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train.py
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train.py
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import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import numpy as np
import argparse
import os
import time
from styler import STYLER
from loss import STYLERLoss, DomainAdversarialTrainingLoss
from dataset import Dataset
from optimizer import ScheduledOptim
from evaluate import evaluate
import hparams as hp
import utils
import audio as Audio
def main(args):
torch.manual_seed(0)
# Get device
device = torch.device('cuda'if torch.cuda.is_available()else 'cpu')
# Get dataset
dataset = Dataset("train.txt")
loader = DataLoader(dataset, batch_size=hp.batch_size**2, shuffle=True,
collate_fn=dataset.collate_fn, drop_last=True, num_workers=0)
# Define model
model = nn.DataParallel(STYLER()).to(device)
print("Model Has Been Defined")
# Parameters
num_param = utils.get_param_num(model)
text_encoder = utils.get_param_num(model.module.style_modeling.style_encoder.text_encoder)
audio_encoder = utils.get_param_num(model.module.style_modeling.style_encoder.audio_encoder)
predictors = utils.get_param_num(model.module.style_modeling.duration_predictor)\
+ utils.get_param_num(model.module.style_modeling.pitch_predictor)\
+ utils.get_param_num(model.module.style_modeling.energy_predictor)
decoder = utils.get_param_num(model.module.decoder)
print('Number of Model Parameters :', num_param)
print('Number of Text Encoder Parameters :', text_encoder)
print('Number of Audio Encoder Parameters :', audio_encoder)
print('Number of Predictor Parameters :', predictors)
print('Number of Decoder Parameters :', decoder)
# Optimizer and loss
optimizer = torch.optim.Adam(
model.parameters(), betas=hp.betas, eps=hp.eps, weight_decay=hp.weight_decay)
scheduled_optim = ScheduledOptim(
optimizer, hp.decoder_hidden, hp.n_warm_up_step, args.restore_step)
Loss = STYLERLoss().to(device)
DATLoss = DomainAdversarialTrainingLoss().to(device)
print("Optimizer and Loss Function Defined.")
# Load checkpoint if exists
checkpoint_path = os.path.join(hp.checkpoint_path())
try:
checkpoint = torch.load(os.path.join(
checkpoint_path, 'checkpoint_{}.pth.tar'.format(args.restore_step)))
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("\n---Model Restored at Step {}---\n".format(args.restore_step))
except:
print("\n---Start New Training---\n")
if not os.path.exists(checkpoint_path):
os.makedirs(checkpoint_path)
# Load vocoder
vocoder = utils.get_vocoder()
# Init logger
log_path = hp.log_path()
if not os.path.exists(log_path):
os.makedirs(log_path)
os.makedirs(os.path.join(log_path, 'train'))
os.makedirs(os.path.join(log_path, 'validation'))
train_logger = SummaryWriter(os.path.join(log_path, 'train'))
val_logger = SummaryWriter(os.path.join(log_path, 'validation'))
# Init synthesis directory
synth_path = hp.synth_path()
if not os.path.exists(synth_path):
os.makedirs(synth_path)
# Define Some Information
Time = np.array([])
Start = time.perf_counter()
# Training
model = model.train()
for epoch in range(hp.epochs):
# Get Training Loader
total_step = hp.epochs * len(loader) * hp.batch_size
for i, batchs in enumerate(loader):
for j, data_of_batch in enumerate(batchs):
start_time = time.perf_counter()
current_step = i*hp.batch_size + j + args.restore_step + \
epoch*len(loader)*hp.batch_size + 1
# Get Data
text = torch.from_numpy(
data_of_batch["text"]).long().to(device)
mel_target = torch.from_numpy(
data_of_batch["mel_target"]).float().to(device)
mel_aug = torch.from_numpy(
data_of_batch["mel_aug"]).float().to(device)
D = torch.from_numpy(data_of_batch["D"]).long().to(device)
log_D = torch.from_numpy(
data_of_batch["log_D"]).float().to(device)
f0 = torch.from_numpy(data_of_batch["f0"]).float().to(device)
f0_norm = torch.from_numpy(data_of_batch["f0_norm"]).float().to(device)
f0_norm_aug = torch.from_numpy(data_of_batch["f0_norm_aug"]).float().to(device)
energy = torch.from_numpy(
data_of_batch["energy"]).float().to(device)
energy_input = torch.from_numpy(
data_of_batch["energy_input"]).float().to(device)
energy_input_aug = torch.from_numpy(
data_of_batch["energy_input_aug"]).float().to(device)
speaker_embed = torch.from_numpy(
data_of_batch["speaker_embed"]).float().to(device)
src_len = torch.from_numpy(
data_of_batch["src_len"]).long().to(device)
mel_len = torch.from_numpy(
data_of_batch["mel_len"]).long().to(device)
max_src_len = np.max(data_of_batch["src_len"]).astype(np.int32)
max_mel_len = np.max(data_of_batch["mel_len"]).astype(np.int32)
# Forward
mel_outputs, mel_postnet_outputs, log_duration_output, f0_output, energy_output, src_mask, mel_mask, _, aug_posteriors = model(
text, mel_target, mel_aug, f0_norm, energy_input, src_len, mel_len, D, f0, energy, max_src_len, max_mel_len, speaker_embed=speaker_embed)
# Cal Loss Clean
mel_output, mel_postnet_output = mel_outputs[0], mel_postnet_outputs[0]
mel_loss, mel_postnet_loss, d_loss, f_loss, e_loss, classifier_loss_a = Loss(
log_duration_output, log_D, f0_output, f0, energy_output, energy, mel_output, mel_postnet_output, mel_target, ~src_mask, ~mel_mask, src_len, mel_len,\
aug_posteriors, torch.zeros(mel_target.size(0)).long().to(device))
# Cal Loss Noisy
mel_output_noisy, mel_postnet_output_noisy = mel_outputs[1], mel_postnet_outputs[1]
mel_noisy_loss, mel_postnet_noisy_loss = Loss.cal_mel_loss(mel_output_noisy, mel_postnet_output_noisy, mel_aug, ~mel_mask)
# Forward DAT
enc_cat = model.module.style_modeling.style_encoder.encoder_input_cat(mel_aug, f0_norm_aug, energy_input_aug, mel_aug)
duration_encoding, pitch_encoding, energy_encoding, _ = model.module.style_modeling.style_encoder.audio_encoder(enc_cat, mel_len, src_len, mask=None)
aug_posterior_d = model.module.style_modeling.augmentation_classifier_d(duration_encoding)
aug_posterior_p = model.module.style_modeling.augmentation_classifier_p(pitch_encoding)
aug_posterior_e = model.module.style_modeling.augmentation_classifier_e(energy_encoding)
# Cal Loss DAT
classifier_loss_a_dat = DATLoss((aug_posterior_d, aug_posterior_p, aug_posterior_e), torch.ones(mel_target.size(0)).long().to(device))
# Total loss
total_loss = mel_loss + mel_postnet_loss + mel_noisy_loss + mel_postnet_noisy_loss + d_loss + f_loss + e_loss\
+ hp.dat_weight*(classifier_loss_a + classifier_loss_a_dat)
# Logger
t_l = total_loss.item()
m_l = mel_loss.item()
m_p_l = mel_postnet_loss.item()
m_n_l = mel_noisy_loss.item()
m_p_n_l = mel_postnet_noisy_loss.item()
d_l = d_loss.item()
f_l = f_loss.item()
e_l = e_loss.item()
cl_a = classifier_loss_a.item()
cl_a_dat = classifier_loss_a_dat.item()
# Backward
total_loss = total_loss / hp.acc_steps
total_loss.backward()
if current_step % hp.acc_steps != 0:
continue
# Clipping gradients to avoid gradient explosion
nn.utils.clip_grad_norm_(
model.parameters(), hp.grad_clip_thresh)
# Update weights
scheduled_optim.step_and_update_lr()
scheduled_optim.zero_grad()
# Print
if current_step == 1 or current_step % hp.log_step == 0:
Now = time.perf_counter()
str1 = "Epoch [{}/{}], Step [{}/{}]:".format(
epoch+1, hp.epochs, current_step, total_step)
str2 = "Total Loss: {:.4f}, Mel Loss: {:.4f}, Mel PostNet Loss: {:.4f}, Duration Loss: {:.4f}, F0 Loss: {:.4f}, Energy Loss: {:.4f};".format(
t_l, m_l, m_p_l, d_l, f_l, e_l)
str3 = "Time Used: {:.3f}s, Estimated Time Remaining: {:.3f}s.".format(
(Now-Start), (total_step-current_step)*np.mean(Time))
print("\n" + str1)
print(str2)
print(str3)
train_logger.add_scalar(
'Loss/total_loss', t_l, current_step)
train_logger.add_scalar('Loss/mel_loss', m_l, current_step)
train_logger.add_scalar(
'Loss/mel_postnet_loss', m_p_l, current_step)
train_logger.add_scalar('Loss/mel_noisy_loss', m_n_l, current_step)
train_logger.add_scalar(
'Loss/mel_postnet_noisy_loss', m_p_n_l, current_step)
train_logger.add_scalar(
'Loss/duration_loss', d_l, current_step)
train_logger.add_scalar('Loss/F0_loss', f_l, current_step)
train_logger.add_scalar(
'Loss/energy_loss', e_l, current_step)
train_logger.add_scalar(
'Loss/dat_clean_loss', cl_a, current_step)
train_logger.add_scalar(
'Loss/dat_noisy_loss', cl_a_dat, current_step)
if current_step % hp.save_step == 0:
torch.save({'model': model.state_dict(), 'optimizer': optimizer.state_dict(
)}, os.path.join(checkpoint_path, 'checkpoint_{}.pth.tar'.format(current_step)))
print("save model at step {} ...".format(current_step))
if current_step == 1 or current_step % hp.synth_step == 0:
length = mel_len[0].item()
mel_target_torch = mel_target[0, :length].detach(
).unsqueeze(0).transpose(1, 2)
mel_aug_torch = mel_aug[0, :length].detach(
).unsqueeze(0).transpose(1, 2)
mel_target = mel_target[0, :length].detach(
).cpu().transpose(0, 1)
mel_aug = mel_aug[0, :length].detach(
).cpu().transpose(0, 1)
mel_torch = mel_output[0, :length].detach(
).unsqueeze(0).transpose(1, 2)
mel_noisy_torch = mel_output_noisy[0, :length].detach(
).unsqueeze(0).transpose(1, 2)
mel = mel_output[0, :length].detach().cpu().transpose(0, 1)
mel_noisy = mel_output_noisy[0, :length].detach().cpu().transpose(0, 1)
mel_postnet_torch = mel_postnet_output[0, :length].detach(
).unsqueeze(0).transpose(1, 2)
mel_postnet_noisy_torch = mel_postnet_output_noisy[0, :length].detach(
).unsqueeze(0).transpose(1, 2)
mel_postnet = mel_postnet_output[0, :length].detach(
).cpu().transpose(0, 1)
mel_postnet_noisy = mel_postnet_output_noisy[0, :length].detach(
).cpu().transpose(0, 1)
# Audio.tools.inv_mel_spec(mel, os.path.join(
# synth_path, "step_{}_{}_griffin_lim.wav".format(current_step, "c")))
# Audio.tools.inv_mel_spec(mel_postnet, os.path.join(
# synth_path, "step_{}_{}_postnet_griffin_lim.wav".format(current_step, "c")))
# Audio.tools.inv_mel_spec(mel_noisy, os.path.join(
# synth_path, "step_{}_{}_griffin_lim.wav".format(current_step, "n")))
# Audio.tools.inv_mel_spec(mel_postnet_noisy, os.path.join(
# synth_path, "step_{}_{}_postnet_griffin_lim.wav".format(current_step, "n")))
wav_mel = utils.vocoder_infer(mel_torch, vocoder, os.path.join(
hp.synth_path(), 'step_{}_{}_{}.wav'.format(current_step, "c", hp.vocoder)))
wav_mel_postnet = utils.vocoder_infer(mel_postnet_torch, vocoder, os.path.join(
hp.synth_path(), 'step_{}_{}_postnet_{}.wav'.format(current_step, "c", hp.vocoder)))
wav_ground_truth = utils.vocoder_infer(mel_target_torch, vocoder, os.path.join(
hp.synth_path(), 'step_{}_{}_ground-truth_{}.wav'.format(current_step, "c", hp.vocoder)))
wav_mel_noisy = utils.vocoder_infer(mel_noisy_torch, vocoder, os.path.join(
hp.synth_path(), 'step_{}_{}_{}.wav'.format(current_step, "n", hp.vocoder)))
wav_mel_postnet_noisy = utils.vocoder_infer(mel_postnet_noisy_torch, vocoder, os.path.join(
hp.synth_path(), 'step_{}_{}_postnet_{}.wav'.format(current_step, "n", hp.vocoder)))
wav_aug = utils.vocoder_infer(mel_aug_torch, vocoder, os.path.join(
hp.synth_path(), 'step_{}_{}_ground-truth_{}.wav'.format(current_step, "n", hp.vocoder)))
# Model duration prediction
log_duration_output = log_duration_output[0, :src_len[0].item()].detach().cpu() # [seg_len]
log_duration_output = torch.clamp(torch.round(torch.exp(log_duration_output)-hp.log_offset), min=0).int()
model_duration = utils.get_alignment_2D(log_duration_output).T # [seg_len, mel_len]
model_duration = utils.plot_alignment([model_duration])
# Model mel prediction
f0 = f0[0, :length].detach().cpu().numpy()
energy = energy[0, :length].detach().cpu().numpy()
f0_output = f0_output[0, :length].detach().cpu().numpy()
energy_output = energy_output[0,
:length].detach().cpu().numpy()
mel_predicted = utils.plot_data([(mel_postnet.numpy(), f0_output, energy_output), (mel_target.numpy(), f0, energy)],
['Synthetized Spectrogram Clean', 'Ground-Truth Spectrogram'], filename=os.path.join(synth_path, 'step_{}_{}.png'.format(current_step, "c")))
mel_noisy_predicted = utils.plot_data([(mel_postnet_noisy.numpy(), f0_output, energy_output), (mel_aug.numpy(), f0, energy)],
['Synthetized Spectrogram Noisy', 'Aug Spectrogram'], filename=os.path.join(synth_path, 'step_{}_{}.png'.format(current_step, "n")))
# Normalize audio for tensorboard logger. See https://github.com/lanpa/tensorboardX/issues/511#issuecomment-537600045
wav_ground_truth = wav_ground_truth/max(wav_ground_truth)
wav_mel = wav_mel/max(wav_mel)
wav_mel_postnet = wav_mel_postnet/max(wav_mel_postnet)
wav_aug = wav_aug/max(wav_aug)
wav_mel_noisy = wav_mel_noisy/max(wav_mel_noisy)
wav_mel_postnet_noisy = wav_mel_postnet_noisy/max(wav_mel_postnet_noisy)
train_logger.add_image(
"model_duration",
model_duration,
current_step, dataformats='HWC')
train_logger.add_image(
"mel_predicted/Clean",
mel_predicted,
current_step, dataformats='HWC')
train_logger.add_image(
"mel_predicted/Noisy",
mel_noisy_predicted,
current_step, dataformats='HWC')
train_logger.add_audio(
"Clean/wav_ground_truth",
wav_ground_truth,
current_step, sample_rate=hp.sampling_rate)
train_logger.add_audio(
"Clean/wav_mel",
wav_mel,
current_step, sample_rate=hp.sampling_rate)
train_logger.add_audio(
"Clean/wav_mel_postnet",
wav_mel_postnet,
current_step, sample_rate=hp.sampling_rate)
train_logger.add_audio(
"Noisy/wav_aug",
wav_aug,
current_step, sample_rate=hp.sampling_rate)
train_logger.add_audio(
"Noisy/wav_mel_noisy",
wav_mel_noisy,
current_step, sample_rate=hp.sampling_rate)
train_logger.add_audio(
"Noisy/wav_mel_postnet_noisy",
wav_mel_postnet_noisy,
current_step, sample_rate=hp.sampling_rate)
if current_step == 1 or current_step % hp.eval_step == 0:
model.eval()
with torch.no_grad():
d_l, f_l, e_l, cl_a, cl_a_dat, m_l, m_p_l, m_n_l, m_p_n_l = evaluate(
model, current_step)
t_l = d_l + f_l + e_l + m_l + m_p_l + m_n_l + m_p_n_l\
+ hp.dat_weight*(cl_a + cl_a_dat)
val_logger.add_scalar(
'Loss/total_loss', t_l, current_step)
val_logger.add_scalar(
'Loss/mel_loss', m_l, current_step)
val_logger.add_scalar(
'Loss/mel_postnet_loss', m_p_l, current_step)
val_logger.add_scalar(
'Loss/mel_noisy_loss', m_n_l, current_step)
val_logger.add_scalar(
'Loss/mel_postnet_noisy_loss', m_p_n_l, current_step)
val_logger.add_scalar(
'Loss/duration_loss', d_l, current_step)
val_logger.add_scalar(
'Loss/F0_loss', f_l, current_step)
val_logger.add_scalar(
'Loss/energy_loss', e_l, current_step)
val_logger.add_scalar(
'Loss/dat_clean_loss', cl_a, current_step)
val_logger.add_scalar(
'Loss/dat_noisy_loss', cl_a_dat, current_step)
model.train()
end_time = time.perf_counter()
Time = np.append(Time, end_time - start_time)
if len(Time) == hp.clear_Time:
temp_value = np.mean(Time)
Time = np.delete(
Time, [i for i in range(len(Time))], axis=None)
Time = np.append(Time, temp_value)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--restore_step', type=int, default=0)
parser.add_argument('--version', type=str, default="default")
parser.add_argument('--batch_size', type=int, default=hp.batch_size)
args = parser.parse_args()
# Set Batch Size
hp.batch_size = args.batch_size
# Version Control
hp.version = args.version + "_batch{}".format(hp.batch_size)
main(args)