speech_recognition/FunASR
1
0
Fork 0
mirror of https://github.com/modelscope/FunASR synced 2025-09-15 14:48:36 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

update

Browse Source
This commit is contained in:
speech_asr 2023-04-19 15:08:34 +08:00
parent 680cdb55bb
commit 124d49c6ee
1 changed files with 33 additions and 11 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

44
funasr/bin/train.py
View File

@ -1,17 +1,21 @@
import logging
import os
import sys
from io import BytesIO
import torch
from funasr.torch_utils.model_summary import model_summary
from funasr.torch_utils.pytorch_version import pytorch_cudnn_version
from funasr.torch_utils.set_all_random_seed import set_all_random_seed
from funasr.utils import config_argparse
from funasr.utils.build_dataloader import build_dataloader
from funasr.utils.build_distributed import build_distributed
from funasr.utils.prepare_data import prepare_data
from funasr.utils.build_optimizer import build_optimizer
from funasr.utils.build_scheduler import build_scheduler
from funasr.utils.prepare_data import prepare_data
from funasr.utils.types import str2bool
from funasr.utils.yaml_no_alias_safe_dump import yaml_no_alias_safe_dump
def get_parser():
@ -326,9 +330,17 @@ if __name__ == '__main__':
parser = get_parser()
args = parser.parse_args()
# set random seed
set_all_random_seed(args.seed)
torch.backends.cudnn.enabled = args.cudnn_enabled
torch.backends.cudnn.benchmark = args.cudnn_benchmark
torch.backends.cudnn.deterministic = args.cudnn_deterministic
# ddp init
args.distributed = args.dist_world_size > 1
distributed_option = build_distributed(args)
# for logging
if not distributed_option.distributed or distributed_option.dist_rank == 0:
logging.basicConfig(
level="INFO",
@ -345,18 +357,28 @@ if __name__ == '__main__':
# prepare files for dataloader
prepare_data(args, distributed_option)
# set random seed
set_all_random_seed(args.seed)
torch.backends.cudnn.enabled = args.cudnn_enabled
torch.backends.cudnn.benchmark = args.cudnn_benchmark
torch.backends.cudnn.deterministic = args.cudnn_deterministic
train_dataloader, valid_dataloader = build_dataloader(args)
model = build_model(args)
optimizer = build_optimizer(args, model=model)
scheduler = build_scheduler(args, optimizer)
logging.info("world size: {}, rank: {}, local_rank: {}".format(distributed_option.dist_world_size,
distributed_option.dist_rank,
distributed_option.local_rank))
logging.info(pytorch_cudnn_version())
logging.info(model_summary(model))
logging.info("Optimizer: {}".format(optimizer))
logging.info("Scheduler: {}".format(scheduler))
model = build_model(args)
optimizers = build_optimizer(args, model=model)
schedule = build_scheduler(args)
# dump args to config.yaml
if not distributed_option.distributed or distributed_option.dist_rank == 0:
os.makedirs(args.output_dir, exist_ok=True)
with open(os.path.join(args.output_dir, "config.yaml"), "w") as f:
logging.info("Saving the configuration in {}/{}".format(args.output_dir, "config.yaml"))
if args.use_pai:
buffer = BytesIO()
torch.save({"config": vars(args)}, buffer)
args.oss_bucket.put_object(os.path.join(args.output_dir, "config.dict"), buffer.getvalue())
else:
yaml_no_alias_safe_dump(vars(args), f, indent=4, sort_keys=False)
train_dataloader, valid_dataloader = build_dataloader(args)