def sample_training(self, output, iteration):
mel_outputs = to_arr(output[0][0])
mel_outputs_postnet = to_arr(output[1][0])
alignments = to_arr(output[2][0]).T
# plot alignment, mel and postnet output
self.add_image("alignment", plot_alignment_to_numpy(alignments),
iteration)
self.add_image("mel_outputs", plot_spectrogram_to_numpy(mel_outputs),
iteration)
self.add_image("mel_outputs_postnet",
plot_spectrogram_to_numpy(mel_outputs_postnet),
iteration)
# save audio
try: # sometimes error
wav = inv_melspectrogram(mel_outputs)
wav /= max(0.01, np.max(np.abs(wav)))
wav_postnet = inv_melspectrogram(mel_outputs_postnet)
wav_postnet /= max(0.01, np.max(np.abs(wav_postnet)))
self.add_audio('pred', wav, iteration, hps.sample_rate)
self.add_audio('pred_postnet', wav_postnet, iteration,
hps.sample_rate)
except:
pass
def audio(output, pth):
mel_outputs, mel_outputs_postnet, _ = output
wav = inv_melspectrogram(to_arr(mel_outputs[0]))
wav_postnet = inv_melspectrogram(to_arr(mel_outputs_postnet[0]))
save_wav(wav, pth + '.wav')
save_wav(wav_postnet, pth + '_post.wav')
print('wav save to:', pth + '.wav')
print('postnet_wav save to:', pth + '_post.wav')
def audio(output, pth):
mel_outputs, mel_outputs_postnet, _ = output
#wav = inv_melspectrogram(to_arr(mel_outputs[0]))
wav_postnet = inv_melspectrogram(to_arr(mel_outputs_postnet[0]))
#save_wav(wav, pth+'.wav')
save_wav(wav_postnet, pth + '.wav')