app.py

from fastapi import FastAPI, UploadFile, File, Form
from faster_whisper import WhisperModel
import tempfile
import shutil
import os
import torch
from fastapi.middleware.cors import CORSMiddleware
import time

device = "cuda" if torch.cuda.is_available() else "cpu"
compute_type = "float16" if device=="cuda" else "int8"

app = FastAPI()

app.add_middleware(
    CORSMiddleware,
    allow_origins=["https://www.cabane-data.fr", "https://www.cabane-data.fr"],  # à restreindre à ton domaine
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

AVAILABLE_MODELS = ["tiny", "base", "small", "medium", "large-v2"]

def load_model(model_name: str):
    return WhisperModel(model_name, device=device, compute_type=compute_type)

@app.post("/transcribe")
async def transcribe(file: UploadFile = File(...), model_name: str = Form("base")):
    if model_name not in AVAILABLE_MODELS:
        return {"error": f"Modèle non reconnu. Choisissez parmi {AVAILABLE_MODELS}"}

    start = time.time()
    model = load_model(model_name)

    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp:
        shutil.copyfileobj(file.file, tmp)
        tmp_path = tmp.name

    segments, info = model.transcribe(tmp_path, beam_size=5)
    text_result = " ".join([s.text for s in segments])
    os.remove(tmp_path)

    end = time.time()
    duration = round(end - start, 2)

    return {
        "text": text_result,
        "duration": duration,
        "model_used": model_name
    }