108 lines
3.3 KiB
Python
108 lines
3.3 KiB
Python
import asyncio
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import aiohttp
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import logging
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import os
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from typing import AsyncGenerator
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from pipecat.pipeline.aggregators import (
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SentenceAggregator,
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)
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from pipecat.pipeline.frames import (
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Frame,
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LLMMessagesFrame,
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TextFrame,
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SendAppMessageFrame,
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)
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from pipecat.pipeline.frame_processor import FrameProcessor
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.transports.daily_transport import DailyTransport
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from pipecat.services.azure_ai_services import AzureTTSService
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from pipecat.services.open_ai_services import OpenAILLMService
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from pipecat.pipeline.aggregators import LLMFullResponseAggregator
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from runner import configure
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from dotenv import load_dotenv
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load_dotenv(override=True)
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logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
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logger = logging.getLogger("pipecat")
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logger.setLevel(logging.DEBUG)
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"""
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This example looks a bit different than the chatbot example, because it isn't waiting on the user to stop talking to start translating.
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It also isn't saving what the user or bot says into the context object for use in subsequent interactions.
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"""
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# We need to use a custom service here to yield LLM frames without saving
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# any context
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class TranslationProcessor(FrameProcessor):
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def __init__(self, language):
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self._language = language
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async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
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if isinstance(frame, TextFrame):
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context = [
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{
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"role": "system",
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"content": f"You will be provided with a sentence in English, and your task is to translate it into {self._language}.",
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},
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{"role": "user", "content": frame.text},
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]
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yield LLMMessagesFrame(context)
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else:
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yield frame
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class TranslationSubtitles(FrameProcessor):
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def __init__(self, language):
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self._language = language
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async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
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if isinstance(frame, TextFrame):
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app_message = {
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"language": self._language,
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"text": frame.text
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}
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yield SendAppMessageFrame(app_message, None)
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yield frame
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else:
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yield frame
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async def main(room_url: str, token):
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async with aiohttp.ClientSession() as session:
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transport = DailyTransport(
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room_url,
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token,
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"Translator",
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duration_minutes=5,
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start_transcription=True,
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mic_enabled=True,
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mic_sample_rate=16000,
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camera_enabled=False,
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)
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tts = AzureTTSService(
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api_key=os.getenv("AZURE_SPEECH_API_KEY"),
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region=os.getenv("AZURE_SPEECH_REGION"),
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voice="es-ES-AlvaroNeural",
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)
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llm = OpenAILLMService(
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api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4-turbo-preview"
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)
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sa = SentenceAggregator()
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tp = TranslationProcessor("Spanish")
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lfra = LLMFullResponseAggregator()
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ts = TranslationSubtitles("spanish")
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pipeline = Pipeline([sa, tp, llm, lfra, ts, tts])
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await transport.run(pipeline)
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if __name__ == "__main__":
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(url, token) = configure()
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asyncio.run(main(url, token))
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