138 lines
4.6 KiB
Python
138 lines
4.6 KiB
Python
#
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# Copyright (c) 2025, 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 List
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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.frames.frames import TranscriptionMessage, TranscriptionUpdateFrame
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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.openai_llm_context import OpenAILLMContext
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from pipecat.processors.transcript_processor import TranscriptProcessor
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from pipecat.services.anthropic import AnthropicLLMService
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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.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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class TranscriptHandler:
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"""Simple handler to demonstrate transcript processing.
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Maintains a list of conversation messages and logs them with timestamps.
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"""
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def __init__(self):
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self.messages: List[TranscriptionMessage] = []
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async def on_transcript_update(
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self, processor: TranscriptProcessor, frame: TranscriptionUpdateFrame
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):
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"""Handle new transcript messages.
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Args:
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processor: The TranscriptProcessor that emitted the update
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frame: TranscriptionUpdateFrame containing new messages
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"""
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self.messages.extend(frame.messages)
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# Log the new messages
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logger.info("New transcript messages:")
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for msg in frame.messages:
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timestamp = f"[{msg.timestamp}] " if msg.timestamp else ""
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logger.info(f"{timestamp}{msg.role}: {msg.content}")
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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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transport = DailyTransport(
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room_url,
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None,
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"Respond bot",
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DailyParams(
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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"))
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
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)
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llm = AnthropicLLMService(
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api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-5-sonnet-20241022"
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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 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, helpful, and brief way.",
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},
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{"role": "user", "content": "Say hello."},
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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 transcript processor and handler
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transcript = TranscriptProcessor()
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transcript_handler = TranscriptHandler()
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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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transcript.user(), # User transcripts
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context_aggregator.user(), # User responses
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llm, # LLM
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tts, # TTS
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transport.output(), # Transport bot output
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context_aggregator.assistant(), # Assistant spoken responses
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transcript.assistant(), # Assistant transcripts
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]
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)
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task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
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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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# Kick off the conversation.
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await task.queue_frames([context_aggregator.user().get_context_frame()])
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# Register event handler for transcript updates
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@transcript.event_handler("on_transcript_update")
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async def on_transcript_update(processor, frame):
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await transcript_handler.on_transcript_update(processor, frame)
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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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