158 lines
5.5 KiB
Python
158 lines
5.5 KiB
Python
#
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# Copyright (c) 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 aiohttp
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from dotenv import load_dotenv
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from loguru import logger
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from openai.types.chat import ChatCompletionToolParam
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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 LLMMessagesFrame
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from pipecat.pipeline.parallel_pipeline import ParallelPipeline
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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.filters.function_filter import FunctionFilter
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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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current_voice = "News Lady"
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async def switch_voice(function_name, tool_call_id, args, llm, context, result_callback):
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global current_voice
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current_voice = args["voice"]
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await result_callback(
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{
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"voice": f"You are now using your {current_voice} voice. Your responses should now be as if you were a {current_voice}."
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}
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)
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async def news_lady_filter(frame) -> bool:
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return current_voice == "News Lady"
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async def british_lady_filter(frame) -> bool:
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return current_voice == "British Lady"
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async def barbershop_man_filter(frame) -> bool:
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return current_voice == "Barbershop Man"
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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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"Pipecat",
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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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news_lady = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="bf991597-6c13-47e4-8411-91ec2de5c466", # Newslady
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)
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british_lady = 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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barbershop_man = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="a0e99841-438c-4a64-b679-ae501e7d6091", # Barbershop Man
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)
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
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llm.register_function("switch_voice", switch_voice)
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tools = [
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ChatCompletionToolParam(
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type="function",
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function={
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"name": "switch_voice",
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"description": "Switch your voice only when the user asks you to",
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"parameters": {
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"type": "object",
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"properties": {
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"voice": {
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"type": "string",
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"description": "The voice the user wants you to use",
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},
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},
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"required": ["voice"],
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},
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},
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)
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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. Respond to what the user said in a creative and helpful way. Your output should not include non-alphanumeric characters. You can do the following voices: 'News Lady', 'British Lady' and 'Barbershop Man'.",
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},
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]
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context = OpenAILLMContext(messages, tools)
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context_aggregator = llm.create_context_aggregator(context)
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pipeline = Pipeline(
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[
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transport.input(), # Transport user input
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context_aggregator.user(), # User responses
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llm, # LLM
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ParallelPipeline( # TTS (one of the following vocies)
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[FunctionFilter(news_lady_filter), news_lady], # News Lady voice
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[FunctionFilter(british_lady_filter), british_lady], # British Lady voice
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[FunctionFilter(barbershop_man_filter), barbershop_man], # Barbershop Man voice
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),
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transport.output(), # Transport bot output
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context_aggregator.assistant(), # Assistant spoken responses
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]
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)
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task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
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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(
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{
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"role": "system",
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"content": f"Please introduce yourself to the user and let them know the voices you can do. Your initial responses should be as if you were a {current_voice}.",
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}
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)
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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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