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#!/usr/bin/env python3
import logging
import pathlib
from argparse import ArgumentParser
from common import MODEL_TYPE_LIBRISPEECH, MODEL_TYPE_MUSTC, MODEL_TYPE_TEDLIUM3
from librispeech.lightning import LibriSpeechRNNTModule
from mustc.lightning import MuSTCRNNTModule
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import ModelCheckpoint
from tedlium3.lightning import TEDLIUM3RNNTModule
def get_trainer(args):
checkpoint_dir = args.exp_dir / "checkpoints"
checkpoint = ModelCheckpoint(
checkpoint_dir,
monitor="Losses/val_loss",
mode="min",
save_top_k=5,
save_weights_only=True,
verbose=True,
)
train_checkpoint = ModelCheckpoint(
checkpoint_dir,
monitor="Losses/train_loss",
mode="min",
save_top_k=5,
save_weights_only=True,
verbose=True,
)
callbacks = [
checkpoint,
train_checkpoint,
]
return Trainer(
default_root_dir=args.exp_dir,
max_epochs=args.epochs,
num_nodes=args.num_nodes,
gpus=args.gpus,
accelerator="gpu",
strategy="ddp",
gradient_clip_val=args.gradient_clip_val,
callbacks=callbacks,
)
def get_lightning_module(args):
if args.model_type == MODEL_TYPE_LIBRISPEECH:
return LibriSpeechRNNTModule(
librispeech_path=str(args.dataset_path),
sp_model_path=str(args.sp_model_path),
global_stats_path=str(args.global_stats_path),
)
elif args.model_type == MODEL_TYPE_TEDLIUM3:
return TEDLIUM3RNNTModule(
tedlium_path=str(args.dataset_path),
sp_model_path=str(args.sp_model_path),
global_stats_path=str(args.global_stats_path),
)
elif args.model_type == MODEL_TYPE_MUSTC:
return MuSTCRNNTModule(
mustc_path=str(args.dataset_path),
sp_model_path=str(args.sp_model_path),
global_stats_path=str(args.global_stats_path),
)
else:
raise ValueError(f"Encountered unsupported model type {args.model_type}.")
def parse_args():
parser = ArgumentParser()
parser.add_argument(
"--model-type", type=str, choices=[MODEL_TYPE_LIBRISPEECH, MODEL_TYPE_TEDLIUM3, MODEL_TYPE_MUSTC], required=True
)
parser.add_argument(
"--global-stats-path",
default=pathlib.Path("global_stats.json"),
type=pathlib.Path,
help="Path to JSON file containing feature means and stddevs.",
required=True,
)
parser.add_argument(
"--dataset-path",
type=pathlib.Path,
help="Path to datasets.",
required=True,
)
parser.add_argument(
"--sp-model-path",
type=pathlib.Path,
help="Path to SentencePiece model.",
required=True,
)
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(
"--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(
"--epochs",
default=120,
type=int,
help="Number of epochs to train for. (Default: 120)",
)
parser.add_argument(
"--gradient-clip-val", default=10.0, type=float, help="Value to clip gradient values to. (Default: 10.0)"
)
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)
model = get_lightning_module(args)
trainer = get_trainer(args)
trainer.fit(model)
if __name__ == "__main__":
cli_main()
|
