Update app.py

app.py

@@ -66,11 +66,11 @@ if uploaded_file is not None:
     audio = np.expand_dims(audio, axis=0)  # Ajouter une dimension batch
 
     # 🔹 Préparer les entrées pour Whisper
-    inputs = processor(audio, sampling_rate=16000, return_tensors="pt")
+    inputs = processor(audio, sampling_rate=16000, return_tensors="pt", language="fr", task="transcribe")
 
     # 🔹 Générer la transcription
     with torch.no_grad():
-        predicted_ids = model.generate(inputs.input_features, language="fr")
+        predicted_ids = model.generate(inputs.input_features)
 
     # 🔹 Décoder la sortie
     transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]