Error in song genre classifier using neural network from chapter 4

[Kashyapdevesh]

ValueError                                Traceback (most recent call last)
<ipython-input-50-9bfdddf06ca4> in <module>
----> 1 features, labels = generate_features_and_labels()

<ipython-input-49-e1fbb8357030> in generate_features_and_labels()
     15     label_uniq_ids, label_row_ids = np.unique(all_labels, return_inverse=True)
     16     label_row_ids = label_row_ids.astype(np.int32, copy=False)
---> 17     onehot_labels = to_categorical(label_row_ids, len(label_uniq_ids))
     18     return np.stack(all_features), onehot_labels
     19 

/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/utils/np_utils.py in to_categorical(y, num_classes, dtype)
     73   y = y.ravel()
     74   if not num_classes:
---> 75     num_classes = np.max(y) + 1
     76   n = y.shape[0]
     77   categorical = np.zeros((n, num_classes), dtype=dtype)

<__array_function__ internals> in amax(*args, **kwargs)

/opt/conda/lib/python3.7/site-packages/numpy/core/fromnumeric.py in amax(a, axis, out, keepdims, initial, where)
   2704     """
   2705     return _wrapreduction(a, np.maximum, 'max', axis, None, out,
-> 2706                           keepdims=keepdims, initial=initial, where=where)
   2707 
   2708 

/opt/conda/lib/python3.7/site-packages/numpy/core/fromnumeric.py in _wrapreduction(obj, ufunc, method, axis, dtype, out, **kwargs)
     85                 return reduction(axis=axis, out=out, **passkwargs)
     86 
---> 87     return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
     88 
     89 

ValueError: zero-size array to reduction operation maximum which has no identity

Hello,I'm facing this ValueError when calling 'generate_features_and_labels()' function ,I tried wrapping this with special case but it didn't work ,please help.

generate_features_and_labels() function:

def generate_features_and_labels():
    all_features = []
    all_labels = []

    genres = ['blues', 'classical', 'country', 'disco', 'hiphop', 'jazz', 'metal', 'pop', 'reggae', 'rock']
    for genre in genres:
        sound_files = glob.glob('genres/'+genre+'/*.au')
        print('Processing %d songs in %s genre...' % (len(sound_files), genre))
        for f in sound_files:
            features = extract_features_song(f)
            all_features.append(features)
            all_labels.append(genre)

    # convert labels to one-hot encoding
    label_uniq_ids, label_row_ids = np.unique(all_labels, return_inverse=True)
    label_row_ids = label_row_ids.astype(np.int32, copy=False)
    onehot_labels = to_categorical(label_row_ids, len(label_uniq_ids))
    return np.stack(all_features), onehot_labels