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import argparse
import os
import torch
import yaml
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
from utils.model import get_model, get_vocoder, get_param_num
from utils.tools import to_device, log, synth_one_sample
from model import FastSpeech2Loss
from dataset import Dataset
from evaluate import evaluate
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def main(args, configs):
print("Prepare training ...")
preprocess_config, model_config, train_config = configs
# Get dataset
dataset = Dataset(
"train.txt", preprocess_config, train_config, sort=True, drop_last=True
)
batch_size = train_config["optimizer"]["batch_size"]
group_size = 4 # Set this larger than 1 to enable sorting in Dataset
assert batch_size * group_size < len(dataset)
loader = DataLoader(
dataset,
batch_size=batch_size * group_size,
shuffle=True,
collate_fn=dataset.collate_fn,
)
# Prepare model
model, optimizer = get_model(args, configs, device, train=True)
model = nn.DataParallel(model)
num_param = get_param_num(model)
Loss = FastSpeech2Loss(preprocess_config, model_config).to(device)
print("Number of FastSpeech2 Parameters:", num_param)
# Load vocoder
vocoder = get_vocoder(model_config, device)
# Init logger
for p in train_config["path"].values():
os.makedirs(p, exist_ok=True)
train_log_path = os.path.join(train_config["path"]["log_path"], "train")
val_log_path = os.path.join(train_config["path"]["log_path"], "val")
os.makedirs(train_log_path, exist_ok=True)
os.makedirs(val_log_path, exist_ok=True)
train_logger = SummaryWriter(train_log_path)
val_logger = SummaryWriter(val_log_path)
# Training
step = args.restore_step + 1
epoch = 1
grad_acc_step = train_config["optimizer"]["grad_acc_step"]
grad_clip_thresh = train_config["optimizer"]["grad_clip_thresh"]
total_step = train_config["step"]["total_step"]
log_step = train_config["step"]["log_step"]
save_step = train_config["step"]["save_step"]
synth_step = train_config["step"]["synth_step"]
val_step = train_config["step"]["val_step"]
outer_bar = tqdm(total=total_step, desc="Training", position=0)
outer_bar.n = args.restore_step
outer_bar.update()
while True:
inner_bar = tqdm(total=len(loader), desc="Epoch {}".format(epoch), position=1)
for batchs in loader:
for batch in batchs:
batch = to_device(batch, device)
# Forward
output = model(*(batch[2:]))
# Cal Loss
losses = Loss(batch, output)
total_loss = losses[0]
# Backward
total_loss = total_loss / grad_acc_step
total_loss.backward()
if step % grad_acc_step == 0:
# Clipping gradients to avoid gradient explosion
nn.utils.clip_grad_norm_(model.parameters(), grad_clip_thresh)
# Update weights
optimizer.step_and_update_lr()
optimizer.zero_grad()
if step % log_step == 0:
losses = [l.item() for l in losses]
message1 = "Step {}/{}, ".format(step, total_step)
message2 = "Total Loss: {:.4f}, Mel Loss: {:.4f}, Mel PostNet Loss: {:.4f}, Pitch Loss: {:.4f}, Energy Loss: {:.4f}, Duration Loss: {:.4f}".format(
*losses
)
with open(os.path.join(train_log_path, "log.txt"), "a") as f:
f.write(message1 + message2 + "\n")
outer_bar.write(message1 + message2)
log(train_logger, step, losses=losses)
if step % synth_step == 0:
fig, wav_reconstruction, wav_prediction, tag = synth_one_sample(
batch,
output,
vocoder,
model_config,
preprocess_config,
)
log(
train_logger,
fig=fig,
tag="Training/step_{}_{}".format(step, tag),
)
sampling_rate = preprocess_config["preprocessing"]["audio"][
"sampling_rate"
]
log(
train_logger,
audio=wav_reconstruction,
sampling_rate=sampling_rate,
tag="Training/step_{}_{}_reconstructed".format(step, tag),
)
log(
train_logger,
audio=wav_prediction,
sampling_rate=sampling_rate,
tag="Training/step_{}_{}_synthesized".format(step, tag),
)
if step % val_step == 0:
model.eval()
message = evaluate(model, step, configs, val_logger, vocoder)
with open(os.path.join(val_log_path, "log.txt"), "a") as f:
f.write(message + "\n")
outer_bar.write(message)
model.train()
if step % save_step == 0:
torch.save(
{
"model": model.module.state_dict(),
"optimizer": optimizer._optimizer.state_dict(),
},
os.path.join(
train_config["path"]["ckpt_path"],
"{}.pth.tar".format(step),
),
)
if step == total_step:
quit()
step += 1
outer_bar.update(1)
inner_bar.update(1)
epoch += 1
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--restore_step", type=int, default=0)
parser.add_argument(
"-p",
"--preprocess_config",
type=str,
required=True,
help="path to preprocess.yaml",
)
parser.add_argument(
"-m", "--model_config", type=str, required=True, help="path to model.yaml"
)
parser.add_argument(
"-t", "--train_config", type=str, required=True, help="path to train.yaml"
)
args = parser.parse_args()
# Read Config
preprocess_config = yaml.load(
open(args.preprocess_config, "r"), Loader=yaml.FullLoader
)
model_config = yaml.load(open(args.model_config, "r"), Loader=yaml.FullLoader)
train_config = yaml.load(open(args.train_config, "r"), Loader=yaml.FullLoader)
configs = (preprocess_config, model_config, train_config)
main(args, configs)
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