0e12fe12ea2f001ea3e4bf4d6cb5d6c6cab2a8cb,open_seq2seq/models/text2speech.py,Text2Speech,finalize_evaluation,#Text2Speech#Any#Any#,308

Before Change

    predicted_final_spectrogram_sample = predicted_final_spectrogram_sample[:audio_length-1,:]
    if audio_length > 2:
      if "magnitude" in self.get_data_layer().params["output_type"]:
        predicted_final_spectrogram_sample = self.get_data_layer().denormalize(predicted_final_spectrogram_sample)
        predicted_final_spectrogram_sample = np.exp(predicted_final_spectrogram_sample)
        wav_summary = save_audio(predicted_final_spectrogram_sample, self.params["logdir"], step, mode="eval",
          save_to_tensorboard = self.save_to_tensorboard)
        dict_to_log["audio"] = wav_summary
      elif "mel" in self.get_data_layer().params["output_type"]:
        predicted_final_spectrogram_sample = self.get_data_layer().inverse_mel(predicted_final_spectrogram_sample)
        wav_summary = save_audio(predicted_final_spectrogram_sample, self.params["logdir"], step, mode="eval",
          save_to_tensorboard = self.save_to_tensorboard)
        dict_to_log["audio"] = wav_summary

    if self.save_to_tensorboard:
      return dict_to_log
    else:
      return {}

After Change

    if audio_length > 2:
      predicted_final_spectrogram_sample = predicted_final_spectrogram_sample[:audio_length-1,:]

      predicted_final_spectrogram_sample = self.get_data_layer().get_magnitude_spec(predicted_final_spectrogram_sample)
      wav_summary = save_audio(predicted_final_spectrogram_sample, self.params["logdir"], step, mode="eval",
        save_to_tensorboard = self.save_to_tensorboard)
      dict_to_log["audio"] = wav_summary

Instances