150 lines
4.6 KiB
Python
150 lines
4.6 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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import wave
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import aiohttp
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from dotenv import load_dotenv
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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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Frame,
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LLMFullResponseEndFrame,
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OutputAudioRawFrame,
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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 (
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OpenAILLMContext,
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OpenAILLMContextFrame,
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)
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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from pipecat.processors.logger import FrameLogger
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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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sounds = {}
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sound_files = ["ding1.wav", "ding2.wav"]
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script_dir = os.path.dirname(__file__)
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for file in sound_files:
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# Build the full path to the image file
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full_path = os.path.join(script_dir, "assets", file)
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# Get the filename without the extension to use as the dictionary key
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filename = os.path.splitext(os.path.basename(full_path))[0]
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# Open the image and convert it to bytes
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with wave.open(full_path) as audio_file:
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sounds[file] = OutputAudioRawFrame(
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audio_file.readframes(-1), audio_file.getframerate(), audio_file.getnchannels()
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)
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class OutboundSoundEffectWrapper(FrameProcessor):
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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 isinstance(frame, LLMFullResponseEndFrame):
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await self.push_frame(sounds["ding1.wav"])
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# In case anything else downstream needs it
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await self.push_frame(frame, direction)
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else:
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await self.push_frame(frame, direction)
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class InboundSoundEffectWrapper(FrameProcessor):
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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 isinstance(frame, OpenAILLMContextFrame):
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await self.push_frame(sounds["ding2.wav"])
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# In case anything else downstream needs it
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await self.push_frame(frame, direction)
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else:
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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, 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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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
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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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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. 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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out_sound = OutboundSoundEffectWrapper()
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in_sound = InboundSoundEffectWrapper()
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fl = FrameLogger("LLM Out")
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fl2 = FrameLogger("Transcription In")
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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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in_sound,
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fl2,
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llm,
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fl,
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tts,
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out_sound,
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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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@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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await tts.say("Hi, I'm listening!")
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await transport.send_audio(sounds["ding1.wav"])
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runner = PipelineRunner()
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task = PipelineTask(pipeline)
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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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