Update app.py

app.py

@@ -8,13 +8,13 @@ model_id = f"{username}/distil-wav2vec2-finetuned-giga-speech"
 device = "cuda:0" if torch.cuda.is_available() else "cpu"
 pipe = pipeline("audio-classification", model=model_id, device=device)
 
-# def predict_trunc(filepath):
-#     preprocessed = pipe.preprocess(filepath)
-#     truncated = pipe.feature_extractor.pad(preprocessed,truncation=True, max_length = 16_000*30)
-#     model_outputs = pipe.forward(truncated)
-#     outputs = pipe.postprocess(model_outputs)
+def predict_trunc(filepath):
+    preprocessed = pipe.preprocess(filepath)
+    truncated = pipe.feature_extractor.pad(preprocessed,truncation=True, max_length = 16_000*30)
+    model_outputs = pipe.forward(truncated)
+    outputs = pipe.postprocess(model_outputs)
 
-#     return outputs
+    return outputs
 
 
 def classify_audio(filepath):
@@ -26,8 +26,10 @@ def classify_audio(filepath):
      {"Gaming":  0.8339303731918335, "Audiobook":0.11914275586605072}
     """
     start_time = timer()
-    preds = pipe(filepath)
-    # preds = predict_trunc(filepath)
+    #preds = pipe(filepath)
+    
+    preds = predict_trunc(filepath)
+    
     outputs = {}
     pred_time = round(timer() - start_time, 5)
     for p in preds: