_out_framerate
- self._camera_out_frame_reset = self._camera_out_frame_duration * 5
+ async def _video_out_task_handler(self):
+ self._video_out_start_time = None
+ self._video_out_frame_index = 0
+ self._video_out_frame_duration = 1 / self._params.video_out_framerate
+ self._video_out_frame_reset = self._video_out_frame_duration * 5
while True:
- if self._params.camera_out_is_live:
- await self._camera_out_is_live_handler()
- elif self._camera_images:
- image = next(self._camera_images)
+ if self._params.video_out_is_live:
+ await self._video_out_is_live_handler()
+ elif self._video_images:
+ image = next(self._video_images)
await self._draw_image(image)
- await asyncio.sleep(self._camera_out_frame_duration)
+ await asyncio.sleep(self._video_out_frame_duration)
else:
- await asyncio.sleep(self._camera_out_frame_duration)
+ await asyncio.sleep(self._video_out_frame_duration)
- async def _camera_out_is_live_handler(self):
- image = await self._camera_out_queue.get()
+ async def _video_out_is_live_handler(self):
+ image = await self._video_out_queue.get()
# We get the start time as soon as we get the first image.
- if not self._camera_out_start_time:
- self._camera_out_start_time = time.time()
- self._camera_out_frame_index = 0
+ if not self._video_out_start_time:
+ self._video_out_start_time = time.time()
+ self._video_out_frame_index = 0
# Calculate how much time we need to wait before rendering next image.
- real_elapsed_time = time.time() - self._camera_out_start_time
- real_render_time = self._camera_out_frame_index * self._camera_out_frame_duration
- delay_time = self._camera_out_frame_duration + real_render_time - real_elapsed_time
+ real_elapsed_time = time.time() - self._video_out_start_time
+ real_render_time = self._video_out_frame_index * self._video_out_frame_duration
+ delay_time = self._video_out_frame_duration + real_render_time - real_elapsed_time
- if abs(delay_time) > self._camera_out_frame_reset:
- self._camera_out_start_time = time.time()
- self._camera_out_frame_index = 0
+ if abs(delay_time) > self._video_out_frame_reset:
+ self._video_out_start_time = time.time()
+ self._video_out_frame_index = 0
elif delay_time > 0:
await asyncio.sleep(delay_time)
- self._camera_out_frame_index += 1
+ self._video_out_frame_index += 1
# Render image
await self._draw_image(image)
- self._camera_out_queue.task_done()
+ self._video_out_queue.task_done()
diff --git a/src/pipecat/transports/base_transport.py b/src/pipecat/transports/base_transport.py
index 30e126ae3..b3d537fa4 100644
--- a/src/pipecat/transports/base_transport.py
+++ b/src/pipecat/transports/base_transport.py
@@ -11,6 +11,7 @@ from pydantic import BaseModel, ConfigDict
from pipecat.audio.filters.base_audio_filter import BaseAudioFilter
from pipecat.audio.mixers.base_audio_mixer import BaseAudioMixer
+from pipecat.audio.turn.base_turn_analyzer import BaseTurnAnalyzer
from pipecat.audio.vad.vad_analyzer import VADAnalyzer
from pipecat.processors.frame_processor import FrameProcessor
from pipecat.utils.base_object import BaseObject
@@ -31,15 +32,26 @@ class TransportParams(BaseModel):
audio_out_sample_rate: Optional[int] = None
audio_out_channels: int = 1
audio_out_bitrate: int = 96000
+ audio_out_10ms_chunks: int = 4
audio_out_mixer: Optional[BaseAudioMixer] = None
audio_in_enabled: bool = False
audio_in_sample_rate: Optional[int] = None
audio_in_channels: int = 1
audio_in_filter: Optional[BaseAudioFilter] = None
audio_in_stream_on_start: bool = True
+ audio_in_passthrough: bool = True
+ video_in_enabled: bool = False
+ video_out_enabled: bool = False
+ video_out_is_live: bool = False
+ video_out_width: int = 1024
+ video_out_height: int = 768
+ video_out_bitrate: int = 800000
+ video_out_framerate: int = 30
+ video_out_color_format: str = "RGB"
vad_enabled: bool = False
vad_audio_passthrough: bool = False
vad_analyzer: Optional[VADAnalyzer] = None
+ turn_analyzer: Optional[BaseTurnAnalyzer] = None
class BaseTransport(BaseObject):
diff --git a/src/pipecat/transports/local/tk.py b/src/pipecat/transports/local/tk.py
index 4338992e3..1695d2cab 100644
--- a/src/pipecat/transports/local/tk.py
+++ b/src/pipecat/transports/local/tk.py
@@ -137,7 +137,7 @@ class TkOutputTransport(BaseOutputTransport):
self._executor, self._out_stream.write, frames
)
- async def write_frame_to_camera(self, frame: OutputImageRawFrame):
+ async def write_raw_video_frame(self, frame: OutputImageRawFrame):
self.get_event_loop().call_soon(self._write_frame_to_tk, frame)
def _write_frame_to_tk(self, frame: OutputImageRawFrame):
diff --git a/src/pipecat/transports/network/fastapi_websocket.py b/src/pipecat/transports/network/fastapi_websocket.py
index 937dda80c..047ae9c69 100644
--- a/src/pipecat/transports/network/fastapi_websocket.py
+++ b/src/pipecat/transports/network/fastapi_websocket.py
@@ -61,6 +61,10 @@ class FastAPIWebsocketClient:
self._closing = False
self._is_binary = is_binary
self._callbacks = callbacks
+ self._leave_counter = 0
+
+ async def setup(self, _: StartFrame):
+ self._leave_counter += 1
def receive(self) -> typing.AsyncIterator[bytes | str]:
return self._websocket.iter_bytes() if self._is_binary else self._websocket.iter_text()
@@ -73,6 +77,10 @@ class FastAPIWebsocketClient:
await self._websocket.send_text(data)
async def disconnect(self):
+ self._leave_counter -= 1
+ if self._leave_counter > 0:
+ return
+
if self.is_connected and not self.is_closing:
self._closing = True
await self._websocket.close()
@@ -116,6 +124,7 @@ class FastAPIWebsocketInputTransport(BaseInputTransport):
async def start(self, frame: StartFrame):
await super().start(frame)
+ await self._client.setup(frame)
await self._params.serializer.setup(frame)
if not self._monitor_websocket_task and self._params.session_timeout:
self._monitor_websocket_task = self.create_task(self._monitor_websocket())
@@ -192,15 +201,18 @@ class FastAPIWebsocketOutputTransport(BaseOutputTransport):
async def start(self, frame: StartFrame):
await super().start(frame)
+ await self._client.setup(frame)
await self._params.serializer.setup(frame)
self._send_interval = (self._audio_chunk_size / self.sample_rate) / 2
async def stop(self, frame: EndFrame):
await super().stop(frame)
+ await self._write_frame(frame)
await self._client.disconnect()
async def cancel(self, frame: CancelFrame):
await super().cancel(frame)
+ await self._write_frame(frame)
await self._client.disconnect()
async def cleanup(self):
diff --git a/src/pipecat/transports/network/small_webrtc.py b/src/pipecat/transports/network/small_webrtc.py
index 987c412fd..6304d8d59 100644
--- a/src/pipecat/transports/network/small_webrtc.py
+++ b/src/pipecat/transports/network/small_webrtc.py
@@ -17,13 +17,19 @@ from pydantic import BaseModel
from pipecat.frames.frames import (
CancelFrame,
EndFrame,
+ Frame,
InputAudioRawFrame,
InputImageRawFrame,
+ OutputAudioRawFrame,
OutputImageRawFrame,
+ SpriteFrame,
StartFrame,
TransportMessageFrame,
TransportMessageUrgentFrame,
+ UserImageRawFrame,
+ UserImageRequestFrame,
)
+from pipecat.processors.frame_processor import FrameDirection
from pipecat.transports.base_input import BaseInputTransport
from pipecat.transports.base_output import BaseOutputTransport
from pipecat.transports.base_transport import BaseTransport, TransportParams
@@ -51,61 +57,52 @@ class RawAudioTrack(AudioStreamTrack):
def __init__(self, sample_rate):
super().__init__()
self._sample_rate = sample_rate
- self._samples_per_frame = self._sample_rate // 50 # 20ms per frame
+ self._samples_per_10ms = sample_rate * 10 // 1000
+ self._bytes_per_10ms = self._samples_per_10ms * 2 # 16-bit (2 bytes per sample)
self._timestamp = 0
- self._audio_buffer = deque()
self._start = time.time()
+ # Queue of (bytes, future), broken into 10ms sub chunks as needed
+ self._chunk_queue = deque()
def add_audio_bytes(self, audio_bytes: bytes):
- """
- Adds bytes to the audio buffer and returns a Future that completes when the data is processed.
- """
- if len(audio_bytes) % 2 != 0:
- raise ValueError("Audio bytes length must be even (16-bit samples).")
+ """Adds bytes to the audio buffer and returns a Future that completes when the data is processed."""
+ if len(audio_bytes) % self._bytes_per_10ms != 0:
+ raise ValueError("Audio bytes must be a multiple of 10ms size.")
future = asyncio.get_running_loop().create_future()
- self._audio_buffer.append((audio_bytes, future))
+
+ # Break input into 10ms chunks
+ for i in range(0, len(audio_bytes), self._bytes_per_10ms):
+ chunk = audio_bytes[i : i + self._bytes_per_10ms]
+ # Only the last chunk carries the future to be resolved once fully consumed
+ fut = future if i + self._bytes_per_10ms >= len(audio_bytes) else None
+ self._chunk_queue.append((chunk, fut))
+
return future
async def recv(self):
- """
- Returns the next audio frame, generating silence if needed.
- """
+ """Returns the next audio frame, generating silence if needed."""
# Compute required wait time for synchronization
if self._timestamp > 0:
wait = self._start + (self._timestamp / self._sample_rate) - time.time()
if wait > 0:
await asyncio.sleep(wait)
- # Check if we have enough data
- needed_bytes = self._samples_per_frame * 2 # 16-bit (2 bytes per sample)
- available_bytes = sum(len(audio_bytes) for audio_bytes, _ in self._audio_buffer)
- consumed_futures = [] # Track futures for processed data
- if available_bytes >= needed_bytes:
- # Extract data from deque
- chunk = bytearray()
- while len(chunk) < needed_bytes:
- audio_bytes, future = self._audio_buffer.popleft()
- chunk.extend(audio_bytes)
- consumed_futures.append(future) # Track the future
- chunk = bytes(chunk[:needed_bytes]) # Trim excess bytes
+ if self._chunk_queue:
+ chunk, future = self._chunk_queue.popleft()
+ if future and not future.done():
+ future.set_result(True)
else:
- chunk = bytes(needed_bytes) # Generate silent frame
+ chunk = bytes(self._bytes_per_10ms) # silence
# Convert the byte data to an ndarray of int16 samples
samples = np.frombuffer(chunk, dtype=np.int16)
# Create AudioFrame
frame = AudioFrame.from_ndarray(samples[None, :], layout="mono")
- self._timestamp += self._samples_per_frame
- frame.pts = self._timestamp
frame.sample_rate = self._sample_rate
+ frame.pts = self._timestamp
frame.time_base = fractions.Fraction(1, self._sample_rate)
-
- # Resolve all futures corresponding to consumed data
- for future in consumed_futures:
- if not future.done():
- future.set_result(True)
-
+ self._timestamp += self._samples_per_10ms
return frame
@@ -138,6 +135,13 @@ class RawVideoTrack(VideoStreamTrack):
class SmallWebRTCClient:
+ FORMAT_CONVERSIONS = {
+ "yuv420p": cv2.COLOR_YUV2RGB_I420,
+ "yuvj420p": cv2.COLOR_YUV2RGB_I420, # OpenCV treats both the same
+ "nv12": cv2.COLOR_YUV2RGB_NV12,
+ "gray": cv2.COLOR_GRAY2RGB,
+ }
+
def __init__(self, webrtc_connection: SmallWebRTCConnection, callbacks: SmallWebRTCCallbacks):
self._webrtc_connection = webrtc_connection
self._closing = False
@@ -176,9 +180,31 @@ class SmallWebRTCClient:
async def on_app_message(connection: SmallWebRTCConnection, message: Any):
await self._handle_app_message(message)
- async def read_video_frame(self):
+ def _convert_frame(self, frame_array: np.ndarray, format_name: str) -> np.ndarray:
+ """Convert a given frame to RGB format based on the input format.
+
+ Args:
+ frame_array (np.ndarray): The input frame.
+ format_name (str): The format of the input frame.
+
+ Returns:
+ np.ndarray: The converted RGB frame.
+
+ Raises:
+ ValueError: If the format is unsupported.
"""
- Reads a video frame from the given MediaStreamTrack, converts it to RGB,
+ if format_name.startswith("rgb"): # Already in RGB, no conversion needed
+ return frame_array
+
+ conversion_code = SmallWebRTCClient.FORMAT_CONVERSIONS.get(format_name)
+
+ if conversion_code is None:
+ raise ValueError(f"Unsupported format: {format_name}")
+
+ return cv2.cvtColor(frame_array, conversion_code)
+
+ async def read_video_frame(self):
+ """Reads a video frame from the given MediaStreamTrack, converts it to RGB,
and creates an InputImageRawFrame.
"""
while True:
@@ -203,21 +229,9 @@ class SmallWebRTCClient:
continue
format_name = frame.format.name
-
# Convert frame to NumPy array in its native format
frame_array = frame.to_ndarray(format=format_name)
-
- # Handle different formats dynamically
- if format_name == "yuv420p":
- frame_rgb = cv2.cvtColor(frame_array, cv2.COLOR_YUV2RGB_I420)
- elif format_name == "nv12":
- frame_rgb = cv2.cvtColor(frame_array, cv2.COLOR_YUV2RGB_NV12)
- elif format_name == "gray":
- frame_rgb = cv2.cvtColor(frame_array, cv2.COLOR_GRAY2RGB)
- elif format_name.startswith("rgb"): # Already RGB, no conversion needed
- frame_rgb = frame_array
- else:
- raise ValueError(f"Unsupported format: {format_name}")
+ frame_rgb = self._convert_frame(frame_array, format_name)
image_frame = InputImageRawFrame(
image=frame_rgb.tobytes(),
@@ -228,9 +242,7 @@ class SmallWebRTCClient:
yield image_frame
async def read_audio_frame(self):
- """
- Reads 20ms of audio from the given MediaStreamTrack and creates an InputAudioRawFrame.
- """
+ """Reads 20ms of audio from the given MediaStreamTrack and creates an InputAudioRawFrame."""
while True:
if self._audio_input_track is None:
await asyncio.sleep(0.01)
@@ -276,7 +288,7 @@ class SmallWebRTCClient:
if self._can_send() and self._audio_output_track:
await self._audio_output_track.add_audio_bytes(data)
- async def write_frame_to_camera(self, frame: OutputImageRawFrame):
+ async def write_raw_video_frame(self, frame: OutputImageRawFrame):
if self._can_send() and self._video_output_track:
self._video_output_track.add_video_frame(frame)
@@ -298,7 +310,7 @@ class SmallWebRTCClient:
if self.is_connected and not self.is_closing:
logger.info(f"Disconnecting to Small WebRTC")
self._closing = True
- await self._webrtc_connection.close()
+ await self._webrtc_connection.disconnect()
await self._handle_client_disconnected()
async def send_message(self, frame: TransportMessageFrame | TransportMessageUrgentFrame):
@@ -316,9 +328,9 @@ class SmallWebRTCClient:
self._audio_output_track = RawAudioTrack(sample_rate=self._out_sample_rate)
self._webrtc_connection.replace_audio_track(self._audio_output_track)
- if self._params.camera_out_enabled:
+ if self._params.video_out_enabled:
self._video_output_track = RawVideoTrack(
- width=self._params.camera_out_width, height=self._params.camera_out_height
+ width=self._params.video_out_width, height=self._params.video_out_height
)
self._webrtc_connection.replace_video_track(self._video_output_track)
@@ -365,16 +377,21 @@ class SmallWebRTCInputTransport(BaseInputTransport):
self._params = params
self._receive_audio_task = None
self._receive_video_task = None
+ self._image_requests = {}
+
+ async def process_frame(self, frame: Frame, direction: FrameDirection):
+ await super().process_frame(frame, direction)
+
+ if isinstance(frame, UserImageRequestFrame):
+ await self.request_participant_image(frame)
async def start(self, frame: StartFrame):
await super().start(frame)
await self._client.setup(self._params, frame)
await self._client.connect()
- if not self._receive_audio_task and (
- self._params.audio_in_enabled or self._params.vad_enabled
- ):
+ if not self._receive_audio_task and self._params.audio_in_enabled:
self._receive_audio_task = self.create_task(self._receive_audio())
- if not self._receive_video_task and self._params.camera_in_enabled:
+ if not self._receive_video_task and self._params.video_in_enabled:
self._receive_video_task = self.create_task(self._receive_video())
async def _stop_tasks(self):
@@ -410,6 +427,22 @@ class SmallWebRTCInputTransport(BaseInputTransport):
if video_frame:
await self.push_frame(video_frame)
+ # Check if there are any pending image requests and create UserImageRawFrame
+ if self._image_requests:
+ for req_id, request_frame in list(self._image_requests.items()):
+ # Create UserImageRawFrame using the current video frame
+ image_frame = UserImageRawFrame(
+ user_id=request_frame.user_id,
+ request=request_frame,
+ image=video_frame.image,
+ size=video_frame.size,
+ format=video_frame.format,
+ )
+ # Push the frame to the pipeline
+ await self.push_frame(image_frame)
+ # Remove from pending requests
+ del self._image_requests[req_id]
+
except Exception as e:
logger.error(f"{self} exception receiving data: {e.__class__.__name__} ({e})")
@@ -418,6 +451,24 @@ class SmallWebRTCInputTransport(BaseInputTransport):
frame = TransportMessageUrgentFrame(message=message)
await self.push_frame(frame)
+ # Add this method similar to DailyInputTransport.request_participant_image
+ async def request_participant_image(self, frame: UserImageRequestFrame):
+ """Requests an image frame from the participant's video stream.
+
+ When a UserImageRequestFrame is received, this method will store the request
+ and the next video frame received will be converted to a UserImageRawFrame.
+ """
+ logger.debug(f"Requesting image from participant: {frame.user_id}")
+
+ # Store the request
+ request_id = f"{frame.function_name}:{frame.tool_call_id}"
+ self._image_requests[request_id] = frame
+
+ # If we're not already receiving video, try to get a frame now
+ if not self._receive_video_task and self._params.video_in_enabled:
+ # Start video reception if it's not already running
+ self._receive_video_task = self.create_task(self._receive_video())
+
class SmallWebRTCOutputTransport(BaseOutputTransport):
def __init__(
@@ -449,8 +500,8 @@ class SmallWebRTCOutputTransport(BaseOutputTransport):
async def write_raw_audio_frames(self, frames: bytes):
await self._client.write_raw_audio_frames(frames)
- async def write_frame_to_camera(self, frame: OutputImageRawFrame):
- await self._client.write_frame_to_camera(frame)
+ async def write_raw_video_frame(self, frame: OutputImageRawFrame):
+ await self._client.write_raw_video_frame(frame)
class SmallWebRTCTransport(BaseTransport):
@@ -473,10 +524,8 @@ class SmallWebRTCTransport(BaseTransport):
self._client = SmallWebRTCClient(webrtc_connection, self._callbacks)
- self._input = SmallWebRTCInputTransport(self._client, self._params, name=self._input_name)
- self._output = SmallWebRTCOutputTransport(
- self._client, self._params, name=self._output_name
- )
+ self._input: Optional[SmallWebRTCInputTransport] = None
+ self._output: Optional[SmallWebRTCOutputTransport] = None
# Register supported handlers. The user will only be able to register
# these handlers.
@@ -499,6 +548,14 @@ class SmallWebRTCTransport(BaseTransport):
)
return self._output
+ async def send_image(self, frame: OutputImageRawFrame | SpriteFrame):
+ if self._output:
+ await self._output.queue_frame(frame, FrameDirection.DOWNSTREAM)
+
+ async def send_audio(self, frame: OutputAudioRawFrame):
+ if self._output:
+ await self._output.queue_frame(frame, FrameDirection.DOWNSTREAM)
+
async def _on_app_message(self, message: Any):
if self._input:
await self._input.push_app_message(message)
diff --git a/src/pipecat/transports/network/webrtc_connection.py b/src/pipecat/transports/network/webrtc_connection.py
index 671ff6622..81ece3af7 100644
--- a/src/pipecat/transports/network/webrtc_connection.py
+++ b/src/pipecat/transports/network/webrtc_connection.py
@@ -7,25 +7,84 @@
import asyncio
import json
import time
-from enum import Enum
-from typing import Any, Optional
+from typing import Any, Literal, Optional, Union
+from av.frame import Frame
from loguru import logger
+from pydantic import BaseModel, TypeAdapter
from pipecat.utils.base_object import BaseObject
try:
- from aiortc import RTCConfiguration, RTCIceServer, RTCPeerConnection, RTCSessionDescription
+ from aiortc import (
+ MediaStreamTrack,
+ RTCConfiguration,
+ RTCIceServer,
+ RTCPeerConnection,
+ RTCSessionDescription,
+ )
+ from aiortc.rtcrtpreceiver import RemoteStreamTrack
except ModuleNotFoundError as e:
logger.error(f"Exception: {e}")
logger.error("In order to use the SmallWebRTC, you need to `pip install pipecat-ai[webrtc]`.")
raise Exception(f"Missing module: {e}")
SIGNALLING_TYPE = "signalling"
+AUDIO_TRANSCEIVER_INDEX = 0
+VIDEO_TRANSCEIVER_INDEX = 1
-class SignallingMessage(Enum):
- RENEGOTIATE = "renegotiate"
+class TrackStatusMessage(BaseModel):
+ type: Literal["trackStatus"]
+ receiver_index: int
+ enabled: bool
+
+
+class RenegotiateMessage(BaseModel):
+ type: Literal["renegotiate"] = "renegotiate"
+
+
+class PeerLeftMessage(BaseModel):
+ type: Literal["peerLeft"] = "peerLeft"
+
+
+class SignallingMessage:
+ Inbound = Union[TrackStatusMessage] # in case we need to add new messages in the future
+ outbound = Union[RenegotiateMessage]
+
+
+class SmallWebRTCTrack:
+ def __init__(self, track: MediaStreamTrack):
+ self._track = track
+ self._enabled = True
+
+ def set_enabled(self, enabled: bool) -> None:
+ self._enabled = enabled
+
+ def is_enabled(self) -> bool:
+ return self._enabled
+
+ async def discard_old_frames(self):
+ remote_track = self._track
+ if isinstance(remote_track, RemoteStreamTrack):
+ if not hasattr(remote_track, "_queue") or not isinstance(
+ remote_track._queue, asyncio.Queue
+ ):
+ print("Warning: _queue does not exist or has changed in aiortc.")
+ return
+ logger.debug("Discarding old frames")
+ while not remote_track._queue.empty():
+ remote_track._queue.get_nowait() # Remove the oldest frame
+ remote_track._queue.task_done()
+
+ async def recv(self) -> Optional[Frame]:
+ if not self._enabled:
+ return None
+ return await self._track.recv()
+
+ def __getattr__(self, name):
+ # Forward other attribute/method calls to the underlying track
+ return getattr(self._track, name)
class SmallWebRTCConnection(BaseObject):
@@ -36,6 +95,12 @@ class SmallWebRTCConnection(BaseObject):
else:
self.ice_servers = []
self._connect_invoked = False
+ self._track_map = {}
+ self._track_getters = {
+ AUDIO_TRANSCEIVER_INDEX: self.audio_input_track,
+ VIDEO_TRANSCEIVER_INDEX: self.video_input_track,
+ }
+
self._initialize()
# Register supported handlers. The user will only be able to register
@@ -67,16 +132,24 @@ class SmallWebRTCConnection(BaseObject):
self._pc = RTCPeerConnection(rtc_config)
self._pc_id = self.name
self._setup_listeners()
- self._tracks = set()
self._data_channel = None
self._renegotiation_in_progress = False
self._last_received_time = None
+ self._message_queue = []
def _setup_listeners(self):
@self._pc.on("datachannel")
def on_datachannel(channel):
self._data_channel = channel
+ # Flush queued messages once the data channel is open
+ @channel.on("open")
+ async def on_open():
+ logger.debug("Data channel is open, flushing queued messages")
+ while self._message_queue:
+ message = self._message_queue.pop(0)
+ self._data_channel.send(message)
+
@channel.on("message")
async def on_message(message):
try:
@@ -86,7 +159,10 @@ class SmallWebRTCConnection(BaseObject):
self._last_received_time = time.time()
else:
json_message = json.loads(message)
- await self._call_event_handler("app-message", json_message)
+ if json_message["type"] == SIGNALLING_TYPE and json_message.get("message"):
+ self._handle_signalling_message(json_message["message"])
+ else:
+ await self._call_event_handler("app-message", json_message)
except Exception as e:
logger.exception(f"Error parsing JSON message {message}, {e}")
@@ -111,13 +187,11 @@ class SmallWebRTCConnection(BaseObject):
@self._pc.on("track")
async def on_track(track):
logger.debug(f"Track {track.kind} received")
- self._tracks.add(track)
await self._call_event_handler("track-started", track)
@track.on("ended")
async def on_ended():
logger.debug(f"Track {track.kind} ended")
- self._tracks.discard(track)
await self._call_event_handler("track-ended", track)
async def _create_answer(self, sdp: str, type: str):
@@ -145,6 +219,9 @@ class SmallWebRTCConnection(BaseObject):
await self._call_event_handler("connected")
# We are renegotiating here, because likely we have loose the first video frames
# and aiortc does not handle that pretty well.
+ video_input_track = self.video_input_track()
+ if video_input_track:
+ await self.video_input_track().discard_old_frames()
self.ask_to_renegotiate()
async def renegotiate(self, sdp: str, type: str, restart_pc: bool = False):
@@ -155,7 +232,7 @@ class SmallWebRTCConnection(BaseObject):
logger.debug("Closing old peer connection")
# removing the listeners to prevent the bot from closing
self._pc.remove_all_listeners()
- await self.close()
+ await self._close()
# we are initializing a new peer connection in this case.
self._initialize()
@@ -200,9 +277,15 @@ class SmallWebRTCConnection(BaseObject):
else:
logger.warning("Video transceiver not found. Cannot replace video track.")
- async def close(self):
+ async def disconnect(self):
+ self.send_app_message({"type": SIGNALLING_TYPE, "message": PeerLeftMessage().model_dump()})
+ await self._close()
+
+ async def _close(self):
if self._pc:
await self._pc.close()
+ self._message_queue.clear()
+ self._track_map = {}
def get_answer(self):
if not self._answer:
@@ -216,11 +299,14 @@ class SmallWebRTCConnection(BaseObject):
async def _handle_new_connection_state(self):
state = self._pc.connectionState
+ if state == "connected" and not self._connect_invoked:
+ # We are going to wait until the pipeline is ready before triggering the event
+ return
logger.debug(f"Connection state changed to: {state}")
await self._call_event_handler(state)
if state == "failed":
logger.warning("Connection failed, closing peer connection.")
- await self.close()
+ await self._close()
# Despite the fact that aiortc provides this listener, they don't have a status for "disconnected"
# So, there is no advantage in looking at self._pc.connectionState
@@ -239,34 +325,46 @@ class SmallWebRTCConnection(BaseObject):
return (time.time() - self._last_received_time) < 3
def audio_input_track(self):
+ if self._track_map.get(AUDIO_TRANSCEIVER_INDEX):
+ return self._track_map[AUDIO_TRANSCEIVER_INDEX]
+
# Transceivers always appear in creation-order for both peers
# For now we are only considering that we are going to have 02 transceivers,
# one for audio and one for video
transceivers = self._pc.getTransceivers()
- if len(transceivers) == 0 or not transceivers[0].receiver:
+ if len(transceivers) == 0 or not transceivers[AUDIO_TRANSCEIVER_INDEX].receiver:
logger.warning("No audio transceiver is available")
return None
- return transceivers[0].receiver.track
+ track = transceivers[AUDIO_TRANSCEIVER_INDEX].receiver.track
+ audio_track = SmallWebRTCTrack(track) if track else None
+ self._track_map[AUDIO_TRANSCEIVER_INDEX] = audio_track
+ return audio_track
def video_input_track(self):
+ if self._track_map.get(VIDEO_TRANSCEIVER_INDEX):
+ return self._track_map[VIDEO_TRANSCEIVER_INDEX]
+
# Transceivers always appear in creation-order for both peers
# For now we are only considering that we are going to have 02 transceivers,
# one for audio and one for video
transceivers = self._pc.getTransceivers()
- if len(transceivers) <= 1 or not transceivers[1].receiver:
+ if len(transceivers) <= 1 or not transceivers[VIDEO_TRANSCEIVER_INDEX].receiver:
logger.warning("No video transceiver is available")
return None
- return transceivers[1].receiver.track
-
- def tracks(self):
- return self._tracks
+ track = transceivers[VIDEO_TRANSCEIVER_INDEX].receiver.track
+ video_track = SmallWebRTCTrack(track) if track else None
+ self._track_map[VIDEO_TRANSCEIVER_INDEX] = video_track
+ return video_track
def send_app_message(self, message: Any):
- if self._data_channel:
- json_message = json.dumps(message)
+ json_message = json.dumps(message)
+ if self._data_channel and self._data_channel.readyState == "open":
self._data_channel.send(json_message)
+ else:
+ logger.debug("Data channel not ready, queuing message")
+ self._message_queue.append(json_message)
def ask_to_renegotiate(self):
if self._renegotiation_in_progress:
@@ -274,5 +372,17 @@ class SmallWebRTCConnection(BaseObject):
self._renegotiation_in_progress = True
self.send_app_message(
- {"type": SIGNALLING_TYPE, "message": SignallingMessage.RENEGOTIATE.value}
+ {"type": SIGNALLING_TYPE, "message": RenegotiateMessage().model_dump()}
)
+
+ def _handle_signalling_message(self, message):
+ logger.debug(f"Signalling message received: {message}")
+ inbound_adapter = TypeAdapter(SignallingMessage.Inbound)
+ signalling_message = inbound_adapter.validate_python(message)
+ match signalling_message:
+ case TrackStatusMessage():
+ track = (
+ self._track_getters.get(signalling_message.receiver_index) or (lambda: None)
+ )()
+ if track:
+ track.set_enabled(signalling_message.enabled)
diff --git a/src/pipecat/transports/network/websocket_client.py b/src/pipecat/transports/network/websocket_client.py
index bf6670883..e45a525fd 100644
--- a/src/pipecat/transports/network/websocket_client.py
+++ b/src/pipecat/transports/network/websocket_client.py
@@ -56,8 +56,8 @@ class WebsocketClientSession:
self._callbacks = callbacks
self._transport_name = transport_name
+ self._leave_counter = 0
self._task_manager: Optional[BaseTaskManager] = None
-
self._websocket: Optional[websockets.WebSocketClientProtocol] = None
@property
@@ -69,6 +69,7 @@ class WebsocketClientSession:
return self._task_manager
async def setup(self, frame: StartFrame):
+ self._leave_counter += 1
if not self._task_manager:
self._task_manager = frame.task_manager
@@ -87,7 +88,8 @@ class WebsocketClientSession:
logger.error(f"Timeout connecting to {self._uri}")
async def disconnect(self):
- if not self._websocket:
+ self._leave_counter -= 1
+ if not self._websocket or self._leave_counter > 0:
return
await self.task_manager.cancel_task(self._client_task)
diff --git a/src/pipecat/transports/network/websocket_server.py b/src/pipecat/transports/network/websocket_server.py
index 8947cbab3..7ca395922 100644
--- a/src/pipecat/transports/network/websocket_server.py
+++ b/src/pipecat/transports/network/websocket_server.py
@@ -157,7 +157,8 @@ class WebsocketServerInputTransport(BaseInputTransport):
self, websocket: websockets.WebSocketServerProtocol, session_timeout: int
):
"""Wait for session_timeout seconds, if the websocket is still open,
- trigger timeout event."""
+ trigger timeout event.
+ """
try:
await asyncio.sleep(session_timeout)
if not websocket.closed:
@@ -195,6 +196,14 @@ class WebsocketServerOutputTransport(BaseOutputTransport):
await self._params.serializer.setup(frame)
self._send_interval = (self._audio_chunk_size / self.sample_rate) / 2
+ async def stop(self, frame: EndFrame):
+ await super().stop(frame)
+ await self._write_frame(frame)
+
+ async def cancel(self, frame: CancelFrame):
+ await super().cancel(frame)
+ await self._write_frame(frame)
+
async def cleanup(self):
await super().cleanup()
await self._transport.cleanup()
diff --git a/src/pipecat/transports/services/daily.py b/src/pipecat/transports/services/daily.py
index af7d2308c..23aef56ac 100644
--- a/src/pipecat/transports/services/daily.py
+++ b/src/pipecat/transports/services/daily.py
@@ -169,6 +169,8 @@ class DailyCallbacks(BaseModel):
on_error: Called when an error occurs.
on_app_message: Called when receiving an app message.
on_call_state_updated: Called when call state changes.
+ on_client_connected: Called when a client (participant) connects.
+ on_client_disconnected: Called when a client (participant) disconnects.
on_dialin_connected: Called when dial-in is connected.
on_dialin_ready: Called when dial-in is ready.
on_dialin_stopped: Called when dial-in is stopped.
@@ -193,6 +195,8 @@ class DailyCallbacks(BaseModel):
on_error: Callable[[str], Awaitable[None]]
on_app_message: Callable[[Any, str], Awaitable[None]]
on_call_state_updated: Callable[[str], Awaitable[None]]
+ on_client_connected: Callable[[Mapping[str, Any]], Awaitable[None]]
+ on_client_disconnected: Callable[[Mapping[str, Any]], Awaitable[None]]
on_dialin_connected: Callable[[Any], Awaitable[None]]
on_dialin_ready: Callable[[str], Awaitable[None]]
on_dialin_stopped: Callable[[Any], Awaitable[None]]
@@ -369,7 +373,7 @@ class DailyTransportClient(EventHandler):
self._mic.write_frames(frames, completion=completion_callback(future))
await future
- async def write_frame_to_camera(self, frame: OutputImageRawFrame):
+ async def write_raw_video_frame(self, frame: OutputImageRawFrame):
if not self._camera:
return None
@@ -379,12 +383,12 @@ class DailyTransportClient(EventHandler):
self._in_sample_rate = self._params.audio_in_sample_rate or frame.audio_in_sample_rate
self._out_sample_rate = self._params.audio_out_sample_rate or frame.audio_out_sample_rate
- if self._params.camera_out_enabled and not self._camera:
+ if self._params.video_out_enabled and not self._camera:
self._camera = Daily.create_camera_device(
self._camera_name(),
- width=self._params.camera_out_width,
- height=self._params.camera_out_height,
- color_format=self._params.camera_out_color_format,
+ width=self._params.video_out_width,
+ height=self._params.video_out_height,
+ color_format=self._params.video_out_color_format,
)
if self._params.audio_out_enabled and not self._mic:
@@ -395,7 +399,7 @@ class DailyTransportClient(EventHandler):
non_blocking=True,
)
- if (self._params.audio_in_enabled or self._params.vad_enabled) and not self._speaker:
+ if self._params.audio_in_enabled and not self._speaker:
self._speaker = Daily.create_speaker_device(
self._speaker_name(),
sample_rate=self._in_sample_rate,
@@ -483,7 +487,7 @@ class DailyTransportClient(EventHandler):
client_settings={
"inputs": {
"camera": {
- "isEnabled": self._params.camera_out_enabled,
+ "isEnabled": self._params.video_out_enabled,
"settings": {
"deviceId": self._camera_name(),
},
@@ -506,8 +510,8 @@ class DailyTransportClient(EventHandler):
"maxQuality": "low",
"encodings": {
"low": {
- "maxBitrate": self._params.camera_out_bitrate,
- "maxFramerate": self._params.camera_out_framerate,
+ "maxBitrate": self._params.video_out_bitrate,
+ "maxFramerate": self._params.video_out_framerate,
}
},
}
@@ -842,7 +846,7 @@ class DailyInputTransport(BaseInputTransport):
def start_audio_in_streaming(self):
# Create audio task. It reads audio frames from Daily and push them
# internally for VAD processing.
- if not self._audio_in_task and (self._params.audio_in_enabled or self._params.vad_enabled):
+ if not self._audio_in_task and self._params.audio_in_enabled:
logger.debug(f"Start receiving audio")
self._audio_in_task = self.create_task(self._audio_in_task_handler())
@@ -859,9 +863,6 @@ class DailyInputTransport(BaseInputTransport):
await self._client.setup(frame)
# Join the room.
await self._client.join()
- # Inialize WebRTC VAD if needed.
- if self._params.vad_enabled and not self._params.vad_analyzer:
- self._vad_analyzer = WebRTCVADAnalyzer(sample_rate=self.sample_rate)
if self._params.audio_in_stream_on_start:
self.start_audio_in_streaming()
@@ -871,7 +872,7 @@ class DailyInputTransport(BaseInputTransport):
# Leave the room.
await self._client.leave()
# Stop audio thread.
- if self._audio_in_task and (self._params.audio_in_enabled or self._params.vad_enabled):
+ if self._audio_in_task and self._params.audio_in_enabled:
await self.cancel_task(self._audio_in_task)
self._audio_in_task = None
@@ -881,7 +882,7 @@ class DailyInputTransport(BaseInputTransport):
# Leave the room.
await self._client.leave()
# Stop audio thread.
- if self._audio_in_task and (self._params.audio_in_enabled or self._params.vad_enabled):
+ if self._audio_in_task and self._params.audio_in_enabled:
await self.cancel_task(self._audio_in_task)
self._audio_in_task = None
@@ -1034,8 +1035,8 @@ class DailyOutputTransport(BaseOutputTransport):
async def write_raw_audio_frames(self, frames: bytes):
await self._client.write_raw_audio_frames(frames)
- async def write_frame_to_camera(self, frame: OutputImageRawFrame):
- await self._client.write_frame_to_camera(frame)
+ async def write_raw_video_frame(self, frame: OutputImageRawFrame):
+ await self._client.write_raw_video_frame(frame)
class DailyTransport(BaseTransport):
@@ -1070,6 +1071,8 @@ class DailyTransport(BaseTransport):
on_error=self._on_error,
on_app_message=self._on_app_message,
on_call_state_updated=self._on_call_state_updated,
+ on_client_connected=self._on_client_connected,
+ on_client_disconnected=self._on_client_disconnected,
on_dialin_connected=self._on_dialin_connected,
on_dialin_ready=self._on_dialin_ready,
on_dialin_stopped=self._on_dialin_stopped,
@@ -1103,6 +1106,8 @@ class DailyTransport(BaseTransport):
self._register_event_handler("on_error")
self._register_event_handler("on_app_message")
self._register_event_handler("on_call_state_updated")
+ self._register_event_handler("on_client_connected")
+ self._register_event_handler("on_client_disconnected")
self._register_event_handler("on_dialin_connected")
self._register_event_handler("on_dialin_ready")
self._register_event_handler("on_dialin_stopped")
@@ -1246,6 +1251,12 @@ class DailyTransport(BaseTransport):
async def _on_call_state_updated(self, state: str):
await self._call_event_handler("on_call_state_updated", state)
+ async def _on_client_connected(self, participant: Any):
+ await self._call_event_handler("on_client_connected", participant)
+
+ async def _on_client_disconnected(self, participant: Any):
+ await self._call_event_handler("on_client_disconnected", participant)
+
async def _handle_dialin_ready(self, sip_endpoint: str):
if not self._params.dialin_settings:
return
@@ -1321,11 +1332,15 @@ class DailyTransport(BaseTransport):
await self._call_event_handler("on_first_participant_joined", participant)
await self._call_event_handler("on_participant_joined", participant)
+ # Also call on_client_connected for compatibility with other transports
+ await self._call_event_handler("on_client_connected", participant)
async def _on_participant_left(self, participant, reason):
id = participant["id"]
logger.info(f"Participant left {id}")
await self._call_event_handler("on_participant_left", participant, reason)
+ # Also call on_client_disconnected for compatibility with other transports
+ await self._call_event_handler("on_client_disconnected", participant)
async def _on_participant_updated(self, participant):
await self._call_event_handler("on_participant_updated", participant)
diff --git a/src/pipecat/transports/services/helpers/daily_rest.py b/src/pipecat/transports/services/helpers/daily_rest.py
index b64aba2d6..8bc6cf3ce 100644
--- a/src/pipecat/transports/services/helpers/daily_rest.py
+++ b/src/pipecat/transports/services/helpers/daily_rest.py
@@ -68,9 +68,9 @@ class DailyRoomProperties(BaseModel, extra="allow"):
exp: Optional[float] = None
enable_chat: bool = False
- enable_prejoin_ui: bool = True
+ enable_prejoin_ui: bool = False
enable_emoji_reactions: bool = False
- eject_at_room_exp: bool = True
+ eject_at_room_exp: bool = False
enable_dialout: Optional[bool] = None
enable_recording: Optional[Literal["cloud", "local", "raw-tracks"]] = None
geo: Optional[str] = None
@@ -291,6 +291,7 @@ class DailyRESTHelper:
self,
room_url: str,
expiry_time: float = 60 * 60,
+ eject_at_token_exp: bool = False,
owner: bool = True,
params: Optional[DailyMeetingTokenParams] = None,
) -> str:
@@ -324,12 +325,16 @@ class DailyRESTHelper:
if params is None:
params = DailyMeetingTokenParams(
properties=DailyMeetingTokenProperties(
- room_name=room_name, is_owner=owner, exp=expiration
+ room_name=room_name,
+ is_owner=owner,
+ exp=expiration,
+ eject_at_token_exp=eject_at_token_exp,
)
)
else:
params.properties.room_name = room_name
params.properties.exp = expiration
+ params.properties.eject_at_token_exp = eject_at_token_exp
params.properties.is_owner = owner
json = params.model_dump(exclude_none=True)
diff --git a/src/pipecat/transports/services/livekit.py b/src/pipecat/transports/services/livekit.py
index 8ce5c885c..2e56ebddf 100644
--- a/src/pipecat/transports/services/livekit.py
+++ b/src/pipecat/transports/services/livekit.py
@@ -368,7 +368,7 @@ class LiveKitInputTransport(BaseInputTransport):
await super().start(frame)
await self._client.setup(frame)
await self._client.connect()
- if not self._audio_in_task and (self._params.audio_in_enabled or self._params.vad_enabled):
+ if not self._audio_in_task and self._params.audio_in_enabled:
self._audio_in_task = self.create_task(self._audio_in_task_handler())
logger.info("LiveKitInputTransport started")
@@ -382,7 +382,7 @@ class LiveKitInputTransport(BaseInputTransport):
async def cancel(self, frame: CancelFrame):
await super().cancel(frame)
await self._client.disconnect()
- if self._audio_in_task and (self._params.audio_in_enabled or self._params.vad_enabled):
+ if self._audio_in_task and self._params.audio_in_enabled:
await self.cancel_task(self._audio_in_task)
async def cleanup(self):
diff --git a/src/pipecat/utils/base_object.py b/src/pipecat/utils/base_object.py
index 1dee24ce7..deb10d1b1 100644
--- a/src/pipecat/utils/base_object.py
+++ b/src/pipecat/utils/base_object.py
@@ -38,7 +38,7 @@ class BaseObject(ABC):
async def cleanup(self):
if self._event_tasks:
event_names, tasks = zip(*self._event_tasks)
- logger.debug(f"{self} wating on event handlers to finish {list(event_names)}...")
+ logger.debug(f"{self} waiting on event handlers to finish {list(event_names)}...")
await asyncio.wait(tasks)
def event_handler(self, event_name: str):
diff --git a/tests/integration/test_integration_unified_function_calling.py b/tests/integration/test_integration_unified_function_calling.py
index bf3e7b00a..09611fd3a 100644
--- a/tests/integration/test_integration_unified_function_calling.py
+++ b/tests/integration/test_integration_unified_function_calling.py
@@ -17,9 +17,9 @@ from pipecat.processors.aggregators.openai_llm_context import (
OpenAILLMContext,
OpenAILLMContextFrame,
)
-from pipecat.services.ai_services import LLMService
from pipecat.services.anthropic.llm import AnthropicLLMService
from pipecat.services.google.llm import GoogleLLMService
+from pipecat.services.llm_service import LLMService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.tests.utils import run_test
@@ -80,7 +80,7 @@ async def _test_llm_function_calling(llm: LLMService):
@pytest.mark.skipif(os.getenv("OPENAI_API_KEY") is None, reason="OPENAI_API_KEY is not set")
@pytest.mark.asyncio
async def test_unified_function_calling_openai():
- llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+ llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
# This will fail if an exception is raised
await _test_llm_function_calling(llm)
diff --git a/tests/test_context_aggregators.py b/tests/test_context_aggregators.py
index b735ed08e..dfe210e07 100644
--- a/tests/test_context_aggregators.py
+++ b/tests/test_context_aggregators.py
@@ -26,7 +26,11 @@ from pipecat.frames.frames import (
UserStartedSpeakingFrame,
UserStoppedSpeakingFrame,
)
-from pipecat.processors.aggregators.llm_response import LLMUserContextAggregator
+from pipecat.processors.aggregators.llm_response import (
+ LLMAssistantAggregatorParams,
+ LLMUserAggregatorParams,
+ LLMUserContextAggregator,
+)
from pipecat.processors.aggregators.openai_llm_context import (
OpenAILLMContext,
OpenAILLMContextFrame,
@@ -41,7 +45,10 @@ from pipecat.services.google.llm import (
GoogleLLMContext,
GoogleUserContextAggregator,
)
-from pipecat.services.openai import OpenAIAssistantContextAggregator, OpenAIUserContextAggregator
+from pipecat.services.openai.llm import (
+ OpenAIAssistantContextAggregator,
+ OpenAIUserContextAggregator,
+)
from pipecat.tests.utils import SleepFrame, run_test
AGGREGATION_TIMEOUT = 0.1
@@ -160,7 +167,9 @@ class BaseTestUserContextAggregator:
assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass"
context = self.CONTEXT_CLASS()
- aggregator = self.AGGREGATOR_CLASS(context, aggregation_timeout=AGGREGATION_TIMEOUT)
+ aggregator = self.AGGREGATOR_CLASS(
+ context, params=LLMUserAggregatorParams(aggregation_timeout=AGGREGATION_TIMEOUT)
+ )
frames_to_send = [
UserStartedSpeakingFrame(),
InterimTranscriptionFrame(text="How ", user_id="cat", timestamp=""),
@@ -186,7 +195,9 @@ class BaseTestUserContextAggregator:
assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass"
context = self.CONTEXT_CLASS()
- aggregator = self.AGGREGATOR_CLASS(context, aggregation_timeout=AGGREGATION_TIMEOUT)
+ aggregator = self.AGGREGATOR_CLASS(
+ context, params=LLMUserAggregatorParams(aggregation_timeout=AGGREGATION_TIMEOUT)
+ )
frames_to_send = [
UserStartedSpeakingFrame(),
InterimTranscriptionFrame(text="How ", user_id="cat", timestamp=""),
@@ -213,7 +224,9 @@ class BaseTestUserContextAggregator:
assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass"
context = self.CONTEXT_CLASS()
- aggregator = self.AGGREGATOR_CLASS(context, aggregation_timeout=AGGREGATION_TIMEOUT)
+ aggregator = self.AGGREGATOR_CLASS(
+ context, params=LLMUserAggregatorParams(aggregation_timeout=AGGREGATION_TIMEOUT)
+ )
frames_to_send = [
UserStartedSpeakingFrame(),
UserStoppedSpeakingFrame(),
@@ -237,7 +250,9 @@ class BaseTestUserContextAggregator:
assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass"
context = self.CONTEXT_CLASS()
- aggregator = self.AGGREGATOR_CLASS(context, aggregation_timeout=AGGREGATION_TIMEOUT)
+ aggregator = self.AGGREGATOR_CLASS(
+ context, params=LLMUserAggregatorParams(aggregation_timeout=AGGREGATION_TIMEOUT)
+ )
frames_to_send = [
UserStartedSpeakingFrame(),
UserStoppedSpeakingFrame(),
@@ -262,7 +277,9 @@ class BaseTestUserContextAggregator:
assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass"
context = self.CONTEXT_CLASS()
- aggregator = self.AGGREGATOR_CLASS(context, aggregation_timeout=AGGREGATION_TIMEOUT)
+ aggregator = self.AGGREGATOR_CLASS(
+ context, params=LLMUserAggregatorParams(aggregation_timeout=AGGREGATION_TIMEOUT)
+ )
frames_to_send = [
UserStartedSpeakingFrame(),
TranscriptionFrame(text="Hello Pipecat!", user_id="cat", timestamp=""),
@@ -290,7 +307,9 @@ class BaseTestUserContextAggregator:
assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass"
context = self.CONTEXT_CLASS()
- aggregator = self.AGGREGATOR_CLASS(context, aggregation_timeout=AGGREGATION_TIMEOUT)
+ aggregator = self.AGGREGATOR_CLASS(
+ context, params=LLMUserAggregatorParams(aggregation_timeout=AGGREGATION_TIMEOUT)
+ )
frames_to_send = [
UserStartedSpeakingFrame(),
UserStoppedSpeakingFrame(),
@@ -315,7 +334,9 @@ class BaseTestUserContextAggregator:
assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass"
context = self.CONTEXT_CLASS()
- aggregator = self.AGGREGATOR_CLASS(context, aggregation_timeout=AGGREGATION_TIMEOUT)
+ aggregator = self.AGGREGATOR_CLASS(
+ context, params=LLMUserAggregatorParams(aggregation_timeout=AGGREGATION_TIMEOUT)
+ )
frames_to_send = [
UserStartedSpeakingFrame(),
InterimTranscriptionFrame(text="Hello ", user_id="cat", timestamp=""),
@@ -343,7 +364,9 @@ class BaseTestUserContextAggregator:
assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass"
context = self.CONTEXT_CLASS()
- aggregator = self.AGGREGATOR_CLASS(context, aggregation_timeout=AGGREGATION_TIMEOUT)
+ aggregator = self.AGGREGATOR_CLASS(
+ context, params=LLMUserAggregatorParams(aggregation_timeout=AGGREGATION_TIMEOUT)
+ )
frames_to_send = [
TranscriptionFrame(text="Hello!", user_id="cat", timestamp=""),
SleepFrame(sleep=AGGREGATION_SLEEP),
@@ -363,7 +386,9 @@ class BaseTestUserContextAggregator:
assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass"
context = self.CONTEXT_CLASS()
- aggregator = self.AGGREGATOR_CLASS(context, aggregation_timeout=AGGREGATION_TIMEOUT)
+ aggregator = self.AGGREGATOR_CLASS(
+ context, params=LLMUserAggregatorParams(aggregation_timeout=AGGREGATION_TIMEOUT)
+ )
frames_to_send = [
InterimTranscriptionFrame(text="Hello ", user_id="cat", timestamp=""),
SleepFrame(),
@@ -386,8 +411,7 @@ class BaseTestUserContextAggregator:
context = self.CONTEXT_CLASS()
aggregator = self.AGGREGATOR_CLASS(
- context,
- aggregation_timeout=AGGREGATION_TIMEOUT,
+ context, params=LLMUserAggregatorParams(aggregation_timeout=AGGREGATION_TIMEOUT)
)
frames_to_send = [
UserStartedSpeakingFrame(),
@@ -466,7 +490,9 @@ class BaseTestAssistantContextAggreagator:
assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass"
context = self.CONTEXT_CLASS()
- aggregator = self.AGGREGATOR_CLASS(context, expect_stripped_words=False)
+ aggregator = self.AGGREGATOR_CLASS(
+ context, params=LLMAssistantAggregatorParams(expect_stripped_words=False)
+ )
frames_to_send = [
LLMFullResponseStartFrame(),
TextFrame(text="Hello "),
@@ -510,7 +536,9 @@ class BaseTestAssistantContextAggreagator:
assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass"
context = self.CONTEXT_CLASS()
- aggregator = self.AGGREGATOR_CLASS(context, expect_stripped_words=False)
+ aggregator = self.AGGREGATOR_CLASS(
+ context, params=LLMAssistantAggregatorParams(expect_stripped_words=False)
+ )
frames_to_send = [
LLMFullResponseStartFrame(),
TextFrame(text="Hello "),
@@ -535,7 +563,9 @@ class BaseTestAssistantContextAggreagator:
assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass"
context = self.CONTEXT_CLASS()
- aggregator = self.AGGREGATOR_CLASS(context, expect_stripped_words=False)
+ aggregator = self.AGGREGATOR_CLASS(
+ context, params=LLMAssistantAggregatorParams(expect_stripped_words=False)
+ )
frames_to_send = [
LLMFullResponseStartFrame(),
TextFrame(text="Hello "),
diff --git a/tests/test_langchain.py b/tests/test_langchain.py
index 6534f6bb0..3d907e084 100644
--- a/tests/test_langchain.py
+++ b/tests/test_langchain.py
@@ -20,6 +20,7 @@ from pipecat.frames.frames import (
)
from pipecat.pipeline.pipeline import Pipeline
from pipecat.processors.aggregators.llm_response import (
+ LLMAssistantAggregatorParams,
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
@@ -63,7 +64,9 @@ class TestLangchain(unittest.IsolatedAsyncioTestCase):
self.mock_proc = self.MockProcessor("token_collector")
tma_in = LLMUserResponseAggregator(messages)
- tma_out = LLMAssistantResponseAggregator(messages, expect_stripped_words=False)
+ tma_out = LLMAssistantResponseAggregator(
+ messages, params=LLMAssistantAggregatorParams(expect_stripped_words=False)
+ )
pipeline = Pipeline([tma_in, proc, self.mock_proc, tma_out])
diff --git a/tests/test_producer_consumer.py b/tests/test_producer_consumer.py
new file mode 100644
index 000000000..578d14706
--- /dev/null
+++ b/tests/test_producer_consumer.py
@@ -0,0 +1,120 @@
+#
+# Copyright (c) 2024-2025 Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import unittest
+
+from pipecat.frames.frames import Frame, InputAudioRawFrame, TextFrame
+from pipecat.pipeline.parallel_pipeline import ParallelPipeline
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.processors.consumer_processor import ConsumerProcessor
+from pipecat.processors.producer_processor import ProducerProcessor
+from pipecat.tests.utils import SleepFrame, run_test
+
+
+async def text_frame_filter(frame: Frame):
+ return isinstance(frame, TextFrame)
+
+
+class TestProducerConsumerProcessor(unittest.IsolatedAsyncioTestCase):
+ async def test_produce_passthrough(self):
+ producer = ProducerProcessor(filter=text_frame_filter)
+ consumer = ConsumerProcessor(producer=producer)
+ pipeline = Pipeline([producer, consumer])
+ frames_to_send = [
+ TextFrame("Hello!"),
+ SleepFrame(), # So we let the consumer go first.
+ ]
+ expected_down_frames = [
+ TextFrame, # Consumer frame
+ TextFrame, # Pass-through frame
+ ]
+ await run_test(
+ pipeline,
+ frames_to_send=frames_to_send,
+ expected_down_frames=expected_down_frames,
+ )
+
+ async def test_produce_no_passthrough(self):
+ producer = ProducerProcessor(filter=text_frame_filter, passthrough=False)
+ consumer = ConsumerProcessor(producer=producer)
+ pipeline = Pipeline([producer, consumer])
+ frames_to_send = [TextFrame("Hello!")]
+ expected_down_frames = [TextFrame]
+ await run_test(
+ pipeline,
+ frames_to_send=frames_to_send,
+ expected_down_frames=expected_down_frames,
+ )
+
+ async def test_produce_multiple_consumer_no_passthrough(self):
+ producer = ProducerProcessor(filter=text_frame_filter, passthrough=False)
+ consumer1 = ConsumerProcessor(producer=producer)
+ consumer2 = ConsumerProcessor(producer=producer)
+ pipeline = Pipeline([producer, consumer1, consumer2])
+ frames_to_send = [TextFrame("Hello!")]
+ expected_down_frames = [
+ TextFrame, # From consumer1 or consumer2 (depending on who runs first)
+ TextFrame, # From consumer1 or consumer2 (depending on who runs first)
+ ]
+ await run_test(
+ pipeline,
+ frames_to_send=frames_to_send,
+ expected_down_frames=expected_down_frames,
+ )
+
+ async def test_produce_parallel_pipeline_no_passthrough(self):
+ producer = ProducerProcessor(filter=text_frame_filter, passthrough=False)
+ consumer = ConsumerProcessor(producer=producer)
+ pipeline = Pipeline([ParallelPipeline([producer], [consumer])])
+ frames_to_send = [TextFrame("Hello!")]
+ expected_down_frames = [TextFrame]
+ await run_test(
+ pipeline,
+ frames_to_send=frames_to_send,
+ expected_down_frames=expected_down_frames,
+ )
+
+ async def test_produce_passthrough_transform(self):
+ async def audio_transformer(_: Frame) -> Frame:
+ return InputAudioRawFrame(audio=b"", sample_rate=16000, num_channels=1)
+
+ producer = ProducerProcessor(filter=text_frame_filter, transformer=audio_transformer)
+ consumer = ConsumerProcessor(producer=producer)
+ pipeline = Pipeline([producer, consumer])
+ frames_to_send = [
+ TextFrame("Hello!"),
+ SleepFrame(), # So we let the consumer go first.
+ ]
+ expected_down_frames = [
+ InputAudioRawFrame, # Consumer frame
+ TextFrame, # Pass-through frame
+ ]
+ await run_test(
+ pipeline,
+ frames_to_send=frames_to_send,
+ expected_down_frames=expected_down_frames,
+ )
+
+ async def test_produce_passthrough_consumer_transform(self):
+ async def audio_transformer(_: Frame) -> Frame:
+ return InputAudioRawFrame(audio=b"", sample_rate=16000, num_channels=1)
+
+ producer = ProducerProcessor(filter=text_frame_filter)
+ consumer = ConsumerProcessor(producer=producer, transformer=audio_transformer)
+ pipeline = Pipeline([producer, consumer])
+ frames_to_send = [
+ TextFrame("Hello!"),
+ SleepFrame(), # So we let the consumer go first.
+ ]
+ expected_down_frames = [
+ InputAudioRawFrame, # Consumer frame
+ TextFrame, # Pass-through frame
+ ]
+ await run_test(
+ pipeline,
+ frames_to_send=frames_to_send,
+ expected_down_frames=expected_down_frames,
+ )
diff --git a/tests/test_transcript_processor.py b/tests/test_transcript_processor.py
index d13246b2c..601631a8e 100644
--- a/tests/test_transcript_processor.py
+++ b/tests/test_transcript_processor.py
@@ -238,8 +238,8 @@ class TestUserTranscriptProcessor(unittest.IsolatedAsyncioTestCase):
TTSTextFrame(text="world!"),
SleepFrame(sleep=0.1),
StartInterruptionFrame(), # User interrupts here
- BotStartedSpeakingFrame(),
SleepFrame(sleep=0.1),
+ BotStartedSpeakingFrame(),
TTSTextFrame(text="New"),
TTSTextFrame(text="response"),
SleepFrame(sleep=0.1),
@@ -251,8 +251,8 @@ class TestUserTranscriptProcessor(unittest.IsolatedAsyncioTestCase):
BotStartedSpeakingFrame,
TTSTextFrame, # "Hello"
TTSTextFrame, # "world!"
+ StartInterruptionFrame,
TranscriptionUpdateFrame, # First message (emitted due to interruption)
- StartInterruptionFrame, # Interruption frame comes after the update
BotStartedSpeakingFrame,
TTSTextFrame, # "New"
TTSTextFrame, # "response"
diff --git a/tests/test_user_idle_processor.py b/tests/test_user_idle_processor.py
index a2f2fd386..5f212e22b 100644
--- a/tests/test_user_idle_processor.py
+++ b/tests/test_user_idle_processor.py
@@ -71,6 +71,10 @@ class TestUserIdleProcessor(unittest.IsolatedAsyncioTestCase):
SleepFrame(sleep=0.1),
# Another bot speaking frame resets timer again
BotSpeakingFrame(),
+ # Give some time for the idle timeout task to start (Python 3.10
+ # doesn't really like when you create a task and then cancel it
+ # right away).
+ SleepFrame(sleep=0.1),
]
expected_down_frames = [
+ 
- 
+ 
- 
+ 
+