mirror of https://github.com/coqui-ai/TTS.git
small fixes
Eren Golge
parent
448a70823d
commit
a31d1cb2d9
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@ -43,7 +43,7 @@
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// VALIDATION
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"run_eval": true,
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"test_delay_epochs": 5, //Until attention is aligned, testing only wastes computation time.
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"test_delay_epochs": 10, //Until attention is aligned, testing only wastes computation time.
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"test_sentences_file": null, // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
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// OPTIMIZER
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24
train.py
24
train.py
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@ -8,7 +8,6 @@ import traceback
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import numpy as np
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import torch
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import torch.nn as nn
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from torch import optim
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from torch.utils.data import DataLoader
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from TTS.datasets.TTSDataset import MyDataset
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@ -171,7 +170,7 @@ def train(model, criterion, criterion_st, optimizer, optimizer_st, scheduler,
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stop_targets) if c.stopnet else torch.zeros(1)
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if c.loss_masking:
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decoder_loss = criterion(decoder_output, mel_input, mel_lengths)
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if c.model in ["Tacotron"]:
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if c.model in ["Tacotron", "TacotronGST"]:
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postnet_loss = criterion(postnet_output, linear_input,
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mel_lengths)
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else:
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@ -179,7 +178,7 @@ def train(model, criterion, criterion_st, optimizer, optimizer_st, scheduler,
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mel_lengths)
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else:
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decoder_loss = criterion(decoder_output, mel_input)
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if c.model in ["Tacotron"]:
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if c.model in ["Tacotron", "TacotronGST"]:
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postnet_loss = criterion(postnet_output, linear_input)
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else:
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postnet_loss = criterion(postnet_output, mel_input)
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@ -277,7 +276,7 @@ def train(model, criterion, criterion_st, optimizer, optimizer_st, scheduler,
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# Diagnostic visualizations
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const_spec = postnet_output[0].data.cpu().numpy()
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gt_spec = linear_input[0].data.cpu().numpy() if c.model in [
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"Tacotron"
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"Tacotron", "TacotronGST"
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] else mel_input[0].data.cpu().numpy()
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align_img = alignments[0].data.cpu().numpy()
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@ -293,7 +292,7 @@ def train(model, criterion, criterion_st, optimizer, optimizer_st, scheduler,
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tb_logger.tb_train_figures(global_step, figures)
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# Sample audio
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if c.model in ["Tacotron"]:
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if c.model in ["Tacotron", "TacotronGST"]:
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train_audio = ap.inv_spectrogram(const_spec.T)
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else:
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train_audio = ap.inv_mel_spectrogram(const_spec.T)
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@ -370,7 +369,7 @@ def evaluate(model, criterion, criterion_st, ap, global_step, epoch):
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if c.loss_masking:
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decoder_loss = criterion(decoder_output, mel_input,
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mel_lengths)
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if c.model in ["Tacotron"]:
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if c.model in ["Tacotron", "TacotronGST"]:
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postnet_loss = criterion(postnet_output, linear_input,
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mel_lengths)
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else:
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@ -378,7 +377,7 @@ def evaluate(model, criterion, criterion_st, ap, global_step, epoch):
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mel_lengths)
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else:
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decoder_loss = criterion(decoder_output, mel_input)
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if c.model in ["Tacotron"]:
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if c.model in ["Tacotron", "TacotronGST"]:
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postnet_loss = criterion(postnet_output, linear_input)
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else:
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postnet_loss = criterion(postnet_output, mel_input)
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@ -434,7 +433,7 @@ def evaluate(model, criterion, criterion_st, ap, global_step, epoch):
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idx = np.random.randint(mel_input.shape[0])
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const_spec = postnet_output[idx].data.cpu().numpy()
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gt_spec = linear_input[idx].data.cpu().numpy() if c.model in [
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"Tacotron"
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"Tacotron", "TacotronGST"
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] else mel_input[idx].data.cpu().numpy()
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align_img = alignments[idx].data.cpu().numpy()
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@ -445,7 +444,7 @@ def evaluate(model, criterion, criterion_st, ap, global_step, epoch):
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}
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# Sample audio
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if c.model in ["Tacotron"]:
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if c.model in ["Tacotron", "TacotronGST"]:
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eval_audio = ap.inv_spectrogram(const_spec.T)
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else:
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eval_audio = ap.inv_mel_spectrogram(const_spec.T)
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@ -466,7 +465,6 @@ def evaluate(model, criterion, criterion_st, ap, global_step, epoch):
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tb_logger.tb_eval_stats(global_step, epoch_stats)
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tb_logger.tb_eval_figures(global_step, eval_figures)
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if args.rank == 0 and epoch > c.test_delay_epochs:
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if c.test_sentences_file is None:
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test_sentences = [
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@ -562,10 +560,10 @@ def main(args): # pylint: disable=redefined-outer-name
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optimizer_st = None
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if c.loss_masking:
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criterion = L1LossMasked() if c.model in ["Tacotron"
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criterion = L1LossMasked() if c.model in ["Tacotron", "TacotronGST"
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] else MSELossMasked()
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else:
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criterion = nn.L1Loss() if c.model in ["Tacotron"
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criterion = nn.L1Loss() if c.model in ["Tacotron", "TacotronGST"
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] else nn.MSELoss()
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criterion_st = nn.BCEWithLogitsLoss(
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pos_weight=torch.tensor(10)) if c.stopnet else None
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@ -686,7 +684,7 @@ if __name__ == '__main__':
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args.restore_path = latest_model_file
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print(f" > Training continues for {args.restore_path}")
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# setup output paths and read configs
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# setup output paths and read configs
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c = load_config(args.config_path)
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_ = os.path.dirname(os.path.realpath(__file__))
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