Medix-AI/coqui-tts
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coqui-tts
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Eren G 2018-07-11 12:42:59 +02:00
parent cc26cd6c7b
commit 7fad94d8a7
1 changed files with 14 additions and 3 deletions
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train.py
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@ -152,13 +152,14 @@ def train(model, criterion, criterion_st, data_loader, optimizer, optimizer_st,
if current_step % c.print_step == 0: if current_step % c.print_step == 0:
print(" | | > Step:{}\tGlobalStep:{}\tTotalLoss:{:.5f}\tLinearLoss:{:.5f}\tMelLoss:\ print(" | | > Step:{}\tGlobalStep:{}\tTotalLoss:{:.5f}\tLinearLoss:{:.5f}\tMelLoss:\
{:.5f}\tStopLoss:{:.5f}\tGradNorm:{:.5f}\t\ {:.5f}\tStopLoss:{:.5f}\tGradNorm:{:.5f}\t\
GradNormST: {:.5f}".format(num_iter, current_step, GradNormST:{:.5f}\tStepTime:{:.2f}".format(num_iter, current_step,
loss.item(), loss.item(),
linear_loss.item(), linear_loss.item(),
mel_loss.item(), mel_loss.item(),
stop_loss.item(), stop_loss.item(),
grad_norm.item(), grad_norm.item(),
grad_norm_st.item())) grad_norm_st.item(),
step_time))
avg_linear_loss += linear_loss.item() avg_linear_loss += linear_loss.item()
avg_mel_loss += mel_loss.item() avg_mel_loss += mel_loss.item()
@ -213,6 +214,16 @@ def train(model, criterion, criterion_st, data_loader, optimizer, optimizer_st,
avg_stop_loss /= (num_iter + 1) avg_stop_loss /= (num_iter + 1)
avg_total_loss = avg_mel_loss + avg_linear_loss + avg_stop_loss avg_total_loss = avg_mel_loss + avg_linear_loss + avg_stop_loss
# print epoch stats
print(" | | > EPOCH END -- GlobalStep:{}\tAvgTotalLoss:{:.5f}\t\
AvgLinearLoss:{:.5f}\tAvgMelLoss:{:.5f}\t\
AvgStopLoss:{:.5f}\tEpochTime:{:.2f}".format(current_step,
avg_total_loss,
avg_linear_loss,
avg_mel_loss,
avg_stop_loss,
epoch_time))
# 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)
@ -423,7 +434,7 @@ def main(args):
train_loss, current_step = train( train_loss, current_step = train(
model, criterion, criterion_st, train_loader, optimizer, optimizer_st, epoch) model, criterion, criterion_st, train_loader, optimizer, optimizer_st, epoch)
val_loss = evaluate(model, criterion, criterion_st, val_loader, current_step) val_loss = evaluate(model, criterion, criterion_st, val_loader, current_step)
print(" >>> Train Loss: {:.5f}\t Validation Loss: {:.5f}".format(train_loss, val_loss)) print(" | > Train Loss: {:.5f}\t Validation Loss: {:.5f}".format(train_loss, val_loss))
best_loss = save_best_model(model, optimizer, val_loss, best_loss = save_best_model(model, optimizer, val_loss,
best_loss, OUT_PATH, best_loss, OUT_PATH,
current_step, epoch) current_step, epoch)