Updating foundation examples to use SmallWebRTCTransport and pipecat-ai-small-webrtc-prebuilt (#1534)
Co-authored-by: Filipi Fuchter <filipi@daily.co>
This commit is contained in:
Mark Backman
@@ -46,18 +46,14 @@ Note:
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handling merged and separate audio tracks respectively.
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"""
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import asyncio
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import datetime
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import io
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import os
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import sys
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import wave
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import aiofiles
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import aiohttp
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from dotenv import load_dotenv
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from loguru import logger
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from runner import configure
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.pipeline.pipeline import Pipeline
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@@ -68,11 +64,11 @@ from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
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from pipecat.services.cartesia.tts import CartesiaTTSService
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from pipecat.services.deepgram.stt import DeepgramSTTService
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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from pipecat.transports.base_transport import TransportParams
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from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
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from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
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load_dotenv(override=True)
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logger.remove(0)
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logger.add(sys.stderr, level="DEBUG")
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async def save_audio_file(audio: bytes, filename: str, sample_rate: int, num_channels: int):
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@@ -89,102 +85,101 @@ async def save_audio_file(audio: bytes, filename: str, sample_rate: int, num_cha
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logger.info(f"Audio saved to {filename}")
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async def main():
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async with aiohttp.ClientSession() as session:
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(room_url, token) = await configure(session)
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async def run_bot(webrtc_connection: SmallWebRTCConnection):
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logger.info(f"Starting bot")
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transport = DailyTransport(
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room_url,
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token,
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"Recording bot",
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DailyParams(
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# audio_in_enabled=True,
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audio_out_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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vad_audio_passthrough=True,
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),
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)
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transport = SmallWebRTCTransport(
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webrtc_connection=webrtc_connection,
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params=TransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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vad_audio_passthrough=True,
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),
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)
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stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"), audio_passthrough=True)
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stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"), audio_passthrough=True)
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",
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)
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",
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)
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4")
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4")
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# Create audio buffer processor
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audiobuffer = AudioBufferProcessor()
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# Create audio buffer processor
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audiobuffer = AudioBufferProcessor()
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messages = [
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{
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"role": "system",
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"content": "You are a helpful assistant demonstrating audio recording capabilities. Keep your responses brief and clear.",
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},
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messages = [
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{
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"role": "system",
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"content": "You are a helpful assistant demonstrating audio recording capabilities. Keep your responses brief and clear.",
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},
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]
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context = OpenAILLMContext(messages)
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context_aggregator = llm.create_context_aggregator(context)
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pipeline = Pipeline(
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[
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transport.input(),
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stt,
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context_aggregator.user(),
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llm,
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tts,
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transport.output(),
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audiobuffer, # Add audio buffer to pipeline
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context_aggregator.assistant(),
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]
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)
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context = OpenAILLMContext(messages)
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context_aggregator = llm.create_context_aggregator(context)
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task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
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pipeline = Pipeline(
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[
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transport.input(),
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stt,
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context_aggregator.user(),
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llm,
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tts,
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transport.output(),
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audiobuffer, # Add audio buffer to pipeline
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context_aggregator.assistant(),
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]
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)
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@transport.event_handler("on_client_connected")
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async def on_client_connected(transport, client):
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logger.info(f"Client connected")
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# Start recording audio
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await audiobuffer.start_recording()
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# Start conversation - empty prompt to let LLM follow system instructions
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await task.queue_frames([context_aggregator.user().get_context_frame()])
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task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info(f"Client disconnected")
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@transport.event_handler("on_first_participant_joined")
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async def on_first_participant_joined(transport, participant):
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await transport.capture_participant_transcription(participant["id"])
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await audiobuffer.start_recording()
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messages.append(
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{
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"role": "system",
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"content": "Greet the user and explain that this conversation will be recorded.",
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}
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)
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await task.queue_frames([context_aggregator.user().get_context_frame()])
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@transport.event_handler("on_client_closed")
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async def on_client_closed(transport, client):
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logger.info(f"Client closed connection")
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await task.cancel()
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@transport.event_handler("on_participant_left")
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async def on_participant_left(transport, participant, reason):
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await audiobuffer.stop_recording()
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await task.cancel()
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# Handler for merged audio
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@audiobuffer.event_handler("on_audio_data")
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async def on_audio_data(buffer, audio, sample_rate, num_channels):
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timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
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filename = f"recordings/merged_{timestamp}.wav"
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os.makedirs("recordings", exist_ok=True)
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await save_audio_file(audio, filename, sample_rate, num_channels)
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# Handler for merged audio
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@audiobuffer.event_handler("on_audio_data")
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async def on_audio_data(buffer, audio, sample_rate, num_channels):
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timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
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filename = f"recordings/merged_{timestamp}.wav"
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os.makedirs("recordings", exist_ok=True)
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await save_audio_file(audio, filename, sample_rate, num_channels)
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# Handler for separate tracks
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@audiobuffer.event_handler("on_track_audio_data")
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async def on_track_audio_data(buffer, user_audio, bot_audio, sample_rate, num_channels):
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timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
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os.makedirs("recordings", exist_ok=True)
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# Handler for separate tracks
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@audiobuffer.event_handler("on_track_audio_data")
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async def on_track_audio_data(buffer, user_audio, bot_audio, sample_rate, num_channels):
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timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
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os.makedirs("recordings", exist_ok=True)
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# Save user audio
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user_filename = f"recordings/user_{timestamp}.wav"
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await save_audio_file(user_audio, user_filename, sample_rate, 1)
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# Save user audio
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user_filename = f"recordings/user_{timestamp}.wav"
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await save_audio_file(user_audio, user_filename, sample_rate, 1)
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# Save bot audio
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bot_filename = f"recordings/bot_{timestamp}.wav"
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await save_audio_file(bot_audio, bot_filename, sample_rate, 1)
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# Save bot audio
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bot_filename = f"recordings/bot_{timestamp}.wav"
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await save_audio_file(bot_audio, bot_filename, sample_rate, 1)
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runner = PipelineRunner()
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await runner.run(task)
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runner = PipelineRunner(handle_sigint=False)
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await runner.run(task)
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if __name__ == "__main__":
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asyncio.run(main())
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from run import main
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main()
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