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 sample_training(self, output, target, iteration):
mel_outputs = to_arr(output[0][0])
mel_target = to_arr(target[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_test", plot_alignment_to_numpy(alignments),
iteration)
self.add_image("mel_outputs_test",
plot_spectrogram_to_numpy(mel_outputs), iteration)
self.add_image("mel_outputs_postnet_test",
plot_spectrogram_to_numpy(mel_outputs_postnet),
iteration)
self.add_image("mel_target_test",
plot_spectrogram_to_numpy(mel_target), iteration)
# save audio
# try: # sometimes error
wav = inv_mel_spectrogram(mel_outputs, hps)
# wav *= 32767 / max(0.01, np.max(np.abs(wav)))
# wav /= max(0.01, np.max(np.abs(wav)))
wav_postnet = inv_mel_spectrogram(mel_outputs_postnet, hps)
# wav_postnet *= 32767 / max(0.01, np.max(np.abs(wav_postnet)))
# wav_postnet /= max(0.01, np.max(np.abs(wav_postnet)))
wav_target = inv_mel_spectrogram(mel_target, hps)
# wav_target *= 32767 / max(0.01, np.max(np.abs(wav_target)))
# wav_target /= max(0.01, np.max(np.abs(wav_target)))
self.add_audio('pred_test', wav, iteration, hps.sample_rate)
self.add_audio('pred_postnet_test', wav_postnet, iteration,
hps.sample_rate)
self.add_audio('target_test', wav_target, iteration, hps.sample_rate)
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 sample_training(self, real, pred, iteration):
real = to_arr(real)
pred = to_arr(pred)
# save audio
real /= max(0.01, np.max(np.abs(real)))
pred /= max(0.01, np.max(np.abs(pred)))
self.add_audio('real', real, iteration, hps.sample_rate)
self.add_audio('pred', pred, iteration, hps.sample_rate)
def infer(model, src_pth):
src = load_wav(src_pth, seg=False)
mel = melspectrogram(src).astype(np.float32)
mel = mode(torch.Tensor([mel]))
with torch.no_grad():
res = model.infer(mel)[0]
return [src, to_arr(res)]
def infer(wav_path, text, model):
sequence = text_to_sequence(text, hps.text_cleaners)
sequence = to_var(torch.IntTensor(sequence)[None, :]).long()
mel = melspectrogram(load_wav(wav_path))
mel_in = to_var(torch.Tensor([mel]))
r = mel_in.shape[2] % hps.n_frames_per_step
if r != 0:
mel_in = mel_in[:, :, :-r]
sequence = torch.cat([sequence, sequence], 0)
mel_in = torch.cat([mel_in, mel_in], 0)
_, mel_outputs_postnet, _, _ = model.teacher_infer(sequence, mel_in)
ret = mel
if r != 0:
ret[:, :-r] = to_arr(mel_outputs_postnet[0])
else:
ret = to_arr(mel_outputs_postnet[0])
return ret
def log_training_vid(self, output, target, reduced_loss, grad_norm,
learning_rate, iteration):
mel_loss, mel_loss_post, l1_loss, gate_loss = reduced_loss
self.add_scalar("training.mel_loss", mel_loss, iteration)
self.add_scalar("training.mel_loss_post", mel_loss_post, iteration)
self.add_scalar("training.l1_loss", l1_loss, iteration)
self.add_scalar("training.gate_loss", gate_loss, iteration)
self.add_scalar("grad.norm", grad_norm, iteration)
self.add_scalar("learning.rate", learning_rate, iteration)
mel_outputs = to_arr(output[0][0])
mel_target = to_arr(target[0][0])
mel_outputs_postnet = to_arr(output[1][0])
alignments = to_arr(output[3][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)
self.add_image("mel_target", plot_spectrogram_to_numpy(mel_target),
iteration)
# save audio
# try: # sometimes error
wav = inv_mel_spectrogram(mel_outputs, hps)
wav *= 32767 / max(0.01, np.max(np.abs(wav)))
# wav /= max(0.01, np.max(np.abs(wav)))
wav_postnet = inv_mel_spectrogram(mel_outputs_postnet, hps)
wav_postnet *= 32767 / max(0.01, np.max(np.abs(wav_postnet)))
# wav_postnet /= max(0.01, np.max(np.abs(wav_postnet)))
wav_target = inv_mel_spectrogram(mel_target, hps)
wav_target *= 32767 / max(0.01, np.max(np.abs(wav_target)))
# wav_target /= max(0.01, np.max(np.abs(wav_target)))
self.add_audio('pred', wav, iteration, hps.sample_rate)
self.add_audio('pred_postnet', wav_postnet, iteration, hps.sample_rate)
self.add_audio('target', wav_target, iteration, hps.sample_rate)
def save_mel(output, pth):
mel_outputs, mel_outputs_postnet, _ = output
#np.save(pth+'.npy', to_arr(mel_outputs[0]).T)
np.save(pth + '.npy', to_arr(mel_outputs[0]).T)
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')
def plot(output, pth):
mel_outputs, mel_outputs_postnet, alignments = output
plot_data((to_arr(mel_outputs[0]), to_arr(mel_outputs_postnet[0]),
to_arr(alignments[0]).T))
plt.savefig(pth + '.png')