File size: 3,413 Bytes
24303c9 | 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 | from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning.strategies import DDPStrategy
from pytorch_lightning.loggers import WandbLogger
from torch import nn
from asr.data_module import ASRDataModule
from asr.meldataset import build_dataloader
from utils import *
from asr.models import build_model
from asr.trainer import ASRTrainer
import os
import os.path as osp
import yaml
import shutil
import click
def get_data_path_list(train_path=None, val_path=None):
if train_path is None:
train_path = "Data/train_list.txt"
if val_path is None:
val_path = "Data/val_list.txt"
with open(train_path, 'r') as f:
train_list = f.readlines()
with open(val_path, 'r') as f:
val_list = f.readlines()
return train_list, val_list
def build_criterion(critic_params={}):
criterion = {
"ce": nn.CrossEntropyLoss(ignore_index=-1),
"ctc": torch.nn.CTCLoss(**critic_params.get('ctc', {})),
}
return criterion
@click.command()
@click.option('-c', '--config_path', default='configs/asr.yml', type=str)
def main(config_path):
config = yaml.safe_load(open(config_path))
log_dir = config['log_dir']
os.makedirs(log_dir,exist_ok=True)
shutil.copy(config_path, osp.join(log_dir, osp.basename(config_path)))
batch_size = config.get('batch_size', 10)
epochs = config.get('epochs', 1000)
save_freq = config.get('save_freq', 20)
train_path = config.get('train_data', None)
val_path = config.get('val_data', None)
# 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=config.get('dataset_params', {}))
#
# val_dataloader = build_dataloader(val_list,
# batch_size=batch_size,
# validation=True,
# num_workers=2,
# dataset_config=config.get('dataset_params', {}))
data_module = ASRDataModule(data_dir='dump', batch_size=batch_size, num_workers=8)
model = build_model(model_params=config['model_params'] or {})
checkpoint_callback = ModelCheckpoint(
dirpath=log_dir,
filename=('{epoch}-{step}'),
every_n_train_steps=None,
every_n_epochs=1,
verbose=True,
save_last=True
)
logger = WandbLogger(project="asr-align")
blank_index = 0
criterion = build_criterion(critic_params={
'ctc': {'blank': blank_index},
})
training_wrapper = ASRTrainer(model=model, criterion=criterion,mono_start_epoch=1,lr=1e-4)
if config.get('pretrained_model',None):
training_wrapper.load_checkpoint(config['pretrained_model'])
trainer: Trainer = Trainer(
max_epochs=epochs,
accelerator='gpu',
devices=-1,
benchmark=False,
fast_dev_run=False,
strategy=DDPStrategy(find_unused_parameters=False),
logger=logger,
precision='16-mixed',
callbacks=[checkpoint_callback])
trainer.fit(training_wrapper, data_module, ckpt_path='logs/asr/epoch=12-step=54140.ckpt')
if __name__ == "__main__":
main() |