138 lines
4.5 KiB
Python
138 lines
4.5 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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import time
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import aiohttp
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from loguru import logger
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from runner import configure
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from pipecat.frames.frames import (
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BotSpeakingFrame,
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EndFrame,
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Frame,
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StartInterruptionFrame,
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StopInterruptionFrame,
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TranscriptionFrame,
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UserStartedSpeakingFrame,
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UserStoppedSpeakingFrame,
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)
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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 PipelineTask
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from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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from pipecat.services.cartesia import CartesiaTTSService
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from pipecat.services.openai import OpenAILLMService
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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logger.remove(0)
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logger.add(sys.stderr, level="DEBUG")
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class DebugProcessor(FrameProcessor):
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def __init__(self, name, **kwargs):
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self._name = name
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super().__init__(**kwargs)
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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await super().process_frame(frame, direction)
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if not (
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isinstance(frame, BotSpeakingFrame)
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):
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logger.debug(f"--- {self._name}: {frame} {direction}")
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await self.push_frame(frame, direction)
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async def main():
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async with aiohttp.ClientSession() as session:
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(room_url, _) = await configure(session)
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transport = DailyTransport(
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room_url, None, "Say One Thing", DailyParams(audio_out_enabled=True)
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)
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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 = OpenAILLMService(api_key=os.environ["OPENAI_API_KEY"], model="gpt-4o")
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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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dp = DebugProcessor("dp")
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context = OpenAILLMContext(messages)
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context_aggregator = llm.create_context_aggregator(context)
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runner = PipelineRunner()
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task = PipelineTask(
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Pipeline(
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[
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dp,
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context_aggregator.user(),
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llm,
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tts,
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transport.output(),
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context_aggregator.assistant(),
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]
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)
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)
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# Register an event handler so we can play the audio when the
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# participant joins.
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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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participant_id = participant.get("info", {}).get("participantId", "")
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await task.queue_frames(
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[
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UserStartedSpeakingFrame(),
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TranscriptionFrame("Tell a joke about dogs.", participant_id, time.time()),
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UserStoppedSpeakingFrame(),
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]
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)
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# await asyncio.sleep(5) # Small delay between frame sets
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# Create frames for 60 seconds
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start_time = time.time()
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while time.time() - start_time < 30:
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elapsed_time = round(time.time() - start_time)
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logger.info(f"Running for {elapsed_time} seconds")
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await asyncio.sleep(5) # Small delay between frame sets
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await task.queue_frames(
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[
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StartInterruptionFrame(),
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TranscriptionFrame("Tell a joke about cats.", participant_id, time.time()),
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StopInterruptionFrame(),
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]
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)
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await asyncio.sleep(5) # Small delay between frame sets
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await task.queue_frames(
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[
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StartInterruptionFrame(),
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TranscriptionFrame("Tell a joke about dogs.", participant_id, time.time()),
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StopInterruptionFrame(),
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]
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)
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await task.queue_frame(EndFrame())
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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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