Spaces:
Runtime error
Runtime error
File size: 7,919 Bytes
11120b4 |
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 |
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
# This program is free software; you can redistribute it and/or modify
# it under the terms of the MIT License.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# MIT License for more details.
import numpy as np
from tqdm import tqdm
import torch
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import params
from model import GradTTS
from data import TextMelSpeakerDataset, TextMelSpeakerBatchCollate
from utils import plot_tensor, save_plot
from text.symbols import symbols
train_filelist_path = params.train_filelist_path
valid_filelist_path = params.valid_filelist_path
cmudict_path = params.cmudict_path
add_blank = params.add_blank
n_spks = params.n_spks
spk_emb_dim = params.spk_emb_dim
log_dir = params.log_dir
n_epochs = params.n_epochs
batch_size = params.batch_size
out_size = params.out_size
learning_rate = params.learning_rate
random_seed = params.seed
nsymbols = len(symbols) + 1 if add_blank else len(symbols)
n_enc_channels = params.n_enc_channels
filter_channels = params.filter_channels
filter_channels_dp = params.filter_channels_dp
n_enc_layers = params.n_enc_layers
enc_kernel = params.enc_kernel
enc_dropout = params.enc_dropout
n_heads = params.n_heads
window_size = params.window_size
n_feats = params.n_feats
n_fft = params.n_fft
sample_rate = params.sample_rate
hop_length = params.hop_length
win_length = params.win_length
f_min = params.f_min
f_max = params.f_max
dec_dim = params.dec_dim
beta_min = params.beta_min
beta_max = params.beta_max
pe_scale = params.pe_scale
num_workers = params.num_workers
if __name__ == "__main__":
torch.manual_seed(random_seed)
np.random.seed(random_seed)
print('Initializing logger...')
logger = SummaryWriter(log_dir=log_dir)
print('Initializing data loaders...')
train_dataset = TextMelSpeakerDataset(train_filelist_path, cmudict_path, add_blank,
n_fft, n_feats, sample_rate, hop_length,
win_length, f_min, f_max)
batch_collate = TextMelSpeakerBatchCollate()
loader = DataLoader(dataset=train_dataset, batch_size=batch_size,
collate_fn=batch_collate, drop_last=True,
num_workers=num_workers, shuffle=True)
test_dataset = TextMelSpeakerDataset(valid_filelist_path, cmudict_path, add_blank,
n_fft, n_feats, sample_rate, hop_length,
win_length, f_min, f_max)
print('Initializing model...')
model = GradTTS(nsymbols, n_spks, spk_emb_dim, n_enc_channels,
filter_channels, filter_channels_dp,
n_heads, n_enc_layers, enc_kernel, enc_dropout, window_size,
n_feats, dec_dim, beta_min, beta_max, pe_scale).cuda()
print('Number of encoder parameters = %.2fm' % (model.encoder.nparams/1e6))
print('Number of decoder parameters = %.2fm' % (model.decoder.nparams/1e6))
print('Initializing optimizer...')
optimizer = torch.optim.Adam(params=model.parameters(), lr=learning_rate)
print('Logging test batch...')
test_batch = test_dataset.sample_test_batch(size=params.test_size)
for item in test_batch:
mel, spk = item['y'], item['spk']
i = int(spk.cpu())
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')
print('Start training...')
iteration = 0
for epoch in range(1, n_epochs + 1):
model.eval()
print('Synthesis...')
with torch.no_grad():
for item in test_batch:
x = item['x'].to(torch.long).unsqueeze(0).cuda()
x_lengths = torch.LongTensor([x.shape[-1]]).cuda()
spk = item['spk'].to(torch.long).cuda()
i = int(spk.cpu())
y_enc, y_dec, attn = model(x, x_lengths, n_timesteps=50, spk=spk)
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')
model.train()
dur_losses = []
prior_losses = []
diff_losses = []
with tqdm(loader, total=len(train_dataset)//batch_size) as progress_bar:
for batch in progress_bar:
model.zero_grad()
x, x_lengths = batch['x'].cuda(), batch['x_lengths'].cuda()
y, y_lengths = batch['y'].cuda(), batch['y_lengths'].cuda()
spk = batch['spk'].cuda()
dur_loss, prior_loss, diff_loss = model.compute_loss(x, x_lengths,
y, y_lengths,
spk=spk, out_size=out_size)
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()
logger.add_scalar('training/duration_loss', dur_loss,
global_step=iteration)
logger.add_scalar('training/prior_loss', prior_loss,
global_step=iteration)
logger.add_scalar('training/diffusion_loss', diff_loss,
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)
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)
dur_losses.append(dur_loss.item())
prior_losses.append(prior_loss.item())
diff_losses.append(diff_loss.item())
iteration += 1
msg = 'Epoch %d: duration loss = %.3f ' % (epoch, np.mean(dur_losses))
msg += '| prior loss = %.3f ' % np.mean(prior_losses)
msg += '| diffusion loss = %.3f\n' % np.mean(diff_losses)
with open(f'{log_dir}/train.log', 'a') as f:
f.write(msg)
if epoch % params.save_every > 0:
continue
ckpt = model.state_dict()
torch.save(ckpt, f=f"{log_dir}/grad_{epoch}.pt")
|