155 lines
5.2 KiB
Python
155 lines
5.2 KiB
Python
#
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# Copyright (c) 2024–2025, 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 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 BotInterruptionFrame, EndFrame
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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.serializers.protobuf import ProtobufFrameSerializer
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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.network.websocket_server import (
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WebsocketServerParams,
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WebsocketServerTransport,
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)
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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 SessionTimeoutHandler:
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"""Handles actions to be performed when a session times out.
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Inputs:
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- task: Pipeline task (used to queue frames).
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- tts: TTS service (used to generate speech output).
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"""
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def __init__(self, task, tts):
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self.task = task
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self.tts = tts
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self.background_tasks = set()
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async def handle_timeout(self, client_address):
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"""Handles the timeout event for a session."""
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try:
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logger.info(f"Connection timeout for {client_address}")
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# Queue a BotInterruptionFrame to notify the user
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await self.task.queue_frames([BotInterruptionFrame()])
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# Send the TTS message to inform the user about the timeout
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await self.tts.say(
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"I'm sorry, we are ending the call now. Please feel free to reach out again if you need assistance."
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)
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# Start the process to gracefully end the call in the background
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end_call_task = asyncio.create_task(self._end_call())
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self.background_tasks.add(end_call_task)
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end_call_task.add_done_callback(self.background_tasks.discard)
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except Exception as e:
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logger.error(f"Error during session timeout handling: {e}")
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async def _end_call(self):
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"""Completes the session termination process after the TTS message."""
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try:
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# Wait for a duration to ensure TTS has completed
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await asyncio.sleep(15)
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# Queue both BotInterruptionFrame and EndFrame to conclude the session
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await self.task.queue_frames([BotInterruptionFrame(), EndFrame()])
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logger.info("TTS completed and EndFrame pushed successfully.")
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except Exception as e:
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logger.error(f"Error during call termination: {e}")
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async def main():
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transport = WebsocketServerTransport(
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params=WebsocketServerParams(
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serializer=ProtobufFrameSerializer(),
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audio_out_enabled=True,
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add_wav_header=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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session_timeout=60 * 3, # 3 minutes
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)
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)
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
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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="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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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 and helpful way.",
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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(), # Websocket input from client
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stt, # Speech-To-Text
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context_aggregator.user(),
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llm, # LLM
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tts, # Text-To-Speech
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transport.output(), # Websocket output to client
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context_aggregator.assistant(),
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]
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)
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task = PipelineTask(
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pipeline,
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params=PipelineParams(
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audio_in_sample_rate=16000,
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audio_out_sample_rate=16000,
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allow_interruptions=True,
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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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# Kick off the conversation.
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messages.append({"role": "system", "content": "Please introduce yourself to the user."})
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await task.queue_frames([context_aggregator.user().get_context_frame()])
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@transport.event_handler("on_session_timeout")
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async def on_session_timeout(transport, client):
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logger.info(f"Entering in timeout for {client.remote_address}")
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timeout_handler = SessionTimeoutHandler(task, tts)
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await timeout_handler.handle_timeout(client)
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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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