130 lines
4.3 KiB
Python
130 lines
4.3 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 argparse
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import os
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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.audio.vad.vad_analyzer import VADParams
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from pipecat.frames.frames import TranscriptionMessage
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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.gemini_multimodal_live.gemini import GeminiMultimodalLiveLLMService
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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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async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
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logger.info(f"Starting bot")
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# Initialize the SmallWebRTCTransport with the connection
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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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# set stop_secs to something roughly similar to the internal setting
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# of the Multimodal Live api, just to align events. This doesn't really
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# matter because we can only use the Multimodal Live API's phrase
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# endpointing, for now.
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vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
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),
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)
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llm = GeminiMultimodalLiveLLMService(
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api_key=os.getenv("GOOGLE_API_KEY"),
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voice_id="Aoede", # Puck, Charon, Kore, Fenrir, Aoede
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# system_instruction="Talk like a pirate."
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transcribe_user_audio=True,
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# inference_on_context_initialization=False,
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)
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context = OpenAILLMContext(
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[
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{
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"role": "user",
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"content": "Say hello. Then ask if I want to hear a joke.",
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},
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# {"role": "assistant", "content": "Hello! Why don't scientists trust atoms?"},
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# {
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# "role": "user",
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# "content": [
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# {
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# "type": "text",
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# "text": "Oh, I know this one: because they make up everything.",
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# }
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# ],
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# },
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],
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)
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context_aggregator = llm.create_context_aggregator(context)
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transcript = TranscriptProcessor()
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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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transcript.user(),
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llm,
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transport.output(),
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transcript.assistant(),
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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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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_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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@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_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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# 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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for msg in frame.messages:
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if isinstance(msg, TranscriptionMessage):
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timestamp = f"[{msg.timestamp}] " if msg.timestamp else ""
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line = f"{timestamp}{msg.role}: {msg.content}"
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logger.info(f"Transcript: {line}")
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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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from run import main
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main()
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