Fork of https://github.com/alokprasad/fastspeech_squeezewave to also fix denoising in squeezewave
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
29 lines
1016 B
29 lines
1016 B
import torch
|
|
import torch.nn as nn
|
|
|
|
|
|
class FastSpeechLoss(nn.Module):
|
|
""" FastSPeech Loss """
|
|
|
|
def __init__(self):
|
|
super(FastSpeechLoss, self).__init__()
|
|
self.mse_loss = nn.MSELoss()
|
|
self.l1_loss = nn.L1Loss()
|
|
|
|
def forward(self, mel, mel_postnet, duration_predicted, mel_target, duration_predictor_target):
|
|
mel_target.requires_grad = False
|
|
mel_loss = self.mse_loss(mel, mel_target)
|
|
mel_postnet_loss = self.mse_loss(mel_postnet, mel_target)
|
|
|
|
duration_predictor_target.requires_grad = False
|
|
# duration_predictor_target = duration_predictor_target + 1
|
|
# duration_predictor_target = torch.log(
|
|
# duration_predictor_target.float())
|
|
|
|
# print(duration_predictor_target)
|
|
# print(duration_predicted)
|
|
|
|
duration_predictor_loss = self.l1_loss(
|
|
duration_predicted, duration_predictor_target.float())
|
|
|
|
return mel_loss, mel_postnet_loss, duration_predictor_loss
|