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train_first.py
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train_first.py
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import os
import os.path as osp
import re
import sys
import yaml
import shutil
import numpy as np
import torch
import click
import warnings
warnings.simplefilter('ignore')
# load packages
import random
import yaml
from munch import Munch
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
import torchaudio
import librosa
from models import *
from meldataset import build_dataloader
from utils import *
from optimizers import build_optimizer
import time
from accelerate import Accelerator
from accelerate.utils import LoggerType
from accelerate import DistributedDataParallelKwargs
from torch.utils.tensorboard import SummaryWriter
# # simple fix for dataparallel that allows access to class attributes
# class MyDataParallel(torch.nn.DataParallel):
# def __getattr__(self, name):
# try:
# return super().__getattr__(name)
# except AttributeError:
# return getattr(self.module, name)
import logging
from logging import StreamHandler
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
handler = StreamHandler()
handler.setLevel(logging.DEBUG)
logger.addHandler(handler)
@click.command()
@click.option('-p', '--config_path', default='Configs/config.yml', type=str)
def main(config_path):
config = yaml.safe_load(open(config_path))
log_dir = config['log_dir']
if not osp.exists(log_dir): os.makedirs(log_dir, exist_ok=True)
shutil.copy(config_path, osp.join(log_dir, osp.basename(config_path)))
ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
accelerator = Accelerator(project_dir=log_dir, split_batches=True, kwargs_handlers=[ddp_kwargs], mixed_precision='bf16')
if accelerator.is_main_process:
writer = SummaryWriter(log_dir + "/tensorboard")
# write logs
file_handler = logging.FileHandler(osp.join(log_dir, 'train.log'))
file_handler.setLevel(logging.DEBUG)
file_handler.setFormatter(logging.Formatter('%(levelname)s:%(asctime)s: %(message)s'))
logger.addHandler(file_handler)
batch_size = config.get('batch_size', 10)
device = accelerator.device
epochs = config.get('epochs_1st', 200)
save_freq = config.get('save_freq', 2)
train_path = config.get('train_data', None)
val_path = config.get('val_data', None)
multigpu = config.get('multigpu', False)
log_interval = config.get('log_interval', 10)
saving_epoch = config.get('save_freq', 2)
# load data
train_list, val_list = get_data_path_list(train_path, val_path)
train_dataloader = build_dataloader(train_list,
batch_size=batch_size,
num_workers=8,
dataset_config={},
device=device)
val_dataloader = build_dataloader(val_list,
batch_size=batch_size,
validation=True,
num_workers=2,
device=device,
dataset_config={})
with accelerator.main_process_first():
# load pretrained ASR model
ASR_config = config.get('ASR_config', False)
ASR_path = config.get('ASR_path', False)
text_aligner = load_ASR_models(ASR_path, ASR_config)
# load pretrained F0 model
F0_path = config.get('F0_path', False)
pitch_extractor = load_F0_models(F0_path)
scheduler_params = {
"max_lr": float(config['optimizer_params'].get('lr', 1e-4)),
"pct_start": float(config['optimizer_params'].get('pct_start', 0.0)),
"epochs": epochs,
"steps_per_epoch": len(train_dataloader),
}
model = build_model(Munch(config['model_params']), text_aligner, pitch_extractor)
for k in model:
model[k] = accelerator.prepare(model[k])
train_dataloader, val_dataloader = accelerator.prepare(
train_dataloader, val_dataloader
)
_ = [model[key].to(device) for key in model]
optimizer = build_optimizer({key: model[key].parameters() for key in model},
scheduler_params_dict= {key: scheduler_params.copy() for key in model})
for k, v in optimizer.optimizers.items():
optimizer.optimizers[k] = accelerator.prepare(optimizer.optimizers[k])
optimizer.schedulers[k] = accelerator.prepare(optimizer.schedulers[k])
# # multi-GPU support
# if multigpu:
# for key in model:
# model[key] = MyDataParallel(model[key])
with accelerator.main_process_first():
if config.get('pretrained_model', '') != '':
model, optimizer, start_epoch, iters = load_checkpoint(model, optimizer, config['pretrained_model'],
load_only_params=config.get('load_only_params', True))
else:
start_epoch = 0
iters = 0
try:
n_down = model.text_aligner.module.n_down
except:
n_down = model.text_aligner.n_down
best_loss = float('inf') # best test loss
loss_train_record = list([])
loss_test_record = list([])
loss_params = Munch(config['loss_params'])
TMA_epoch = loss_params.TMA_epoch
TMA_CEloss = loss_params.TMA_CEloss
for epoch in range(start_epoch, epochs):
running_loss = 0
start_time = time.time()
criterion = nn.L1Loss()
_ = [model[key].train() for key in model]
for i, batch in enumerate(train_dataloader):
batch = [b.to(device) for b in batch]
texts, input_lengths, mels, mel_input_length = batch
mask = length_to_mask(mel_input_length // (2 ** n_down)).to('cuda')
m = length_to_mask(input_lengths)
text_mask = length_to_mask(input_lengths).to(texts.device)
ppgs, s2s_pred, s2s_attn_feat = model.text_aligner(mels, mask, texts)
s2s_attn_feat = s2s_attn_feat.transpose(-1, -2)
s2s_attn_feat = s2s_attn_feat[..., 1:]
s2s_attn_feat = s2s_attn_feat.transpose(-1, -2)
with torch.no_grad():
text_mask = length_to_mask(input_lengths).to(texts.device)
attn_mask = (~mask).unsqueeze(-1).expand(mask.shape[0], mask.shape[1], text_mask.shape[-1]).float().transpose(-1, -2)
attn_mask = attn_mask.float() * (~text_mask).unsqueeze(-1).expand(text_mask.shape[0], text_mask.shape[1], mask.shape[-1]).float()
attn_mask = (attn_mask < 1)
s2s_attn_feat.masked_fill_(attn_mask, -float("inf"))
if TMA_CEloss:
s2s_attn = F.softmax(s2s_attn_feat, dim=1) # along the mel dimension
else:
s2s_attn = F.softmax(s2s_attn_feat, dim=-1) # along the text dimension
# get monotonic version
with torch.no_grad():
mask_ST = mask_from_lens(s2s_attn, input_lengths, mel_input_length // (2 ** model.text_aligner.n_down))
s2s_attn_mono = maximum_path(s2s_attn, mask_ST)
s2s_attn = torch.nan_to_num(s2s_attn)
# encode
t_en = model.text_encoder(texts, input_lengths, m)
# 50% of chance of using monotonic version
if bool(random.getrandbits(1)):
asr = (t_en @ s2s_attn)
else:
asr = (t_en @ s2s_attn_mono)
# get clips
mel_input_length_all = accelerator.gather(mel_input_length) # for balanced load
mel_len = int(mel_input_length_all.min().item() / 2 - 1)
en = []
gt = []
for bib in range(len(mel_input_length)):
mel_length = int(mel_input_length[bib].item() / 2)
random_start = np.random.randint(0, mel_length - mel_len)
en.append(asr[bib, :, random_start:random_start+mel_len])
gt.append(mels[bib, :, (random_start * 2):((random_start+mel_len) * 2)])
en = torch.stack(en)
gt = torch.stack(gt).detach()
# clip too short to be used by the style encoder
if gt.shape[-1] < 80:
continue
real_norm = log_norm(gt.unsqueeze(1)).squeeze(1).detach()
F0_real, _, _ = model.pitch_extractor(gt.unsqueeze(1))
s = model.style_encoder(gt.unsqueeze(1))
# reconstruction
mel_rec = model.decoder(en, F0_real, real_norm, s)
# discriminator loss
optimizer.zero_grad()
gt.requires_grad_()
out, _ = model.discriminator(gt.unsqueeze(1))
loss_real = adv_loss(out, 1)
loss_reg = r1_reg(out, gt)
out, _ = model.discriminator(mel_rec.detach().unsqueeze(1))
loss_fake = adv_loss(out, 0)
d_loss = loss_real + loss_fake + loss_reg * loss_params.lambda_reg
accelerator.backward(d_loss)
optimizer.step('discriminator')
# generator loss
optimizer.zero_grad()
loss_mel = criterion(mel_rec, gt)
if epoch > TMA_epoch: # start TMA training
loss_s2s = 0
for _s2s_pred, _text_input, _text_length in zip(s2s_pred, texts, input_lengths):
loss_s2s += F.cross_entropy(_s2s_pred[:_text_length], _text_input[:_text_length])
loss_s2s /= texts.size(0)
if TMA_CEloss:
# cross entropy loss for monotonic alignment
log_attn = torch.nan_to_num(F.log_softmax(s2s_attn_feat, dim=1)) # along the mel dimension
loss_mono = -(torch.mul(log_attn, s2s_attn_mono).sum(axis=[-1, -2]) / input_lengths).mean()
else:
# L1 loss for monotonic alignment
loss_mono = F.l1_loss(s2s_attn, s2s_attn_mono) * 10
else:
loss_s2s = 0
loss_mono = 0
# adversarial loss
with torch.no_grad():
_, f_real = model.discriminator(gt.unsqueeze(1))
out_rec, f_fake = model.discriminator(mel_rec.unsqueeze(1))
loss_adv = adv_loss(out_rec, 1)
# feature matching loss
loss_fm = 0
for m in range(len(f_real)):
for k in range(len(f_real[m])):
loss_fm += torch.mean(torch.abs(f_real[m][k] - f_fake[m][k]))
g_loss = loss_params.lambda_mel * loss_mel + \
loss_params.lambda_adv * loss_adv + \
loss_params.lambda_fm * loss_fm + \
loss_params.lambda_mono * loss_mono + \
loss_params.lambda_s2s * loss_s2s
running_loss += accelerator.gather(loss_mel).mean().item()
accelerator.backward(g_loss)
optimizer.step('text_encoder')
optimizer.step('style_encoder')
optimizer.step('decoder')
if epoch > TMA_epoch:
optimizer.step('text_aligner')
optimizer.step('pitch_extractor')
iters = iters + 1
if (i+1)%log_interval == 0 and accelerator.is_main_process:
logger.info ('Epoch [%d/%d], Step [%d/%d], Mel Loss: %.5f, Adv Loss: %.5f, Disc Loss: %.5f, Mono Loss: %.5f, S2S Loss: %.5f'
%(epoch+1, epochs, i+1, len(train_list)//batch_size, running_loss / log_interval, loss_adv.item(), d_loss.item(), loss_mono, loss_s2s))
writer.add_scalar('train/mel_loss', running_loss / log_interval, iters)
writer.add_scalar('train/adv_loss', loss_adv.item(), iters)
writer.add_scalar('train/d_loss', d_loss.item(), iters)
writer.add_scalar('train/mono_loss', loss_mono, iters)
writer.add_scalar('train/s2s_loss', loss_s2s, iters)
running_loss = 0
print('Time elasped:', time.time()-start_time)
loss_test = 0
_ = [model[key].eval() for key in model]
with torch.no_grad():
iters_test = 0
for batch_idx, batch in enumerate(val_dataloader):
optimizer.zero_grad()
batch = [b.to(device) for b in batch]
texts, input_lengths, mels, mel_input_length = batch
with torch.no_grad():
mask = length_to_mask(mel_input_length // (2 ** model.text_aligner.n_down)).to('cuda')
m = length_to_mask(input_lengths)
ppgs, s2s_pred, s2s_attn_feat = model.text_aligner(mels, mask, texts)
s2s_attn_feat = s2s_attn_feat.transpose(-1, -2)
s2s_attn_feat = s2s_attn_feat[..., 1:]
s2s_attn_feat = s2s_attn_feat.transpose(-1, -2)
with torch.no_grad():
text_mask = length_to_mask(input_lengths).to(texts.device)
attn_mask = (~mask).unsqueeze(-1).expand(mask.shape[0], mask.shape[1], text_mask.shape[-1]).float().transpose(-1, -2)
attn_mask = attn_mask.float() * (~text_mask).unsqueeze(-1).expand(text_mask.shape[0], text_mask.shape[1], mask.shape[-1]).float()
attn_mask = (attn_mask < 1)
s2s_attn_feat.masked_fill_(attn_mask, -float("inf"))
if TMA_CEloss:
s2s_attn = F.softmax(s2s_attn_feat, dim=1) # along the mel dimension
else:
s2s_attn = F.softmax(s2s_attn_feat, dim=-1) # along the text dimension
# get monotonic version
with torch.no_grad():
mask_ST = mask_from_lens(s2s_attn, input_lengths, mel_input_length // (2 ** model.text_aligner.n_down))
s2s_attn_mono = maximum_path(s2s_attn, mask_ST)
s2s_attn = torch.nan_to_num(s2s_attn)
# encode
t_en = model.text_encoder(texts, input_lengths, m)
asr = (t_en @ s2s_attn_mono)
# get clips
mel_input_length_all = accelerator.gather(mel_input_length) # for balanced load
mel_len = int(mel_input_length_all.min().item() / 2 - 1)
en = []
gt = []
for bib in range(len(mel_input_length)):
mel_length = int(mel_input_length[bib].item() / 2)
random_start = np.random.randint(0, mel_length - mel_len)
en.append(asr[bib, :, random_start:random_start+mel_len])
gt.append(mels[bib, :, (random_start * 2):((random_start+mel_len) * 2)])
en = torch.stack(en)
gt = torch.stack(gt).detach()
with torch.no_grad():
F0_real, _, F0 = model.pitch_extractor(gt.unsqueeze(1))
F0 = F0.reshape(F0.shape[0], F0.shape[1] * 2, F0.shape[2], 1).squeeze()
# reconstruct
s = model.style_encoder(gt.unsqueeze(1))
real_norm = log_norm(gt.unsqueeze(1)).squeeze(1)
mel_rec = model.decoder(en, F0_real, real_norm, s)
mel_rec = mel_rec[..., :gt.shape[-1]]
loss_mel = criterion(mel_rec, gt)
loss_test += accelerator.gather(loss_mel).mean().item()
iters_test += 1
if accelerator.is_main_process:
print('Epochs:', epoch + 1)
logger.info('Validation mel loss: %.3f' % (loss_test / iters_test))
print('\n\n\n')
writer.add_scalar('eval/mel_loss', loss_test / iters_test, epoch + 1)
attn_image = get_image(s2s_attn[0].cpu().numpy().squeeze())
writer.add_figure('eval/attn', attn_image, epoch)
mel_image = get_image(mel_rec[0].cpu().numpy().squeeze())
writer.add_figure('eval/mel_rec', mel_image, epoch)
if epoch % saving_epoch == 0:
if (loss_test / iters_test) < best_loss:
best_loss = loss_test / iters_test
print('Saving..')
state = {
'net': {key: model[key].state_dict() for key in model},
'optimizer': optimizer.state_dict(),
'iters': iters,
'val_loss': loss_test / iters_test,
'epoch': epoch,
}
save_path = osp.join(log_dir, 'epoch_1st_%05d.pth' % epoch)
torch.save(state, save_path)
if accelerator.is_main_process:
print('Saving..')
state = {
'net': {key: model[key].state_dict() for key in model},
'optimizer': optimizer.state_dict(),
'iters': iters,
'val_loss': loss_test / iters_test,
'epoch': epoch,
}
save_path = osp.join(log_dir, config.get('first_stage_path', 'first_stage.pth'))
torch.save(state, save_path)
if __name__=="__main__":
main()