import argparse
import os
import sys
import time
import traceback
import torch
from torch.utils.data import DataLoader
from TTS.datasets.preprocess import load_meta_data
from TTS.speaker_encoder.dataset import MyDataset
from TTS.speaker_encoder.loss import GE2ELoss
from TTS.speaker_encoder.model import SpeakerEncoder
from TTS.speaker_encoder.visual import plot_embeddings
from TTS.speaker_encoder.generic_utils import save_best_model
from TTS.utils.audio import AudioProcessor
from TTS.utils.generic_utils import (NoamLR, check_update, copy_config_file,
count_parameters,
create_experiment_folder, get_git_branch,
load_config,
remove_experiment_folder, set_init_dict)
from TTS.utils.logger import Logger
from TTS.utils.radam import RAdam
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
torch.manual_seed(54321)
use_cuda = torch.cuda.is_available()
num_gpus = torch.cuda.device_count()
print(" > Using CUDA: ", use_cuda)
print(" > Number of GPUs: ", num_gpus)
def setup_loader(ap, is_val=False, verbose=False):
if is_val:
loader = None
else:
dataset = MyDataset(ap,
meta_data_eval if is_val else meta_data_train,
voice_len=1.6,
num_utter_per_speaker=10,
skip_speakers=False,
verbose=verbose)
# sampler = DistributedSampler(dataset) if num_gpus > 1 else None
loader = DataLoader(dataset,
batch_size=c.num_speakers_in_batch,
shuffle=False,
num_workers=0,
collate_fn=dataset.collate_fn)
return loader
def train(model, criterion, optimizer, scheduler, ap, global_step):
data_loader = setup_loader(ap, is_val=False, verbose=True)
model.train()
epoch_time = 0
best_loss = float('inf')
avg_loss = 0
end_time = time.time()
for _, data in enumerate(data_loader):
start_time = time.time()
# setup input data
inputs = data[0]
loader_time = time.time() - end_time
global_step += 1
# setup lr
if c.lr_decay:
scheduler.step()
optimizer.zero_grad()
# dispatch data to GPU
if use_cuda:
inputs = inputs.cuda(non_blocking=True)
# labels = labels.cuda(non_blocking=True)
# forward pass model
outputs = model(inputs)
# loss computation
loss = criterion(
outputs.view(c.num_speakers_in_batch,
outputs.shape[0] // c.num_speakers_in_batch, -1))
loss.backward()
grad_norm, _ = check_update(model, c.grad_clip)
optimizer.step()
step_time = time.time() - start_time
epoch_time += step_time
avg_loss = 0.01 * loss.item(
) + 0.99 * avg_loss if avg_loss != 0 else loss.item()
current_lr = optimizer.param_groups[0]['lr']
if global_step % c.steps_plot_stats == 0:
# Plot Training Epoch Stats
train_stats = {
"GE2Eloss": avg_loss,
"lr": current_lr,
"grad_norm": grad_norm,
"step_time": step_time
}
tb_logger.tb_train_epoch_stats(global_step, train_stats)
figures = {
# FIXME: not constant
"UMAP Plot": plot_embeddings(outputs.detach().cpu().numpy(),
10),
}
tb_logger.tb_train_figures(global_step, figures)
if global_step % c.print_step == 0:
print(
" | > Step:{} Loss:{:.5f} AvgLoss:{:.5f} GradNorm:{:.5f} "
"StepTime:{:.2f} LoaderTime:{:.2f} LR:{:.6f}".format(
global_step, loss.item(), avg_loss, grad_norm, step_time,
loader_time, current_lr),
flush=True)
# save best model
best_loss = save_best_model(model, optimizer, avg_loss, best_loss,
OUT_PATH, global_step)
end_time = time.time()
return avg_loss, global_step
def main(args): # pylint: disable=redefined-outer-name
# pylint: disable=global-variable-undefined
global meta_data_train
global meta_data_eval
ap = AudioProcessor(**c.audio)
model = SpeakerEncoder(input_dim=40,
proj_dim=128,
lstm_dim=384,
num_lstm_layers=3)
optimizer = RAdam(model.parameters(), lr=c.lr)
criterion = GE2ELoss(loss_method='softmax')
if args.restore_path:
checkpoint = torch.load(args.restore_path)
try:
# TODO: fix optimizer init, model.cuda() needs to be called before
# optimizer restore
# optimizer.load_state_dict(checkpoint['optimizer'])
if c.reinit_layers:
raise RuntimeError
model.load_state_dict(checkpoint['model'])
except KeyError:
print(" > Partial model initialization.")
model_dict = model.state_dict()
model_dict = set_init_dict(model_dict, checkpoint, c)
model.load_state_dict(model_dict)
del model_dict
for group in optimizer.param_groups:
group['lr'] = c.lr
print(" > Model restored from step %d" % checkpoint['step'],
flush=True)
args.restore_step = checkpoint['step']
else:
args.restore_step = 0
if use_cuda:
model = model.cuda()
criterion.cuda()
if c.lr_decay:
scheduler = NoamLR(optimizer,
warmup_steps=c.warmup_steps,
last_epoch=args.restore_step - 1)
else:
scheduler = None
num_params = count_parameters(model)
print("\n > Model has {} parameters".format(num_params), flush=True)
# pylint: disable=redefined-outer-name
meta_data_train, meta_data_eval = load_meta_data(c.datasets)
global_step = args.restore_step
train_loss, global_step = train(model, criterion, optimizer, scheduler, ap,
global_step)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--restore_path',
type=str,
help='Path to model outputs (checkpoint, tensorboard etc.).',
default=0)
parser.add_argument(
'--config_path',
type=str,
help='Path to config file for training.',
)
parser.add_argument('--debug',
type=bool,
default=True,
help='Do not verify commit integrity to run training.')
parser.add_argument(
'--data_path',
type=str,
default='',
help='Defines the data path. It overwrites config.json.')
parser.add_argument('--output_path',
type=str,
help='path for training outputs.',
default='')
parser.add_argument('--output_folder',
type=str,
default='',
help='folder name for training outputs.')
args = parser.parse_args()
# setup output paths and read configs
c = load_config(args.config_path)
_ = os.path.dirname(os.path.realpath(__file__))
if args.data_path != '':
c.data_path = args.data_path
if args.output_path == '':
OUT_PATH = os.path.join(_, c.output_path)
else:
OUT_PATH = args.output_path
if args.output_folder == '':
OUT_PATH = create_experiment_folder(OUT_PATH, c.run_name, args.debug)
else:
OUT_PATH = os.path.join(OUT_PATH, args.output_folder)
new_fields = {}
if args.restore_path:
new_fields["restore_path"] = args.restore_path
new_fields["github_branch"] = get_git_branch()
copy_config_file(args.config_path, os.path.join(OUT_PATH, 'config.json'),
new_fields)
LOG_DIR = OUT_PATH
tb_logger = Logger(LOG_DIR)
try:
main(args)
except KeyboardInterrupt:
remove_experiment_folder(OUT_PATH)
try:
sys.exit(0)
except SystemExit:
os._exit(0) # pylint: disable=protected-access
except Exception: # pylint: disable=broad-except
remove_experiment_folder(OUT_PATH)
traceback.print_exc()
sys.exit(1)