mask pad value to 0

wenet/tts/vits/models.py

@@ -15,6 +15,7 @@
 from wenet.tts.vits.commons import init_weights, get_padding
 from wenet.tts.vits.losses import generator_loss, discriminator_loss, feature_loss, kl_loss
 from wenet.tts.vits.mel_processing import mel_spectrogram_torch
+from wenet.utils.mask import make_pad_mask
 
 
 class StochasticDurationPredictor(nn.Module):
@@ -791,6 +792,8 @@ def __init__(self, n_vocab, spec_channels, **kwargs):
     def forward(self, batch: dict, device: torch.device):
         x = batch['target'].to(device)
         x_lengths = batch['target_lengths'].to(device)
+        x_mask = make_pad_mask(x_lengths)
+        x = x.masked_fill(x_mask, 0)  # change pad value(IGNORE_ID = -1) to 0
         spec = batch['feats'].to(device)
         spec_lengths = batch['feats_lengths'].to(device)
         spec = spec.transpose(1, 2)