94 lines
3.0 KiB
Python
94 lines
3.0 KiB
Python
#
|
|
# Copyright (c) 2024, Daily
|
|
#
|
|
# SPDX-License-Identifier: BSD 2-Clause License
|
|
#
|
|
|
|
import aiohttp
|
|
import asyncio
|
|
import os
|
|
import sys
|
|
|
|
from pipecat.frames.frames import LLMMessagesFrame
|
|
from pipecat.pipeline.pipeline import Pipeline
|
|
from pipecat.pipeline.runner import PipelineRunner
|
|
from pipecat.pipeline.task import PipelineTask
|
|
from pipecat.processors.aggregators.llm_response import (
|
|
LLMAssistantResponseAggregator,
|
|
LLMUserResponseAggregator
|
|
)
|
|
from pipecat.services.deepgram import DeepgramSTTService
|
|
from pipecat.services.elevenlabs import ElevenLabsTTSService
|
|
from pipecat.services.openai import OpenAILLMService
|
|
from pipecat.transports.network.websocket_server import WebsocketServerParams, WebsocketServerTransport
|
|
from pipecat.vad.silero import SileroVADAnalyzer
|
|
|
|
from loguru import logger
|
|
|
|
from dotenv import load_dotenv
|
|
load_dotenv(override=True)
|
|
|
|
logger.remove(0)
|
|
logger.add(sys.stderr, level="DEBUG")
|
|
|
|
|
|
async def main():
|
|
async with aiohttp.ClientSession() as session:
|
|
transport = WebsocketServerTransport(
|
|
params=WebsocketServerParams(
|
|
audio_out_enabled=True,
|
|
add_wav_header=True,
|
|
vad_enabled=True,
|
|
vad_analyzer=SileroVADAnalyzer(),
|
|
vad_audio_passthrough=True
|
|
)
|
|
)
|
|
|
|
llm = OpenAILLMService(
|
|
api_key=os.getenv("OPENAI_API_KEY"),
|
|
model="gpt-4o")
|
|
|
|
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
|
|
|
tts = ElevenLabsTTSService(
|
|
aiohttp_session=session,
|
|
api_key=os.getenv("ELEVENLABS_API_KEY"),
|
|
voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
|
|
)
|
|
|
|
messages = [
|
|
{
|
|
"role": "system",
|
|
"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.",
|
|
},
|
|
]
|
|
|
|
tma_in = LLMUserResponseAggregator(messages)
|
|
tma_out = LLMAssistantResponseAggregator(messages)
|
|
|
|
pipeline = Pipeline([
|
|
transport.input(), # Websocket input from client
|
|
stt, # Speech-To-Text
|
|
tma_in, # User responses
|
|
llm, # LLM
|
|
tts, # Text-To-Speech
|
|
transport.output(), # Websocket output to client
|
|
tma_out # LLM responses
|
|
])
|
|
|
|
task = PipelineTask(pipeline)
|
|
|
|
@transport.event_handler("on_client_connected")
|
|
async def on_client_connected(transport, client):
|
|
# Kick off the conversation.
|
|
messages.append(
|
|
{"role": "system", "content": "Please introduce yourself to the user."})
|
|
await task.queue_frames([LLMMessagesFrame(messages)])
|
|
|
|
runner = PipelineRunner()
|
|
|
|
await runner.run(task)
|
|
|
|
if __name__ == "__main__":
|
|
asyncio.run(main())
|