| 291 | ) |
| 292 | |
| 293 | def p_losses(self, x_start, t, cond, noise=None, nonpadding=None): |
| 294 | noise = default(noise, lambda: torch.randn_like(x_start)) |
| 295 | |
| 296 | x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) |
| 297 | x_recon = self.denoise_fn(x_noisy, t, cond) |
| 298 | |
| 299 | if self.loss_type == 'l1': |
| 300 | if nonpadding is not None: |
| 301 | loss = ((noise - x_recon).abs() * nonpadding.unsqueeze(1)).mean() |
| 302 | else: |
| 303 | # print('are you sure w/o nonpadding?') |
| 304 | loss = (noise - x_recon).abs().mean() |
| 305 | |
| 306 | elif self.loss_type == 'l2': |
| 307 | loss = F.mse_loss(noise, x_recon) |
| 308 | else: |
| 309 | raise NotImplementedError() |
| 310 | |
| 311 | return loss |
| 312 | |
| 313 | def forward(self, txt_tokens, mel2ph=None, spk_embed=None, |
| 314 | ref_mels=None, f0=None, uv=None, energy=None, infer=False): |