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| import tqdm | |
| import torch | |
| import torch.nn.functional as F | |
| def validate(hp, args, generator, discriminator, valloader, stft, writer, step, device): | |
| generator.eval() | |
| discriminator.eval() | |
| torch.backends.cudnn.benchmark = False | |
| loader = tqdm.tqdm(valloader, desc='Validation loop') | |
| mel_loss = 0.0 | |
| for idx, (ppg, ppg_l, vec, pit, spk, spec, spec_l, audio, audio_l) in enumerate(loader): | |
| ppg = ppg.to(device) | |
| vec = vec.to(device) | |
| pit = pit.to(device) | |
| spk = spk.to(device) | |
| ppg_l = ppg_l.to(device) | |
| audio = audio.to(device) | |
| if hasattr(generator, 'module'): | |
| fake_audio = generator.module.infer(ppg, vec, pit, spk, ppg_l)[ | |
| :, :, :audio.size(2)] | |
| else: | |
| fake_audio = generator.infer(ppg, vec, pit, spk, ppg_l)[ | |
| :, :, :audio.size(2)] | |
| mel_fake = stft.mel_spectrogram(fake_audio.squeeze(1)) | |
| mel_real = stft.mel_spectrogram(audio.squeeze(1)) | |
| mel_loss += F.l1_loss(mel_fake, mel_real).item() | |
| if idx < hp.log.num_audio: | |
| spec_fake = stft.linear_spectrogram(fake_audio.squeeze(1)) | |
| spec_real = stft.linear_spectrogram(audio.squeeze(1)) | |
| audio = audio[0][0].cpu().detach().numpy() | |
| fake_audio = fake_audio[0][0].cpu().detach().numpy() | |
| spec_fake = spec_fake[0].cpu().detach().numpy() | |
| spec_real = spec_real[0].cpu().detach().numpy() | |
| writer.log_fig_audio( | |
| audio, fake_audio, spec_fake, spec_real, idx, step) | |
| mel_loss = mel_loss / len(valloader.dataset) | |
| writer.log_validation(mel_loss, generator, discriminator, step) | |
| torch.backends.cudnn.benchmark = True | |