Medix-AI/coqui-tts
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coqui-tts
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masked loss

This commit is contained in:
Eren Golge 2018-03-22 21:13:33 -07:00
parent 96efe83a41
commit 46a2b10038
2 changed files with 3 additions and 3 deletions
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4
layers/losses.py
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@ -47,5 +47,5 @@ def L1LossMasked(input, target, length):
# mask: (batch, max_len) # mask: (batch, max_len)
mask = _sequence_mask(sequence_length=length, max_len=target.size(1)).unsqueeze(2) mask = _sequence_mask(sequence_length=length, max_len=target.size(1)).unsqueeze(2)
losses = losses * mask.float() losses = losses * mask.float()
loss = losses.sum() / length.float().sum() loss = losses.sum() / (length.float().sum() * target.shape[2])
return loss / input.shape[0] return loss

2
train.py
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@ -240,7 +240,7 @@ def evaluate(model, criterion, data_loader, current_step):
mel_output, linear_output, alignments = model.forward(text_input_var, mel_spec_var) mel_output, linear_output, alignments = model.forward(text_input_var, mel_spec_var)
# loss computation # loss computation
mel_loss = criterion(mel_output, mel_spec_var, mel_lengths) mel_loss = criterion(mel_output, mel_spec_var, mel_lengths_var)
linear_loss = 0.5 * criterion(linear_output, linear_spec_var, mel_lengths_var) \ linear_loss = 0.5 * criterion(linear_output, linear_spec_var, mel_lengths_var) \
+ 0.5 * criterion(linear_output[:, :, :n_priority_freq], + 0.5 * criterion(linear_output[:, :, :n_priority_freq],
linear_spec_var[: ,: ,:n_priority_freq], linear_spec_var[: ,: ,:n_priority_freq],