import tensorflow as tf


        encoder_cells = tf.nn.rnn_cell.MultiRNNCell([cells() for _ in range(num_layers)])
        encoder_out, encoder_state = tf.nn.dynamic_rnn(
            cell=encoder_cells, inputs=forward, sequence_length=seq_lens, dtype=tf.float32
        )

        encoder_state = tuple(encoder_state[-1] for _ in range(num_layers))
        decoder_cell = attention(encoder_out, seq_lens)
        dense_layer = tf.layers.Dense(n_mels * resampled)

        training_helper = tf.contrib.seq2seq.TrainingHelper(
            inputs=self.decoder_inputs, sequence_length=seq_lens, time_major=False
        )
        training_decoder = tf.contrib.seq2seq.BasicDecoder(
            cell=decoder_cell,
            helper=training_helper,
            initial_state=decoder_cell.zero_state(batch_size, tf.float32).clone(
                cell_state=encoder_state
            ),
            output_layer=dense_layer,
