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| from torch.utils.tensorboard import SummaryWriter | |
| import numpy as np | |
| import librosa | |
| from .plotting import plot_waveform_to_numpy, plot_spectrogram_to_numpy | |
| class MyWriter(SummaryWriter): | |
| def __init__(self, hp, logdir): | |
| super(MyWriter, self).__init__(logdir) | |
| self.sample_rate = hp.data.sampling_rate | |
| def log_training(self, g_loss, d_loss, mel_loss, stft_loss, k_loss, r_loss, score_loss, step): | |
| self.add_scalar('train/g_loss', g_loss, step) | |
| self.add_scalar('train/d_loss', d_loss, step) | |
| self.add_scalar('train/score_loss', score_loss, step) | |
| self.add_scalar('train/stft_loss', stft_loss, step) | |
| self.add_scalar('train/mel_loss', mel_loss, step) | |
| self.add_scalar('train/kl_f_loss', k_loss, step) | |
| self.add_scalar('train/kl_r_loss', r_loss, step) | |
| def log_validation(self, mel_loss, generator, discriminator, step): | |
| self.add_scalar('validation/mel_loss', mel_loss, step) | |
| def log_fig_audio(self, real, fake, spec_fake, spec_real, idx, step): | |
| if idx == 0: | |
| spec_fake = librosa.amplitude_to_db(spec_fake, ref=np.max,top_db=80.) | |
| spec_real = librosa.amplitude_to_db(spec_real, ref=np.max,top_db=80.) | |
| self.add_image(f'spec_fake/{step}', plot_spectrogram_to_numpy(spec_fake), step) | |
| self.add_image(f'wave_fake/{step}', plot_waveform_to_numpy(fake), step) | |
| self.add_image(f'spec_real/{step}', plot_spectrogram_to_numpy(spec_real), step) | |
| self.add_image(f'wave_real/{step}', plot_waveform_to_numpy(real), step) | |
| self.add_audio(f'fake/{step}', fake, step, self.sample_rate) | |
| self.add_audio(f'real/{step}', real, step, self.sample_rate) | |
| def log_histogram(self, model, step): | |
| for tag, value in model.named_parameters(): | |
| self.add_histogram(tag.replace('.', '/'), value.cpu().detach().numpy(), step) | |