85 lines
2.8 KiB
Python
85 lines
2.8 KiB
Python
import asyncio
|
|
import aiohttp
|
|
import logging
|
|
import os
|
|
from typing import AsyncGenerator
|
|
|
|
from dailyai.pipeline.aggregators import (
|
|
SentenceAggregator,
|
|
)
|
|
from dailyai.pipeline.frames import Frame, LLMMessagesQueueFrame, TextFrame
|
|
from dailyai.pipeline.frame_processor import FrameProcessor
|
|
from dailyai.pipeline.pipeline import Pipeline
|
|
from dailyai.transports.daily_transport import DailyTransport
|
|
from dailyai.services.azure_ai_services import AzureTTSService
|
|
from dailyai.services.open_ai_services import OpenAILLMService
|
|
|
|
from runner import configure
|
|
|
|
from dotenv import load_dotenv
|
|
load_dotenv(override=True)
|
|
|
|
logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
|
|
logger = logging.getLogger("dailyai")
|
|
logger.setLevel(logging.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):
|
|
self._language = language
|
|
|
|
async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
|
|
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},
|
|
]
|
|
yield LLMMessagesQueueFrame(context)
|
|
else:
|
|
yield frame
|
|
|
|
|
|
async def main(room_url: str, token):
|
|
async with aiohttp.ClientSession() as session:
|
|
transport = DailyTransport(
|
|
room_url,
|
|
token,
|
|
"Translator",
|
|
duration_minutes=5,
|
|
start_transcription=True,
|
|
mic_enabled=True,
|
|
mic_sample_rate=16000,
|
|
camera_enabled=False,
|
|
vad_enabled=True,
|
|
)
|
|
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-4-turbo-preview")
|
|
sa = SentenceAggregator()
|
|
tp = TranslationProcessor("Spanish")
|
|
pipeline = Pipeline([sa, tp, llm, tts])
|
|
|
|
transport.transcription_settings["extra"]["endpointing"] = True
|
|
transport.transcription_settings["extra"]["punctuate"] = True
|
|
await transport.run(pipeline)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
(url, token) = configure()
|
|
asyncio.run(main(url, token))
|