138 lines
4.3 KiB
Python
138 lines
4.3 KiB
Python
import asyncio
|
|
import aiohttp
|
|
import os
|
|
import sys
|
|
|
|
from pipecat.frames.frames import Frame, LLMMessagesFrame, TextFrame
|
|
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 LLMFullResponseAggregator
|
|
from pipecat.processors.aggregators.sentence import SentenceAggregator
|
|
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
|
from pipecat.services.azure import AzureTTSService
|
|
from pipecat.services.openai import OpenAILLMService
|
|
from pipecat.transports.services.daily import DailyParams, DailyTranscriptionSettings, DailyTransport, DailyTransportMessageFrame
|
|
|
|
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")
|
|
|
|
|
|
"""
|
|
This example looks a bit different than the chatbot example, because it isn't waiting on the user to stop talking to start translating.
|
|
It also isn't saving what the user or bot says into the context object for use in subsequent interactions.
|
|
"""
|
|
|
|
|
|
# We need to use a custom service here to yield LLM frames without saving
|
|
# any context
|
|
class TranslationProcessor(FrameProcessor):
|
|
|
|
def __init__(self, language):
|
|
super().__init__()
|
|
self._language = language
|
|
|
|
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
|
await super().process_frame(frame, direction)
|
|
|
|
if isinstance(frame, TextFrame):
|
|
context = [
|
|
{
|
|
"role": "system",
|
|
"content": f"You will be provided with a sentence in English, and your task is to translate it into {self._language}.",
|
|
},
|
|
{"role": "user", "content": frame.text},
|
|
]
|
|
await self.push_frame(LLMMessagesFrame(context))
|
|
else:
|
|
await self.push_frame(frame)
|
|
|
|
|
|
class TranslationSubtitles(FrameProcessor):
|
|
def __init__(self, language):
|
|
super().__init__()
|
|
self._language = language
|
|
|
|
#
|
|
# This doesn't do anything unless the receiver recognizes the message being
|
|
# sent. For example, in this case, we are sending a message to the transport
|
|
# so an application running at the other end of the transport could display
|
|
# subtitles.
|
|
#
|
|
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
|
await super().process_frame(frame, direction)
|
|
|
|
if isinstance(frame, TextFrame):
|
|
message = {
|
|
"language": self._language,
|
|
"text": frame.text
|
|
}
|
|
await self.push_frame(DailyTransportMessageFrame(message))
|
|
|
|
await self.push_frame(frame)
|
|
|
|
|
|
async def main(room_url: str, token):
|
|
async with aiohttp.ClientSession() as session:
|
|
transport = DailyTransport(
|
|
room_url,
|
|
token,
|
|
"Translator",
|
|
DailyParams(
|
|
audio_out_enabled=True,
|
|
transcription_enabled=True,
|
|
transcription_settings=DailyTranscriptionSettings(extra={
|
|
"interim_results": False
|
|
})
|
|
)
|
|
)
|
|
|
|
tts = AzureTTSService(
|
|
api_key=os.getenv("AZURE_SPEECH_API_KEY"),
|
|
region=os.getenv("AZURE_SPEECH_REGION"),
|
|
voice="es-ES-AlvaroNeural",
|
|
)
|
|
|
|
llm = OpenAILLMService(
|
|
api_key=os.getenv("OPENAI_API_KEY"),
|
|
model="gpt-4o"
|
|
)
|
|
|
|
sa = SentenceAggregator()
|
|
tp = TranslationProcessor("Spanish")
|
|
lfra = LLMFullResponseAggregator()
|
|
ts = TranslationSubtitles("spanish")
|
|
|
|
pipeline = Pipeline([
|
|
transport.input(),
|
|
sa,
|
|
tp,
|
|
llm,
|
|
lfra,
|
|
ts,
|
|
tts,
|
|
transport.output()
|
|
])
|
|
|
|
task = PipelineTask(pipeline)
|
|
|
|
@transport.event_handler("on_first_participant_joined")
|
|
async def on_first_participant_joined(transport, participant):
|
|
transport.capture_participant_transcription(participant["id"])
|
|
|
|
runner = PipelineRunner()
|
|
|
|
await runner.run(task)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
(url, token) = configure()
|
|
asyncio.run(main(url, token))
|