def prepare_minibatch(self, audio_paths, texts):
        """ Featurize a minibatch of audio, zero pad them and return a dictionary
        Params:
            audio_paths (list(str)): List of paths to audio files
            texts (list(str)): List of texts corresponding to the audio files
        Returns:
            dict: See below for contents
        """
        assert len(audio_paths) == len(texts),\
            "Inputs and outputs to the network must be of the same number"
        # Features is a list of (timesteps, feature_dim) arrays
        # Calculate the features for each audio clip, as the log of the
        # Fourier Transform of the audio
        features = [self.featurize(a) for a in audio_paths]
        input_lengths = [f.shape[0] for f in features]
        max_length = max(input_lengths)
        feature_dim = features[0].shape[1]
        mb_size = len(features)
        # Pad all the inputs so that they are all the same length
        x = np.zeros((mb_size, max_length, feature_dim))
        y = []
        label_lengths = []
        for i in range(mb_size):
            feat = features[i]
            feat = self.normalize(feat)  # Center using means and std
            x[i, :feat.shape[0], :] = feat
            label = text_to_int_sequence(texts[i])
            y.append(label)
            label_lengths.append(len(label))
        # Flatten labels to comply with warp-CTC signature
        y = reduce(lambda i, j: i + j, y)
        return {
            'x': x,  # (0-padded features of shape(mb_size,timesteps,feat_dim)
            'y': y,  # list(int) Flattened labels (integer sequences)
            'texts': texts,  # list(str) Original texts
            'input_lengths': input_lengths,  # list(int) Length of each input
            'label_lengths': label_lengths  # list(int) Length of each label
        }
 Featurize a minibatch of audio, zero pad them and return a dictionary
        Params:
            audio_paths (list(str)): List of paths to audio files
            texts (list(str)): List of texts corresponding to the audio files
        Returns:
            dict: See below for contents
        
data_generator.py