def log_attn_ws(self,
attn_ws,
ilens,
olens,
iteration,
name=None,
num=None):
for w in attn_ws:
for k in w.keys():
att = w[k].transpose(1, 2)[0][:olens[0], :ilens[0]].cpu().data
if name is not None:
self.add_figure(name + '_' + num + '_' + k,
plot_alignment_to_numpy(att), iteration)
else:
self.add_figure(k, plot_alignment_to_numpy(att), iteration)
def log_validation(self, reduced_loss, model, y, y_pred, iteration):
self.add_scalar("validation.loss", reduced_loss, iteration)
_, mel_outputs, gate_outputs, alignments = y_pred
mel_targets, gate_targets = y
# plot distribution of parameters
for tag, value in model.named_parameters():
tag = tag.replace('.', '/')
self.add_histogram(tag, value.data.cpu().numpy(), iteration)
# plot alignment, mel target and predicted, gate target and predicted
idx = random.randint(0, alignments.size(0) - 1)
self.add_image("alignment",
plot_alignment_to_numpy(
alignments[idx].data.cpu().numpy().T),
iteration,
dataformats='HWC')
self.add_image("mel_target",
plot_spectrogram_to_numpy(
mel_targets[idx].data.cpu().numpy()),
iteration,
dataformats='HWC')
self.add_image("mel_predicted",
plot_spectrogram_to_numpy(
mel_outputs[idx].data.cpu().numpy()),
iteration,
dataformats='HWC')
self.add_image("gate",
plot_gate_outputs_to_numpy(
gate_targets[idx].data.cpu().numpy(),
torch.sigmoid(
gate_outputs[idx]).data.cpu().numpy()),
iteration,
dataformats='HWC')
def log(self, y, y_pred, idx, iteration):
_, mel_outputs, gate_outputs, alignments = y_pred
mel_targets, gate_targets = y
self.add_image("alignment",
plot_alignment_to_numpy(
alignments[idx].data.cpu().numpy().T),
iteration,
dataformats='HWC')
self.add_image("mel_target",
plot_spectrogram_to_numpy(
mel_targets[idx].data.cpu().numpy()),
iteration,
dataformats='HWC')
self.add_image("mel_predicted",
plot_spectrogram_to_numpy(
mel_outputs[idx].data.cpu().numpy()),
iteration,
dataformats='HWC')
self.add_image("gate",
plot_gate_outputs_to_numpy(
gate_targets[idx].data.cpu().numpy(),
torch.sigmoid(
gate_outputs[idx]).data.cpu().numpy()),
iteration,
dataformats='HWC')
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 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)