| import torch | |
| import torch.nn as nn | |
| import hparams as hp | |
| class FastSpeech2Loss(nn.Module): | |
| """ FastSpeech2 Loss """ | |
| def __init__(self): | |
| super(FastSpeech2Loss, self).__init__() | |
| self.mse_loss = nn.MSELoss() | |
| self.mae_loss = nn.L1Loss() | |
| def forward(self, log_d_predicted, log_d_target, p_predicted, p_target, e_predicted, e_target, mel, mel_postnet, mel_target, src_mask, mel_mask): | |
| log_d_target.requires_grad = False | |
| p_target.requires_grad = False | |
| e_target.requires_grad = False | |
| mel_target.requires_grad = False | |
| log_d_predicted = log_d_predicted.masked_select(src_mask) | |
| log_d_target = log_d_target.masked_select(src_mask) | |
| p_predicted = p_predicted.masked_select(src_mask) | |
| p_target = p_target.masked_select(src_mask) | |
| e_predicted = e_predicted.masked_select(src_mask) | |
| e_target = e_target.masked_select(src_mask) | |
| mel = mel.masked_select(mel_mask.unsqueeze(-1)) | |
| mel_postnet = mel_postnet.masked_select(mel_mask.unsqueeze(-1)) | |
| mel_target = mel_target.masked_select(mel_mask.unsqueeze(-1)) | |
| mel_loss = self.mse_loss(mel, mel_target) | |
| mel_postnet_loss = self.mse_loss(mel_postnet, mel_target) | |
| d_loss = self.mae_loss(log_d_predicted, log_d_target) | |
| p_loss = self.mae_loss(p_predicted, p_target) | |
| e_loss = self.mae_loss(e_predicted, e_target) | |
| return mel_loss, mel_postnet_loss, d_loss, p_loss, e_loss | |