132 lines
4.3 KiB
Python
132 lines
4.3 KiB
Python
#
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# Copyright (c) 2024, Daily
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#
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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 typing import Any, Mapping
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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 pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import LLMMessagesFrame
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from pipecat.pipeline.pipeline import Pipeline
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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.llm_response import (
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LLMAssistantResponseAggregator,
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LLMUserResponseAggregator,
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)
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from pipecat.services.cartesia import CartesiaTTSService
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from pipecat.services.deepgram import DeepgramSTTService
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from pipecat.services.openai import OpenAILLMService
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from pipecat.services.tavus import TavusVideoService
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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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 main():
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async with aiohttp.ClientSession() as session:
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tavus = TavusVideoService(
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api_key=os.getenv("TAVUS_API_KEY"),
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replica_id=os.getenv("TAVUS_REPLICA_ID"),
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persona_id=os.getenv("TAVUS_PERSONA_ID", "pipecat0"),
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session=session,
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)
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# get persona, look up persona_name, set this as the bot name to ignore
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persona_name = await tavus.get_persona_name()
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room_url = await tavus.initialize()
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transport = DailyTransport(
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room_url=room_url,
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token=None,
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bot_name="Pipecat bot",
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params=DailyParams(
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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"))
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="a167e0f3-df7e-4d52-a9c3-f949145efdab",
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)
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llm = OpenAILLMService(model="gpt-4o-mini")
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messages = [
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{
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"role": "system",
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"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.",
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},
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]
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tma_in = LLMUserResponseAggregator(messages)
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tma_out = LLMAssistantResponseAggregator(messages)
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pipeline = Pipeline(
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[
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transport.input(), # Transport user input
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stt, # STT
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tma_in, # User responses
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llm, # LLM
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tts, # TTS
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tavus, # Tavus output layer
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transport.output(), # Transport bot output
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tma_out, # Assistant spoken responses
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]
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)
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task = PipelineTask(
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pipeline,
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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_participant_joined")
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async def on_participant_joined(
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transport: DailyTransport, participant: Mapping[str, Any]
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) -> None:
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# Ignore the Tavus replica's microphone
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if participant.get("info", {}).get("userName", "") == persona_name:
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logger.debug(f"Ignoring {participant['id']}'s microphone")
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await transport.update_subscriptions(
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participant_settings={
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participant["id"]: {
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"media": {"microphone": "unsubscribed"},
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}
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}
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)
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if participant.get("info", {}).get("userName", "") != persona_name:
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# Kick off the conversation.
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messages.append(
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{"role": "system", "content": "Please introduce yourself to the user."}
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)
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await task.queue_frames([LLMMessagesFrame(messages)])
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runner = PipelineRunner()
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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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