Medix-AI/coqui-tts
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coqui-tts
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make attn guiding optional

This commit is contained in:
Eren Golge 2018-04-25 05:43:29 -07:00
parent a2a1b9931b
commit cbd24d9a42
1 changed files with 12 additions and 12 deletions
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24
train.py
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@ -91,9 +91,6 @@ def train(model, criterion, data_loader, optimizer, epoch):
params_group['lr'] = current_lr params_group['lr'] = current_lr
optimizer.zero_grad() optimizer.zero_grad()
# setup mk
mk = mk_decay(c.mk, c.epochs, epoch)
# convert inputs to variables # convert inputs to variables
text_input_var = Variable(text_input) text_input_var = Variable(text_input)
@ -109,9 +106,11 @@ def train(model, criterion, data_loader, optimizer, epoch):
linear_spec_var = linear_spec_var.cuda() linear_spec_var = linear_spec_var.cuda()
# create attention mask # create attention mask
N = text_input_var.shape[1] if c.mk > 0.0:
T = mel_spec_var.shape[1] // c.r N = text_input_var.shape[1]
M = create_attn_mask(N, T, 0.03) T = mel_spec_var.shape[1] // c.r
M = create_attn_mask(N, T, 0.03)
mk = mk_decay(c.mk, c.epochs, epoch)
# forward pass # forward pass
mel_output, linear_output, alignments =\ mel_output, linear_output, alignments =\
@ -123,9 +122,10 @@ def train(model, criterion, data_loader, optimizer, epoch):
+ 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],
mel_lengths_var) mel_lengths_var)
attention_loss = criterion(alignments, M, mel_lengths_var) loss = mel_loss + linear_loss
print(mk) if c.mk > 0.0:
loss = mel_loss + linear_loss + mk * attention_loss attention_loss = criterion(alignments, M, mel_lengths_var)
loss += mk * attention_loss
# backpass and check the grad norm # backpass and check the grad norm
loss.backward() loss.backward()
@ -155,7 +155,6 @@ def train(model, criterion, data_loader, optimizer, epoch):
tb.add_scalar('TrainIterLoss/LinearLoss', linear_loss.data[0], tb.add_scalar('TrainIterLoss/LinearLoss', linear_loss.data[0],
current_step) current_step)
tb.add_scalar('TrainIterLoss/MelLoss', mel_loss.data[0], current_step) tb.add_scalar('TrainIterLoss/MelLoss', mel_loss.data[0], current_step)
tb.add_scalar('TrainIterLoss/AttnLoss', attention_loss.data[0], current_step)
tb.add_scalar('Params/LearningRate', optimizer.param_groups[0]['lr'], tb.add_scalar('Params/LearningRate', optimizer.param_groups[0]['lr'],
current_step) current_step)
tb.add_scalar('Params/GradNorm', grad_norm, current_step) tb.add_scalar('Params/GradNorm', grad_norm, current_step)
@ -196,14 +195,15 @@ def train(model, criterion, data_loader, optimizer, epoch):
avg_linear_loss /= (num_iter + 1) avg_linear_loss /= (num_iter + 1)
avg_mel_loss /= (num_iter + 1) avg_mel_loss /= (num_iter + 1)
avg_attn_loss /= (num_iter + 1)
avg_total_loss = avg_mel_loss + avg_linear_loss avg_total_loss = avg_mel_loss + avg_linear_loss
# Plot Training Epoch Stats # Plot Training Epoch Stats
tb.add_scalar('TrainEpochLoss/TotalLoss', avg_total_loss, current_step) tb.add_scalar('TrainEpochLoss/TotalLoss', avg_total_loss, current_step)
tb.add_scalar('TrainEpochLoss/LinearLoss', avg_linear_loss, current_step) tb.add_scalar('TrainEpochLoss/LinearLoss', avg_linear_loss, current_step)
tb.add_scalar('TrainEpochLoss/MelLoss', avg_mel_loss, current_step) tb.add_scalar('TrainEpochLoss/MelLoss', avg_mel_loss, current_step)
tb.add_scalar('TrainEpochLoss/AttnLoss', avg_attn_loss, current_step) if c.mk > 0:
avg_attn_loss /= (num_iter + 1)
tb.add_scalar('TrainEpochLoss/AttnLoss', avg_attn_loss, current_step)
tb.add_scalar('Time/EpochTime', epoch_time, epoch) tb.add_scalar('Time/EpochTime', epoch_time, epoch)
epoch_time = 0 epoch_time = 0