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Merge pull request #451 from Canonical-AI-Inc/recording
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Audio recording FrameProcessor
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aconchillo authored Oct 11, 2024
2 parents 66a76af + 4330374 commit dc73b20
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161 changes: 161 additions & 0 deletions examples/canonical-metrics/.gitignore
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# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
recordings/
# C extensions
*.so

# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST

# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec

# Installer logs
pip-log.txt
pip-delete-this-directory.txt

# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/

# Translations
*.mo
*.pot

# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal

# Flask stuff:
instance/
.webassets-cache

# Scrapy stuff:
.scrapy

# Sphinx documentation
docs/_build/

# PyBuilder
.pybuilder/
target/

# Jupyter Notebook
.ipynb_checkpoints

# IPython
profile_default/
ipython_config.py

# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version

# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock

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#pdm.lock
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.pdm.toml

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__pypackages__/

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celerybeat-schedule
celerybeat.pid

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*.sage.py

# Environments
.env
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venv/
ENV/
env.bak/
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# Spyder project settings
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.pytype/

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# PyCharm
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#.idea/
runpod.toml
16 changes: 16 additions & 0 deletions examples/canonical-metrics/Dockerfile
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FROM python:3.10-bullseye

RUN mkdir /app
RUN mkdir /app/assets
RUN mkdir /app/utils
COPY *.py /app/
COPY requirements.txt /app/
copy assets/* /app/assets/
copy utils/* /app/utils/

WORKDIR /app
RUN pip3 install -r requirements.txt

EXPOSE 7860

CMD ["python3", "server.py"]
37 changes: 37 additions & 0 deletions examples/canonical-metrics/README.md
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# Simple Chatbot

<img src="image.png" width="420px">

This app connects you to a chatbot powered by GPT-4, complete with animations generated by Stable Video Diffusion.

See a video of it in action: https://x.com/kwindla/status/1778628911817183509

And a quick video walkthrough of the code: https://www.loom.com/share/13df1967161f4d24ade054e7f8753416

ℹ️ The first time, things might take extra time to get started since VAD (Voice Activity Detection) model needs to be downloaded.

## Get started

```python
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

cp env.example .env # and add your credentials

```

## Run the server

```bash
python server.py
```

Then, visit `http://localhost:7860/start` in your browser to start a chatbot session.

## Build and test the Docker image

```
docker build -t chatbot .
docker run --env-file .env -p 7860:7860 chatbot
```
149 changes: 149 additions & 0 deletions examples/canonical-metrics/bot.py
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#
# Copyright (c) 2024, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#

import asyncio
import os
import sys
import uuid

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 PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
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
from pipecat.vad.silero import SileroVADAnalyzer

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,
"Chatbot",
DailyParams(
audio_out_enabled=True,
audio_in_enabled=True,
camera_out_enabled=False,
vad_enabled=True,
vad_audio_passthrough=True,
vad_analyzer=SileroVADAnalyzer(),
transcription_enabled=True,
#
# Spanish
#
# transcription_settings=DailyTranscriptionSettings(
# language="es",
# tier="nova",
# model="2-general"
# )
),
)

tts = ElevenLabsTTSService(
api_key=os.getenv("ELEVENLABS_API_KEY"),
#
# English
#
voice_id="cgSgspJ2msm6clMCkdW9",
aiohttp_session=session,
#
# Spanish
#
# model="eleven_multilingual_v2",
# voice_id="gD1IexrzCvsXPHUuT0s3",
)

llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")

messages = [
{
"role": "system",
#
# English
#
"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. Keep all your responses to 12 words or fewer.",
#
# Spanish
#
# "content": "Eres Chatbot, un amigable y útil robot. Tu objetivo es demostrar tus capacidades de una manera breve. Tus respuestas se convertiran a audio así que nunca no debes incluir caracteres especiales. Contesta a lo que el usuario pregunte de una manera creativa, útil y breve. Empieza por presentarte a ti mismo.",
},
]

user_response = LLMUserResponseAggregator()
assistant_response = LLMAssistantResponseAggregator()

"""
CanonicalMetrics uses AudioBufferProcessor under the hood to buffer the audio. On
call completion, CanonicalMetrics will send the audio buffer to Canonical for
analysis. Visit https://voice.canonical.chat to learn more.
"""
audio_buffer_processor = AudioBufferProcessor()
canonical = CanonicalMetricsService(
audio_buffer_processor=audio_buffer_processor,
aiohttp_session=session,
api_key=os.getenv("CANONICAL_API_KEY"),
api_url=os.getenv("CANONICAL_API_URL"),
call_id=str(uuid.uuid4()),
assistant="pipecat-chatbot",
assistant_speaks_first=True,
)
pipeline = Pipeline(
[
transport.input(), # microphone
user_response,
llm,
tts,
transport.output(),
audio_buffer_processor, # captures audio into a buffer
canonical, # uploads audio buffer to Canonical AI for metrics
assistant_response,
]
)

task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))

@transport.event_handler("on_first_participant_joined")
async def on_first_participant_joined(transport, participant):
transport.capture_participant_transcription(participant["id"])
await task.queue_frames([LLMMessagesFrame(messages)])

@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
print(f"Participant left: {participant}")
await task.queue_frame(EndFrame())

@transport.event_handler("on_call_state_updated")
async def on_call_state_updated(transport, state):
if state == "left":
await task.queue_frame(EndFrame())

runner = PipelineRunner()

await runner.run(task)


if __name__ == "__main__":
asyncio.run(main())
5 changes: 5 additions & 0 deletions examples/canonical-metrics/env.example
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DAILY_SAMPLE_ROOM_URL=https://yourdomain.daily.co/yourroom # (for joining the bot to the same room repeatedly for local dev)
DAILY_API_KEY=7df...
OPENAI_API_KEY=sk-PL...
ELEVENLABS_API_KEY=aeb...
CANONICAL_API_KEY=can...
5 changes: 5 additions & 0 deletions examples/canonical-metrics/requirements.txt
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python-dotenv
fastapi[all]
uvicorn
pipecat-ai[daily,openai,silero,elevenlabs,canonical]

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