Update app.py

app.py

@@ -9,33 +9,34 @@ from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
 # Initialize the model outside the main app function to load it only once
 
 
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
 
-model_id = "distil-whisper/distil-large-v2"
-
-model = AutoModelForSpeechSeq2Seq.from_pretrained(
-    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
-)
-model.to(device)
-
-processor = AutoProcessor.from_pretrained(model_id)
-
-pipe = pipeline(
-    "automatic-speech-recognition",
-    model=model,
-    tokenizer=processor.tokenizer,
-    feature_extractor=processor.feature_extractor,
-    max_new_tokens=128,
-    chunk_length_s=15,
-    batch_size=16,
-    torch_dtype=torch_dtype,
-    device=device,
-)
 
 
 
 def transcribe_audio(audio_file):
+    device = "cuda:0" if torch.cuda.is_available() else "cpu"
+    torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+    
+    model_id = "distil-whisper/distil-large-v2"
+    
+    model = AutoModelForSpeechSeq2Seq.from_pretrained(
+        model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
+    )
+    model.to(device)
+    
+    processor = AutoProcessor.from_pretrained(model_id)
+    
+    pipe = pipeline(
+        "automatic-speech-recognition",
+        model=model,
+        tokenizer=processor.tokenizer,
+        feature_extractor=processor.feature_extractor,
+        max_new_tokens=128,
+        chunk_length_s=15,
+        batch_size=16,
+        torch_dtype=torch_dtype,
+        device=device,
+    )
     # Save the audio file to a temporary file
     with open("temp_audio_file", "wb") as f:
         f.write(audio_file.getbuffer())