67 lines
2.2 KiB
Python
67 lines
2.2 KiB
Python
import argparse
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import asyncio
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import re
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from dailyai.services.daily_transport_service import DailyTransportService
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from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
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from dailyai.queue_frame import QueueFrame, FrameType
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async def main(room_url:str):
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global transport
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global llm
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global tts
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transport = DailyTransportService(
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room_url,
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None,
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"Say Two Things Bot",
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1,
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)
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transport.mic_enabled = True
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transport.mic_sample_rate = 16000
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transport.camera_enabled = False
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llm = AzureLLMService()
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tts = AzureTTSService()
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@transport.event_handler("on_participant_joined")
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async def on_joined(transport, participant):
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if participant["id"] == transport.my_participant_id:
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return
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# queue two pieces of speech: one specified as a text literal,
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# and one generated by an llm. We'll kick off the llm first, and let
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# it generate a response while we're speaking the literal string.
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#
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# Note that in this case, we don't use `run_llm_async` because we're
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# taking advantage of the time spent speaking the first phrase to generate
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# the entire LLM response, and this happens asynchronously in a task.
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llm_response_task = asyncio.create_task(llm.run_llm(
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[{"role": "system", "content": "tell the user a joke about llamas"}]
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))
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async for audio_chunk in tts.run_tts("My friend the LLM is now going to tell a joke about llamas."):
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transport.output_queue.put(QueueFrame(FrameType.AUDIO_FRAME, audio_chunk))
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llm_response = await llm_response_task
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async for audio_chunk in tts.run_tts(llm_response):
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transport.output_queue.put(QueueFrame(FrameType.AUDIO_FRAME, audio_chunk))
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# wait for the output queue to be empty, then leave the meeting
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transport.output_queue.join()
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transport.stop()
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await transport.run()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Simple Daily Bot Sample")
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parser.add_argument(
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"-u", "--url", type=str, required=True, help="URL of the Daily room to join"
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)
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args: argparse.Namespace = parser.parse_args()
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asyncio.run(main(args.url))
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