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from fastapi import FastAPI, WebSocket
from urllib.parse import urlparse, parse_qsl
from transcribe.whisper_llm_serve import PyWhiperCppServe
from uuid import uuid1
from logging import getLogger
import numpy as np
from transcribe.translatepipes import TranslatePipes
from contextlib import asynccontextmanager
from multiprocessing import Process, freeze_support
from fastapi.staticfiles import StaticFiles
logger = getLogger(__name__)
async def get_audio_from_websocket(websocket)->np.array:
"""
Receives audio buffer from websocket and creates a numpy array out of it.
Args:
websocket: The websocket to receive audio from.
Returns:
A numpy array containing the audio.
"""
frame_data = await websocket.receive_bytes()
if frame_data == b"END_OF_AUDIO":
return False
return np.frombuffer(frame_data, dtype=np.int16).astype(np.float32) / 32768.0
@asynccontextmanager
async def lifespan(app:FastAPI):
global pipe
pipe = TranslatePipes()
pipe.wait_ready()
logger.info("Pipeline is ready.")
yield
app = FastAPI(lifespan=lifespan)
app.mount("/translate", StaticFiles(directory="frontend"),)
pipe = None
@app.websocket("/ws")
async def translate(websocket: WebSocket):
query_parameters_dict = websocket.query_params
from_lang, to_lang = query_parameters_dict.get('from'), query_parameters_dict.get('to')
client = PyWhiperCppServe(
websocket,
pipe,
language="en",
client_uid=f"{uuid1()}",
)
if from_lang and to_lang:
client.set_lang(from_lang, to_lang)
logger.info(f"Source lange: {from_lang} -> Dst lange: {to_lang}")
await websocket.accept()
while True:
frame_data = await get_audio_from_websocket(websocket)
client.add_frames(frame_data)
if __name__ == '__main__':
freeze_support()
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=9090) |