minor updates to get started and working on latest modal

CHANGELOG.md

@@ -5,41 +5,10 @@ All notable changes to **Pipecat** will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
-## [0.0.65] - 2025-04-23 "Sant Jordi's release"
-
-https://en.wikipedia.org/wiki/Saint_George%27s_Day_in_Catalonia
+## [Unreleased]
 
 ### Added
 
-- Added automatic hangup logic to the Telnyx serializer. This feature hangs up
-  the Telnyx call when an `EndFrame` or `CancelFrame` is received. It is
-  enabled by default and is configurable via the `auto_hang_up` `InputParam`.
-
-- Added a keepalive task to `GladiaSTTService` to prevent the websocket from
-  disconnecting after 30 seconds of no audio input.
-
-### Changed
-
-- The `InputParams` for `ElevenLabsTTSService` and `ElevenLabsHttpTTSService`
-  no longer require that `stability` and `similarity_boost` be set. You can
-  individually set each param.
-
-- In `TwilioFrameSerializer`, `call_sid` is Optional so as to avoid a breaking
-  changed. `call_sid` is required to automatically hang up.
-
-### Fixed
-
-- Fixed an issue where `TwilioFrameSerializer` would send two hang up commands:
-  one for the `EndFrame` and one for the `CancelFrame`.
-
-## [0.0.64] - 2025-04-22
-
-### Added
-
-- Added automatic hangup logic to the Twilio serializer. This feature hangs up
-  the Twilio call when an `EndFrame` or `CancelFrame` is received. It is
-  enabled by default and is configurable via the `auto_hang_up` `InputParam`.
-
 - Added `SmartTurnMetricsData`, which contains end-of-turn prediction metrics,
   to the `MetricsFrame`. Using `MetricsFrame`, you can now retrieve prediction
   confidence scores and processing time metrics from the smart turn analyzers.
@@ -48,9 +17,9 @@ https://en.wikipedia.org/wiki/Saint_George%27s_Day_in_Catalonia
   `GoogleSTTService`, `GoogleTTSService`, and `GoogleVertexLLMService`.
 
 - Added support for Smart Turn Detection via the `turn_analyzer` transport
-  parameter. You can now choose between `HttpSmartTurnAnalyzer()` or
-  `FalSmartTurnAnalyzer()` for remote inference or
-  `LocalCoreMLSmartTurnAnalyzer()` for on-device inference using Core ML.
+  parameter. You can now choose between `SmartTurnAnalyzer()` for remote
+  inference or `LocalCoreMLSmartTurnAnalyzer()` for on-device inference using
+  Core ML.
 
 - `DeepgramTTSService` accepts `base_url` argument again, allowing you to
   connect to an on-prem service.
@@ -75,8 +44,6 @@ https://en.wikipedia.org/wiki/Saint_George%27s_Day_in_Catalonia
 
 ### Changed
 
-- `GrokLLMService` now uses `grok-3-beta` as its default model.
-
 - Daily's REST helpers now include an `eject_at_token_exp` param, which ejects
   the user when their token expires. This new parameter defaults to False.
   Also, the default value for `enable_prejoin_ui` changed to False and
@@ -111,10 +78,6 @@ https://en.wikipedia.org/wiki/Saint_George%27s_Day_in_Catalonia
 - Fixed an issue where LLM input parameters were not working and applied correctly in `GoogleVertexLLMService`, causing
   unexpected behavior during inference.
 
-### Other
-
-- Updated the `twilio-chatbot` example to use the auto-hangup feature.
-
 ## [0.0.63] - 2025-04-11
 
 ### Added

dot-env.template

@@ -96,8 +96,4 @@ PIPER_BASE_URL=...
 
 # Smart turn
 LOCAL_SMART_TURN_MODEL_PATH=
-FAL_SMART_TURN_API_KEY=...
-
-# Twilio
-TWILIO_ACCOUNT_SID=
-TWILIO_AUTH_TOKEN=
+REMOTE_SMART_TURN_URL=

examples/deployment/modal-example/app.py

@@ -10,24 +10,27 @@ import aiohttp
 import modal
 from bot import _voice_bot_process
 from fastapi import HTTPException
-from fastapi.responses import JSONResponse
+from fastapi.responses import RedirectResponse
 from loguru import logger
 
 MAX_SESSION_TIME = 15 * 60  # 15 minutes
 
-app = modal.App("pipecat-modal")
-
-
-image = modal.Image.debian_slim(python_version="3.12").pip_install_from_requirements(
-    "requirements.txt"
+image = (
+    modal.Image.debian_slim(python_version="3.13")
+    .apt_install("ffmpeg")
+    .pip_install_from_requirements("requirements.txt")
+    .pip_install("pipecat-ai[daily,silero,cartesia,openai]")
+    .add_local_python_source("bot")
 )
 
+app = modal.App("pipecat-modal", image=image)
+
 
 @app.function(
     image=image,
     cpu=1.0,
     secrets=[modal.Secret.from_dotenv()],
-    keep_warm=1,
+    min_containers=1,
     enable_memory_snapshot=True,
     max_inputs=1,  # Do not reuse instances across requests
     retries=0,
@@ -40,7 +43,7 @@ def launch_bot_process(room_url: str, token: str):
     image=image,
     secrets=[modal.Secret.from_dotenv()],
 )
-@modal.web_endpoint(method="POST")
+@modal.fastapi_endpoint(method="GET")
 async def start():
     from pipecat.transports.services.helpers.daily_rest import (
         DailyRESTHelper,
@@ -77,4 +80,4 @@ async def start():
 
         # Return room URL to the user to join
         # Note: in production, you would want to return a token to the user
-        return JSONResponse(content={"room_url": room.url, token: token})
+        return RedirectResponse(room.url)

examples/deployment/modal-example/requirements.txt

@@ -1,5 +1,4 @@
 python-dotenv==1.0.1
 modal==0.71.3
-pipecat-ai[daily,silero,cartesia,openai]==0.0.52
 fastapi==0.115.6
 aiohttp==3.11.11

examples/foundational/38-smart-turn-fal.py

@@ -1,113 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-
-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.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):
-    logger.info(f"Starting bot")
-
-    async with aiohttp.ClientSession() as session:
-        transport = SmallWebRTCTransport(
-            webrtc_connection=webrtc_connection,
-            params=TransportParams(
-                audio_in_enabled=True,
-                audio_out_enabled=True,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-                vad_audio_passthrough=True,
-                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 = 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()

examples/foundational/38-smart-turn.py

@@ -0,0 +1,111 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.turn.smart_turn import SmartTurnAnalyzer
+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):
+    logger.info(f"Starting bot")
+
+    remote_smart_turn_url = os.getenv("REMOTE_SMART_TURN_URL")
+
+    transport = SmallWebRTCTransport(
+        webrtc_connection=webrtc_connection,
+        params=TransportParams(
+            audio_in_enabled=True,
+            audio_out_enabled=True,
+            vad_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
+            vad_audio_passthrough=True,
+            turn_analyzer=SmartTurnAnalyzer(url=remote_smart_turn_url),
+        ),
+    )
+
+    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()

examples/foundational/38a-local-smart-turn.py

@@ -9,8 +9,8 @@ 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.turn.base_smart_turn import SmartTurnParams
+from pipecat.audio.turn.local_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

examples/foundational/run.py

@@ -7,6 +7,7 @@
 import argparse
 import asyncio
 import importlib.util
+import logging
 import os
 import sys
 from contextlib import asynccontextmanager
@@ -17,7 +18,6 @@ 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
@@ -25,6 +25,14 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 # Load environment variables
 load_dotenv(override=True)
 
+# Configure logger
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(message)s",
+    handlers=[logging.StreamHandler()],
+)
+logger = logging.getLogger("pipecat-server")
+
 app = FastAPI()
 
 # Store connections by pc_id
@@ -154,11 +162,10 @@ def main():
     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")
+        logging.basicConfig(level=logging.DEBUG)
     else:
-        logger.add(sys.stderr, level="DEBUG")
+        logging.basicConfig(level=logging.INFO)
 
     # Infer the bot file from the caller if not provided explicitly
     bot_file = args.bot_file

examples/p2p-webrtc/daily-interop-bridge/bot.py

@@ -26,6 +26,9 @@ from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 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):

examples/p2p-webrtc/daily-interop-bridge/server.py

@@ -1,12 +1,6 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
 import argparse
 import asyncio
-import sys
+import logging
 from contextlib import asynccontextmanager
 from typing import Dict
 
@@ -15,7 +9,6 @@ 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
@@ -23,6 +16,8 @@ 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
@@ -86,10 +81,9 @@ if __name__ == "__main__":
     parser.add_argument("--verbose", "-v", action="count")
     args = parser.parse_args()
 
-    logger.remove(0)
     if args.verbose:
-        logger.add(sys.stderr, level="TRACE")
+        logging.basicConfig(level=logging.DEBUG)
     else:
-        logger.add(sys.stderr, level="DEBUG")
+        logging.basicConfig(level=logging.INFO)
 
     uvicorn.run(app, host=args.host, port=args.port)

examples/p2p-webrtc/video-transform/server/bot.py

@@ -25,6 +25,9 @@ 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):

examples/p2p-webrtc/video-transform/server/server.py

@@ -1,12 +1,6 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
 import argparse
 import asyncio
-import sys
+import logging
 from contextlib import asynccontextmanager
 from typing import Dict
 
@@ -15,7 +9,6 @@ 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
@@ -23,6 +16,8 @@ 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
@@ -86,10 +81,9 @@ if __name__ == "__main__":
     parser.add_argument("--verbose", "-v", action="count")
     args = parser.parse_args()
 
-    logger.remove(0)
     if args.verbose:
-        logger.add(sys.stderr, level="TRACE")
+        logging.basicConfig(level=logging.DEBUG)
     else:
-        logger.add(sys.stderr, level="DEBUG")
+        logging.basicConfig(level=logging.INFO)
 
     uvicorn.run(app, host=args.host, port=args.port)

examples/p2p-webrtc/voice-agent/bot.py

@@ -20,6 +20,10 @@ 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.
 

examples/p2p-webrtc/voice-agent/server.py

@@ -1,12 +1,6 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
 import argparse
 import asyncio
-import sys
+import logging
 from contextlib import asynccontextmanager
 from typing import Dict
 
@@ -15,13 +9,14 @@ 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
@@ -78,10 +73,9 @@ if __name__ == "__main__":
     parser.add_argument("--verbose", "-v", action="count")
     args = parser.parse_args()
 
-    logger.remove(0)
     if args.verbose:
-        logger.add(sys.stderr, level="TRACE")
+        logging.basicConfig(level=logging.DEBUG)
     else:
-        logger.add(sys.stderr, level="DEBUG")
+        logging.basicConfig(level=logging.INFO)
 
     uvicorn.run(app, host=args.host, port=args.port)

examples/phone-chatbot/env.example

@@ -1,7 +1,6 @@
 DAILY_SAMPLE_ROOM_URL=https://yourdomain.daily.co/yourroom # (optional: for joining the bot to the same room repeatedly for local dev)
 DAILY_API_KEY=
-DAILY_API_URL=https://api.daily.co/v1
-DEEPGRAM_API_KEY=
+DAILY_API_URL=api.daily.co/v1
 OPENAI_API_KEY=
 GOOGLE_API_KEY
 CARTESIA_API_KEY=

examples/phone-chatbot/requirements.txt

@@ -1,5 +1,5 @@
-pipecat-ai[daily,cartesia,deepgram,openai,google,silero]
-fastapi==0.115.6
+pipecat-ai[daily,cartesia,openai,google,silero]
+fastapi==3.11.12
 uvicorn
 python-dotenv
 twilio

examples/telnyx-chatbot/README.md

@@ -63,35 +63,20 @@ This project is a FastAPI-based chatbot that integrates with Telnyx to handle We
    ngrok http 8765
    ```
 
-2. **Purchase a number**
+2. **Update the Telnyx TeXML applications Webhook**:
 
-   If you haven't already, purchase a number from Telnyx.
+   - Go to your TeXML configuration page
+   - Provide the ngrok URL to the Webhook URL field and ensure the POST method is selected
+   - Click Save at the bottom of the page
 
-   - Log in to the Telnyx developer portal: https://portal.telnyx.com/
-   - Buy a number: https://portal.telnyx.com/#/numbers/buy-numbers
-
-3. **Update the Telnyx TeXML applications Webhook**:
-
-   - Go to your TeXML configuration page: https://portal.telnyx.com/#/call-control/texml
-   - Create a new TeXML app, if one doesn't exist already:
-     - Add an application name
-     - Under Webhooks, select POST as the "Voice Method"
-     - Select "Custom URL" under Webhook URL Method
-     - Enter your ngrok URL in the "Webhook URL" field (e.g. https://your-name.ngrok.io)
-     - Click "Create" to save
-       Note: You'll see subsequent pages to set up SIP and Outbound, both are not required, so just skip.
-   - Navigate to "Manage Numbers" (https://portal.telnyx.com/#/numbers/my-numbers) and under SIP connection, select the pencil icon to edit and select the TeXML application that you just created.
-
-   Now your number is ready to call.
-
-4. **Configure streams.xml**:
+3. **Configure streams.xml**:
    - Copy the template file to create your local version:
      ```sh
      cp templates/streams.xml.template templates/streams.xml
      ```
    - In `templates/streams.xml`, replace `<your server url>` with your ngrok URL (without `https://`)
-   - 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 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 inbound encoding can be controlled from the application configuration for inbound calls and dial/transfer commands for outbound calls.
 
 ## Running the Application

examples/telnyx-chatbot/bot.py

@@ -33,18 +33,9 @@ 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(
@@ -53,7 +44,7 @@ async def run_bot(
             vad_enabled=True,
             vad_analyzer=SileroVADAnalyzer(),
             vad_audio_passthrough=True,
-            serializer=serializer,
+            serializer=TelnyxFrameSerializer(stream_id, outbound_encoding, inbound_encoding),
         ),
     )
 

examples/telnyx-chatbot/server.py

@@ -37,10 +37,9 @@ 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, call_control_id, outbound_encoding, "PCMU")
+    await run_bot(websocket, stream_id, outbound_encoding, "PCMU")
 
 
 if __name__ == "__main__":

examples/twilio-chatbot/bot.py

@@ -54,14 +54,7 @@ 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, 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", ""),
-    )
-
+async def run_bot(websocket_client: WebSocket, stream_sid: str, testing: bool):
     transport = FastAPIWebsocketTransport(
         websocket=websocket_client,
         params=FastAPIWebsocketParams(
@@ -71,7 +64,7 @@ async def run_bot(websocket_client: WebSocket, stream_sid: str, call_sid: str, t
             vad_enabled=True,
             vad_analyzer=SileroVADAnalyzer(),
             vad_audio_passthrough=True,
-            serializer=serializer,
+            serializer=TwilioFrameSerializer(stream_sid),
         ),
     )
 

examples/twilio-chatbot/server.py

@@ -38,9 +38,8 @@ 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, call_sid, app.state.testing)
+    await run_bot(websocket, stream_sid, app.state.testing)
 
 
 if __name__ == "__main__":

src/pipecat/audio/turn/base_smart_turn.py

@@ -30,10 +30,6 @@ class SmartTurnParams(BaseModel):
     # 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()
@@ -46,7 +42,7 @@ class BaseSmartTurn(BaseTurnAnalyzer):
         self._audio_buffer = []
         self._speech_triggered = False
         self._silence_ms = 0
-        self._speech_start_time = 0
+        self._speech_start_time = None
 
     @property
     def speech_triggered(self) -> bool:
@@ -64,7 +60,7 @@ class BaseSmartTurn(BaseTurnAnalyzer):
             # Reset silence tracking on speech
             self._silence_ms = 0
             self._speech_triggered = True
-            if self._speech_start_time == 0:
+            if self._speech_start_time is None:
                 self._speech_start_time = time.time()
         else:
             if self._speech_triggered:
@@ -91,8 +87,8 @@ class BaseSmartTurn(BaseTurnAnalyzer):
 
         return state
 
-    async def analyze_end_of_turn(self) -> Tuple[EndOfTurnState, Optional[MetricsData]]:
-        state, result = await self._process_speech_segment(self._audio_buffer)
+    def analyze_end_of_turn(self) -> Tuple[EndOfTurnState, Optional[MetricsData]]:
+        state, result = 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}")
@@ -102,12 +98,10 @@ class BaseSmartTurn(BaseTurnAnalyzer):
         # 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._speech_start_time = None
         self._silence_ms = 0
 
-    async def _process_speech_segment(
-        self, audio_buffer
-    ) -> Tuple[EndOfTurnState, Optional[MetricsData]]:
+    def _process_speech_segment(self, audio_buffer) -> Tuple[EndOfTurnState, Optional[MetricsData]]:
         state = EndOfTurnState.INCOMPLETE
 
         if not audio_buffer:
@@ -137,41 +131,30 @@ class BaseSmartTurn(BaseTurnAnalyzer):
 
         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()
+            result = 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
+            # Calculate processing time
+            e2e_processing_time_ms = (end_time - start_time) * 1000
 
-                # Prepare the result data
-                result_data = SmartTurnMetricsData(
-                    processor="BaseSmartTurn",
-                    is_complete=result["prediction"] == 1,
-                    probability=result["probability"],
-                    inference_time_ms=result.get("inference_time", 0) * 1000,
-                    server_total_time_ms=result.get("total_time", 0) * 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
+            # Prepare the result data
+            result_data = SmartTurnMetricsData(
+                processor="BaseSmartTurn",
+                is_complete=result["prediction"] == 1,
+                probability=result["probability"],
+                inference_time_ms=result.get("inference_time", 0) * 1000,
+                server_total_time_ms=result.get("total_time", 0) * 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")
         else:
             logger.trace(f"params: {self._params}, stop_ms: {self._stop_ms}")
             logger.trace("Captured empty audio segment, skipping prediction.")
@@ -179,11 +162,11 @@ class BaseSmartTurn(BaseTurnAnalyzer):
         return state, result_data
 
     @abstractmethod
-    async def _predict_endpoint(self, audio_array: np.ndarray) -> Dict[str, Any]:
+    def _predict_endpoint(self, buffer: 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.
+            buffer: Float32 numpy array of audio samples at 16kHz.
 
         Returns:
             Dictionary with:

src/pipecat/audio/turn/base_turn_analyzer.py

@@ -71,7 +71,7 @@ class BaseTurnAnalyzer(ABC):
         pass
 
     @abstractmethod
-    async def analyze_end_of_turn(self) -> Tuple[EndOfTurnState, Optional[MetricsData]]:
+    def analyze_end_of_turn(self) -> Tuple[EndOfTurnState, Optional[MetricsData]]:
         """Analyzes if an end of turn has occurred based on the audio input.
 
         Returns:

src/pipecat/audio/turn/local_smart_turn.py

@@ -5,16 +5,17 @@
 #
 
 
-from typing import Any, Dict
+import os
+from typing import Dict
 
 import numpy as np
+import torch
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import BaseSmartTurn
+from pipecat.audio.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}")
@@ -25,7 +26,7 @@ except ModuleNotFoundError as e:
 
 
 class LocalCoreMLSmartTurnAnalyzer(BaseSmartTurn):
-    def __init__(self, *, smart_turn_model_path: str, **kwargs):
+    def __init__(self, smart_turn_model_path: str, **kwargs):
         super().__init__(**kwargs)
 
         if not smart_turn_model_path:
@@ -40,7 +41,7 @@ class LocalCoreMLSmartTurnAnalyzer(BaseSmartTurn):
         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]:
+    def _predict_endpoint(self, audio_array: np.ndarray) -> Dict[str, any]:
         inputs = self._turn_processor(
             audio_array,
             sampling_rate=16000,

src/pipecat/audio/turn/smart_turn.py

@@ -0,0 +1,75 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+
+import io
+import os
+from typing import Dict
+
+import numpy as np
+import requests
+from loguru import logger
+
+from pipecat.audio.turn.base_smart_turn import BaseSmartTurn
+
+
+class SmartTurnAnalyzer(BaseSmartTurn):
+    def __init__(self, url: str, **kwargs):
+        super().__init__(**kwargs)
+        self.remote_smart_turn_url = url
+
+        if not self.remote_smart_turn_url:
+            logger.error("remote_smart_turn_url is not set.")
+            raise Exception("remote_smart_turn_url must be provided.")
+
+        # Use a session to reuse connections (keep-alive)
+        self.session = requests.Session()
+        self.session.headers.update({"Connection": "keep-alive"})
+
+    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
+
+    def _send_raw_request(self, data_bytes: bytes):
+        headers = {"Content-Type": "application/octet-stream"}
+        logger.trace(
+            f"Sending {len(data_bytes)} bytes as raw body to {self.remote_smart_turn_url}..."
+        )
+        try:
+            response = self.session.post(
+                self.remote_smart_turn_url,
+                data=data_bytes,
+                headers=headers,
+                timeout=60,
+            )
+
+            logger.trace("\n--- Response ---")
+            logger.trace(f"Status Code: {response.status_code}")
+
+            if response.ok:
+                try:
+                    logger.trace("Response JSON:")
+                    logger.trace(response.json())
+                    return response.json()
+                except requests.exceptions.JSONDecodeError:
+                    logger.trace("Response Content (non-JSON):")
+                    logger.trace(response.text)
+            else:
+                logger.trace("Response Content (Error):")
+                logger.trace(response.text)
+                response.raise_for_status()
+
+        except requests.exceptions.RequestException 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.")
+
+    def _predict_endpoint(self, audio_array: np.ndarray) -> Dict[str, any]:
+        serialized_array = self._serialize_array(audio_array)
+        return self._send_raw_request(serialized_array)

src/pipecat/audio/turn/smart_turn/fal_smart_turn.py

@@ -1,26 +0,0 @@
-#
-# Copyright (c) 2024–2025, 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)

src/pipecat/audio/turn/smart_turn/http_smart_turn.py

@@ -1,80 +0,0 @@
-#
-# Copyright (c) 2024–2025, 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)

src/pipecat/serializers/telnyx.py

@@ -8,8 +8,6 @@ import base64
 import json
 from typing import Optional
 
-import aiohttp
-from loguru import logger
 from pydantic import BaseModel
 
 from pipecat.audio.utils import (
@@ -21,8 +19,6 @@ from pipecat.audio.utils import (
 )
 from pipecat.frames.frames import (
     AudioRawFrame,
-    CancelFrame,
-    EndFrame,
     Frame,
     InputAudioRawFrame,
     InputDTMFFrame,
@@ -34,120 +30,38 @@ 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):
-        """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
+        telnyx_sample_rate: int = 8000  # Default Telnyx rate (8kHz)
+        sample_rate: Optional[int] = None  # Pipeline input rate
         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:
-        """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):
+        if isinstance(frame, AudioRawFrame):
             data = frame.audio
 
             # Output: Convert PCM at frame's rate to 8kHz encoded for Telnyx
@@ -170,58 +84,11 @@ class TelnyxFrameSerializer(FrameSerializer):
 
             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}")
+        if isinstance(frame, StartInterruptionFrame):
+            answer = {"event": "clear"}
+            return json.dumps(answer)
 
     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":

src/pipecat/serializers/twilio.py

@@ -8,14 +8,11 @@ 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,
@@ -29,107 +26,28 @@ from pipecat.serializers.base_serializer import FrameSerializer, FrameSerializer
 
 
 class TwilioFrameSerializer(FrameSerializer):
-    """Serializer for Twilio Media Streams WebSocket protocol.
-
-    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.
+        twilio_sample_rate: int = 8000  # Default Twilio rate (8kHz)
+        sample_rate: Optional[int] = None  # Pipeline input rate
 
-        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.
-        """
+    def __init__(self, stream_sid: str, params: InputParams = InputParams()):
         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:
-        """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):
+        if isinstance(frame, StartInterruptionFrame):
             answer = {"event": "clear", "streamSid": self._stream_sid}
             return json.dumps(answer)
         elif isinstance(frame, AudioRawFrame):
@@ -150,70 +68,7 @@ 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":

src/pipecat/services/elevenlabs/tts.py

@@ -126,14 +126,31 @@ def build_elevenlabs_voice_settings(
         settings: Dictionary containing voice settings parameters
 
     Returns:
-        Dictionary of voice settings or None if no valid settings are provided
+        Dictionary of voice settings or None if required parameters are missing
     """
-    voice_setting_keys = ["stability", "similarity_boost", "style", "use_speaker_boost", "speed"]
-
     voice_settings = {}
-    for key in voice_setting_keys:
-        if key in settings and settings[key] is not None:
-            voice_settings[key] = settings[key]
+    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."
+            )
 
     return voice_settings or None
 

src/pipecat/services/gladia/stt.py

@@ -4,7 +4,6 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-import asyncio
 import base64
 import json
 import warnings
@@ -225,7 +224,6 @@ class GladiaSTTService(STTService):
         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."""
@@ -289,22 +287,14 @@ class GladiaSTTService(STTService):
         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
@@ -312,15 +302,7 @@ class GladiaSTTService(STTService):
     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
-
+        await self._websocket.close()
         if self._receive_task:
             await self.cancel_task(self._receive_task)
             self._receive_task = None
@@ -359,24 +341,6 @@ class GladiaSTTService(STTService):
         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:

src/pipecat/services/grok/llm.py

@@ -42,7 +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-3-beta"
+        model (str, optional): The model identifier to use. Defaults to "grok-2"
         **kwargs: Additional keyword arguments passed to OpenAILLMService
     """
 
@@ -51,7 +51,7 @@ class GrokLLMService(OpenAILLMService):
         *,
         api_key: str,
         base_url: str = "https://api.x.ai/v1",
-        model: str = "grok-3-beta",
+        model: str = "grok-2",
         **kwargs,
     ):
         super().__init__(api_key=api_key, base_url=base_url, model=model, **kwargs)

src/pipecat/transports/base_input.py

@@ -222,8 +222,12 @@ class BaseInputTransport(FrameProcessor):
 
     async def _handle_end_of_turn(self):
         if self.turn_analyzer:
-            state, prediction = await self.turn_analyzer.analyze_end_of_turn()
+            state, prediction = await self.get_event_loop().run_in_executor(
+                self._executor, 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):

src/pipecat/transports/network/fastapi_websocket.py

@@ -207,12 +207,10 @@ class FastAPIWebsocketOutputTransport(BaseOutputTransport):
 
     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):

src/pipecat/transports/network/websocket_server.py

@@ -157,8 +157,7 @@ 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:
@@ -196,14 +195,6 @@ 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()