Update app.py

app.py

@@ -19,12 +19,10 @@ def classify_audio(audio_file):
     
     sr, raw_wav = audio_file
     
-    print(audio_file, audio_file[1].dtype)
     y = raw_wav.astype(np.float32)
     y /= np.max(np.abs(y))
 
     
-    #raw_wav, _ librosa.load(audio_file, sr=16000)
     norm_wav = (y - mean) / (std+0.000001)
 
     mask = torch.ones(1, len(norm_wav))
@@ -33,7 +31,7 @@ def classify_audio(audio_file):
     pred = model(wavs, mask).detach().numpy()
     
     pred = {}
-    for i, audio_pred in enumerate(pred.numpy()):
+    for i, audio_pred in enumerate(pred):
         pred[i] = {}
         for att_i, att_val in enumerate(audio_pred):
             pred[i][id2label[att_i]] = att_val