Create app.py

app.py

@@ -0,0 +1,60 @@
+from fastapi import FastAPI, UploadFile, File, Form
+from faster_whisper import WhisperModel
+import uvicorn
+import tempfile
+import shutil
+import torch
+import os
+from fastapi.middleware.cors import CORSMiddleware
+device = "cuda" if torch.cuda.is_available() else "cpu"
+compute_type = "float16" if device == "cuda" else "int8"
+origins = [
+    "cabane-data.fr",  # ton WordPress
+]
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+
+# === Dictionnaire des modèles dispo ===
+AVAILABLE_MODELS = ["tiny", "base", "small", "medium", "large-v2"]
+
+def load_model(model_name: str):
+    """Charger un modèle Whisper avec CPU (modifiable si GPU dispo)"""
+    return WhisperModel(model_name, device=device, compute_type="int8")
+
+
+# === Endpoint API REST ===
+@app.post("/transcribe")
+async def transcribe(
+    file: UploadFile = File(...),
+    model_name: str = Form("base")  # par défaut "base"
+):
+    if model_name not in AVAILABLE_MODELS:
+        return {"error": f"Modèle non reconnu. Choisissez parmi {AVAILABLE_MODELS}"}
+
+    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([segment.text for segment in segments])
+
+    os.remove(tmp_path)
+
+    return {
+        "model_used": model_name,
+        "language": info.language,
+        "probability": info.language_probability,
+        "transcription": text_result,
+    }
+