app.py

from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse
import os
from pydub import AudioSegment
import aiofiles
import faster_whisper

# Initialize the FastAPI app
app = FastAPI()

# Initialize the model with GPU support
model = faster_whisper.WhisperModel('ivrit-ai/faster-whisper-v2-d4')

# Define file paths
TEMP_FILE_PATH = "temp_audio_file.m4a"
WAV_FILE_PATH = "temp_audio_file.wav"

@app.post("/transcribe")
async def transcribe(request: Request):
    # Stream the file directly to a temporary file on disk
    async with aiofiles.open(TEMP_FILE_PATH, 'wb') as out_file:
        async for chunk in request.stream():
            await out_file.write(chunk)
    print("File saved successfully.")

    # Convert M4A to WAV
    try:
        audio = AudioSegment.from_file(TEMP_FILE_PATH, format="m4a")
        audio.export(WAV_FILE_PATH, format="wav")
        print("Conversion to WAV successful.")
    except Exception as e:
        print("Error during conversion:", e)
        return JSONResponse({"detail": "Error in audio conversion"}, status_code=400)

    # Transcribe the WAV audio file
    segments, _ = model.transcribe(WAV_FILE_PATH, language='he')
    transcribed_text = ' '.join([s.text for s in segments])

    # Clean up temporary files
    os.remove(TEMP_FILE_PATH)
    os.remove(WAV_FILE_PATH)

    return JSONResponse({"transcribed_text": transcribed_text})