mirror of https://github.com/coqui-ai/TTS.git
print average text length, fix for Nancy preprocessor
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@ -55,6 +55,6 @@ def nancy(root_path, meta_file):
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id = line.split()[1]
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id = line.split()[1]
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text = line[line.find('"')+1:line.rfind('"')-1]
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text = line[line.find('"')+1:line.rfind('"')-1]
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wav_file = root_path + 'wavn/' + id + '.wav'
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wav_file = root_path + 'wavn/' + id + '.wav'
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items.append(text, wav_file)
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items.append([text, wav_file])
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random.shuffle(items)
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random.shuffle(items)
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return items
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return items
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17
train.py
17
train.py
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@ -51,6 +51,7 @@ def train(model, criterion, criterion_st, data_loader, optimizer, optimizer_st,
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mel_input = data[3]
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mel_input = data[3]
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mel_lengths = data[4]
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mel_lengths = data[4]
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stop_targets = data[5]
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stop_targets = data[5]
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avg_text_length = torch.mean(text_lengths.float())
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# set stop targets view, we predict a single stop token per r frames prediction
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# set stop targets view, we predict a single stop token per r frames prediction
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stop_targets = stop_targets.view(text_input.shape[0],
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stop_targets = stop_targets.view(text_input.shape[0],
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@ -68,12 +69,12 @@ def train(model, criterion, criterion_st, data_loader, optimizer, optimizer_st,
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# dispatch data to GPU
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# dispatch data to GPU
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if use_cuda:
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if use_cuda:
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text_input = text_input.cuda()
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text_input = text_input.cuda(non_blocking=True)
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text_lengths = text_lengths.cuda()
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text_lengths = text_lengths.cuda(non_blocking=True)
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mel_input = mel_input.cuda()
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mel_input = mel_input.cuda(non_blocking=True)
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mel_lengths = mel_lengths.cuda()
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mel_lengths = mel_lengths.cuda(non_blocking=True)
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linear_input = linear_input.cuda()
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linear_input = linear_input.cuda(non_blocking=True)
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stop_targets = stop_targets.cuda()
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stop_targets = stop_targets.cuda(non_blocking=True)
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# compute mask for padding
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# compute mask for padding
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mask = sequence_mask(text_lengths)
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mask = sequence_mask(text_lengths)
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@ -129,10 +130,10 @@ def train(model, criterion, criterion_st, data_loader, optimizer, optimizer_st,
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print(
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print(
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" | > Step:{}/{} GlobalStep:{} TotalLoss:{:.5f} LinearLoss:{:.5f} "
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" | > Step:{}/{} GlobalStep:{} TotalLoss:{:.5f} LinearLoss:{:.5f} "
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"MelLoss:{:.5f} StopLoss:{:.5f} GradNorm:{:.5f} "
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"MelLoss:{:.5f} StopLoss:{:.5f} GradNorm:{:.5f} "
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"GradNormST:{:.5f} StepTime:{:.2f} LR:{:.6f}".format(
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"GradNormST:{:.5f} AvgTextLen:{:.1f} StepTime:{:.2f} LR:{:.6f}".format(
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num_iter, batch_n_iter, current_step, loss.item(),
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num_iter, batch_n_iter, current_step, loss.item(),
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linear_loss.item(), mel_loss.item(), stop_loss.item(),
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linear_loss.item(), mel_loss.item(), stop_loss.item(),
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grad_norm, grad_norm_st, step_time, current_lr),
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grad_norm, grad_norm_st, avg_text_length, step_time, current_lr),
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flush=True)
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flush=True)
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avg_linear_loss += linear_loss.item()
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avg_linear_loss += linear_loss.item()
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