Compare commits
1 Commits
hush/custo
...
cb/extra-l
| Author | SHA1 | Date | |
|---|---|---|---|
|
b8e2227a21 |
54
.github/workflows/coverage.yaml
vendored
54
.github/workflows/coverage.yaml
vendored
@@ -1,54 +0,0 @@
|
||||
name: coverage
|
||||
|
||||
on:
|
||||
workflow_dispatch:
|
||||
push:
|
||||
branches:
|
||||
- main
|
||||
pull_request:
|
||||
branches:
|
||||
- "**"
|
||||
paths-ignore:
|
||||
- "docs/**"
|
||||
|
||||
jobs:
|
||||
coverage:
|
||||
name: "Coverage"
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Checkout repo
|
||||
uses: actions/checkout@v4
|
||||
- name: Set up Python
|
||||
id: setup_python
|
||||
uses: actions/setup-python@v4
|
||||
with:
|
||||
python-version: "3.10"
|
||||
- name: Cache virtual environment
|
||||
uses: actions/cache@v3
|
||||
with:
|
||||
# We are hashing dev-requirements.txt and test-requirements.txt which
|
||||
# contain all dependencies needed to run the tests.
|
||||
key: venv-${{ runner.os }}-${{ steps.setup_python.outputs.python-version}}-${{ hashFiles('dev-requirements.txt') }}-${{ hashFiles('test-requirements.txt') }}
|
||||
path: .venv
|
||||
- name: Install system packages
|
||||
id: install_system_packages
|
||||
run: |
|
||||
sudo apt-get install -y portaudio19-dev
|
||||
- name: Setup virtual environment
|
||||
run: |
|
||||
python -m venv .venv
|
||||
- name: Install basic Python dependencies
|
||||
run: |
|
||||
source .venv/bin/activate
|
||||
python -m pip install --upgrade pip
|
||||
pip install -r dev-requirements.txt -r test-requirements.txt
|
||||
- name: Run tests with coverage
|
||||
run: |
|
||||
source .venv/bin/activate
|
||||
coverage run
|
||||
coverage xml
|
||||
- name: Upload coverage to Codecov
|
||||
uses: codecov/codecov-action@v5
|
||||
with:
|
||||
token: ${{ secrets.CODECOV_TOKEN }}
|
||||
slug: pipecat-ai/pipecat
|
||||
15
.gitignore
vendored
15
.gitignore
vendored
@@ -32,21 +32,6 @@ fly.toml
|
||||
|
||||
# Example files
|
||||
pipecat/examples/twilio-chatbot/templates/streams.xml
|
||||
pipecat/examples/bot-ready-signalling/client/react-native/node_modules/
|
||||
pipecat/examples/bot-ready-signalling/client/react-native/.expo/
|
||||
pipecat/examples/bot-ready-signalling/client/react-native/dist/
|
||||
pipecat/examples/bot-ready-signalling/client/react-native/npm-debug.*
|
||||
pipecat/examples/bot-ready-signalling/client/react-native/*.jks
|
||||
pipecat/examples/bot-ready-signalling/client/react-native/*.p8
|
||||
pipecat/examples/bot-ready-signalling/client/react-native/*.p12
|
||||
pipecat/examples/bot-ready-signalling/client/react-native/*.key
|
||||
pipecat/examples/bot-ready-signalling/client/react-native/*.mobileprovision
|
||||
pipecat/examples/bot-ready-signalling/client/react-native/*.orig.*
|
||||
pipecat/examples/bot-ready-signalling/client/react-native/web-build/
|
||||
|
||||
# macOS
|
||||
.DS_Store
|
||||
|
||||
|
||||
# Documentation
|
||||
docs/api/_build/
|
||||
|
||||
@@ -1,8 +1,7 @@
|
||||
repos:
|
||||
- repo: https://github.com/astral-sh/ruff-pre-commit
|
||||
rev: v0.9.7
|
||||
- repo: local
|
||||
hooks:
|
||||
- id: ruff
|
||||
language_version: python3
|
||||
args: [ --select, I, ]
|
||||
- id: ruff-format
|
||||
- id: ruff-format-hook
|
||||
name: Check ruff formatting
|
||||
entry: sh scripts/pre-commit.sh
|
||||
language: system
|
||||
|
||||
993
CHANGELOG.md
993
CHANGELOG.md
File diff suppressed because it is too large
Load Diff
@@ -26,52 +26,11 @@ git commit -m "Description of your changes"
|
||||
git push origin your-branch-name
|
||||
```
|
||||
|
||||
8. **Submit a Pull Request (PR)**: Open a PR from your forked repository to the main branch of this repo.
|
||||
> Important: Describe the changes you've made clearly!
|
||||
9. **Submit a Pull Request (PR)**: Open a PR from your forked repository to the main branch of this repo.
|
||||
> Important: Describe the changes you've made clearly!
|
||||
|
||||
Our maintainers will review your PR, and once everything is good, your contributions will be merged!
|
||||
|
||||
## Code Style and Documentation
|
||||
|
||||
### Python Code Style
|
||||
|
||||
We use Ruff for code linting and formatting. Please ensure your code passes all linting checks before submitting a PR.
|
||||
|
||||
### Docstring Conventions
|
||||
|
||||
We follow Google-style docstrings with these specific conventions:
|
||||
|
||||
- Class docstrings should fully document all parameters used in `__init__`
|
||||
- We don't require separate docstrings for `__init__` methods when parameters are documented in the class docstring
|
||||
- Property methods should have docstrings explaining their purpose and return value
|
||||
|
||||
Example of correctly documented class:
|
||||
|
||||
```python
|
||||
class MyClass:
|
||||
"""Class description.
|
||||
|
||||
Additional details about the class.
|
||||
|
||||
Args:
|
||||
param1: Description of first parameter.
|
||||
param2: Description of second parameter.
|
||||
"""
|
||||
|
||||
def __init__(self, param1, param2):
|
||||
# No docstring required here as parameters are documented above
|
||||
self.param1 = param1
|
||||
self.param2 = param2
|
||||
|
||||
@property
|
||||
def some_property(self) -> str:
|
||||
"""Get the formatted property value.
|
||||
|
||||
Returns:
|
||||
A string representation of the property.
|
||||
"""
|
||||
return f"Property: {self.param1}"
|
||||
```
|
||||
|
||||
# Contributor Covenant Code of Conduct
|
||||
|
||||
@@ -92,23 +51,23 @@ diverse, inclusive, and healthy community.
|
||||
Examples of behavior that contributes to a positive environment for our
|
||||
community include:
|
||||
|
||||
- Demonstrating empathy and kindness toward other people
|
||||
- Being respectful of differing opinions, viewpoints, and experiences
|
||||
- Giving and gracefully accepting constructive feedback
|
||||
- Accepting responsibility and apologizing to those affected by our mistakes,
|
||||
* Demonstrating empathy and kindness toward other people
|
||||
* Being respectful of differing opinions, viewpoints, and experiences
|
||||
* Giving and gracefully accepting constructive feedback
|
||||
* Accepting responsibility and apologizing to those affected by our mistakes,
|
||||
and learning from the experience
|
||||
- Focusing on what is best not just for us as individuals, but for the overall
|
||||
* Focusing on what is best not just for us as individuals, but for the overall
|
||||
community
|
||||
|
||||
Examples of unacceptable behavior include:
|
||||
|
||||
- The use of sexualized language or imagery, and sexual attention or advances of
|
||||
* The use of sexualized language or imagery, and sexual attention or advances of
|
||||
any kind
|
||||
- Trolling, insulting or derogatory comments, and personal or political attacks
|
||||
- Public or private harassment
|
||||
- Publishing others' private information, such as a physical or email address,
|
||||
* Trolling, insulting or derogatory comments, and personal or political attacks
|
||||
* Public or private harassment
|
||||
* Publishing others' private information, such as a physical or email address,
|
||||
without their explicit permission
|
||||
- Other conduct which could reasonably be considered inappropriate in a
|
||||
* Other conduct which could reasonably be considered inappropriate in a
|
||||
professional setting
|
||||
|
||||
## Enforcement Responsibilities
|
||||
@@ -203,4 +162,4 @@ For answers to common questions about this code of conduct, see the FAQ at
|
||||
[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html
|
||||
[Mozilla CoC]: https://github.com/mozilla/diversity
|
||||
[FAQ]: https://www.contributor-covenant.org/faq
|
||||
[translations]: https://www.contributor-covenant.org/translations
|
||||
[translations]: https://www.contributor-covenant.org/translations
|
||||
47
README.md
47
README.md
@@ -2,7 +2,7 @@
|
||||
<img alt="pipecat" width="300px" height="auto" src="https://raw.githubusercontent.com/pipecat-ai/pipecat/main/pipecat.png">
|
||||
</div></h1>
|
||||
|
||||
[](https://pypi.org/project/pipecat-ai)  [](https://codecov.io/gh/pipecat-ai/pipecat) [](https://docs.pipecat.ai) [](https://discord.gg/pipecat)
|
||||
[](https://pypi.org/project/pipecat-ai)  [](https://docs.pipecat.ai) [](https://discord.gg/pipecat) <a href="https://app.commanddash.io/agent/github_pipecat-ai_pipecat"><img src="https://img.shields.io/badge/AI-Code%20Agent-EB9FDA"></a>
|
||||
|
||||
Pipecat is an open source Python framework for building voice and multimodal conversational agents. It handles the complex orchestration of AI services, network transport, audio processing, and multimodal interactions, letting you focus on creating engaging experiences.
|
||||
|
||||
@@ -55,18 +55,17 @@ pip install "pipecat-ai[option,...]"
|
||||
|
||||
### Available services
|
||||
|
||||
| Category | Services | Install Command Example |
|
||||
| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------- |
|
||||
| Speech-to-Text | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [Fal Wizper](https://docs.pipecat.ai/server/services/stt/fal), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Google](https://docs.pipecat.ai/server/services/stt/google), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [Parakeet (NVIDIA)](https://docs.pipecat.ai/server/services/stt/parakeet), [Ultravox](https://docs.pipecat.ai/server/services/stt/ultravox), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper) | `pip install "pipecat-ai[deepgram]"` |
|
||||
| LLMs | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/server/services/llm/perplexity), [Qwen](https://docs.pipecat.ai/server/services/llm/qwen), [Together AI](https://docs.pipecat.ai/server/services/llm/together) | `pip install "pipecat-ai[openai]"` |
|
||||
| Text-to-Speech | [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [FastPitch (NVIDIA)](https://docs.pipecat.ai/server/services/tts/fastpitch), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [Piper](https://docs.pipecat.ai/server/services/tts/piper), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) | `pip install "pipecat-ai[cartesia]"` |
|
||||
| Speech-to-Speech | [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai) | `pip install "pipecat-ai[google]"` |
|
||||
| Transport | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local | `pip install "pipecat-ai[daily]"` |
|
||||
| Video | [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli) | `pip install "pipecat-ai[tavus,simli]"` |
|
||||
| Memory | [mem0](https://docs.pipecat.ai/server/services/memory/mem0) | `pip install "pipecat-ai[mem0]"` |
|
||||
| Vision & Image | [fal](https://docs.pipecat.ai/server/services/image-generation/fal), [Google Imagen](https://docs.pipecat.ai/server/services/image-generation/fal), [Moondream](https://docs.pipecat.ai/server/services/vision/moondream) | `pip install "pipecat-ai[moondream]"` |
|
||||
| Audio Processing | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [Noisereduce](https://docs.pipecat.ai/server/utilities/audio/noisereduce-filter) | `pip install "pipecat-ai[silero]"` |
|
||||
| Analytics & Metrics | [Canonical AI](https://docs.pipecat.ai/server/services/analytics/canonical), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry) | `pip install "pipecat-ai[canonical]"` |
|
||||
| Category | Services | Install Command Example |
|
||||
| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------- |
|
||||
| Speech-to-Text | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper) | `pip install "pipecat-ai[deepgram]"` |
|
||||
| LLMs | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Together AI](https://docs.pipecat.ai/server/services/llm/together) | `pip install "pipecat-ai[openai]"` |
|
||||
| Text-to-Speech | [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) | `pip install "pipecat-ai[cartesia]"` |
|
||||
| Speech-to-Speech | [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai) | `pip install "pipecat-ai[openai]"` |
|
||||
| Transport | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local | `pip install "pipecat-ai[daily]"` |
|
||||
| Video | [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli) | `pip install "pipecat-ai[tavus,simli]"` |
|
||||
| Vision & Image | [Moondream](https://docs.pipecat.ai/server/services/vision/moondream), [fal](https://docs.pipecat.ai/server/services/image-generation/fal) | `pip install "pipecat-ai[moondream]"` |
|
||||
| Audio Processing | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [Noisereduce](https://docs.pipecat.ai/server/utilities/audio/noisereduce-filter) | `pip install "pipecat-ai[silero]"` |
|
||||
| Analytics & Metrics | [Canonical AI](https://docs.pipecat.ai/server/services/analytics/canonical), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry) | `pip install "pipecat-ai[canonical]"` |
|
||||
|
||||
📚 [View full services documentation →](https://docs.pipecat.ai/server/services/supported-services)
|
||||
|
||||
@@ -150,40 +149,36 @@ Sign up [here](https://dashboard.daily.co/u/signup) and [create a room](https://
|
||||
|
||||
## Hacking on the framework itself
|
||||
|
||||
_Note: You may need to set up a virtual environment before following these instructions. From the root of the repo:_
|
||||
_Note that you may need to set up a virtual environment before following the instructions below. For instance, you might need to run the following from the root of the repo:_
|
||||
|
||||
```shell
|
||||
python3 -m venv venv
|
||||
source venv/bin/activate
|
||||
```
|
||||
|
||||
Install the development dependencies:
|
||||
From the root of this repo, run the following:
|
||||
|
||||
```shell
|
||||
pip install -r dev-requirements.txt
|
||||
```
|
||||
|
||||
Install the git pre-commit hooks (these help ensure your code follows project rules):
|
||||
This will install the necessary development dependencies. Also, make sure you install the git pre-commit hooks:
|
||||
|
||||
```shell
|
||||
pre-commit install
|
||||
```
|
||||
|
||||
Install the `pipecat-ai` package locally in editable mode:
|
||||
The hooks will just save you time when you submit a PR by making sure your code follows the project rules.
|
||||
|
||||
To use the package locally (e.g. to run sample files), run:
|
||||
|
||||
```shell
|
||||
pip install -e .
|
||||
pip install --editable ".[option,...]"
|
||||
```
|
||||
|
||||
The `-e` or `--editable` option allows you to modify the code without reinstalling.
|
||||
The `--editable` option makes sure you don't have to run `pip install` again and you can just edit the project files locally.
|
||||
|
||||
To include optional dependencies, add them to the install command. For example:
|
||||
|
||||
```shell
|
||||
pip install -e ".[daily,deepgram,cartesia,openai,silero]" # Updated for the services you're using
|
||||
```
|
||||
|
||||
If you want to use this package from another directory:
|
||||
If you want to use this package from another directory, you can run:
|
||||
|
||||
```shell
|
||||
pip install "path_to_this_repo[option,...]"
|
||||
|
||||
11
codecov.yml
11
codecov.yml
@@ -1,11 +0,0 @@
|
||||
coverage:
|
||||
range: 50..90 # coverage lower than 50 is red, higher than 90 green, between color code
|
||||
|
||||
status:
|
||||
project:
|
||||
default:
|
||||
target: auto # auto % coverage target
|
||||
threshold: 5% # allow for 5% reduction of coverage without failing
|
||||
|
||||
# do not run coverage on patch nor changes
|
||||
patch: false
|
||||
@@ -1,13 +1,11 @@
|
||||
build~=1.2.2
|
||||
coverage~=7.6.12
|
||||
grpcio-tools~=1.67.1
|
||||
grpcio-tools~=1.69.0
|
||||
pip-tools~=7.4.1
|
||||
pre-commit~=4.0.1
|
||||
pyright~=1.1.397
|
||||
pyright~=1.1.392
|
||||
pytest~=8.3.4
|
||||
pytest-asyncio~=0.25.3
|
||||
pytest-aiohttp==1.1.0
|
||||
ruff~=0.11.1
|
||||
setuptools~=70.0.0
|
||||
pytest-asyncio~=0.25.2
|
||||
ruff~=0.9.1
|
||||
setuptools~=75.8.0
|
||||
setuptools_scm~=8.1.0
|
||||
python-dotenv~=1.0.1
|
||||
|
||||
@@ -50,14 +50,6 @@ autodoc_mock_imports = [
|
||||
"pyht.protos",
|
||||
"pyht.protos.api_pb2",
|
||||
"pipecat_ai_playht", # PlayHT wrapper
|
||||
"vllm",
|
||||
"aiortc",
|
||||
"aiortc.mediastreams",
|
||||
"cv2",
|
||||
"av",
|
||||
"pyneuphonic",
|
||||
"mem0",
|
||||
"mlx_whisper",
|
||||
"anthropic",
|
||||
"assemblyai",
|
||||
"boto3",
|
||||
|
||||
@@ -45,10 +45,8 @@ Transport & Serialization
|
||||
Utilities
|
||||
~~~~~~~~~
|
||||
|
||||
* :mod:`Adapters <pipecat.adapters>`
|
||||
* :mod:`Clocks <pipecat.clocks>`
|
||||
* :mod:`Metrics <pipecat.metrics>`
|
||||
* :mod:`Observers <pipecat.observers>`
|
||||
* :mod:`Sync <pipecat.sync>`
|
||||
* :mod:`Transcriptions <pipecat.transcriptions>`
|
||||
* :mod:`Utils <pipecat.utils>`
|
||||
@@ -58,12 +56,10 @@ Utilities
|
||||
:caption: API Reference
|
||||
:hidden:
|
||||
|
||||
Adapters <api/pipecat.adapters>
|
||||
Audio <api/pipecat.audio>
|
||||
Clocks <api/pipecat.clocks>
|
||||
Frames <api/pipecat.frames>
|
||||
Metrics <api/pipecat.metrics>
|
||||
Observers <api/pipecat.observers>
|
||||
Pipeline <api/pipecat.pipeline>
|
||||
Processors <api/pipecat.processors>
|
||||
Serializers <api/pipecat.serializers>
|
||||
|
||||
@@ -12,29 +12,22 @@ pipecat-ai[aws]
|
||||
pipecat-ai[azure]
|
||||
pipecat-ai[canonical]
|
||||
pipecat-ai[cartesia]
|
||||
pipecat-ai[cerebras]
|
||||
pipecat-ai[deepseek]
|
||||
pipecat-ai[daily]
|
||||
pipecat-ai[deepgram]
|
||||
pipecat-ai[elevenlabs]
|
||||
pipecat-ai[fal]
|
||||
pipecat-ai[fireworks]
|
||||
pipecat-ai[fish]
|
||||
pipecat-ai[gladia]
|
||||
pipecat-ai[google]
|
||||
pipecat-ai[grok]
|
||||
pipecat-ai[groq]
|
||||
# pipecat-ai[krisp] # Mocked
|
||||
pipecat-ai[koala]
|
||||
# pipecat-ai[krisp] # Mocked instead
|
||||
pipecat-ai[langchain]
|
||||
pipecat-ai[livekit]
|
||||
pipecat-ai[lmnt]
|
||||
pipecat-ai[local]
|
||||
# pipecat-ai[mem0] # Mocked
|
||||
# pipecat-ai[mlx-whisper] # Mocked
|
||||
pipecat-ai[moondream]
|
||||
pipecat-ai[nim]
|
||||
# pipecat-ai[neuphonic] # Mocked
|
||||
pipecat-ai[noisereduce]
|
||||
pipecat-ai[openai]
|
||||
# pipecat-ai[openpipe]
|
||||
@@ -43,9 +36,5 @@ pipecat-ai[riva]
|
||||
pipecat-ai[silero]
|
||||
pipecat-ai[simli]
|
||||
pipecat-ai[soundfile]
|
||||
pipecat-ai[tavus]
|
||||
pipecat-ai[together]
|
||||
# pipecat-ai[ultravox] # Mocked
|
||||
# pipecat-ai[webrtc] # Mocked
|
||||
pipecat-ai[websocket]
|
||||
pipecat-ai[whisper]
|
||||
@@ -18,9 +18,6 @@ AZURE_DALLE_API_KEY=...
|
||||
AZURE_DALLE_ENDPOINT=https://...
|
||||
AZURE_DALLE_MODEL=...
|
||||
|
||||
# Cartesia
|
||||
CARTESIA_API_KEY=...
|
||||
|
||||
# Daily
|
||||
DAILY_API_KEY=...
|
||||
DAILY_SAMPLE_ROOM_URL=https://...
|
||||
@@ -29,9 +26,6 @@ DAILY_SAMPLE_ROOM_URL=https://...
|
||||
ELEVENLABS_API_KEY=...
|
||||
ELEVENLABS_VOICE_ID=...
|
||||
|
||||
# Neuphonic
|
||||
NEUPHONIC_API_KEY=...
|
||||
|
||||
# Fal
|
||||
FAL_KEY=...
|
||||
|
||||
@@ -90,10 +84,3 @@ ASSEMBLYAI_API_KEY=...
|
||||
|
||||
# OpenRouter
|
||||
OPENROUTER_API_KEY=...
|
||||
|
||||
# Piper
|
||||
PIPER_BASE_URL=...
|
||||
|
||||
# Smart turn
|
||||
LOCAL_SMART_TURN_MODEL_PATH=
|
||||
REMOTE_SMART_TURN_URL=
|
||||
@@ -12,7 +12,7 @@
|
||||
"@daily-co/daily-js": "0.74.0"
|
||||
},
|
||||
"devDependencies": {
|
||||
"vite": "^6.0.9"
|
||||
"vite": "^6.0.2"
|
||||
}
|
||||
},
|
||||
"node_modules/@babel/runtime": {
|
||||
@@ -1007,14 +1007,15 @@
|
||||
}
|
||||
},
|
||||
"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": "6.0.7",
|
||||
"resolved": "https://registry.npmjs.org/vite/-/vite-6.0.7.tgz",
|
||||
"integrity": "sha512-RDt8r/7qx9940f8FcOIAH9PTViRrghKaK2K1jY3RaAURrEUbm9Du1mJ72G+jlhtG3WwodnfzY8ORQZbBavZEAQ==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"esbuild": "^0.24.2",
|
||||
"postcss": "^8.5.1",
|
||||
"rollup": "^4.30.1"
|
||||
"postcss": "^8.4.49",
|
||||
"rollup": "^4.23.0"
|
||||
},
|
||||
"bin": {
|
||||
"vite": "bin/vite.js"
|
||||
|
||||
@@ -12,7 +12,7 @@
|
||||
"license": "ISC",
|
||||
"description": "",
|
||||
"devDependencies": {
|
||||
"vite": "^6.0.9"
|
||||
"vite": "^6.0.2"
|
||||
},
|
||||
"dependencies": {
|
||||
"@daily-co/daily-js": "0.74.0"
|
||||
|
||||
@@ -1 +0,0 @@
|
||||
22.14
|
||||
@@ -1,60 +0,0 @@
|
||||
# React Native Implementation
|
||||
|
||||
Basic implementation using the [Pipecat React Native SDK](https://docs.pipecat.ai/client/react-native/introduction).
|
||||
|
||||
## Usage
|
||||
|
||||
### Expo requirements
|
||||
|
||||
This project cannot be used with an [Expo Go](https://docs.expo.dev/workflow/expo-go/) app because [it requires custom native code](https://docs.expo.io/workflow/customizing/).
|
||||
|
||||
When a project requires custom native code or a config plugin, we need to transition from using [Expo Go](https://docs.expo.dev/workflow/expo-go/)
|
||||
to a [development build](https://docs.expo.dev/development/introduction/).
|
||||
|
||||
More details about the custom native code used by this demo can be found in [rn-daily-js-expo-config-plugin](https://github.com/daily-co/rn-daily-js-expo-config-plugin).
|
||||
|
||||
### Building remotely
|
||||
|
||||
If you do not have experience with Xcode and Android Studio builds or do not have them installed locally on your computer, you will need to follow [this guide from Expo to use EAS Build](https://docs.expo.dev/development/create-development-builds/#create-and-install-eas-build).
|
||||
|
||||
### Building locally
|
||||
|
||||
You will need to have installed locally on your computer:
|
||||
- [Xcode](https://developer.apple.com/xcode/) to build for iOS;
|
||||
- [Android Studio](https://developer.android.com/studio) to build for Android;
|
||||
|
||||
#### Install the demo dependencies
|
||||
|
||||
```bash
|
||||
# Use the version of node specified in .nvmrc
|
||||
nvm i
|
||||
|
||||
# Install dependencies
|
||||
npm i
|
||||
|
||||
# Before a native app can be compiled, the native source code must be generated.
|
||||
npx expo prebuild
|
||||
|
||||
# Configure the environment variable to connect to the local server
|
||||
cp env.example .env
|
||||
# edit .env and add your local ip address, for example: http://192.168.1.16:7860
|
||||
```
|
||||
|
||||
#### Running on Android
|
||||
|
||||
After plugging in an Android device [configured for debugging](https://developer.android.com/studio/debug/dev-options), run the following command:
|
||||
|
||||
```
|
||||
npm run android
|
||||
```
|
||||
|
||||
#### Running on iOS
|
||||
|
||||
Run the following command:
|
||||
|
||||
```
|
||||
npm run ios
|
||||
```
|
||||
|
||||
#### Connect to the server
|
||||
Use the http://localhost:5173 in your app.
|
||||
@@ -1,75 +0,0 @@
|
||||
{
|
||||
"expo": {
|
||||
"name": "bot-ready-rn",
|
||||
"slug": "bot-ready-rn",
|
||||
"version": "1.0.0",
|
||||
"orientation": "portrait",
|
||||
"icon": "./assets/icon.png",
|
||||
"userInterfaceStyle": "light",
|
||||
"splash": {
|
||||
"image": "./assets/splash.png",
|
||||
"resizeMode": "contain",
|
||||
"backgroundColor": "#ffffff"
|
||||
},
|
||||
"updates": {
|
||||
"fallbackToCacheTimeout": 0
|
||||
},
|
||||
"assetBundlePatterns": [
|
||||
"**/*"
|
||||
],
|
||||
"ios": {
|
||||
"supportsTablet": true,
|
||||
"bitcode": false,
|
||||
"bundleIdentifier": "co.daily.expo.BotReady",
|
||||
"infoPlist": {
|
||||
"UIBackgroundModes": [
|
||||
"voip"
|
||||
]
|
||||
},
|
||||
"appleTeamId": "EEBGKV9N3N"
|
||||
},
|
||||
"android": {
|
||||
"adaptiveIcon": {
|
||||
"foregroundImage": "./assets/adaptive-icon.png",
|
||||
"backgroundColor": "#FFFFFF"
|
||||
},
|
||||
"package": "co.daily.expo.BotReady",
|
||||
"permissions": [
|
||||
"android.permission.ACCESS_NETWORK_STATE",
|
||||
"android.permission.BLUETOOTH",
|
||||
"android.permission.CAMERA",
|
||||
"android.permission.INTERNET",
|
||||
"android.permission.MODIFY_AUDIO_SETTINGS",
|
||||
"android.permission.RECORD_AUDIO",
|
||||
"android.permission.SYSTEM_ALERT_WINDOW",
|
||||
"android.permission.WAKE_LOCK",
|
||||
"android.permission.FOREGROUND_SERVICE",
|
||||
"android.permission.FOREGROUND_SERVICE_CAMERA",
|
||||
"android.permission.FOREGROUND_SERVICE_MICROPHONE",
|
||||
"android.permission.FOREGROUND_SERVICE_MEDIA_PROJECTION",
|
||||
"android.permission.POST_NOTIFICATIONS"
|
||||
]
|
||||
},
|
||||
"web": {
|
||||
"favicon": "./assets/favicon.png"
|
||||
},
|
||||
"plugins": [
|
||||
"@config-plugins/react-native-webrtc",
|
||||
"@daily-co/config-plugin-rn-daily-js",
|
||||
[
|
||||
"expo-build-properties",
|
||||
{
|
||||
"android": {
|
||||
"minSdkVersion": 24,
|
||||
"compileSdkVersion": 35,
|
||||
"targetSdkVersion": 34,
|
||||
"buildToolsVersion": "35.0.0"
|
||||
},
|
||||
"ios": {
|
||||
"deploymentTarget": "15.1"
|
||||
}
|
||||
}
|
||||
]
|
||||
]
|
||||
}
|
||||
}
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 17 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 1.4 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 22 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 46 KiB |
@@ -1,7 +0,0 @@
|
||||
module.exports = function(api) {
|
||||
api.cache(true);
|
||||
return {
|
||||
presets: ['babel-preset-expo'],
|
||||
plugins: [["module:react-native-dotenv"]],
|
||||
};
|
||||
};
|
||||
@@ -1 +0,0 @@
|
||||
API_BASE_URL=http://YOUR_LOCAL_IP:7860
|
||||
@@ -1,7 +0,0 @@
|
||||
import { registerRootComponent } from "expo";
|
||||
|
||||
import App from "./src/App";
|
||||
|
||||
// registerRootComponent calls AppRegistry.registerComponent('main', () => App);
|
||||
// It also ensures that the environment is set up appropriately
|
||||
registerRootComponent(App);
|
||||
@@ -1,4 +0,0 @@
|
||||
// Learn more https://docs.expo.io/guides/customizing-metro
|
||||
const { getDefaultConfig } = require('expo/metro-config');
|
||||
|
||||
module.exports = getDefaultConfig(__dirname);
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,31 +0,0 @@
|
||||
{
|
||||
"name": "bot-ready-rn",
|
||||
"version": "1.0.0",
|
||||
"scripts": {
|
||||
"start": "expo start --dev-client",
|
||||
"android": "expo run:android --device",
|
||||
"ios": "expo run:ios --device",
|
||||
"web": "expo start --web"
|
||||
},
|
||||
"dependencies": {
|
||||
"@config-plugins/react-native-webrtc": "^10.0.0",
|
||||
"@daily-co/config-plugin-rn-daily-js": "0.0.7",
|
||||
"@daily-co/react-native-daily-js": "^0.70.0",
|
||||
"@daily-co/react-native-webrtc": "^118.0.3-daily.2",
|
||||
"@react-native-async-storage/async-storage": "1.23.1",
|
||||
"expo": "^52.0.0",
|
||||
"expo-build-properties": "~0.13.1",
|
||||
"expo-dev-client": "~5.0.5",
|
||||
"expo-splash-screen": "~0.29.16",
|
||||
"expo-status-bar": "~2.0.0",
|
||||
"react": "18.3.1",
|
||||
"react-native": "0.76.3",
|
||||
"react-native-background-timer": "^2.4.1",
|
||||
"react-native-dotenv": "^3.4.11",
|
||||
"react-native-get-random-values": "^1.11.0"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@babel/core": "^7.12.9"
|
||||
},
|
||||
"private": true
|
||||
}
|
||||
@@ -1,121 +0,0 @@
|
||||
import React, { useState, useEffect } from 'react';
|
||||
import {SafeAreaView, View, Text, Button, StyleSheet, ScrollView} from 'react-native';
|
||||
import Daily from "@daily-co/react-native-daily-js";
|
||||
import { API_BASE_URL } from "@env";
|
||||
|
||||
const CallScreen = () => {
|
||||
const [connectionStatus, setConnectionStatus] = useState('Disconnected');
|
||||
const [isConnected, setIsConnected] = useState(false);
|
||||
const [callObject, setCallObject] = useState(null);
|
||||
const [logs, setLogs] = useState([]);
|
||||
|
||||
useEffect(() => {
|
||||
if (callObject) {
|
||||
setupTrackListeners(callObject);
|
||||
}
|
||||
}, [callObject]);
|
||||
|
||||
const log = (message) => {
|
||||
setLogs((prevLogs) => [...prevLogs, `${new Date().toISOString()} - ${message}`]);
|
||||
console.log(message);
|
||||
};
|
||||
|
||||
const setupTrackListeners = (callObject) => {
|
||||
callObject.on("joined-meeting", () => {
|
||||
setConnectionStatus('Connected');
|
||||
setIsConnected(true);
|
||||
log('Client connected');
|
||||
});
|
||||
callObject.on("left-meeting", () => {
|
||||
setConnectionStatus('Disconnected');
|
||||
setIsConnected(false);
|
||||
log('Client disconnected');
|
||||
});
|
||||
callObject.on("participant-left", () => {
|
||||
// When the bot leaves, we are also disconnecting from the call
|
||||
disconnect().catch((err) => {
|
||||
log(`Failed to disconnect ${err}`);
|
||||
})
|
||||
});
|
||||
// Trigger so the bot can start sending audio
|
||||
callObject.on("track-started", (evt) => {
|
||||
if (evt.track.kind === "audio" && evt.participant.local === false) {
|
||||
handleEventToConsole(evt)
|
||||
log("Sending the message that will trigger the bot to play the audio.")
|
||||
callObject.sendAppMessage("playable")
|
||||
}
|
||||
});
|
||||
callObject.on("error", (evt) => log(`Error: ${evt.error}`));
|
||||
// Other events just for awareness
|
||||
callObject.on("track-stopped", handleEventToConsole);
|
||||
callObject.on("participant-joined", handleEventToConsole);
|
||||
callObject.on("participant-updated", handleEventToConsole);
|
||||
};
|
||||
|
||||
const handleEventToConsole = (evt) => {
|
||||
log(`Received event: ${evt.action}`);
|
||||
};
|
||||
|
||||
const connect = async () => {
|
||||
try {
|
||||
const callObject = Daily.createCallObject({ subscribeToTracksAutomatically: true });
|
||||
setCallObject(callObject);
|
||||
const connectionUrl = `${API_BASE_URL}/connect`
|
||||
const res = await fetch(connectionUrl, { method: "POST", headers: { "Content-Type": "application/json" } });
|
||||
const roomInfo = await res.json();
|
||||
await callObject.join({ url: roomInfo.room_url });
|
||||
} catch (error) {
|
||||
log(`Error connecting: ${error.message}`);
|
||||
}
|
||||
};
|
||||
|
||||
const disconnect = async () => {
|
||||
if (callObject) {
|
||||
try {
|
||||
await callObject.leave();
|
||||
await callObject.destroy();
|
||||
setCallObject(null);
|
||||
} catch (error) {
|
||||
log(`Error disconnecting: ${error.message}`);
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
return (
|
||||
<SafeAreaView style={styles.safeArea}>
|
||||
<View style={styles.container}>
|
||||
<View style={styles.statusBar}>
|
||||
<Text>Status: <Text style={styles.status}>{connectionStatus}</Text></Text>
|
||||
<View style={styles.controls}>
|
||||
<Button
|
||||
title={isConnected ? "Disconnect" : "Connect"}
|
||||
onPress={isConnected ? disconnect : connect}
|
||||
/>
|
||||
</View>
|
||||
</View>
|
||||
|
||||
<View style={styles.debugPanel}>
|
||||
<Text style={styles.debugTitle}>Debug Info</Text>
|
||||
<ScrollView style={styles.debugLog}>
|
||||
{logs.map((logEntry, index) => (
|
||||
<Text key={index} style={styles.logText}>{logEntry}</Text>
|
||||
))}
|
||||
</ScrollView>
|
||||
</View>
|
||||
</View>
|
||||
</SafeAreaView>
|
||||
);
|
||||
};
|
||||
|
||||
const styles = StyleSheet.create({
|
||||
safeArea: { flex: 1, backgroundColor: '#f0f0f0', padding: 20 },
|
||||
container: { flex: 1, margin: 20 },
|
||||
statusBar: { flexDirection: 'row', justifyContent: 'space-between', alignItems: 'center', padding: 10, backgroundColor: '#fff', borderRadius: 8, marginBottom: 20 },
|
||||
status: { fontWeight: 'bold' },
|
||||
controls: { flexDirection: 'row', gap: 10 },
|
||||
debugPanel: { height: '80%', backgroundColor: '#fff', borderRadius: 8, padding: 20},
|
||||
debugTitle: { fontSize: 16, fontWeight: 'bold' },
|
||||
debugLog: { height: '100%', overflow: 'scroll', backgroundColor: '#f8f8f8', padding: 10, borderRadius: 4, fontFamily: 'monospace', fontSize: 12, lineHeight: 1.4 },
|
||||
});
|
||||
|
||||
export default CallScreen;
|
||||
@@ -6,7 +6,6 @@
|
||||
|
||||
import argparse
|
||||
import os
|
||||
from typing import Optional
|
||||
|
||||
import aiohttp
|
||||
|
||||
@@ -19,7 +18,7 @@ async def configure(aiohttp_session: aiohttp.ClientSession):
|
||||
|
||||
|
||||
async def configure_with_args(
|
||||
aiohttp_session: aiohttp.ClientSession, parser: Optional[argparse.ArgumentParser] = None
|
||||
aiohttp_session: aiohttp.ClientSession, parser: argparse.ArgumentParser | None = None
|
||||
):
|
||||
if not parser:
|
||||
parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")
|
||||
|
||||
@@ -18,7 +18,7 @@ from pipecat.frames.frames import AudioRawFrame, EndFrame, OutputAudioRawFrame,
|
||||
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.services.cartesia import CartesiaTTSService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
@@ -31,15 +31,16 @@ logger.add(sys.stderr, level="DEBUG")
|
||||
class SilenceFrame(OutputAudioRawFrame):
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
sample_rate: int,
|
||||
duration: float,
|
||||
audio: bytes = None,
|
||||
sample_rate: int = 16000,
|
||||
num_channels: int = 1,
|
||||
duration: float = 0.1,
|
||||
):
|
||||
# Initialize the parent class with the silent frame's data
|
||||
super().__init__(
|
||||
audio=self.create_silent_audio_frame(sample_rate, 1, duration).audio,
|
||||
audio=self.create_silent_audio_frame(sample_rate, num_channels, duration).audio,
|
||||
sample_rate=sample_rate,
|
||||
num_channels=1,
|
||||
num_channels=num_channels,
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
@@ -64,7 +65,7 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
runner = PipelineRunner()
|
||||
@@ -79,10 +80,7 @@ async def main():
|
||||
return
|
||||
await task.queue_frames(
|
||||
[
|
||||
SilenceFrame(
|
||||
sample_rate=task.params.audio_out_sample_rate,
|
||||
duration=0.5,
|
||||
),
|
||||
SilenceFrame(duration=0.5),
|
||||
TTSSpeakFrame(f"Hello there, how are you doing today ?"),
|
||||
EndFrame(),
|
||||
]
|
||||
|
||||
@@ -21,9 +21,9 @@ 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.audio_buffer_processor import AudioBufferProcessor
|
||||
from pipecat.services.canonical.metrics import CanonicalMetricsService
|
||||
from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.services.canonical import CanonicalMetricsService
|
||||
from pipecat.services.elevenlabs import ElevenLabsTTSService
|
||||
from pipecat.services.openai import OpenAILLMService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
@@ -65,6 +65,7 @@ async def main():
|
||||
# English
|
||||
#
|
||||
voice_id="cgSgspJ2msm6clMCkdW9",
|
||||
aiohttp_session=session,
|
||||
#
|
||||
# Spanish
|
||||
#
|
||||
@@ -72,7 +73,7 @@ async def main():
|
||||
# voice_id="gD1IexrzCvsXPHUuT0s3",
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
|
||||
|
||||
messages = [
|
||||
{
|
||||
@@ -113,17 +114,16 @@ async def main():
|
||||
llm,
|
||||
tts,
|
||||
transport.output(),
|
||||
canonical, # uploads audio buffer to Canonical AI for metrics
|
||||
audio_buffer_processor, # captures audio into a buffer
|
||||
canonical, # uploads audio buffer to Canonical AI for metrics
|
||||
context_aggregator.assistant(),
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
|
||||
task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
|
||||
|
||||
@transport.event_handler("on_first_participant_joined")
|
||||
async def on_first_participant_joined(transport, participant):
|
||||
await audio_buffer_processor.start_recording()
|
||||
await transport.capture_participant_transcription(participant["id"])
|
||||
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
||||
|
||||
|
||||
@@ -23,8 +23,8 @@ 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.audio_buffer_processor import AudioBufferProcessor
|
||||
from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.services.elevenlabs import ElevenLabsTTSService
|
||||
from pipecat.services.openai import OpenAILLMService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
@@ -32,16 +32,10 @@ load_dotenv(override=True)
|
||||
logger.remove(0)
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
# Create the recordings directory if it doesn't exist
|
||||
os.makedirs("recordings", exist_ok=True)
|
||||
|
||||
|
||||
async def save_audio(audio: bytes, sample_rate: int, num_channels: int, name: str):
|
||||
async def save_audio(audio: bytes, sample_rate: int, num_channels: int):
|
||||
if len(audio) > 0:
|
||||
filename = os.path.join(
|
||||
"recordings",
|
||||
f"{name}_conversation_recording{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}.wav",
|
||||
)
|
||||
filename = f"conversation_recording{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}.wav"
|
||||
with io.BytesIO() as buffer:
|
||||
with wave.open(buffer, "wb") as wf:
|
||||
wf.setsampwidth(2)
|
||||
@@ -88,6 +82,7 @@ async def main():
|
||||
# English
|
||||
#
|
||||
voice_id="cgSgspJ2msm6clMCkdW9",
|
||||
aiohttp_session=session,
|
||||
#
|
||||
# Spanish
|
||||
#
|
||||
@@ -95,7 +90,7 @@ async def main():
|
||||
# voice_id="gD1IexrzCvsXPHUuT0s3",
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
|
||||
|
||||
messages = [
|
||||
{
|
||||
@@ -114,9 +109,8 @@ async def main():
|
||||
context = OpenAILLMContext(messages)
|
||||
context_aggregator = llm.create_context_aggregator(context)
|
||||
|
||||
# NOTE: Watch out! This will save all the conversation in memory. You
|
||||
# can pass `buffer_size` to get periodic callbacks.
|
||||
audiobuffer = AudioBufferProcessor(enable_turn_audio=True)
|
||||
# Save audio every 10 seconds.
|
||||
audiobuffer = AudioBufferProcessor(buffer_size=480000)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
@@ -130,23 +124,14 @@ async def main():
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
|
||||
task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
|
||||
|
||||
@audiobuffer.event_handler("on_audio_data")
|
||||
async def on_audio_data(buffer, audio, sample_rate, num_channels):
|
||||
await save_audio(audio, sample_rate, num_channels, "full")
|
||||
|
||||
@audiobuffer.event_handler("on_user_turn_audio_data")
|
||||
async def on_user_turn_audio_data(buffer, audio, sample_rate, num_channels):
|
||||
await save_audio(audio, sample_rate, num_channels, "user")
|
||||
|
||||
@audiobuffer.event_handler("on_bot_turn_audio_data")
|
||||
async def on_bot_turn_audio_data(buffer, audio, sample_rate, num_channels):
|
||||
await save_audio(audio, sample_rate, num_channels, "bot")
|
||||
await save_audio(audio, sample_rate, num_channels)
|
||||
|
||||
@transport.event_handler("on_first_participant_joined")
|
||||
async def on_first_participant_joined(transport, participant):
|
||||
await audiobuffer.start_recording()
|
||||
await transport.capture_participant_transcription(participant["id"])
|
||||
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
||||
|
||||
|
||||
@@ -1,9 +1,3 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import argparse
|
||||
import asyncio
|
||||
import os
|
||||
@@ -18,8 +12,8 @@ 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.elevenlabs.tts import ElevenLabsTTSService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.services.elevenlabs import ElevenLabsTTSService
|
||||
from pipecat.services.openai import OpenAILLMService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
@@ -53,7 +47,7 @@ async def main(room_url: str, token: str):
|
||||
voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
|
||||
|
||||
messages = [
|
||||
{
|
||||
@@ -76,7 +70,7 @@ async def main(room_url: str, token: str):
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
|
||||
task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
|
||||
|
||||
@transport.event_handler("on_first_participant_joined")
|
||||
async def on_first_participant_joined(transport, participant):
|
||||
|
||||
@@ -1,9 +1,3 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import os
|
||||
|
||||
import aiohttp
|
||||
|
||||
@@ -1,9 +1,3 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
import sys
|
||||
@@ -16,8 +10,8 @@ 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.openai.llm import OpenAILLMService
|
||||
from pipecat.services.cartesia import CartesiaTTSService
|
||||
from pipecat.services.openai import OpenAILLMService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
@@ -40,10 +34,10 @@ async def main(room_url: str, token: str):
|
||||
)
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY", ""), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121"
|
||||
api_key=os.getenv("CARTESIA_API_KEY", ""), voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22"
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
|
||||
|
||||
messages = [
|
||||
{
|
||||
@@ -68,7 +62,7 @@ async def main(room_url: str, token: str):
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -1,178 +0,0 @@
|
||||
# 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
|
||||
}
|
||||
}'
|
||||
```
|
||||
@@ -1,98 +0,0 @@
|
||||
# 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
|
||||
@@ -1,3 +0,0 @@
|
||||
AGENT_NAME="your-agent-name"
|
||||
PIPECAT_CLOUD_API_KEY="your-daily-api-key"
|
||||
PINLESS_HMAC_SECRET="hmac-secret-pinless-dialin"
|
||||
@@ -1,6 +0,0 @@
|
||||
fastapi
|
||||
uvicorn
|
||||
python-dotenv
|
||||
requests
|
||||
pydantic
|
||||
loguru
|
||||
@@ -1,202 +0,0 @@
|
||||
#
|
||||
# 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")
|
||||
@@ -1,53 +0,0 @@
|
||||
# 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
|
||||
@@ -1,115 +0,0 @@
|
||||
# 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)
|
||||
@@ -1,4 +0,0 @@
|
||||
AGENT_NAME=my-first-agent
|
||||
PIPECAT_CLOUD_API_KEY=your_daily_api_key
|
||||
PINLESS_HMAC_SECRET=your_hmac_secret
|
||||
LOG_LEVEL="info"
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,22 +0,0 @@
|
||||
{
|
||||
"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"
|
||||
}
|
||||
}
|
||||
@@ -1,176 +0,0 @@
|
||||
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: ${error.message}`);
|
||||
return res.status(500).json({ error: 'Internal server error', message: error.message });
|
||||
}
|
||||
}
|
||||
|
||||
// Configure body parser to preserve raw body text
|
||||
export const config = {
|
||||
api: {
|
||||
bodyParser: {
|
||||
sizeLimit: '1mb',
|
||||
},
|
||||
},
|
||||
};
|
||||
@@ -1,6 +0,0 @@
|
||||
import { logger } from '../../lib/utils';
|
||||
|
||||
export default function handler(req, res) {
|
||||
logger.info('Received request to /api');
|
||||
res.status(200).json({ message: 'Hello, World! from ᓚᘏᗢ' });
|
||||
}
|
||||
@@ -1,6 +0,0 @@
|
||||
module.exports = {
|
||||
version: 2,
|
||||
buildCommand: "next build",
|
||||
outputDirectory: ".next",
|
||||
cleanUrls: true
|
||||
};
|
||||
@@ -1,94 +0,0 @@
|
||||
# Python
|
||||
__pycache__/
|
||||
*.py[cod]
|
||||
*$py.class
|
||||
*.so
|
||||
.Python
|
||||
build/
|
||||
dist/
|
||||
*.egg-info/
|
||||
*.egg
|
||||
.installed.cfg
|
||||
.eggs/
|
||||
downloads/
|
||||
lib/
|
||||
lib64/
|
||||
parts/
|
||||
sdist/
|
||||
var/
|
||||
wheels/
|
||||
share/python-wheels/
|
||||
MANIFEST
|
||||
|
||||
# Virtual Environments
|
||||
venv/
|
||||
env/
|
||||
.env
|
||||
.venv/
|
||||
ENV/
|
||||
env.bak/
|
||||
venv.bak/
|
||||
|
||||
# IDE
|
||||
.idea/
|
||||
.vscode/
|
||||
.spyderproject
|
||||
.spyproject
|
||||
.ropeproject
|
||||
|
||||
# Testing and Coverage
|
||||
.coverage
|
||||
.coverage.*
|
||||
htmlcov/
|
||||
.pytest_cache/
|
||||
.tox/
|
||||
.nox/
|
||||
.cache
|
||||
nosetests.xml
|
||||
coverage.xml
|
||||
*.cover
|
||||
.hypothesis/
|
||||
cover/
|
||||
|
||||
# Logs and Databases
|
||||
*.log
|
||||
*.db
|
||||
db.sqlite3
|
||||
db.sqlite3-journal
|
||||
pip-log.txt
|
||||
|
||||
# System Files
|
||||
.DS_Store
|
||||
Thumbs.db
|
||||
desktop.ini
|
||||
*.swp
|
||||
*.swo
|
||||
*.bak
|
||||
*.tmp
|
||||
*~
|
||||
|
||||
# Build and Documentation
|
||||
docs/_build/
|
||||
.pybuilder/
|
||||
target/
|
||||
instance/
|
||||
.webassets-cache
|
||||
.pdm.toml
|
||||
.pdm-python
|
||||
.pdm-build/
|
||||
__pypackages__/
|
||||
|
||||
# Other
|
||||
*.mo
|
||||
*.pot
|
||||
*.sage.py
|
||||
.mypy_cache/
|
||||
.dmypy.json
|
||||
dmypy.json
|
||||
.pyre/
|
||||
.pytype/
|
||||
cython_debug/
|
||||
.ipynb_checkpoints
|
||||
|
||||
# Pipecat cloud
|
||||
.pcc-deploy.toml
|
||||
@@ -1,7 +0,0 @@
|
||||
FROM dailyco/pipecat-base:latest
|
||||
|
||||
COPY ./requirements.txt requirements.txt
|
||||
|
||||
RUN pip install --no-cache-dir --upgrade -r requirements.txt
|
||||
|
||||
COPY ./bot.py bot.py
|
||||
@@ -1,196 +0,0 @@
|
||||
# Pipecat Cloud Starter Project
|
||||
|
||||
[](https://docs.pipecat.daily.co) [](https://discord.gg/dailyco)
|
||||
|
||||
A template voice agent for [Pipecat Cloud](https://www.daily.co/products/pipecat-cloud/) that demonstrates building and deploying a conversational AI agent.
|
||||
|
||||
> **For a detailed step-by-step guide, see our [Quickstart Documentation](https://docs.pipecat.daily.co/quickstart).**
|
||||
|
||||
## Prerequisites
|
||||
|
||||
- Python 3.10+
|
||||
- Linux, MacOS, or Windows Subsystem for Linux (WSL)
|
||||
- [Docker](https://www.docker.com) and a Docker repository (e.g., [Docker Hub](https://hub.docker.com))
|
||||
- A Docker Hub account (or other container registry account)
|
||||
- [Pipecat Cloud](https://pipecat.daily.co) account
|
||||
|
||||
> **Note**: If you haven't installed Docker yet, follow the official installation guides for your platform ([Linux](https://docs.docker.com/engine/install/), [Mac](https://docs.docker.com/desktop/setup/install/mac-install/), [Windows](https://docs.docker.com/desktop/setup/install/windows-install/)). For Docker Hub, [create a free account](https://hub.docker.com/signup) and log in via terminal with `docker login`.
|
||||
|
||||
## Get Started
|
||||
|
||||
### 1. Get the starter project
|
||||
|
||||
Clone the starter project from GitHub:
|
||||
|
||||
```bash
|
||||
git clone https://github.com/daily-co/pipecat-cloud-starter
|
||||
cd pipecat-cloud-starter
|
||||
```
|
||||
|
||||
### 2. Set up your Python environment
|
||||
|
||||
We recommend using a virtual environment to manage your Python dependencies.
|
||||
|
||||
```bash
|
||||
# Create a virtual environment
|
||||
python -m venv .venv
|
||||
|
||||
# Activate it
|
||||
source .venv/bin/activate # On Windows: .venv\Scripts\activate
|
||||
|
||||
# Install the Pipecat Cloud CLI
|
||||
pip install pipecatcloud
|
||||
```
|
||||
|
||||
### 3. Authenticate with Pipecat Cloud
|
||||
|
||||
```bash
|
||||
pcc auth login
|
||||
```
|
||||
|
||||
### 4. Acquire required API keys
|
||||
|
||||
This starter requires the following API keys:
|
||||
|
||||
- **OpenAI API Key**: Get from [platform.openai.com/api-keys](https://platform.openai.com/api-keys)
|
||||
- **Cartesia API Key**: Get from [play.cartesia.ai/keys](https://play.cartesia.ai/keys)
|
||||
- **Daily API Key**: Automatically provided through your Pipecat Cloud account
|
||||
|
||||
### 5. Configure to run locally (optional)
|
||||
|
||||
You can test your agent locally before deploying to Pipecat Cloud:
|
||||
|
||||
```bash
|
||||
# Set environment variables with your API keys
|
||||
export CARTESIA_API_KEY="your_cartesia_key"
|
||||
export DAILY_API_KEY="your_daily_key"
|
||||
export OPENAI_API_KEY="your_openai_key"
|
||||
```
|
||||
|
||||
> Your `DAILY_API_KEY` can be found at [https://pipecat.daily.co](https://pipecat.daily.co) under the `Settings` in the `Daily (WebRTC)` tab.
|
||||
|
||||
First install requirements:
|
||||
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
Then, launch the bot.py script locally:
|
||||
|
||||
```bash
|
||||
LOCAL_RUN=1 python bot.py
|
||||
```
|
||||
|
||||
## Deploy & Run
|
||||
|
||||
### 1. Build and push your Docker image
|
||||
|
||||
```bash
|
||||
# Build the image (targeting ARM architecture for cloud deployment)
|
||||
docker build --platform=linux/arm64 -t my-first-agent:latest .
|
||||
|
||||
# Tag with your Docker username and version
|
||||
docker tag my-first-agent:latest your-username/my-first-agent:0.1
|
||||
|
||||
# Push to Docker Hub
|
||||
docker push your-username/my-first-agent:0.1
|
||||
```
|
||||
|
||||
### 2. Create a secret set for your API keys
|
||||
|
||||
The starter project requires API keys for OpenAI and Cartesia:
|
||||
|
||||
```bash
|
||||
# Copy the example env file
|
||||
cp env.example .env
|
||||
|
||||
# Edit .env to add your API keys:
|
||||
# CARTESIA_API_KEY=your_cartesia_key
|
||||
# OPENAI_API_KEY=your_openai_key
|
||||
|
||||
# Create a secret set from your .env file
|
||||
pcc secrets set my-first-agent-secrets --file .env
|
||||
```
|
||||
|
||||
Alternatively, you can create secrets directly via CLI:
|
||||
|
||||
```bash
|
||||
pcc secrets set my-first-agent-secrets \
|
||||
CARTESIA_API_KEY=your_cartesia_key \
|
||||
OPENAI_API_KEY=your_openai_key
|
||||
```
|
||||
|
||||
### 3. Deploy to Pipecat Cloud
|
||||
|
||||
```bash
|
||||
pcc deploy my-first-agent your-username/my-first-agent:0.1 --secrets my-first-agent-secrets
|
||||
```
|
||||
|
||||
> **Note (Optional)**: For a more maintainable approach, you can use the included `pcc-deploy.toml` file:
|
||||
>
|
||||
> ```toml
|
||||
> agent_name = "my-first-agent"
|
||||
> image = "your-username/my-first-agent:0.1"
|
||||
> secret_set = "my-first-agent-secrets"
|
||||
>
|
||||
> [scaling]
|
||||
> min_instances = 0
|
||||
> ```
|
||||
>
|
||||
> Then simply run `pcc deploy` without additional arguments.
|
||||
|
||||
> **Note**: If your repository is private, you'll need to add credentials:
|
||||
>
|
||||
> ```bash
|
||||
> # Create pull secret (you’ll be prompted for credentials)
|
||||
> pcc secrets image-pull-secret pull-secret https://index.docker.io/v1/
|
||||
>
|
||||
> # Deploy with credentials
|
||||
> pcc deploy my-first-agent your-username/my-first-agent:0.1 --credentials pull-secret
|
||||
> ```
|
||||
|
||||
### 4. Check deployment and scaling (optional)
|
||||
|
||||
By default, your agent will use "scale-to-zero" configuration, which means it may have a cold start of around 10 seconds when first used. By default, idle instances are maintained for 5 minutes before being terminated when using scale-to-zero.
|
||||
|
||||
For more responsive testing, you can scale your deployment to keep a minimum of one instance warm:
|
||||
|
||||
```bash
|
||||
# Ensure at least one warm instance is always available
|
||||
pcc deploy my-first-agent your-username/my-first-agent:0.1 --min-instances 1
|
||||
|
||||
# Check the status of your deployment
|
||||
pcc agent status my-first-agent
|
||||
```
|
||||
|
||||
By default, idle instances are maintained for 5 minutes before being terminated when using scale-to-zero.
|
||||
|
||||
### 5. Create an API key
|
||||
|
||||
```bash
|
||||
# Create a public API key for accessing your agent
|
||||
pcc organizations keys create
|
||||
|
||||
# Set it as the default key to use with your agent
|
||||
pcc organizations keys use
|
||||
```
|
||||
|
||||
### 6. Start your agent
|
||||
|
||||
```bash
|
||||
# Start a session with your agent in a Daily room
|
||||
pcc agent start my-first-agent --use-daily
|
||||
```
|
||||
|
||||
This will return a URL, which you can use to connect to your running agent.
|
||||
|
||||
## Documentation
|
||||
|
||||
For more details on Pipecat Cloud and its capabilities:
|
||||
|
||||
- [Pipecat Cloud Documentation](https://docs.pipecat.daily.co)
|
||||
- [Pipecat Project Documentation](https://docs.pipecat.ai)
|
||||
|
||||
## Support
|
||||
|
||||
Join our [Discord community](https://discord.gg/dailyco) for help and discussions.
|
||||
@@ -1,161 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import os
|
||||
|
||||
import aiohttp
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
from pipecatcloud.agent import DailySessionArguments
|
||||
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.frames.frames import LLMMessagesFrame
|
||||
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.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
# Check if we're in local development mode
|
||||
LOCAL_RUN = os.getenv("LOCAL_RUN")
|
||||
if LOCAL_RUN:
|
||||
import asyncio
|
||||
import webbrowser
|
||||
|
||||
try:
|
||||
from local_runner import configure
|
||||
except ImportError:
|
||||
logger.error("Could not import local_runner module. Local development mode may not work.")
|
||||
|
||||
# Load environment variables
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
async def main(room_url: str, token: str):
|
||||
"""Main pipeline setup and execution function.
|
||||
|
||||
Args:
|
||||
room_url: The Daily room URL
|
||||
token: The Daily room token
|
||||
"""
|
||||
logger.debug("Starting bot in room: {}", room_url)
|
||||
|
||||
transport = DailyTransport(
|
||||
room_url,
|
||||
token,
|
||||
"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="79a125e8-cd45-4c13-8a67-188112f4dd22"
|
||||
)
|
||||
|
||||
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(),
|
||||
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):
|
||||
logger.info("First participant joined: {}", participant["id"])
|
||||
await transport.capture_participant_transcription(participant["id"])
|
||||
# Kick off the conversation.
|
||||
messages.append(
|
||||
{
|
||||
"role": "system",
|
||||
"content": "Please start with 'Hello World' and introduce yourself to the user.",
|
||||
}
|
||||
)
|
||||
await task.queue_frames([LLMMessagesFrame(messages)])
|
||||
|
||||
@transport.event_handler("on_participant_left")
|
||||
async def on_participant_left(transport, participant, reason):
|
||||
logger.info("Participant left: {}", participant)
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner()
|
||||
|
||||
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
|
||||
"""
|
||||
logger.info(f"Bot process initialized {args.room_url} {args.token}")
|
||||
|
||||
try:
|
||||
await main(args.room_url, args.token)
|
||||
logger.info("Bot process completed")
|
||||
except Exception as e:
|
||||
logger.exception(f"Error in bot process: {str(e)}")
|
||||
raise
|
||||
|
||||
|
||||
# Local development functions
|
||||
async def local_main():
|
||||
"""Function for local development testing."""
|
||||
try:
|
||||
async with aiohttp.ClientSession() as session:
|
||||
(room_url, token) = await configure(session)
|
||||
logger.warning("_")
|
||||
logger.warning("_")
|
||||
logger.warning(f"Talk to your voice agent here: {room_url}")
|
||||
logger.warning("_")
|
||||
logger.warning("_")
|
||||
webbrowser.open(room_url)
|
||||
await main(room_url, token)
|
||||
except Exception as e:
|
||||
logger.exception(f"Error in local development mode: {e}")
|
||||
|
||||
|
||||
# Local development entry point
|
||||
if LOCAL_RUN and __name__ == "__main__":
|
||||
try:
|
||||
asyncio.run(local_main())
|
||||
except Exception as e:
|
||||
logger.exception(f"Failed to run in local mode: {e}")
|
||||
@@ -1,2 +0,0 @@
|
||||
CARTESIA_API_KEY=
|
||||
OPENAI_API_KEY=
|
||||
@@ -1,46 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import os
|
||||
|
||||
import aiohttp
|
||||
|
||||
from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper, DailyRoomParams
|
||||
|
||||
|
||||
async def configure(aiohttp_session: aiohttp.ClientSession):
|
||||
(url, token) = await configure_with_args(aiohttp_session)
|
||||
return (url, token)
|
||||
|
||||
|
||||
async def configure_with_args(aiohttp_session: aiohttp.ClientSession = None):
|
||||
key = os.getenv("DAILY_API_KEY")
|
||||
if not key:
|
||||
raise Exception(
|
||||
"No Daily API key specified. 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,
|
||||
)
|
||||
|
||||
room = await daily_rest_helper.create_room(
|
||||
DailyRoomParams(properties={"enable_prejoin_ui": False})
|
||||
)
|
||||
if not room.url:
|
||||
raise HTTPException(status_code=500, detail="Failed to create room")
|
||||
|
||||
url = room.url
|
||||
|
||||
# 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)
|
||||
@@ -1,6 +0,0 @@
|
||||
agent_name = "my-first-agent"
|
||||
image = "your-username/my-first-agent:0.1"
|
||||
secret_set = "my-first-agent-secrets"
|
||||
|
||||
[scaling]
|
||||
min_instances = 0
|
||||
@@ -1,3 +0,0 @@
|
||||
pipecatcloud
|
||||
pipecat-ai[cartesia,daily,openai,silero]>=0.0.58
|
||||
python-dotenv~=1.0.1
|
||||
@@ -1,57 +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.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.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")
|
||||
|
||||
# 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 = PiperTTSService(
|
||||
base_url=os.getenv("PIPER_BASE_URL"), aiohttp_session=session, sample_rate=24000
|
||||
)
|
||||
|
||||
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()
|
||||
@@ -1,59 +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.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):
|
||||
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()
|
||||
@@ -4,53 +4,56 @@
|
||||
# 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.frames.frames import EndFrame, TranscriptionFrame, TTSSpeakFrame
|
||||
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.processors.frame_processor import FrameProcessor
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
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.services.cartesia import CartesiaTTSService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
logger.remove(0)
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
async def run_bot(webrtc_connection: SmallWebRTCConnection):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
# Create a transport using the WebRTC connection
|
||||
transport = SmallWebRTCTransport(
|
||||
webrtc_connection=webrtc_connection,
|
||||
params=TransportParams(
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
)
|
||||
async def main():
|
||||
async with aiohttp.ClientSession() as session:
|
||||
(room_url, _) = await configure(session)
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
transport = DailyTransport(
|
||||
room_url, None, "Say One Thing", DailyParams(audio_out_enabled=True)
|
||||
)
|
||||
|
||||
task = PipelineTask(Pipeline([tts, transport.output()]))
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
# 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()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=False)
|
||||
task = PipelineTask(Pipeline([tts, transport.output()]))
|
||||
|
||||
await runner.run(task)
|
||||
# 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)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from run import main
|
||||
|
||||
main()
|
||||
asyncio.run(main())
|
||||
|
||||
@@ -15,8 +15,9 @@ 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.local.audio import LocalAudioTransport, LocalAudioTransportParams
|
||||
from pipecat.services.cartesia import CartesiaTTSService
|
||||
from pipecat.transports.base_transport import TransportParams
|
||||
from pipecat.transports.local.audio import LocalAudioTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
@@ -25,11 +26,11 @@ logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
|
||||
async def main():
|
||||
transport = LocalAudioTransport(LocalAudioTransportParams(audio_out_enabled=True))
|
||||
transport = LocalAudioTransport(TransportParams(audio_out_enabled=True))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
pipeline = Pipeline([tts, transport.output()])
|
||||
@@ -40,7 +41,7 @@ async def main():
|
||||
await asyncio.sleep(1)
|
||||
await task.queue_frames([TTSSpeakFrame("Hello there, how is it going!"), EndFrame()])
|
||||
|
||||
runner = PipelineRunner(handle_sigint=False if sys.platform == "win32" else True)
|
||||
runner = PipelineRunner()
|
||||
|
||||
await asyncio.gather(runner.run(task), say_something())
|
||||
|
||||
|
||||
@@ -1,9 +1,3 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import argparse
|
||||
import asyncio
|
||||
import os
|
||||
@@ -18,7 +12,7 @@ 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.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.cartesia import CartesiaTTSService
|
||||
from pipecat.transports.services.livekit import LiveKitParams, LiveKitTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
@@ -89,7 +83,7 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
runner = PipelineRunner()
|
||||
|
||||
@@ -4,49 +4,51 @@
|
||||
# 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.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.base_transport import TransportParams
|
||||
from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
|
||||
from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
|
||||
from pipecat.services.riva import FastPitchTTSService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
logger.remove(0)
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
async def run_bot(webrtc_connection: SmallWebRTCConnection):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
# Create a transport using the WebRTC connection
|
||||
transport = SmallWebRTCTransport(
|
||||
webrtc_connection=webrtc_connection,
|
||||
params=TransportParams(
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
)
|
||||
async def main():
|
||||
async with aiohttp.ClientSession() as session:
|
||||
(room_url, _) = await configure(session)
|
||||
|
||||
tts = FastPitchTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
|
||||
transport = DailyTransport(
|
||||
room_url, None, "Say One Thing", DailyParams(audio_out_enabled=True)
|
||||
)
|
||||
|
||||
task = PipelineTask(Pipeline([tts, transport.output()]))
|
||||
tts = FastPitchTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
|
||||
|
||||
# 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()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=False)
|
||||
task = PipelineTask(Pipeline([tts, transport.output()]))
|
||||
|
||||
await runner.run(task)
|
||||
# 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)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from run import main
|
||||
|
||||
main()
|
||||
asyncio.run(main())
|
||||
|
||||
@@ -4,62 +4,61 @@
|
||||
# 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.frames.frames import EndFrame, LLMMessagesFrame
|
||||
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.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
|
||||
from pipecat.services.cartesia import CartesiaTTSService
|
||||
from pipecat.services.openai 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 run_bot(webrtc_connection: SmallWebRTCConnection):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
# Create a transport using the WebRTC connection
|
||||
transport = SmallWebRTCTransport(
|
||||
webrtc_connection=webrtc_connection,
|
||||
params=TransportParams(
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
)
|
||||
async def main():
|
||||
async with aiohttp.ClientSession() as session:
|
||||
(room_url, _) = await configure(session)
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
transport = DailyTransport(
|
||||
room_url, None, "Say One Thing From an LLM", DailyParams(audio_out_enabled=True)
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are an LLM in a WebRTC session, and this is a 'hello world' demo. Say hello to the world.",
|
||||
}
|
||||
]
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
|
||||
|
||||
task = PipelineTask(Pipeline([llm, tts, transport.output()]))
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are an LLM in a WebRTC session, and this is a 'hello world' demo. Say hello to the world.",
|
||||
}
|
||||
]
|
||||
|
||||
# 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()])
|
||||
runner = PipelineRunner()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=False)
|
||||
task = PipelineTask(Pipeline([llm, tts, transport.output()]))
|
||||
|
||||
await runner.run(task)
|
||||
@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)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from run import main
|
||||
|
||||
main()
|
||||
asyncio.run(main())
|
||||
|
||||
@@ -4,67 +4,59 @@
|
||||
# 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.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.base_transport import TransportParams
|
||||
from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
|
||||
from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
|
||||
from pipecat.services.fal import FalImageGenService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
logger.remove(0)
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
async def run_bot(webrtc_connection: SmallWebRTCConnection):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
# Create a transport using the WebRTC connection
|
||||
transport = SmallWebRTCTransport(
|
||||
webrtc_connection=webrtc_connection,
|
||||
params=TransportParams(
|
||||
camera_out_enabled=True,
|
||||
camera_out_width=1024,
|
||||
camera_out_height=1024,
|
||||
),
|
||||
)
|
||||
|
||||
# Create an HTTP session
|
||||
async def main():
|
||||
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()]))
|
||||
|
||||
# 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):
|
||||
@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"))
|
||||
|
||||
@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")
|
||||
@transport.event_handler("on_participant_left")
|
||||
async def on_participant_left(transport, participant, reason):
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=False)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from run import main
|
||||
|
||||
main()
|
||||
asyncio.run(main())
|
||||
|
||||
@@ -17,8 +17,9 @@ 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.local.tk import TkLocalTransport, TkTransportParams
|
||||
from pipecat.services.fal import FalImageGenService
|
||||
from pipecat.transports.base_transport import TransportParams
|
||||
from pipecat.transports.local.tk import TkLocalTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
@@ -33,9 +34,7 @@ async def main():
|
||||
|
||||
transport = TkLocalTransport(
|
||||
tk_root,
|
||||
TkTransportParams(
|
||||
camera_out_enabled=True, camera_out_width=1024, camera_out_height=1024
|
||||
),
|
||||
TransportParams(camera_out_enabled=True, camera_out_width=1024, camera_out_height=1024),
|
||||
)
|
||||
|
||||
imagegen = FalImageGenService(
|
||||
|
||||
@@ -4,67 +4,61 @@
|
||||
# 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.frames.frames import TextFrame
|
||||
from pipecat.frames.frames import EndFrame, 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.base_transport import TransportParams
|
||||
from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
|
||||
from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
|
||||
from pipecat.services.google import GoogleImageGenService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
logger.remove(0)
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
async def run_bot(webrtc_connection: SmallWebRTCConnection):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
# Create a transport using the WebRTC connection
|
||||
transport = SmallWebRTCTransport(
|
||||
webrtc_connection=webrtc_connection,
|
||||
params=TransportParams(
|
||||
camera_out_enabled=True,
|
||||
camera_out_width=1024,
|
||||
camera_out_height=1024,
|
||||
),
|
||||
)
|
||||
async def main():
|
||||
async with aiohttp.ClientSession() as session:
|
||||
(room_url, _) = await configure(session)
|
||||
|
||||
imagegen = GoogleImageGenService(
|
||||
api_key=os.getenv("GOOGLE_API_KEY"),
|
||||
)
|
||||
transport = DailyTransport(
|
||||
room_url,
|
||||
None,
|
||||
"Show a still frame image",
|
||||
DailyParams(camera_out_enabled=True, camera_out_width=1024, camera_out_height=1024),
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
Pipeline([imagegen, transport.output()]),
|
||||
params=PipelineParams(enable_metrics=True),
|
||||
)
|
||||
imagegen = GoogleImageGenService(
|
||||
api_key=os.getenv("GOOGLE_API_KEY"),
|
||||
)
|
||||
|
||||
# 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"))
|
||||
runner = PipelineRunner()
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
task = PipelineTask(
|
||||
Pipeline([imagegen, transport.output()]), PipelineParams(enable_metrics=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 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"))
|
||||
|
||||
runner = PipelineRunner(handle_sigint=False)
|
||||
@transport.event_handler("on_participant_left")
|
||||
async def on_participant_left(transport, participant, reason):
|
||||
await task.queue_frame(EndFrame())
|
||||
|
||||
await runner.run(task)
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from run import main
|
||||
|
||||
main()
|
||||
asyncio.run(main())
|
||||
|
||||
@@ -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
|
||||
@@ -51,6 +51,7 @@ async def main():
|
||||
)
|
||||
|
||||
elevenlabs_tts = ElevenLabsTTSService(
|
||||
aiohttp_session=session,
|
||||
api_key=os.getenv("ELEVENLABS_API_KEY"),
|
||||
voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
|
||||
)
|
||||
|
||||
@@ -4,12 +4,15 @@
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import asyncio
|
||||
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,
|
||||
@@ -24,15 +27,16 @@ from pipecat.pipeline.sync_parallel_pipeline import SyncParallelPipeline
|
||||
from pipecat.pipeline.task import PipelineTask
|
||||
from pipecat.processors.aggregators.sentence import SentenceAggregator
|
||||
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.base_transport import TransportParams
|
||||
from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
|
||||
from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
|
||||
from pipecat.services.cartesia import CartesiaHttpTTSService
|
||||
from pipecat.services.fal import FalImageGenService
|
||||
from pipecat.services.openai import OpenAILLMService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
logger.remove(0)
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
|
||||
@dataclass
|
||||
class MonthFrame(DataFrame):
|
||||
@@ -63,33 +67,27 @@ class MonthPrepender(FrameProcessor):
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
async def run_bot(webrtc_connection: SmallWebRTCConnection):
|
||||
"""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,
|
||||
camera_out_enabled=True,
|
||||
camera_out_width=1024,
|
||||
camera_out_height=1024,
|
||||
),
|
||||
)
|
||||
|
||||
# Create an HTTP session for API calls
|
||||
async def main():
|
||||
async with aiohttp.ClientSession() as session:
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
(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")
|
||||
|
||||
tts = CartesiaHttpTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
imagegen = FalImageGenService(
|
||||
@@ -146,30 +144,14 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
|
||||
frames.append(MonthFrame(month=month))
|
||||
frames.append(LLMMessagesFrame(messages))
|
||||
|
||||
runner = PipelineRunner()
|
||||
|
||||
task = PipelineTask(pipeline)
|
||||
|
||||
# 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)
|
||||
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__":
|
||||
from run import main
|
||||
|
||||
main()
|
||||
asyncio.run(main())
|
||||
|
||||
@@ -27,10 +27,11 @@ from pipecat.pipeline.sync_parallel_pipeline import SyncParallelPipeline
|
||||
from pipecat.pipeline.task import PipelineTask
|
||||
from pipecat.processors.aggregators.sentence import SentenceAggregator
|
||||
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.local.tk import TkLocalTransport, TkTransportParams
|
||||
from pipecat.services.cartesia import CartesiaHttpTTSService
|
||||
from pipecat.services.fal import FalImageGenService
|
||||
from pipecat.services.openai import OpenAILLMService
|
||||
from pipecat.transports.base_transport import TransportParams
|
||||
from pipecat.transports.local.tk import TkLocalTransport, TkOutputTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
@@ -93,11 +94,11 @@ async def main():
|
||||
self.frame = frame
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
|
||||
|
||||
tts = CartesiaHttpTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
imagegen = FalImageGenService(
|
||||
@@ -151,7 +152,7 @@ async def main():
|
||||
|
||||
transport = TkLocalTransport(
|
||||
tk_root,
|
||||
TkTransportParams(
|
||||
TransportParams(
|
||||
audio_out_enabled=True,
|
||||
camera_out_enabled=True,
|
||||
camera_out_width=1024,
|
||||
|
||||
@@ -4,13 +4,17 @@
|
||||
# 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.frames.frames import Frame, MetricsFrame, TranscriptionFrame, TTSSpeakFrame
|
||||
from pipecat.frames.frames import Frame, MetricsFrame
|
||||
from pipecat.metrics.metrics import (
|
||||
LLMUsageMetricsData,
|
||||
ProcessingMetricsData,
|
||||
@@ -22,40 +26,17 @@ from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
|
||||
from pipecat.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.base_transport import TransportParams
|
||||
from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
|
||||
from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
|
||||
from pipecat.services.cartesia import CartesiaTTSService
|
||||
from pipecat.services.openai import OpenAILLMService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# Custom processor that prints a message if it receives a TranscriptionFrame that says "banana"
|
||||
class BananaProcessor(FrameProcessor):
|
||||
"""A custom processor that listens for transcription frames containing the word 'banana'."""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
# Ensure the super method is called first
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
if isinstance(frame, TranscriptionFrame):
|
||||
logger.debug(f"Received transcription frame: {frame.text}")
|
||||
if "banana" in frame.text.lower():
|
||||
logger.info("---- Received 'banana' in transcription frame")
|
||||
|
||||
# Push the frame after processing
|
||||
await self.push_frame(frame)
|
||||
logger.remove(0)
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
|
||||
class MetricsLogger(FrameProcessor):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
@@ -75,86 +56,73 @@ class MetricsLogger(FrameProcessor):
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
async def run_bot(webrtc_connection: SmallWebRTCConnection):
|
||||
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_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
vad_audio_passthrough=True,
|
||||
),
|
||||
)
|
||||
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="79a125e8-cd45-4c13-8a67-188112f4dd22", # British 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"))
|
||||
ml = MetricsLogger()
|
||||
|
||||
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)
|
||||
|
||||
banana = BananaProcessor()
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(),
|
||||
stt,
|
||||
banana,
|
||||
context_aggregator.user(),
|
||||
llm,
|
||||
tts,
|
||||
ml,
|
||||
transport.output(),
|
||||
context_aggregator.assistant(),
|
||||
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.",
|
||||
},
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=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.
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(),
|
||||
context_aggregator.user(),
|
||||
llm,
|
||||
tts,
|
||||
ml,
|
||||
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,
|
||||
PipelineParams(enable_metrics=True, enable_usage_metrics=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"])
|
||||
# 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(handle_sigint=False)
|
||||
await runner.run(task)
|
||||
@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__":
|
||||
from run import main
|
||||
|
||||
main()
|
||||
asyncio.run(main())
|
||||
|
||||
@@ -4,11 +4,15 @@
|
||||
# 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 runner import configure
|
||||
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.frames.frames import (
|
||||
@@ -16,21 +20,22 @@ from pipecat.frames.frames import (
|
||||
BotStoppedSpeakingFrame,
|
||||
Frame,
|
||||
OutputImageRawFrame,
|
||||
TextFrame,
|
||||
)
|
||||
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.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
|
||||
from pipecat.services.cartesia import CartesiaHttpTTSService
|
||||
from pipecat.services.openai import OpenAILLMService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
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):
|
||||
@@ -67,90 +72,83 @@ class ImageSyncAggregator(FrameProcessor):
|
||||
await self.push_frame(frame)
|
||||
|
||||
|
||||
async def run_bot(webrtc_connection: SmallWebRTCConnection):
|
||||
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,
|
||||
camera_out_enabled=True,
|
||||
camera_out_width=1024,
|
||||
camera_out_height=1024,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
vad_audio_passthrough=True,
|
||||
),
|
||||
)
|
||||
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(),
|
||||
),
|
||||
)
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
tts = CartesiaHttpTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British 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.",
|
||||
},
|
||||
]
|
||||
|
||||
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(),
|
||||
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.",
|
||||
},
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
report_only_initial_ttfb=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_frames([context_aggregator.user().get_context_frame()])
|
||||
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_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(),
|
||||
context_aggregator.user(),
|
||||
llm,
|
||||
tts,
|
||||
image_sync_aggregator,
|
||||
transport.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,
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
report_only_initial_ttfb=True,
|
||||
),
|
||||
)
|
||||
|
||||
runner = PipelineRunner(handle_sigint=False)
|
||||
await runner.run(task)
|
||||
@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)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from run import main
|
||||
|
||||
main()
|
||||
asyncio.run(main())
|
||||
|
||||
@@ -9,19 +9,17 @@ import os
|
||||
import sys
|
||||
|
||||
import aiohttp
|
||||
import sentry_sdk
|
||||
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.processors.metrics.sentry import SentryMetrics
|
||||
from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.processors.audio.vad.silero import SileroVAD
|
||||
from pipecat.services.cartesia import CartesiaTTSService
|
||||
from pipecat.services.openai import OpenAILLMService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
@@ -37,39 +35,27 @@ async def main():
|
||||
transport = DailyTransport(
|
||||
room_url,
|
||||
token,
|
||||
"Chatbot",
|
||||
"Respond bot",
|
||||
DailyParams(
|
||||
audio_out_enabled=True,
|
||||
audio_in_enabled=True,
|
||||
camera_out_enabled=False,
|
||||
vad_enabled=True,
|
||||
vad_audio_passthrough=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
audio_out_enabled=True,
|
||||
transcription_enabled=True,
|
||||
),
|
||||
)
|
||||
|
||||
# Initialize Sentry
|
||||
sentry_sdk.init(
|
||||
dsn="your-project-dsn",
|
||||
traces_sample_rate=1.0,
|
||||
vad = SileroVAD()
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
tts = ElevenLabsTTSService(
|
||||
api_key=os.getenv("ELEVENLABS_API_KEY"),
|
||||
voice_id="cgSgspJ2msm6clMCkdW9",
|
||||
metrics=SentryMetrics(),
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
metrics=SentryMetrics(),
|
||||
)
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
|
||||
|
||||
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.",
|
||||
"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.",
|
||||
},
|
||||
]
|
||||
|
||||
@@ -78,7 +64,8 @@ async def main():
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # microphone
|
||||
transport.input(),
|
||||
vad,
|
||||
context_aggregator.user(),
|
||||
llm,
|
||||
tts,
|
||||
@@ -89,17 +76,23 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(allow_interruptions=True, enable_metrics=True),
|
||||
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):
|
||||
print(f"Participant left: {participant}")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner()
|
||||
@@ -4,103 +4,99 @@
|
||||
# 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.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
|
||||
from pipecat.services.cartesia import CartesiaTTSService
|
||||
from pipecat.services.openai 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 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,
|
||||
),
|
||||
)
|
||||
async def main():
|
||||
async with aiohttp.ClientSession() as session:
|
||||
(room_url, token) = await configure(session)
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
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
|
||||
)
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British 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.",
|
||||
},
|
||||
]
|
||||
|
||||
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
|
||||
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.",
|
||||
},
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
report_only_initial_ttfb=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.
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
||||
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_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
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):
|
||||
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(handle_sigint=False)
|
||||
@transport.event_handler("on_participant_left")
|
||||
async def on_participant_left(transport, participant, reason):
|
||||
await task.cancel()
|
||||
|
||||
await runner.run(task)
|
||||
runner = PipelineRunner()
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from run import main
|
||||
|
||||
main()
|
||||
asyncio.run(main())
|
||||
|
||||
106
examples/foundational/07a-interruptible-anthropic.py
Normal file
106
examples/foundational/07a-interruptible-anthropic.py
Normal file
@@ -0,0 +1,106 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, 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 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.anthropic import AnthropicLLMService
|
||||
from pipecat.services.cartesia import CartesiaTTSService
|
||||
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="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
llm = AnthropicLLMService(
|
||||
api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-opus-20240229"
|
||||
)
|
||||
|
||||
# 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.",
|
||||
},
|
||||
]
|
||||
|
||||
context = OpenAILLMContext(messages)
|
||||
context_aggregator = llm.create_context_aggregator(context)
|
||||
|
||||
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
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
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())
|
||||
@@ -1,106 +0,0 @@
|
||||
#
|
||||
# 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.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):
|
||||
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()
|
||||
@@ -4,8 +4,11 @@
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import asyncio
|
||||
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
|
||||
@@ -13,6 +16,7 @@ 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
|
||||
@@ -24,15 +28,15 @@ from pipecat.processors.aggregators.llm_response import (
|
||||
LLMUserResponseAggregator,
|
||||
)
|
||||
from pipecat.processors.frameworks.langchain import LangchainProcessor
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
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
|
||||
from pipecat.services.cartesia import CartesiaTTSService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
logger.remove(0)
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
message_store = {}
|
||||
|
||||
|
||||
@@ -42,97 +46,90 @@ def get_session_history(session_id: str) -> BaseChatMessageHistory:
|
||||
return message_store[session_id]
|
||||
|
||||
|
||||
async def run_bot(webrtc_connection: SmallWebRTCConnection):
|
||||
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_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
|
||||
)
|
||||
|
||||
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.",
|
||||
transport = DailyTransport(
|
||||
room_url,
|
||||
token,
|
||||
"Respond bot",
|
||||
DailyParams(
|
||||
audio_out_enabled=True,
|
||||
transcription_enabled=True,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
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)
|
||||
)
|
||||
|
||||
tma_in = LLMUserResponseAggregator()
|
||||
tma_out = LLMAssistantResponseAggregator()
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
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
|
||||
]
|
||||
)
|
||||
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)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
report_only_initial_ttfb=True,
|
||||
),
|
||||
)
|
||||
tma_in = LLMUserResponseAggregator()
|
||||
tma_out = LLMAssistantResponseAggregator()
|
||||
|
||||
@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)])
|
||||
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_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
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):
|
||||
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)])
|
||||
|
||||
runner = PipelineRunner(handle_sigint=False)
|
||||
@transport.event_handler("on_participant_left")
|
||||
async def on_participant_left(transport, participant, reason):
|
||||
await task.cancel()
|
||||
|
||||
await runner.run(task)
|
||||
runner = PipelineRunner()
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from run import main
|
||||
|
||||
main()
|
||||
asyncio.run(main())
|
||||
|
||||
@@ -4,11 +4,15 @@
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import asyncio
|
||||
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,
|
||||
@@ -20,98 +24,93 @@ 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.base_transport import TransportParams
|
||||
from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
|
||||
from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
|
||||
from pipecat.services.deepgram import DeepgramSTTService, DeepgramTTSService
|
||||
from pipecat.services.openai 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 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,
|
||||
),
|
||||
)
|
||||
async def main():
|
||||
async with aiohttp.ClientSession() as session:
|
||||
(room_url, _) = await configure(session)
|
||||
|
||||
stt = DeepgramSTTService(
|
||||
api_key=os.getenv("DEEPGRAM_API_KEY"),
|
||||
live_options=LiveOptions(vad_events=True, utterance_end_ms="1000"),
|
||||
)
|
||||
transport = DailyTransport(
|
||||
room_url,
|
||||
None,
|
||||
"Respond bot",
|
||||
DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
)
|
||||
|
||||
tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
|
||||
stt = DeepgramSTTService(
|
||||
api_key=os.getenv("DEEPGRAM_API_KEY"),
|
||||
live_options=LiveOptions(vad_events=True, utterance_end_ms="1000"),
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
|
||||
|
||||
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")
|
||||
|
||||
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
|
||||
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.",
|
||||
},
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
report_only_initial_ttfb=True,
|
||||
),
|
||||
)
|
||||
context = OpenAILLMContext(messages)
|
||||
context_aggregator = llm.create_context_aggregator(context)
|
||||
|
||||
@stt.event_handler("on_speech_started")
|
||||
async def on_speech_started(stt, *args, **kwargs):
|
||||
await task.queue_frames([BotInterruptionFrame(), UserStartedSpeakingFrame()])
|
||||
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_utterance_end")
|
||||
async def on_utterance_end(stt, *args, **kwargs):
|
||||
await task.queue_frames([StopInterruptionFrame(), UserStoppedSpeakingFrame()])
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
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()])
|
||||
@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_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
@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_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):
|
||||
# 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(handle_sigint=False)
|
||||
@transport.event_handler("on_participant_left")
|
||||
async def on_participant_left(transport, participant, reason):
|
||||
await task.cancel()
|
||||
|
||||
await runner.run(task)
|
||||
runner = PipelineRunner()
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from run import main
|
||||
|
||||
main()
|
||||
asyncio.run(main())
|
||||
|
||||
@@ -4,100 +4,98 @@
|
||||
# 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.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.deepgram.tts import DeepgramTTSService
|
||||
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
|
||||
from pipecat.services.deepgram import DeepgramSTTService, DeepgramTTSService
|
||||
from pipecat.services.openai 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 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,
|
||||
),
|
||||
)
|
||||
async def main():
|
||||
async with aiohttp.ClientSession() as session:
|
||||
(room_url, _) = await configure(session)
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
transport = DailyTransport(
|
||||
room_url,
|
||||
None,
|
||||
"Respond bot",
|
||||
DailyParams(
|
||||
audio_out_enabled=True,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
vad_audio_passthrough=True,
|
||||
),
|
||||
)
|
||||
|
||||
tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
|
||||
|
||||
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")
|
||||
|
||||
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
|
||||
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.",
|
||||
},
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
report_only_initial_ttfb=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.
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
||||
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_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
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):
|
||||
# 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(handle_sigint=False)
|
||||
@transport.event_handler("on_participant_left")
|
||||
async def on_participant_left(transport, participant, reason):
|
||||
await task.cancel()
|
||||
|
||||
await runner.run(task)
|
||||
runner = PipelineRunner()
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from run import main
|
||||
|
||||
main()
|
||||
asyncio.run(main())
|
||||
|
||||
@@ -1,110 +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.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.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,
|
||||
),
|
||||
)
|
||||
|
||||
# Create an HTTP session
|
||||
async with aiohttp.ClientSession() as session:
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = ElevenLabsHttpTTSService(
|
||||
api_key=os.getenv("ELEVENLABS_API_KEY", ""),
|
||||
voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
|
||||
aiohttp_session=session,
|
||||
)
|
||||
|
||||
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()
|
||||
@@ -4,103 +4,99 @@
|
||||
# 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.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
|
||||
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
|
||||
from pipecat.services.elevenlabs import ElevenLabsTTSService
|
||||
from pipecat.services.openai 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 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,
|
||||
),
|
||||
)
|
||||
async def main():
|
||||
async with aiohttp.ClientSession() as session:
|
||||
(room_url, token) = await configure(session)
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
transport = DailyTransport(
|
||||
room_url,
|
||||
token,
|
||||
"Respond bot",
|
||||
DailyParams(
|
||||
audio_out_enabled=True,
|
||||
transcription_enabled=True,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
)
|
||||
|
||||
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"))
|
||||
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(), # Transport user input
|
||||
stt,
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
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.",
|
||||
},
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
report_only_initial_ttfb=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.
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
||||
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_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
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):
|
||||
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(handle_sigint=False)
|
||||
@transport.event_handler("on_participant_left")
|
||||
async def on_participant_left(transport, participant, reason):
|
||||
await task.cancel()
|
||||
|
||||
await runner.run(task)
|
||||
runner = PipelineRunner()
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from run import main
|
||||
|
||||
main()
|
||||
asyncio.run(main())
|
||||
|
||||
@@ -4,104 +4,100 @@
|
||||
# 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.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.services.playht.tts import PlayHTHttpTTSService
|
||||
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.services.openai import OpenAILLMService
|
||||
from pipecat.services.playht import PlayHTHttpTTSService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
logger.remove(0)
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
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,
|
||||
),
|
||||
)
|
||||
async def main():
|
||||
async with aiohttp.ClientSession() as session:
|
||||
(room_url, token) = await configure(session)
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
transport = DailyTransport(
|
||||
room_url,
|
||||
token,
|
||||
"Respond bot",
|
||||
DailyParams(
|
||||
audio_out_enabled=True,
|
||||
transcription_enabled=True,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
)
|
||||
|
||||
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"))
|
||||
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(), # Transport user input
|
||||
stt,
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
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.",
|
||||
},
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
report_only_initial_ttfb=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.
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
||||
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_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
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):
|
||||
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(handle_sigint=False)
|
||||
@transport.event_handler("on_participant_left")
|
||||
async def on_participant_left(transport, participant, reason):
|
||||
await task.cancel()
|
||||
|
||||
await runner.run(task)
|
||||
runner = PipelineRunner()
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from run import main
|
||||
|
||||
main()
|
||||
asyncio.run(main())
|
||||
|
||||
@@ -4,106 +4,102 @@
|
||||
# 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.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.services.playht.tts import PlayHTTTSService
|
||||
from pipecat.services.openai import OpenAILLMService
|
||||
from pipecat.services.playht import PlayHTTTSService
|
||||
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
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
logger.remove(0)
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
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,
|
||||
),
|
||||
)
|
||||
async def main():
|
||||
async with aiohttp.ClientSession() as session:
|
||||
(room_url, token) = await configure(session)
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
transport = DailyTransport(
|
||||
room_url,
|
||||
token,
|
||||
"Respond bot",
|
||||
DailyParams(
|
||||
audio_out_enabled=True,
|
||||
transcription_enabled=True,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
)
|
||||
|
||||
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/d9ff78ba-d016-47f6-b0ef-dd630f59414e/female-cs/manifest.json",
|
||||
params=PlayHTTTSService.InputParams(language=Language.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.",
|
||||
},
|
||||
]
|
||||
|
||||
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
|
||||
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.",
|
||||
},
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
report_only_initial_ttfb=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.
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
||||
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_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
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):
|
||||
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(handle_sigint=False)
|
||||
@transport.event_handler("on_participant_left")
|
||||
async def on_participant_left(transport, participant, reason):
|
||||
await task.cancel()
|
||||
|
||||
await runner.run(task)
|
||||
runner = PipelineRunner()
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from run import main
|
||||
|
||||
main()
|
||||
asyncio.run(main())
|
||||
|
||||
@@ -4,110 +4,108 @@
|
||||
# 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.azure.llm import AzureLLMService
|
||||
from pipecat.services.azure.stt import AzureSTTService
|
||||
from pipecat.services.azure.tts import AzureTTSService
|
||||
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.services.azure import AzureLLMService, AzureSTTService, AzureTTSService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
logger.remove(0)
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
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,
|
||||
),
|
||||
)
|
||||
async def main():
|
||||
async with aiohttp.ClientSession() as session:
|
||||
(room_url, token) = await configure(session)
|
||||
|
||||
stt = AzureSTTService(
|
||||
api_key=os.getenv("AZURE_SPEECH_API_KEY"),
|
||||
region=os.getenv("AZURE_SPEECH_REGION"),
|
||||
)
|
||||
transport = DailyTransport(
|
||||
room_url,
|
||||
token,
|
||||
"Respond bot",
|
||||
DailyParams(
|
||||
audio_out_enabled=True,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
vad_audio_passthrough=True,
|
||||
),
|
||||
)
|
||||
|
||||
tts = AzureTTSService(
|
||||
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"),
|
||||
)
|
||||
|
||||
llm = AzureLLMService(
|
||||
api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
|
||||
endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
|
||||
model=os.getenv("AZURE_CHATGPT_MODEL"),
|
||||
)
|
||||
tts = AzureTTSService(
|
||||
api_key=os.getenv("AZURE_SPEECH_API_KEY"),
|
||||
region=os.getenv("AZURE_SPEECH_REGION"),
|
||||
)
|
||||
|
||||
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 = AzureLLMService(
|
||||
api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
|
||||
endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
|
||||
model=os.getenv("AZURE_CHATGPT_MODEL"),
|
||||
)
|
||||
|
||||
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
|
||||
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.",
|
||||
},
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
report_only_initial_ttfb=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.
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
||||
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_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
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):
|
||||
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(handle_sigint=False)
|
||||
@transport.event_handler("on_participant_left")
|
||||
async def on_participant_left(transport, participant, reason):
|
||||
await task.cancel()
|
||||
|
||||
await runner.run(task)
|
||||
runner = PipelineRunner()
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from run import main
|
||||
|
||||
main()
|
||||
asyncio.run(main())
|
||||
|
||||
@@ -4,52 +4,49 @@
|
||||
# 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.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
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
|
||||
from pipecat.services.openai import OpenAILLMService, OpenAITTSService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
logger.remove(0)
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
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,
|
||||
),
|
||||
)
|
||||
|
||||
# Create an HTTP session
|
||||
async def main():
|
||||
async with aiohttp.ClientSession() as session:
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
(room_url, token) = await configure(session)
|
||||
|
||||
tts = RimeHttpTTSService(
|
||||
api_key=os.getenv("RIME_API_KEY", ""),
|
||||
voice_id="rex",
|
||||
aiohttp_session=session,
|
||||
transport = DailyTransport(
|
||||
room_url,
|
||||
token,
|
||||
"Respond bot",
|
||||
DailyParams(
|
||||
audio_out_enabled=True,
|
||||
audio_out_sample_rate=24000,
|
||||
transcription_enabled=True,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
tts = OpenAITTSService(api_key=os.getenv("OPENAI_API_KEY"), voice="alloy")
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
|
||||
|
||||
messages = [
|
||||
{
|
||||
@@ -64,7 +61,6 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
@@ -75,7 +71,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
@@ -83,28 +79,21 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
|
||||
),
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
@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_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")
|
||||
@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)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from run import main
|
||||
|
||||
main()
|
||||
asyncio.run(main())
|
||||
@@ -1,108 +0,0 @@
|
||||
#
|
||||
# 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.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.openai.llm import OpenAILLMService
|
||||
from pipecat.services.openai.stt import OpenAISTTService
|
||||
from pipecat.services.openai.tts import OpenAITTSService
|
||||
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 = 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")
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
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
|
||||
]
|
||||
)
|
||||
|
||||
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,
|
||||
),
|
||||
)
|
||||
|
||||
@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()
|
||||
@@ -4,109 +4,106 @@
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import asyncio
|
||||
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
|
||||
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.base_transport import TransportParams
|
||||
from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
|
||||
from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
|
||||
from pipecat.services.cartesia import CartesiaTTSService
|
||||
from pipecat.services.openpipe import OpenPipeLLMService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
logger.remove(0)
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
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,
|
||||
),
|
||||
)
|
||||
async def main():
|
||||
async with aiohttp.ClientSession() as session:
|
||||
(room_url, token) = await configure(session)
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
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
|
||||
)
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
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}"},
|
||||
)
|
||||
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}"},
|
||||
)
|
||||
|
||||
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
|
||||
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.",
|
||||
},
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
report_only_initial_ttfb=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.
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
||||
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_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
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):
|
||||
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(handle_sigint=False)
|
||||
@transport.event_handler("on_participant_left")
|
||||
async def on_participant_left(transport, participant, reason):
|
||||
await task.cancel()
|
||||
|
||||
await runner.run(task)
|
||||
runner = PipelineRunner()
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from run import main
|
||||
|
||||
main()
|
||||
asyncio.run(main())
|
||||
|
||||
@@ -4,44 +4,45 @@
|
||||
# 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.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.services.xtts.tts import XTTSService
|
||||
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.services.openai import OpenAILLMService
|
||||
from pipecat.services.xtts import XTTSService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
logger.remove(0)
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
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,
|
||||
),
|
||||
)
|
||||
|
||||
# Create an HTTP session
|
||||
async def main():
|
||||
async with aiohttp.ClientSession() as session:
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
(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 = XTTSService(
|
||||
aiohttp_session=session,
|
||||
@@ -49,7 +50,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
|
||||
base_url="http://localhost:8000",
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
|
||||
|
||||
messages = [
|
||||
{
|
||||
@@ -64,7 +65,6 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
@@ -75,7 +75,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
@@ -83,28 +83,21 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
|
||||
),
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
@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_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")
|
||||
@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)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from run import main
|
||||
|
||||
main()
|
||||
asyncio.run(main())
|
||||
|
||||
@@ -4,111 +4,106 @@
|
||||
# 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.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
|
||||
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
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
logger.remove(0)
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
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,
|
||||
),
|
||||
)
|
||||
async def main():
|
||||
async with aiohttp.ClientSession() as session:
|
||||
(room_url, token) = await configure(session)
|
||||
|
||||
stt = GladiaSTTService(
|
||||
api_key=os.getenv("GLADIA_API_KEY", ""),
|
||||
params=GladiaInputParams(
|
||||
language_config=LanguageConfig(
|
||||
languages=[Language.EN],
|
||||
)
|
||||
),
|
||||
)
|
||||
transport = DailyTransport(
|
||||
room_url,
|
||||
token,
|
||||
"Respond bot",
|
||||
DailyParams(
|
||||
audio_out_enabled=True,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
vad_audio_passthrough=True,
|
||||
),
|
||||
)
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY", ""),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
stt = GladiaSTTService(
|
||||
api_key=os.getenv("GLADIA_API_KEY"),
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY", ""))
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
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.",
|
||||
},
|
||||
]
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
|
||||
|
||||
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
|
||||
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.",
|
||||
},
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
report_only_initial_ttfb=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.
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
||||
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_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
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):
|
||||
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(handle_sigint=False)
|
||||
await runner.run(task)
|
||||
# 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)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from run import main
|
||||
|
||||
main()
|
||||
asyncio.run(main())
|
||||
|
||||
@@ -4,100 +4,97 @@
|
||||
# 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.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.lmnt.tts import LmntTTSService
|
||||
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
|
||||
from pipecat.services.lmnt import LmntTTSService
|
||||
from pipecat.services.openai 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 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,
|
||||
),
|
||||
)
|
||||
async def main():
|
||||
async with aiohttp.ClientSession() as session:
|
||||
(room_url, token) = await configure(session)
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
transport = DailyTransport(
|
||||
room_url,
|
||||
token,
|
||||
"Respond bot",
|
||||
DailyParams(
|
||||
audio_out_enabled=True,
|
||||
audio_out_sample_rate=24000,
|
||||
transcription_enabled=True,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
)
|
||||
|
||||
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"))
|
||||
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(), # Transport user input
|
||||
stt,
|
||||
context_aggregator.user(), # User respones
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
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.",
|
||||
},
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
report_only_initial_ttfb=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.
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
||||
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_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
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):
|
||||
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(handle_sigint=False)
|
||||
@transport.event_handler("on_participant_left")
|
||||
async def on_participant_left(transport, participant, reason):
|
||||
await task.cancel()
|
||||
|
||||
await runner.run(task)
|
||||
runner = PipelineRunner()
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from run import main
|
||||
|
||||
main()
|
||||
asyncio.run(main())
|
||||
|
||||
@@ -1,102 +0,0 @@
|
||||
#
|
||||
# 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.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):
|
||||
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 = 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()
|
||||
115
examples/foundational/07l-interruptible-together.py
Normal file
115
examples/foundational/07l-interruptible-together.py
Normal file
@@ -0,0 +1,115 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, 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 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.services.ai_services import OpenAILLMContext
|
||||
from pipecat.services.cartesia import CartesiaTTSService
|
||||
from pipecat.services.together 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="79a125e8-cd45-4c13-8a67-188112f4dd22", # British 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,
|
||||
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())
|
||||
@@ -4,106 +4,106 @@
|
||||
# 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.aws.tts import PollyTTSService
|
||||
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
|
||||
from pipecat.services.aws import PollyTTSService
|
||||
from pipecat.services.deepgram import DeepgramSTTService
|
||||
from pipecat.services.openai 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 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,
|
||||
),
|
||||
)
|
||||
async def main():
|
||||
async with aiohttp.ClientSession() as session:
|
||||
(room_url, _) = await configure(session)
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
transport = DailyTransport(
|
||||
room_url,
|
||||
None,
|
||||
"Respond bot",
|
||||
DailyParams(
|
||||
audio_out_enabled=True,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
vad_audio_passthrough=True,
|
||||
),
|
||||
)
|
||||
|
||||
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"),
|
||||
)
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_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"),
|
||||
)
|
||||
|
||||
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")
|
||||
|
||||
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
|
||||
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.",
|
||||
},
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
report_only_initial_ttfb=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.
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
||||
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_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
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):
|
||||
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(handle_sigint=False)
|
||||
@transport.event_handler("on_participant_left")
|
||||
async def on_participant_left(transport, participant, reason):
|
||||
await task.cancel()
|
||||
|
||||
await runner.run(task)
|
||||
runner = PipelineRunner()
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from run import main
|
||||
|
||||
main()
|
||||
asyncio.run(main())
|
||||
|
||||
@@ -4,108 +4,105 @@
|
||||
# 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.google.llm import GoogleLLMService
|
||||
from pipecat.services.google.stt import GoogleSTTService
|
||||
from pipecat.services.google.tts import GoogleTTSService
|
||||
from pipecat.services.deepgram import DeepgramSTTService
|
||||
from pipecat.services.google import GoogleTTSService
|
||||
from pipecat.services.openai 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
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
logger.remove(0)
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
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,
|
||||
),
|
||||
)
|
||||
async def main():
|
||||
async with aiohttp.ClientSession() as session:
|
||||
(room_url, _) = await configure(session)
|
||||
|
||||
stt = GoogleSTTService(
|
||||
params=GoogleSTTService.InputParams(languages=Language.EN_US),
|
||||
credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
|
||||
)
|
||||
transport = DailyTransport(
|
||||
room_url,
|
||||
None,
|
||||
"Respond bot",
|
||||
DailyParams(
|
||||
audio_out_enabled=True,
|
||||
audio_out_sample_rate=24000,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
vad_audio_passthrough=True,
|
||||
),
|
||||
)
|
||||
|
||||
tts = GoogleTTSService(
|
||||
voice_id="en-US-Chirp3-HD-Charon",
|
||||
params=GoogleTTSService.InputParams(language=Language.EN_US),
|
||||
credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
|
||||
)
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"))
|
||||
tts = GoogleTTSService(
|
||||
voice_id="en-US-Journey-F",
|
||||
params=GoogleTTSService.InputParams(language=Language.EN_US),
|
||||
)
|
||||
|
||||
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")
|
||||
|
||||
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
|
||||
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.",
|
||||
},
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
report_only_initial_ttfb=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.
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
||||
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_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
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):
|
||||
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(handle_sigint=False)
|
||||
@transport.event_handler("on_participant_left")
|
||||
async def on_participant_left(transport, participant, reason):
|
||||
await task.cancel()
|
||||
|
||||
await runner.run(task)
|
||||
runner = PipelineRunner()
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from run import main
|
||||
|
||||
main()
|
||||
asyncio.run(main())
|
||||
|
||||
@@ -4,105 +4,105 @@
|
||||
# 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.assemblyai.stt import AssemblyAISTTService
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
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
|
||||
from pipecat.services.assemblyai import AssemblyAISTTService
|
||||
from pipecat.services.cartesia import CartesiaTTSService
|
||||
from pipecat.services.openai 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 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,
|
||||
),
|
||||
)
|
||||
async def main():
|
||||
async with aiohttp.ClientSession() as session:
|
||||
(room_url, token) = await configure(session)
|
||||
|
||||
stt = AssemblyAISTTService(
|
||||
api_key=os.getenv("ASSEMBLYAI_API_KEY"),
|
||||
)
|
||||
transport = DailyTransport(
|
||||
room_url,
|
||||
token,
|
||||
"Respond bot",
|
||||
DailyParams(
|
||||
audio_out_enabled=True,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
vad_audio_passthrough=True,
|
||||
),
|
||||
)
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
stt = AssemblyAISTTService(
|
||||
api_key=os.getenv("ASSEMBLYAI_API_KEY"),
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British 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"), model="gpt-4o")
|
||||
|
||||
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
|
||||
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.",
|
||||
},
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
report_only_initial_ttfb=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.
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
||||
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_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
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):
|
||||
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(handle_sigint=False)
|
||||
@transport.event_handler("on_participant_left")
|
||||
async def on_participant_left(transport, participant, reason):
|
||||
await task.cancel()
|
||||
|
||||
await runner.run(task)
|
||||
runner = PipelineRunner()
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from run import main
|
||||
|
||||
main()
|
||||
asyncio.run(main())
|
||||
|
||||
@@ -4,102 +4,100 @@
|
||||
# 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.filters.krisp_filter import KrispFilter
|
||||
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.base_transport import TransportParams
|
||||
from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
|
||||
from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
|
||||
from pipecat.services.deepgram import DeepgramSTTService, DeepgramTTSService
|
||||
from pipecat.services.openai import OpenAILLMService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
from pipecat.vad.silero import SileroVADAnalyzer
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
logger.remove(0)
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
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,
|
||||
audio_in_filter=KrispFilter(),
|
||||
),
|
||||
)
|
||||
async def main():
|
||||
async with aiohttp.ClientSession() as session:
|
||||
(room_url, token) = await configure(session)
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
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(),
|
||||
),
|
||||
)
|
||||
|
||||
tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
|
||||
|
||||
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")
|
||||
|
||||
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
|
||||
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.",
|
||||
},
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
report_only_initial_ttfb=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.
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
||||
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_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
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):
|
||||
# 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(handle_sigint=False)
|
||||
@transport.event_handler("on_participant_left")
|
||||
async def on_participant_left(transport, participant, reason):
|
||||
await task.cancel()
|
||||
|
||||
await runner.run(task)
|
||||
runner = PipelineRunner()
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from run import main
|
||||
|
||||
main()
|
||||
asyncio.run(main())
|
||||
|
||||
@@ -4,103 +4,100 @@
|
||||
# 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.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.services.rime.tts import RimeTTSService
|
||||
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.services.openai import OpenAILLMService
|
||||
from pipecat.services.rime import RimeHttpTTSService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
logger.remove(0)
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
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,
|
||||
),
|
||||
)
|
||||
async def main():
|
||||
async with aiohttp.ClientSession() as session:
|
||||
(room_url, token) = await configure(session)
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
transport = DailyTransport(
|
||||
room_url,
|
||||
token,
|
||||
"Respond bot",
|
||||
DailyParams(
|
||||
audio_out_enabled=True,
|
||||
transcription_enabled=True,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
)
|
||||
|
||||
tts = RimeTTSService(
|
||||
api_key=os.getenv("RIME_API_KEY", ""),
|
||||
voice_id="rex",
|
||||
)
|
||||
tts = RimeHttpTTSService(
|
||||
api_key=os.getenv("RIME_API_KEY", ""),
|
||||
voice_id="rex",
|
||||
params=RimeHttpTTSService.InputParams(reduce_latency=True),
|
||||
)
|
||||
|
||||
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.",
|
||||
},
|
||||
]
|
||||
|
||||
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
|
||||
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.",
|
||||
},
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
report_only_initial_ttfb=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.
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
||||
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_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
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):
|
||||
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(handle_sigint=False)
|
||||
@transport.event_handler("on_participant_left")
|
||||
async def on_participant_left(transport, participant, reason):
|
||||
await task.cancel()
|
||||
|
||||
await runner.run(task)
|
||||
runner = PipelineRunner()
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from run import main
|
||||
|
||||
main()
|
||||
asyncio.run(main())
|
||||
|
||||
@@ -4,100 +4,92 @@
|
||||
# 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.nim.llm import NimLLMService
|
||||
from pipecat.services.riva.stt import ParakeetSTTService
|
||||
from pipecat.services.riva.tts import FastPitchTTSService
|
||||
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.services.nim import NimLLMService
|
||||
from pipecat.services.riva import FastPitchTTSService, ParakeetSTTService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
logger.remove(0)
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
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,
|
||||
),
|
||||
)
|
||||
async def main():
|
||||
async with aiohttp.ClientSession() as session:
|
||||
(room_url, _) = await configure(session)
|
||||
|
||||
stt = ParakeetSTTService(api_key=os.getenv("NVIDIA_API_KEY"))
|
||||
transport = DailyTransport(
|
||||
room_url,
|
||||
None,
|
||||
"Respond bot",
|
||||
DailyParams(
|
||||
audio_out_enabled=True,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
vad_audio_passthrough=True,
|
||||
),
|
||||
)
|
||||
|
||||
llm = NimLLMService(api_key=os.getenv("NVIDIA_API_KEY"), model="meta/llama-3.1-405b-instruct")
|
||||
stt = ParakeetSTTService(api_key=os.getenv("NVIDIA_API_KEY"))
|
||||
|
||||
tts = FastPitchTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
|
||||
llm = NimLLMService(
|
||||
api_key=os.getenv("NVIDIA_API_KEY"), model="meta/llama-3.1-405b-instruct"
|
||||
)
|
||||
|
||||
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.",
|
||||
},
|
||||
]
|
||||
tts = FastPitchTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
|
||||
|
||||
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
|
||||
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.",
|
||||
},
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
report_only_initial_ttfb=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.
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
||||
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_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
task = PipelineTask(pipeline, 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):
|
||||
# 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(handle_sigint=False)
|
||||
@transport.event_handler("on_participant_left")
|
||||
async def on_participant_left(transport, participant, reason):
|
||||
await task.cancel()
|
||||
|
||||
await runner.run(task)
|
||||
runner = PipelineRunner()
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from run import main
|
||||
|
||||
main()
|
||||
asyncio.run(main())
|
||||
|
||||
@@ -4,12 +4,16 @@
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import asyncio
|
||||
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 (
|
||||
@@ -28,15 +32,14 @@ from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
|
||||
from pipecat.processors.frame_processor import FrameProcessor
|
||||
from pipecat.services.google.llm import GoogleLLMService
|
||||
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
|
||||
from pipecat.services.cartesia import CartesiaTTSService
|
||||
from pipecat.services.google import GoogleLLMService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
logger.remove(0)
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
marker = "|----|"
|
||||
system_message = f"""
|
||||
@@ -190,92 +193,89 @@ class TanscriptionContextFixup(FrameProcessor):
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
async def run_bot(webrtc_connection: SmallWebRTCConnection):
|
||||
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,
|
||||
# No transcription at all. just audio input to Gemini!
|
||||
# transcription_enabled=True,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
vad_audio_passthrough=True,
|
||||
),
|
||||
)
|
||||
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,
|
||||
),
|
||||
)
|
||||
|
||||
llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"), model="gemini-2.0-flash-001")
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
tts = GoogleTTSService(
|
||||
voice_id="en-US-Chirp3-HD-Charon",
|
||||
params=GoogleTTSService.InputParams(language=Language.EN_US),
|
||||
credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
|
||||
)
|
||||
llm = GoogleLLMService(
|
||||
model="gemini-1.5-flash-latest",
|
||||
# model="gemini-exp-1114",
|
||||
api_key=os.getenv("GOOGLE_API_KEY"),
|
||||
)
|
||||
|
||||
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,
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": system_message,
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Start by saying hello.",
|
||||
},
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
)
|
||||
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)
|
||||
|
||||
@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()])
|
||||
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_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=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"])
|
||||
# Kick off the conversation.
|
||||
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
||||
|
||||
runner = PipelineRunner(handle_sigint=False)
|
||||
@transport.event_handler("on_participant_left")
|
||||
async def on_participant_left(transport, participant, reason):
|
||||
await task.cancel()
|
||||
|
||||
await runner.run(task)
|
||||
runner = PipelineRunner()
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from run import main
|
||||
|
||||
main()
|
||||
asyncio.run(main())
|
||||
|
||||
@@ -4,103 +4,99 @@
|
||||
# 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.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.fish.tts import FishAudioTTSService
|
||||
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
|
||||
from pipecat.services.fish import FishAudioTTSService
|
||||
from pipecat.services.openai 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 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,
|
||||
),
|
||||
)
|
||||
async def main():
|
||||
async with aiohttp.ClientSession() as session:
|
||||
(room_url, token) = await configure(session)
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
transport = DailyTransport(
|
||||
room_url,
|
||||
token,
|
||||
"Respond bot",
|
||||
DailyParams(
|
||||
audio_out_enabled=True,
|
||||
transcription_enabled=True,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
)
|
||||
|
||||
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"))
|
||||
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(), # Transport user input
|
||||
stt,
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
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.",
|
||||
},
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
report_only_initial_ttfb=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.
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
||||
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_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
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):
|
||||
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(handle_sigint=False)
|
||||
@transport.event_handler("on_participant_left")
|
||||
async def on_participant_left(transport, participant, reason):
|
||||
await task.cancel()
|
||||
|
||||
await runner.run(task)
|
||||
runner = PipelineRunner()
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from run import main
|
||||
|
||||
main()
|
||||
asyncio.run(main())
|
||||
|
||||
@@ -1,95 +0,0 @@
|
||||
#
|
||||
# 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.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.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.ultravox.stt import UltravoxSTTService
|
||||
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)
|
||||
|
||||
# NOTE: This example requires GPU resources to run efficiently.
|
||||
# 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.
|
||||
|
||||
|
||||
# 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_name="fixie-ai/ultravox-v0_5-llama-3_1-8b",
|
||||
hf_token=os.getenv("HF_TOKEN"),
|
||||
)
|
||||
|
||||
|
||||
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(params=VADParams(stop_secs=0.2)),
|
||||
vad_audio_passthrough=True,
|
||||
),
|
||||
)
|
||||
|
||||
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
|
||||
]
|
||||
)
|
||||
|
||||
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")
|
||||
|
||||
@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()
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user