163 lines
5.1 KiB
Python
163 lines
5.1 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 aiohttp
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import numpy as np
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from dotenv import load_dotenv
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from dtmf import detect, generate, model, parse
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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 (
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BotSpeakingFrame,
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Frame,
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InputAudioRawFrame,
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LLMMessagesFrame,
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OutputAudioRawFrame,
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TextFrame,
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TTSAudioRawFrame,
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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 PipelineParams, 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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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 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, InputAudioRawFrame)
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or isinstance(frame, BotSpeakingFrame)
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or isinstance(frame, UserStoppedSpeakingFrame)
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or isinstance(frame, TTSAudioRawFrame)
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or isinstance(frame, TextFrame)
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):
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logger.debug(f"--- DebugProcessor {self._name}: {frame} {direction}")
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await self.push_frame(frame, direction)
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class DTMFProcessor(FrameProcessor):
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def __init__(self, **kwargs):
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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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tones = model.String(
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[
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model.Tone("1"),
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model.Tone("2"),
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model.Tone("3"),
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model.Tone("4"),
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model.Pause(),
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model.Tone("5"),
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model.Tone("6"),
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model.Tone("7"),
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model.Tone("8"),
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model.Tone("9"),
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]
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)
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tone_audio = generate(tones)
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# Convert the generated audio to a numpy array (assuming the generate function returns an iterable of floats)
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audio_data = np.array(list(tone_audio), dtype=np.float32)
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# Create an AudioRawFrame with the audio data
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audio_frame = OutputAudioRawFrame(audio_data, sample_rate=8000, num_channels=1)
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await self.push_frame(audio_frame)
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async def main():
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print(detect, generate, parse)
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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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token,
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"Respond bot",
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DailyParams(
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audio_out_enabled=True,
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transcription_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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),
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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.getenv("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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context = OpenAILLMContext(messages)
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context_aggregator = llm.create_context_aggregator(context)
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dtmf = DTMFProcessor()
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dp = DebugProcessor("dp")
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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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dtmf,
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dp,
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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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task = PipelineTask(
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pipeline,
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PipelineParams(enable_metrics=True, enable_usage_metrics=True),
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)
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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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messages.append({"role": "system", "content": "Please introduce yourself to the user."})
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await task.queue_frames([LLMMessagesFrame(messages)])
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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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