Voice-Clone / TTS /bin /train_encoder.py
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voice-clone with single audio sample input
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import sys
import time
import traceback
import torch
from torch.utils.data import DataLoader
from trainer.torch import NoamLR
from trainer.trainer_utils import get_optimizer
from TTS.encoder.dataset import EncoderDataset
from TTS.encoder.utils.generic_utils import save_best_model, save_checkpoint, setup_encoder_model
from TTS.encoder.utils.training import init_training
from TTS.encoder.utils.visual import plot_embeddings
from TTS.tts.datasets import load_tts_samples
from TTS.utils.audio import AudioProcessor
from TTS.utils.generic_utils import count_parameters, remove_experiment_folder
from TTS.utils.io import copy_model_files
from TTS.utils.samplers import PerfectBatchSampler
from TTS.utils.training import check_update
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: AudioProcessor, is_val: bool = False, verbose: bool = False):
num_utter_per_class = c.num_utter_per_class if not is_val else c.eval_num_utter_per_class
num_classes_in_batch = c.num_classes_in_batch if not is_val else c.eval_num_classes_in_batch
dataset = EncoderDataset(
c,
ap,
meta_data_eval if is_val else meta_data_train,
voice_len=c.voice_len,
num_utter_per_class=num_utter_per_class,
num_classes_in_batch=num_classes_in_batch,
verbose=verbose,
augmentation_config=c.audio_augmentation if not is_val else None,
use_torch_spec=c.model_params.get("use_torch_spec", False),
)
# get classes list
classes = dataset.get_class_list()
sampler = PerfectBatchSampler(
dataset.items,
classes,
batch_size=num_classes_in_batch * num_utter_per_class, # total batch size
num_classes_in_batch=num_classes_in_batch,
num_gpus=1,
shuffle=not is_val,
drop_last=True,
)
if len(classes) < num_classes_in_batch:
if is_val:
raise RuntimeError(
f"config.eval_num_classes_in_batch ({num_classes_in_batch}) need to be <= {len(classes)} (Number total of Classes in the Eval dataset) !"
)
raise RuntimeError(
f"config.num_classes_in_batch ({num_classes_in_batch}) need to be <= {len(classes)} (Number total of Classes in the Train dataset) !"
)
# set the classes to avoid get wrong class_id when the number of training and eval classes are not equal
if is_val:
dataset.set_classes(train_classes)
loader = DataLoader(
dataset,
num_workers=c.num_loader_workers,
batch_sampler=sampler,
collate_fn=dataset.collate_fn,
)
return loader, classes, dataset.get_map_classid_to_classname()
def evaluation(model, criterion, data_loader, global_step):
eval_loss = 0
for _, data in enumerate(data_loader):
with torch.no_grad():
# setup input data
inputs, labels = data
# agroup samples of each class in the batch. perfect sampler produces [3,2,1,3,2,1] we need [3,3,2,2,1,1]
labels = torch.transpose(
labels.view(c.eval_num_utter_per_class, c.eval_num_classes_in_batch), 0, 1
).reshape(labels.shape)
inputs = torch.transpose(
inputs.view(c.eval_num_utter_per_class, c.eval_num_classes_in_batch, -1), 0, 1
).reshape(inputs.shape)
# 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.eval_num_classes_in_batch, outputs.shape[0] // c.eval_num_classes_in_batch, -1), labels
)
eval_loss += loss.item()
eval_avg_loss = eval_loss / len(data_loader)
# save stats
dashboard_logger.eval_stats(global_step, {"loss": eval_avg_loss})
# plot the last batch in the evaluation
figures = {
"UMAP Plot": plot_embeddings(outputs.detach().cpu().numpy(), c.num_classes_in_batch),
}
dashboard_logger.eval_figures(global_step, figures)
return eval_avg_loss
def train(model, optimizer, scheduler, criterion, data_loader, eval_data_loader, global_step):
model.train()
best_loss = float("inf")
avg_loader_time = 0
end_time = time.time()
for epoch in range(c.epochs):
tot_loss = 0
epoch_time = 0
for _, data in enumerate(data_loader):
start_time = time.time()
# setup input data
inputs, labels = data
# agroup samples of each class in the batch. perfect sampler produces [3,2,1,3,2,1] we need [3,3,2,2,1,1]
labels = torch.transpose(labels.view(c.num_utter_per_class, c.num_classes_in_batch), 0, 1).reshape(
labels.shape
)
inputs = torch.transpose(inputs.view(c.num_utter_per_class, c.num_classes_in_batch, -1), 0, 1).reshape(
inputs.shape
)
# ToDo: move it to a unit test
# labels_converted = torch.transpose(labels.view(c.num_utter_per_class, c.num_classes_in_batch), 0, 1).reshape(labels.shape)
# inputs_converted = torch.transpose(inputs.view(c.num_utter_per_class, c.num_classes_in_batch, -1), 0, 1).reshape(inputs.shape)
# idx = 0
# for j in range(0, c.num_classes_in_batch, 1):
# for i in range(j, len(labels), c.num_classes_in_batch):
# if not torch.all(labels[i].eq(labels_converted[idx])) or not torch.all(inputs[i].eq(inputs_converted[idx])):
# print("Invalid")
# print(labels)
# exit()
# idx += 1
# labels = labels_converted
# inputs = inputs_converted
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_classes_in_batch, outputs.shape[0] // c.num_classes_in_batch, -1), labels
)
loss.backward()
grad_norm, _ = check_update(model, c.grad_clip)
optimizer.step()
step_time = time.time() - start_time
epoch_time += step_time
# acumulate the total epoch loss
tot_loss += loss.item()
# Averaged Loader Time
num_loader_workers = c.num_loader_workers if c.num_loader_workers > 0 else 1
avg_loader_time = (
1 / num_loader_workers * loader_time + (num_loader_workers - 1) / num_loader_workers * avg_loader_time
if avg_loader_time != 0
else loader_time
)
current_lr = optimizer.param_groups[0]["lr"]
if global_step % c.steps_plot_stats == 0:
# Plot Training Epoch Stats
train_stats = {
"loss": loss.item(),
"lr": current_lr,
"grad_norm": grad_norm,
"step_time": step_time,
"avg_loader_time": avg_loader_time,
}
dashboard_logger.train_epoch_stats(global_step, train_stats)
figures = {
"UMAP Plot": plot_embeddings(outputs.detach().cpu().numpy(), c.num_classes_in_batch),
}
dashboard_logger.train_figures(global_step, figures)
if global_step % c.print_step == 0:
print(
" | > Step:{} Loss:{:.5f} GradNorm:{:.5f} "
"StepTime:{:.2f} LoaderTime:{:.2f} AvGLoaderTime:{:.2f} LR:{:.6f}".format(
global_step, loss.item(), grad_norm, step_time, loader_time, avg_loader_time, current_lr
),
flush=True,
)
if global_step % c.save_step == 0:
# save model
save_checkpoint(model, optimizer, criterion, loss.item(), OUT_PATH, global_step, epoch)
end_time = time.time()
print("")
print(
">>> Epoch:{} AvgLoss: {:.5f} GradNorm:{:.5f} "
"EpochTime:{:.2f} AvGLoaderTime:{:.2f} ".format(
epoch, tot_loss / len(data_loader), grad_norm, epoch_time, avg_loader_time
),
flush=True,
)
# evaluation
if c.run_eval:
model.eval()
eval_loss = evaluation(model, criterion, eval_data_loader, global_step)
print("\n\n")
print("--> EVAL PERFORMANCE")
print(
" | > Epoch:{} AvgLoss: {:.5f} ".format(epoch, eval_loss),
flush=True,
)
# save the best checkpoint
best_loss = save_best_model(model, optimizer, criterion, eval_loss, best_loss, OUT_PATH, global_step, epoch)
model.train()
return best_loss, global_step
def main(args): # pylint: disable=redefined-outer-name
# pylint: disable=global-variable-undefined
global meta_data_train
global meta_data_eval
global train_classes
ap = AudioProcessor(**c.audio)
model = setup_encoder_model(c)
optimizer = get_optimizer(c.optimizer, c.optimizer_params, c.lr, model)
# pylint: disable=redefined-outer-name
meta_data_train, meta_data_eval = load_tts_samples(c.datasets, eval_split=True)
train_data_loader, train_classes, map_classid_to_classname = setup_loader(ap, is_val=False, verbose=True)
if c.run_eval:
eval_data_loader, _, _ = setup_loader(ap, is_val=True, verbose=True)
else:
eval_data_loader = None
num_classes = len(train_classes)
criterion = model.get_criterion(c, num_classes)
if c.loss == "softmaxproto" and c.model != "speaker_encoder":
c.map_classid_to_classname = map_classid_to_classname
copy_model_files(c, OUT_PATH)
if args.restore_path:
criterion, args.restore_step = model.load_checkpoint(
c, args.restore_path, eval=False, use_cuda=use_cuda, criterion=criterion
)
print(" > Model restored from step %d" % args.restore_step, flush=True)
else:
args.restore_step = 0
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)
if use_cuda:
model = model.cuda()
criterion.cuda()
global_step = args.restore_step
_, global_step = train(model, optimizer, scheduler, criterion, train_data_loader, eval_data_loader, global_step)
if __name__ == "__main__":
args, c, OUT_PATH, AUDIO_PATH, c_logger, dashboard_logger = init_training()
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)