133 lines
4.1 KiB
Python
133 lines
4.1 KiB
Python
#
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# Copyright (c) 2024, 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 aiohttp
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import asyncio
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import os
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import sys
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from pipecat.frames.frames import Frame, LLMMessagesFrame, TextFrame
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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.llm_response import LLMFullResponseAggregator
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from pipecat.processors.aggregators.sentence import SentenceAggregator
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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from pipecat.services.azure import AzureTTSService
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from pipecat.services.openai import OpenAILLMService
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from pipecat.transports.services.daily import (
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DailyParams,
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DailyTranscriptionSettings,
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DailyTransport,
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DailyTransportMessageFrame,
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)
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from runner import configure
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from loguru import logger
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from dotenv import load_dotenv
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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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"""
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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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super().__init__()
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self._language = language
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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, 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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await self.push_frame(LLMMessagesFrame(context))
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else:
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await self.push_frame(frame)
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class TranslationSubtitles(FrameProcessor):
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def __init__(self, language):
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super().__init__()
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self._language = language
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#
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# This doesn't do anything unless the receiver recognizes the message being
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# sent. For example, in this case, we are sending a message to the transport
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# so an application running at the other end of the transport could display
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# subtitles.
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#
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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, TextFrame):
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message = {"language": self._language, "text": frame.text}
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await self.push_frame(DailyTransportMessageFrame(message))
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await self.push_frame(frame)
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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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"Translator",
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DailyParams(
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audio_out_enabled=True,
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transcription_enabled=True,
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transcription_settings=DailyTranscriptionSettings(extra={"interim_results": False}),
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),
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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(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
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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([transport.input(), sa, tp, llm, lfra, ts, tts, transport.output()])
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task = PipelineTask(pipeline)
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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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transport.capture_participant_transcription(participant["id"])
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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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