def load_args(model_dir):
parser = get_parser()
with open(f"{model_dir}/train.log") as f:
cmd_args = f.readlines()[0].split()[2:]
for i in range(len(cmd_args)):
if "/" in cmd_args[i]:
cmd_args[i] = f"{model_dir.parents[1]}/{cmd_args[i]}"
args, _ = parser.parse_known_args(cmd_args)
args.char_list = load_dict(args.dict) if args.dict is not None else None
args.resume = str(Path(args.resume).parent/MODEL_NAME)
return args
def wrap(parser):
return get_parser(parser, required=False)
# Copyright 2020 Shanghai Jiao Tong University (Wangyou Zhang)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import json
import sys
from functools import reduce
from operator import mul
from espnet.bin.asr_train import get_parser
from espnet.nets.pytorch_backend.nets_utils import get_subsample
from espnet.utils.dynamic_import import dynamic_import
if __name__ == "__main__":
cmd_args = sys.argv[1:]
parser = get_parser(required=False)
parser.add_argument("--data-json", type=str, help="data.json")
parser.add_argument("--mode-subsample",
type=str,
required=True,
help='One of ("asr", "mt", "st")')
parser.add_argument(
"--min-io-delta",
type=float,
help="An additional parameter "
"for controlling the input-output length difference",
default=0.0,
)
parser.add_argument(
"--output-json-path",
type=str,
separators=(",", ": "),
)
logging.warning(f"Log saved at {args.outdir}/log")
if args.patience > 0 and early_stop >= args.patience:
test_stats = test("test_best", test_loader, model, save_path)
logging.warning(
f"=====Early stop! Final best test loss: {test_stats['loss']}")
break
if __name__ == "__main__":
# 执行该命令运行4 GPU训练：CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=2 train.py
setup_logging(
verbose=0) # Should come first before other package import logging
parser = get_parser()
add_custom_arguments(parser)
arg_list = sys.argv[1:] + [
"--dict",
'',
#"--dataset", "_".join("cv mt cnh ky dv sl el lv fyNL sah".split()),
]
if "--config" not in arg_list:
arg_list += ["--config", "config/train.yaml"]
if "--outdir" not in arg_list:
arg_list += ["--outdir", '']
args, _ = parser.parse_known_args(arg_list)
# Use all GPUs
ngpu = torch.cuda.device_count() if args.ngpu is None else args.ngpu
