| 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 |
|
|
