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
@@ -44,14 +44,10 @@ Note:
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such as formatting instructions, command recognition, or structured data extraction.
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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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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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@@ -59,14 +55,15 @@ from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
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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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from pipecat.utils.text.pattern_pair_aggregator import PatternMatch, PatternPairAggregator
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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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# Define voice IDs
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VOICE_IDS = {
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@@ -76,57 +73,57 @@ VOICE_IDS = {
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}
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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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"Multi-voice storyteller",
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DailyParams(
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audio_out_enabled=True,
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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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),
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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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# Create pattern pair aggregator for voice switching
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pattern_aggregator = PatternPairAggregator()
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# Create pattern pair aggregator for voice switching
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pattern_aggregator = PatternPairAggregator()
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# Add pattern for voice switching
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pattern_aggregator.add_pattern_pair(
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pattern_id="voice_tag",
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start_pattern="<voice>",
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end_pattern="</voice>",
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remove_match=True,
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)
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# Add pattern for voice switching
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pattern_aggregator.add_pattern_pair(
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pattern_id="voice_tag",
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start_pattern="<voice>",
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end_pattern="</voice>",
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remove_match=True,
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)
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# Register handler for voice switching
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def on_voice_tag(match: PatternMatch):
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voice_name = match.content.strip().lower()
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if voice_name in VOICE_IDS:
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voice_id = VOICE_IDS[voice_name]
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tts.set_voice(voice_id)
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logger.info(f"Switched to {voice_name} voice")
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else:
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logger.warning(f"Unknown voice: {voice_name}")
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# Register handler for voice switching
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def on_voice_tag(match: PatternMatch):
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voice_name = match.content.strip().lower()
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if voice_name in VOICE_IDS:
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voice_id = VOICE_IDS[voice_name]
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tts.set_voice(voice_id)
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logger.info(f"Switched to {voice_name} voice")
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else:
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logger.warning(f"Unknown voice: {voice_name}")
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pattern_aggregator.on_pattern_match("voice_tag", on_voice_tag)
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pattern_aggregator.on_pattern_match("voice_tag", on_voice_tag)
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# Initialize TTS with narrator voice as default
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id=VOICE_IDS["narrator"],
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text_aggregator=pattern_aggregator,
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)
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stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
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# Initialize LLM
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
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# Initialize TTS with narrator voice as default
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id=VOICE_IDS["narrator"],
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text_aggregator=pattern_aggregator,
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)
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# System prompt for storytelling with voice switching
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system_prompt = """You are an engaging storyteller that uses different voices to bring stories to life.
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# Initialize LLM
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
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# System prompt for storytelling with voice switching
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system_prompt = """You are an engaging storyteller that uses different voices to bring stories to life.
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You have three voices to use, but each has a specific purpose:
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@@ -173,58 +170,60 @@ FOLLOW THESE RULES:
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Remember: Use narrator voice for EVERYTHING except the actual quoted dialogue."""
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# Set up LLM context
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messages = [
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{
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"role": "system",
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"content": system_prompt,
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},
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# Set up LLM context
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messages = [
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{
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"role": "system",
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"content": system_prompt,
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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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# Create pipeline
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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, # TTS with pattern aggregator
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transport.output(),
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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(
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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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report_only_initial_ttfb=True,
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),
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)
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# Create pipeline
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pipeline = Pipeline(
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[
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transport.input(),
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context_aggregator.user(),
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llm,
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tts, # TTS with pattern aggregator
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transport.output(),
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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 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(
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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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report_only_initial_ttfb=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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logger.info(f"First participant joined: {participant['id']}")
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await transport.capture_participant_transcription(participant["id"])
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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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# 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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@transport.event_handler("on_participant_left")
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async def on_participant_left(transport, participant, reason):
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logger.info(f"Participant left: {participant['id']}")
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await task.cancel()
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logger.info(f"Starting storytelling bot at: {room_url}")
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logger.info("Join the room to interact with the bot!")
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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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