154 lines
5.2 KiB
Python
154 lines
5.2 KiB
Python
#
|
|
# Copyright (c) 2024, Daily
|
|
#
|
|
# SPDX-License-Identifier: BSD 2-Clause License
|
|
#
|
|
|
|
import asyncio
|
|
import aiohttp
|
|
import os
|
|
import sys
|
|
|
|
from pipecat.frames.frames import LLMMessagesFrame
|
|
from pipecat.pipeline.pipeline import Pipeline
|
|
from pipecat.pipeline.parallel_pipeline import ParallelPipeline
|
|
from pipecat.pipeline.runner import PipelineRunner
|
|
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
|
from pipecat.processors.aggregators.llm_response import (
|
|
LLMAssistantContextAggregator,
|
|
LLMUserContextAggregator
|
|
)
|
|
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
|
|
from pipecat.processors.filters.function_filter import FunctionFilter
|
|
from pipecat.services.elevenlabs import ElevenLabsTTSService
|
|
from pipecat.services.openai import OpenAILLMService
|
|
from pipecat.services.whisper import Model, WhisperSTTService
|
|
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
|
from pipecat.vad.silero import SileroVADAnalyzer
|
|
|
|
from openai.types.chat import ChatCompletionToolParam
|
|
|
|
from runner import configure
|
|
|
|
from loguru import logger
|
|
|
|
from dotenv import load_dotenv
|
|
load_dotenv(override=True)
|
|
|
|
logger.remove(0)
|
|
logger.add(sys.stderr, level="DEBUG")
|
|
|
|
current_language = "English"
|
|
|
|
|
|
async def switch_language(llm, args):
|
|
global current_language
|
|
current_language = args["language"]
|
|
return {"voice": f"Your answers from now on should be in {current_language}."}
|
|
|
|
|
|
async def english_filter(frame) -> bool:
|
|
return current_language == "English"
|
|
|
|
|
|
async def spanish_filter(frame) -> bool:
|
|
return current_language == "Spanish"
|
|
|
|
|
|
async def main(room_url: str, token):
|
|
async with aiohttp.ClientSession() as session:
|
|
transport = DailyTransport(
|
|
room_url,
|
|
token,
|
|
"Pipecat",
|
|
DailyParams(
|
|
audio_in_enabled=True,
|
|
audio_out_enabled=True,
|
|
vad_enabled=True,
|
|
vad_analyzer=SileroVADAnalyzer(),
|
|
vad_audio_passthrough=True
|
|
)
|
|
)
|
|
|
|
stt = WhisperSTTService(model=Model.LARGE)
|
|
|
|
english_tts = ElevenLabsTTSService(
|
|
aiohttp_session=session,
|
|
api_key=os.getenv("ELEVENLABS_API_KEY"),
|
|
voice_id="pNInz6obpgDQGcFmaJgB",
|
|
)
|
|
|
|
spanish_tts = ElevenLabsTTSService(
|
|
aiohttp_session=session,
|
|
api_key=os.getenv("ELEVENLABS_API_KEY"),
|
|
model="eleven_multilingual_v2",
|
|
voice_id="9F4C8ztpNUmXkdDDbz3J",
|
|
)
|
|
|
|
llm = OpenAILLMService(
|
|
api_key=os.getenv("OPENAI_API_KEY"),
|
|
model="gpt-4o")
|
|
llm.register_function("switch_language", switch_language)
|
|
|
|
tools = [
|
|
ChatCompletionToolParam(
|
|
type="function",
|
|
function={
|
|
"name": "switch_language",
|
|
"description": "Switch to another language when the user asks you to",
|
|
"parameters": {
|
|
"type": "object",
|
|
"properties": {
|
|
"language": {
|
|
"type": "string",
|
|
"description": "The language the user wants you to speak",
|
|
},
|
|
},
|
|
"required": ["language"],
|
|
},
|
|
})]
|
|
messages = [
|
|
{
|
|
"role": "system",
|
|
"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 speak the following languages: 'English' and 'Spanish'.",
|
|
},
|
|
]
|
|
|
|
context = OpenAILLMContext(messages, tools)
|
|
tma_in = LLMUserContextAggregator(context)
|
|
tma_out = LLMAssistantContextAggregator(context)
|
|
|
|
pipeline = Pipeline([
|
|
transport.input(), # Transport user input
|
|
stt, # STT
|
|
tma_in, # User responses
|
|
llm, # LLM
|
|
ParallelPipeline( # TTS (bot will speak the chosen language)
|
|
[FunctionFilter(english_filter), english_tts], # English
|
|
[FunctionFilter(spanish_filter), spanish_tts], # Spanish
|
|
),
|
|
transport.output(), # Transport bot output
|
|
tma_out # Assistant spoken responses
|
|
])
|
|
|
|
task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
|
|
|
|
@transport.event_handler("on_first_participant_joined")
|
|
async def on_first_participant_joined(transport, participant):
|
|
transport.capture_participant_transcription(participant["id"])
|
|
# Kick off the conversation.
|
|
messages.append(
|
|
{
|
|
"role": "system",
|
|
"content": f"Please introduce yourself to the user and let them know the languages you speak. Your initial responses should be in {current_language}."})
|
|
await task.queue_frames([LLMMessagesFrame(messages)])
|
|
|
|
runner = PipelineRunner()
|
|
|
|
await runner.run(task)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
(url, token) = configure()
|
|
asyncio.run(main(url, token))
|