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
@@ -4,16 +4,12 @@
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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import asyncio
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import os
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import sys
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from dataclasses import dataclass
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import aiohttp
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import google.ai.generativelanguage as glm
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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.frames.frames import (
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@@ -37,13 +33,12 @@ from pipecat.processors.aggregators.openai_llm_context import (
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from pipecat.processors.frame_processor import FrameProcessor
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from pipecat.services.cartesia.tts import CartesiaTTSService
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from pipecat.services.google.llm import GoogleLLMContext, GoogleLLMService
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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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#
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# The system prompt for the main conversation.
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#
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@@ -273,102 +268,110 @@ class TranscriptionContextFixup(FrameProcessor):
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await self.push_frame(frame, direction)
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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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"Respond bot",
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DailyParams(
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audio_out_enabled=True,
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# No transcription at all. just audio input to Gemini!
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# transcription_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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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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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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)
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conversation_llm = GoogleLLMService(
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name="Conversation",
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model="gemini-2.0-flash-001",
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# model="gemini-exp-1121",
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api_key=os.getenv("GOOGLE_API_KEY"),
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# we can give the GoogleLLMService a system instruction to use directly
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# in the GenerativeModel constructor. Let's do that rather than put
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# our system message in the messages list.
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system_instruction=conversation_system_message,
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)
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input_transcription_llm = GoogleLLMService(
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name="Transcription",
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model="gemini-2.0-flash-001",
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# model="gemini-exp-1121",
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api_key=os.getenv("GOOGLE_API_KEY"),
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system_instruction=transcriber_system_message,
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)
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messages = [
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{
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"role": "user",
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"content": "Start by saying hello.",
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},
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]
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context = OpenAILLMContext(messages)
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context_aggregator = conversation_llm.create_context_aggregator(context)
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audio_collector = UserAudioCollector(context, context_aggregator.user())
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input_transcription_context_filter = InputTranscriptionContextFilter()
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transcription_frames_emitter = InputTranscriptionFrameEmitter()
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fixup_context_messages = TranscriptionContextFixup(context)
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pipeline = Pipeline(
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[
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transport.input(),
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audio_collector,
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context_aggregator.user(),
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ParallelPipeline(
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[ # transcribe
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input_transcription_context_filter,
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input_transcription_llm,
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transcription_frames_emitter,
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],
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[ # conversation inference
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conversation_llm,
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],
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),
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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", # British Reading Lady
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)
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conversation_llm = GoogleLLMService(
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name="Conversation",
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model="gemini-2.0-flash-001",
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# model="gemini-exp-1121",
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api_key=os.getenv("GOOGLE_API_KEY"),
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# we can give the GoogleLLMService a system instruction to use directly
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# in the GenerativeModel constructor. Let's do that rather than put
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# our system message in the messages list.
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system_instruction=conversation_system_message,
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)
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input_transcription_llm = GoogleLLMService(
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name="Transcription",
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model="gemini-2.0-flash-001",
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# model="gemini-exp-1121",
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api_key=os.getenv("GOOGLE_API_KEY"),
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system_instruction=transcriber_system_message,
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)
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messages = [
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{
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"role": "user",
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"content": "Start by saying hello.",
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},
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tts,
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transport.output(),
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context_aggregator.assistant(),
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fixup_context_messages,
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]
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)
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context = OpenAILLMContext(messages)
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context_aggregator = conversation_llm.create_context_aggregator(context)
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audio_collector = UserAudioCollector(context, context_aggregator.user())
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input_transcription_context_filter = InputTranscriptionContextFilter()
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transcription_frames_emitter = InputTranscriptionFrameEmitter()
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fixup_context_messages = TranscriptionContextFixup(context)
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task = PipelineTask(
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pipeline,
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params=PipelineParams(
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allow_interruptions=True,
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enable_metrics=True,
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enable_usage_metrics=True,
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),
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)
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pipeline = Pipeline(
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[
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transport.input(),
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audio_collector,
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context_aggregator.user(),
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ParallelPipeline(
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[ # transcribe
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input_transcription_context_filter,
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input_transcription_llm,
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transcription_frames_emitter,
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],
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[ # conversation inference
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conversation_llm,
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],
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),
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tts,
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transport.output(),
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context_aggregator.assistant(),
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fixup_context_messages,
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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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# Kick off the conversation.
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await task.queue_frames([context_aggregator.user().get_context_frame()])
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task = PipelineTask(
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pipeline,
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params=PipelineParams(
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allow_interruptions=True,
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enable_metrics=True,
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enable_usage_metrics=True,
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),
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)
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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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# Kick off the conversation.
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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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runner = PipelineRunner()
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runner = PipelineRunner(handle_sigint=False)
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await runner.run(task)
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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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