6313ad3830b5fc05926168eeb5faea1afe8e19f3,open_seq2seq/decoders/tacotron2_decoder.py,Tacotron2Decoder,_decode,#Tacotron2Decoder#Any#,260

Before Change

      // helper = tf.contrib.seq2seq.TrainingHelper(
      //   inputs=tgt_inputs,
      //   sequence_length=tgt_lengths)
    elif self._mode == "eval":
      // embedding_fn = lambda ids: tf.cast(tf.nn.embedding_lookup(self._dec_emb_w, ids),
      //                         dtype=self.params["dtype"])
      inputs = tf.zeros((_batch_size, self.num_audio_features))
      helper = TacotronHelper(inputs=inputs,
                              sequence_length=spec_length,
                              enable_prenet=enable_prenet,
                              prenet_units=prenet_units,
                              prenet_layers=prenet_layers,
                              prenet_activation=prenet_activation,
                              mask_decoder_sequence=mask_decoder_sequence)
                              // context=mean_pool)
      // helper = tf.contrib.seq2seq.TrainingHelper(
      //   inputs=encoder_outputs,
      //   sequence_length=enc_src_lengths)

After Change

    time_major = self.params.get("time_major", False)
    use_swap_memory = self.params.get("use_swap_memory", False)
    if self._mode == "train" or self._mode == "eval":
      maximum_iterations = tf.reduce_max(spec_length)
    else:
      maximum_iterations = tf.reduce_max(enc_src_lengths) * 5
    // maximum_iterations = tf.reduce_max(spec_length)

    final_outputs, final_state, final_sequence_lengths = tf.contrib.seq2seq.dynamic_decode(
    // final_outputs, final_state, final_sequence_lengths, final_inputs = dynamic_decode(
      decoder=decoder,
      // impute_finished=False if self._decoder_type == "beam_search" else True,

Instances