0e12fe12ea2f001ea3e4bf4d6cb5d6c6cab2a8cb,open_seq2seq/models/text2speech.py,Text2Speech,finalize_inference,#Text2Speech#Any#Any#,365

Before Change

        // print(predicted_final_spectrogram_sample.shape)
        if audio_length > 2:
          predicted_final_spectrogram_sample = predicted_final_spectrogram_sample[:audio_length-1,:]
          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)
            save_audio(predicted_final_spectrogram_sample, self.params["logdir"], 0, mode="infer",number= i*batch_size+j)
          elif "mel" in self.get_data_layer().params["output_type"]:
            save_audio(predicted_final_spectrogram_sample, self.params["logdir"], 0, mode="infer",number= i*batch_size+j)
          // if "spectrogram" in self.get_data_layer().params["output_type"]:
          //   save_audio(predicted_final_spectrogram_sample, self.params["logdir"], 0, mode="infer", number=i*batch_size+j)

    return {}

After Change

  def finalize_inference(self, results_per_batch, output_file):
    print("output_file is ignored for ts2")
    print("results are logged to the logdir")
    batch_size = len(results_per_batch[0][0]["source_tensors"][0])
    for i, sample in enumerate(results_per_batch):
      input_values = sample[0]
      output_values = sample[1]

Instances