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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 | from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import ModelCheckpoint, LearningRateMonitor
from pytorch_lightning.loggers import TensorBoardLogger
from s1.data_module import QuantizeDataModule
from s1.trainer import Wav2TTS
from pytorch_lightning.strategies import DDPStrategy
import json
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
args = AttrDict()
arg_dict = json.load(open('configs/mq.json', 'r'))
args.__dict__.update(arg_dict)
fname_prefix = f''
if args.accelerator == 'ddp':
args.accelerator = DDPStrategy(find_unused_parameters=True)
checkpoint_callback = ModelCheckpoint(
dirpath=args.saving_path,
filename=(fname_prefix+'{epoch}-{step}'),
every_n_train_steps=(None if args.val_check_interval == 1.0 else args.val_check_interval),
every_n_epochs=(None if args.check_val_every_n_epoch == 1 else args.check_val_every_n_epoch),
verbose=True,
save_last=True
)
logger = TensorBoardLogger(args.sampledir, name="VQ-TTS", version=args.version)
lr_monitor = LearningRateMonitor(logging_interval='step')
wrapper = Trainer(
precision=args.precision,
callbacks=[checkpoint_callback, lr_monitor],
val_check_interval=args.val_check_interval,
num_sanity_val_steps=0,
max_steps=args.training_step,
devices=(-1 if args.distributed else 1),
strategy=(args.accelerator if args.distributed else None),
use_distributed_sampler=False,
accumulate_grad_batches=args.accumulate_grad_batches,
logger=logger,
check_val_every_n_epoch=None
)
training_wrapper = Wav2TTS(args)
# if args.pretrained_path:
# training_wrapper.load()
data_module = QuantizeDataModule(args)
wrapper.fit(training_wrapper, datamodule=data_module, ckpt_path=args.resume_checkpoint) |