space.gpu corrected

app.py

@@ -30,6 +30,7 @@ def safe_copy(src: str, dst: str) -> str:
 # ──────────────────────────────────────────────────────────────────────────────
 # Core processing pipeline
 # ──────────────────────────────────────────────────────────────────────────────
+@spaces.GPU()
 def process_video(
     context: str,
     instruction: str,

scripts/generate_scripts.py

@@ -18,7 +18,6 @@ model_id = "Qwen/Qwen2.5-0.5B"
 tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float32, trust_remote_code=True).to(device)
 
-@spaces.GPU()
 def generate_local(prompt: str, max_new_tokens: int = 350, temperature: float = 0.7) -> str:
     device = model.device  # get the device the model is on
     inputs = tokenizer(prompt, return_tensors="pt").to(device)

scripts/generate_subtitles.py

@@ -84,7 +84,6 @@ def save_subtitles_to_srt(subtitles, output_path):
             f.write(f"{sub['text'].strip()}\n\n")
 
 
-@spaces.GPU()
 def transcribe_audio_to_subs(audio_path):
     """
     Transcrit le fichier audio en texte (via Whisper), retourne la liste

scripts/generate_voice.py

@@ -14,7 +14,6 @@ ENGLISH_VOICES = [
     "am_fenrir"     
 ]
 
-@spaces.GPU()
 def generate_voice(text: str, path: str):
     for voice in random.sample(ENGLISH_VOICES, len(ENGLISH_VOICES)):
         try: