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train.py
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import torch
import torch.nn as nn
from torch.utils.tensorboard import SummaryWriter
import argparse
import os
import json
from models.StyleSpeech import StyleSpeech
from dataloader import prepare_dataloader
from optimizer import ScheduledOptim
from evaluate import evaluate
import utils
def load_checkpoint(checkpoint_path, model, optimizer):
assert os.path.isfile(checkpoint_path)
print("Starting model from checkpoint '{}'".format(checkpoint_path))
checkpoint_dict = torch.load(checkpoint_path)
if 'model' in checkpoint_dict:
model.load_state_dict(checkpoint_dict['model'])
print('Model is loaded!')
if 'optimizer' in checkpoint_dict:
optimizer.load_state_dict(checkpoint_dict['optimizer'])
print('Optimizer is loaded!')
current_step = checkpoint_dict['step'] + 1
return model, optimizer, current_step
def main(args, c):
# Define model
model = StyleSpeech(c).cuda()
print("StyleSpeech Has Been Defined")
num_param = utils.get_param_num(model)
print('Number of StyleSpeech Parameters:', num_param)
with open(os.path.join(args.save_path, "model.txt"), "w") as f_log:
f_log.write(str(model))
# Optimizer
optimizer = torch.optim.Adam(model.parameters(), betas=c.betas, eps=c.eps)
# Loss
Loss = model.get_criterion()
print("Optimizer and Loss Function Defined.")
# Get dataset
data_loader = prepare_dataloader(args.data_path, "train.txt", shuffle=True, batch_size=c.batch_size)
print("Data Loader is Prepared.")
# Load checkpoint if exists
if args.checkpoint_path is not None:
assert os.path.exists(args.checkpoint_path)
model, optimizer, current_step= load_checkpoint(args.checkpoint_path, model, optimizer)
print("\n---Model Restored at Step {}---\n".format(current_step))
else:
print("\n---Start New Training---\n")
current_step = 0
checkpoint_path = os.path.join(args.save_path, 'ckpt')
os.makedirs(checkpoint_path, exist_ok=True)
# Scheduled optimizer
scheduled_optim = ScheduledOptim(optimizer, c.decoder_hidden, c.n_warm_up_step, current_step)
# Init logger
log_path = os.path.join(args.save_path, 'log')
logger = SummaryWriter(os.path.join(log_path, 'board'))
with open(os.path.join(log_path, "log.txt"), "a") as f_log:
f_log.write("Dataset :{}\n Number of Parameters: {}\n".format(c.dataset, num_param))
# Init synthesis directory
synth_path = os.path.join(args.save_path, 'synth')
os.makedirs(synth_path, exist_ok=True)
# Training
model.train()
while current_step < args.max_iter:
# Get Training Loader
for idx, batch in enumerate(data_loader):
if current_step == args.max_iter:
break
# Get Data
sid, text, mel_target, D, log_D, f0, energy, \
src_len, mel_len, max_src_len, max_mel_len = model.parse_batch(batch)
# Forward
scheduled_optim.zero_grad()
mel_output, src_output, style_vector, log_duration_output, f0_output, energy_output, src_mask, mel_mask, _ = model(
text, src_len, mel_target, mel_len, D, f0, energy, max_src_len, max_mel_len)
mel_loss, d_loss, f_loss, e_loss = Loss(mel_output, mel_target,
log_duration_output, log_D, f0_output, f0, energy_output, energy, src_len, mel_len)
# Total loss
total_loss = mel_loss + d_loss + f_loss + e_loss
# Backward
total_loss.backward()
# Clipping gradients to avoid gradient explosion
nn.utils.clip_grad_norm_(model.parameters(), c.grad_clip_thresh)
# Update weights
scheduled_optim.step_and_update_lr()
# Print log
if current_step % args.log_step == 0 and current_step != 0:
t_l = total_loss.item()
m_l = mel_loss.item()
d_l = d_loss.item()
f_l = f_loss.item()
e_l = e_loss.item()
str1 = "Step [{}/{}]:".format(current_step, args.max_iter)
str2 = "Total Loss: {:.4f}\nMel Loss: {:.4f},\n" \
"Duration Loss: {:.4f}, F0 Loss: {:.4f}, Energy Loss: {:.4f} ;" \
.format(t_l, m_l, d_l, f_l, e_l)
print(str1 + "\n" + str2 +"\n")
with open(os.path.join(log_path, "log.txt"), "a") as f_log:
f_log.write(str1 + "\n" + str2 +"\n")
logger.add_scalar('Train/total_loss', t_l, current_step)
logger.add_scalar('Train/mel_loss', m_l, current_step)
logger.add_scalar('Train/duration_loss', d_l, current_step)
logger.add_scalar('Train/f0_loss', f_l, current_step)
logger.add_scalar('Train/energy_loss', e_l, current_step)
# Save Checkpoint
if current_step % args.save_step == 0 and current_step != 0:
torch.save({'model': model.state_dict(), 'optimizer': optimizer.state_dict(), 'step': current_step},
os.path.join(checkpoint_path, 'checkpoint_{}.pth.tar'.format(current_step)))
print("*** Save Checkpoint ***")
print("Save model at step {}...\n".format(current_step))
if current_step % args.synth_step == 0 and current_step != 0:
length = mel_len[0].item()
mel_target = mel_target[0, :length].detach().cpu().transpose(0, 1)
mel = mel_output[0, :length].detach().cpu().transpose(0, 1)
# plotting
utils.plot_data([mel.numpy(), mel_target.numpy()],
['Synthesized Spectrogram', 'Ground-Truth Spectrogram'], filename=os.path.join(synth_path, 'step_{}.png'.format(current_step)))
print("Synth spectrograms at step {}...\n".format(current_step))
if current_step % args.eval_step == 0 and current_step != 0:
model.eval()
with torch.no_grad():
m_l, d_l, f_l, e_l = evaluate(args, model, current_step)
str_v = "*** Validation ***\n" \
"StyleSpeech Step {},\n" \
"Mel Loss: {}\nDuration Loss:{}\nF0 Loss: {}\nEnergy Loss: {}" \
.format(current_step, m_l, d_l, f_l, e_l)
print(str_v + "\n" )
with open(os.path.join(log_path, "eval.txt"), "a") as f_log:
f_log.write(str_v + "\n")
logger.add_scalar('Validation/mel_loss', m_l, current_step)
logger.add_scalar('Validation/duration_loss', d_l, current_step)
logger.add_scalar('Validation/f0_loss', f_l, current_step)
logger.add_scalar('Validation/energy_loss', e_l, current_step)
model.train()
current_step += 1
print("Training Done at Step : {}".format(current_step))
torch.save({'model': model.state_dict(), 'optimizer': scheduled_optim.state_dict(), 'step': current_step},
os.path.join(checkpoint_path, 'checkpoint_last_{}.pth.tar'.format(current_step)))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--data_path', default='dataset/LibriTTS/preprocessed')
parser.add_argument('--save_path', default='exp_stylespeech')
parser.add_argument('--config', default='configs/config.json')
parser.add_argument('--max_iter', default=100000, type=int)
parser.add_argument('--save_step', default=5000, type=int)
parser.add_argument('--synth_step', default=1000, type=int)
parser.add_argument('--eval_step', default=5000, type=int)
parser.add_argument('--log_step', default=100, type=int)
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the pretrained model')
args = parser.parse_args()
torch.backends.cudnn.enabled = True
with open(args.config) as f:
data = f.read()
json_config = json.loads(data)
config = utils.AttrDict(json_config)
utils.build_env(args.config, 'config.json', args.save_path)
main(args, config)