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#!/usr/bin/env python3
"""Train the HuBERTPretrainModel by using labels generated by KMeans clustering.
Example:
python train.py --dataset-path ./exp/data/mfcc/ --feature-type mfcc --num-classes 100
"""
import logging
import pathlib
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, RawDescriptionHelpFormatter
from typing import Tuple
from lightning.pytorch import seed_everything, Trainer
from lightning.pytorch.callbacks import ModelCheckpoint
from lightning_modules import HuBERTPreTrainModule
logger = logging.getLogger(__name__)
class _Formatter(ArgumentDefaultsHelpFormatter, RawDescriptionHelpFormatter):
# To use ArgumentDefaultsHelpFormatter as the formatter_class and
# RawDescriptionHelpFormatter to add custom formatting to description or epilog.
# Check: https://stackoverflow.com/a/18462760
pass
def run_train(args):
seed_everything(1337)
checkpoint_dir = args.exp_dir / f"checkpoints_{args.dataset}_{args.model_name}"
checkpoint = ModelCheckpoint(
checkpoint_dir,
monitor="val_loss",
mode="min",
save_top_k=5,
save_weights_only=False,
verbose=True,
)
train_checkpoint = ModelCheckpoint(
checkpoint_dir,
monitor="train_loss",
mode="min",
save_top_k=5,
save_weights_only=False,
verbose=True,
)
callbacks = [
checkpoint,
train_checkpoint,
]
trainer = Trainer(
default_root_dir=args.exp_dir,
max_steps=args.max_updates,
num_nodes=args.num_nodes,
devices=args.gpus,
accelerator="gpu",
strategy="ddp_find_unused_parameters_true",
use_distributed_sampler=False,
callbacks=callbacks,
reload_dataloaders_every_n_epochs=1,
)
model = HuBERTPreTrainModule(
model_name=args.model_name,
feature_grad_mult=args.feature_grad_mult,
num_classes=args.num_classes,
dataset=args.dataset,
dataset_path=args.dataset_path,
feature_type=args.feature_type,
seconds_per_batch=args.seconds_per_batch,
learning_rate=args.learning_rate,
betas=args.betas,
eps=args.eps,
weight_decay=args.weight_decay,
clip_norm=args.clip_norm,
warmup_updates=args.warmup_updates,
max_updates=args.max_updates,
)
trainer.fit(model, ckpt_path=args.resume_checkpoint)
def _parse_args():
parser = ArgumentParser(
description=__doc__,
formatter_class=_Formatter,
)
parser.add_argument(
"--dataset-path",
type=pathlib.Path,
required=True,
help="Path to the feature and label directories.",
)
parser.add_argument(
"--resume-checkpoint",
type=pathlib.Path,
default=None,
help="Path to the feature and label directories. (Default: None)",
)
parser.add_argument(
"--feature-type",
choices=["mfcc", "hubert"],
type=str,
required=True,
)
parser.add_argument(
"--feature-grad-mult",
default=0.1,
type=float,
help="The scaling factor to multiply the feature extractor gradient. (Default: 0.1)",
)
parser.add_argument(
"--num-classes",
choices=[100, 500],
type=int,
required=True,
help="The ``num_class`` when building the hubert_pretrain_base model.",
)
parser.add_argument(
"--model-name",
default="hubert_pretrain_base",
choices=["hubert_pretrain_base", "hubert_pretrain_large", "hubert_pretrain_xlarge"],
type=str,
help="The HuBERT model to train. (Default: 'hubert_pretrain_base')",
)
parser.add_argument(
"--exp-dir",
default=pathlib.Path("./exp"),
type=pathlib.Path,
help="Directory to save checkpoints and logs to. (Default: './exp')",
)
parser.add_argument(
"--dataset",
default="librispeech",
choices=["librispeech", "librilight"],
type=str,
help="The dataset for training. (Default: 'librispeech')",
)
parser.add_argument(
"--learning-rate",
default=0.0005,
type=float,
help="The peak learning rate. (Default: 0.0005)",
)
parser.add_argument(
"--betas",
default=(0.9, 0.98),
type=Tuple,
help="The coefficients for computing running averages of gradient and its square (Default: (0.9, 0.98))",
)
parser.add_argument(
"--eps",
default=1e-6,
type=float,
help="Epsilon value in Adam optimizer. (Default: 1e-6)",
)
parser.add_argument(
"--weight-decay",
default=0.01,
type=float,
help="Weight decay (L2 penalty) (default: 0.01)",
)
parser.add_argument(
"--clip-norm",
default=10.0,
type=float,
help="The gradient norm value to clip. (Default: 10.0)",
)
parser.add_argument(
"--num-nodes",
default=4,
type=int,
help="Number of nodes to use for training. (Default: 4)",
)
parser.add_argument(
"--gpus",
default=8,
type=int,
help="Number of GPUs per node to use for training. (Default: 8)",
)
parser.add_argument(
"--warmup-updates",
default=32000,
type=int,
help="Number of steps for warm up the learning rate. (Default: 32000)",
)
parser.add_argument(
"--max-updates",
default=250000,
type=int,
help="Total number of training steps. (Default: 250000)",
)
parser.add_argument(
"--seconds-per-batch",
default=87.5,
type=float,
help="Number of seconds of audio in a mini-batch. (Default: 87.5)",
)
parser.add_argument("--debug", action="store_true", help="whether to use debug level for logging")
return parser.parse_args()
def _init_logger(debug):
fmt = "%(asctime)s %(message)s" if debug else "%(message)s"
level = logging.DEBUG if debug else logging.INFO
logging.basicConfig(format=fmt, level=level, datefmt="%Y-%m-%d %H:%M:%S")
def cli_main():
args = _parse_args()
_init_logger(args.debug)
run_train(args)
if __name__ == "__main__":
cli_main()
|
