File size: 9,568 Bytes
ae8e1dd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 |
import numpy as np
from tqdm import tqdm
from copy import deepcopy
import torch
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import data_collate
import data_loader
from utils_data import plot_tensor, save_plot
from model.utils import fix_len_compatibility
from text.symbols import symbols
import utils_data as utils
class ModelEmaV2(torch.nn.Module):
def __init__(self, model, decay=0.9999, device=None):
super(ModelEmaV2, self).__init__()
self.model_state_dict = deepcopy(model.state_dict())
self.decay = decay
self.device = device # perform ema on different device from model if set
def _update(self, model, update_fn):
with torch.no_grad():
for ema_v, model_v in zip(self.model_state_dict.values(), model.state_dict().values()):
if self.device is not None:
model_v = model_v.to(device=self.device)
ema_v.copy_(update_fn(ema_v, model_v))
def update(self, model):
self._update(model, update_fn=lambda e, m: self.decay * e + (1. - self.decay) * m)
def set(self, model):
self._update(model, update_fn=lambda e, m: m)
def state_dict(self, destination=None, prefix='', keep_vars=False):
return self.model_state_dict
if __name__ == "__main__":
hps = utils.get_hparams()
logger_text = utils.get_logger(hps.model_dir)
logger_text.info(hps)
out_size = fix_len_compatibility(2 * hps.data.sampling_rate // hps.data.hop_length) # NOTE: 2-sec of mel-spec
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.manual_seed(hps.train.seed)
np.random.seed(hps.train.seed)
print('Initializing logger...')
log_dir = hps.model_dir
logger = SummaryWriter(log_dir=log_dir)
train_dataset, collate, model = utils.get_correct_class(hps)
test_dataset, _, _ = utils.get_correct_class(hps, train=False)
print('Initializing data loaders...')
batch_collate = collate
loader = DataLoader(dataset=train_dataset, batch_size=hps.train.batch_size,
collate_fn=batch_collate, drop_last=True,
num_workers=4, shuffle=False) # NOTE: if on server, worker can be 4
print('Initializing model...')
model = model(**hps.model).to(device)
print('Number of encoder + duration predictor parameters: %.2fm' % (model.encoder.nparams / 1e6))
print('Number of decoder parameters: %.2fm' % (model.decoder.nparams / 1e6))
print('Total parameters: %.2fm' % (model.nparams / 1e6))
use_gt_dur = getattr(hps.train, "use_gt_dur", False)
if use_gt_dur:
print("++++++++++++++> Using ground truth duration for training")
print('Initializing optimizer...')
optimizer = torch.optim.Adam(params=model.parameters(), lr=hps.train.learning_rate)
print('Logging test batch...')
test_batch = test_dataset.sample_test_batch(size=hps.train.test_size)
for i, item in enumerate(test_batch):
mel = item['mel']
logger.add_image(f'image_{i}/ground_truth', plot_tensor(mel.squeeze()),
global_step=0, dataformats='HWC')
save_plot(mel.squeeze(), f'{log_dir}/original_{i}.png')
try:
model, optimizer, learning_rate, epoch_logged = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "grad_*.pt"), model, optimizer)
epoch_start = epoch_logged + 1
print(f"Loaded checkpoint from {epoch_logged} epoch, resuming training.")
global_step = epoch_logged * (len(train_dataset)/hps.train.batch_size)
except:
print(f"Cannot find trained checkpoint, begin to train from scratch")
epoch_start = 1
global_step = 0
learning_rate = hps.train.learning_rate
ema_model = ModelEmaV2(model, decay=0.9999) # It's necessary that we put this after loading model.
print('Start training...')
used_items = set()
iteration = global_step
for epoch in range(epoch_start, hps.train.n_epochs + 1):
model.train()
dur_losses = []
prior_losses = []
diff_losses = []
with tqdm(loader, total=len(train_dataset) // hps.train.batch_size) as progress_bar:
for batch_idx, batch in enumerate(progress_bar):
model.zero_grad()
x, x_lengths = batch['text_padded'].to(device), \
batch['input_lengths'].to(device)
y, y_lengths = batch['mel_padded'].to(device), \
batch['output_lengths'].to(device)
if hps.xvector:
spk = batch['xvector'].to(device)
else:
spk = batch['spk_ids'].to(torch.long).to(device)
emo = batch['emo_ids'].to(torch.long).to(device)
dur_loss, prior_loss, diff_loss = model.compute_loss(x, x_lengths,
y, y_lengths,
spk=spk,
emo=emo,
out_size=out_size,
use_gt_dur=use_gt_dur,
durs=batch['dur_padded'].to(device) if use_gt_dur else None)
loss = sum([dur_loss, prior_loss, diff_loss])
loss.backward()
enc_grad_norm = torch.nn.utils.clip_grad_norm_(model.encoder.parameters(),
max_norm=1)
dec_grad_norm = torch.nn.utils.clip_grad_norm_(model.decoder.parameters(),
max_norm=1)
optimizer.step()
ema_model.update(model)
logger.add_scalar('training/duration_loss', dur_loss.item(),
global_step=iteration)
logger.add_scalar('training/prior_loss', prior_loss.item(),
global_step=iteration)
logger.add_scalar('training/diffusion_loss', diff_loss.item(),
global_step=iteration)
logger.add_scalar('training/encoder_grad_norm', enc_grad_norm,
global_step=iteration)
logger.add_scalar('training/decoder_grad_norm', dec_grad_norm,
global_step=iteration)
dur_losses.append(dur_loss.item())
prior_losses.append(prior_loss.item())
diff_losses.append(diff_loss.item())
if batch_idx % 5 == 0:
msg = f'Epoch: {epoch}, iteration: {iteration} | dur_loss: {dur_loss.item()}, prior_loss: {prior_loss.item()}, diff_loss: {diff_loss.item()}'
progress_bar.set_description(msg)
iteration += 1
log_msg = 'Epoch %d: duration loss = %.3f ' % (epoch, float(np.mean(dur_losses)))
log_msg += '| prior loss = %.3f ' % np.mean(prior_losses)
log_msg += '| diffusion loss = %.3f\n' % np.mean(diff_losses)
with open(f'{log_dir}/train.log', 'a') as f:
f.write(log_msg)
if epoch % hps.train.save_every > 0:
continue
model.eval()
print('Synthesis...')
with torch.no_grad():
for i, item in enumerate(test_batch):
if item['utt'] + "/truth" not in used_items:
used_items.add(item['utt'] + "/truth")
x = item['text'].to(torch.long).unsqueeze(0).to(device)
if not hps.xvector:
spk = item['spk_ids']
spk = torch.LongTensor([spk]).to(device)
else:
spk = item["xvector"]
spk = spk.unsqueeze(0).to(device)
emo = item['emo_ids']
emo = torch.LongTensor([emo]).to(device)
x_lengths = torch.LongTensor([x.shape[-1]]).to(device)
y_enc, y_dec, attn = model(x, x_lengths, spk=spk, emo=emo, n_timesteps=10)
logger.add_image(f'image_{i}/generated_enc',
plot_tensor(y_enc.squeeze().cpu()),
global_step=iteration, dataformats='HWC')
logger.add_image(f'image_{i}/generated_dec',
plot_tensor(y_dec.squeeze().cpu()),
global_step=iteration, dataformats='HWC')
logger.add_image(f'image_{i}/alignment',
plot_tensor(attn.squeeze().cpu()),
global_step=iteration, dataformats='HWC')
save_plot(y_enc.squeeze().cpu(),
f'{log_dir}/generated_enc_{i}.png')
save_plot(y_dec.squeeze().cpu(),
f'{log_dir}/generated_dec_{i}.png')
save_plot(attn.squeeze().cpu(),
f'{log_dir}/alignment_{i}.png')
ckpt = model.state_dict()
utils.save_checkpoint(ema_model, optimizer, learning_rate, epoch, checkpoint_path=f"{log_dir}/EMA_grad_{epoch}.pt")
utils.save_checkpoint(model, optimizer, learning_rate, epoch, checkpoint_path=f"{log_dir}/grad_{epoch}.pt")
|