update

speech_asr

2023-04-19 58fb22cb2b8144b2e29d38327be44f3510ec8bb1

 funasr/bin/train.py

@@ -6,6 +6,7 @@

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.types import str2bool
@@ -338,14 +339,36 @@
            format=f"[{os.uname()[1].split('.')[0]}]"
                   f" %(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
        )
    logging.info("world size: {}, rank: {}, local_rank: {}".format(distributed_option.dist_world_size,
                                                                   distributed_option.dist_rank,
                                                                   distributed_option.local_rank))

    # 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)

    logging.info("world size: {}, rank: {}, local_rank: {}".format(distributed_option.dist_world_size,
                                                                   distributed_option.dist_rank,
                                                                   distributed_option.local_rank))

    # optimizers = cls.build_optimizers(args, model=model)
    # schedulers = []
    # for i, optim in enumerate(optimizers, 1):
    #     suf = "" if i == 1 else str(i)
    #     name = getattr(args, f"scheduler{suf}")
    #     conf = getattr(args, f"scheduler{suf}_conf")
    #     if name is not None:
    #         cls_ = scheduler_classes.get(name)
    #         if cls_ is None:
    #             raise ValueError(
    #                 f"must be one of {list(scheduler_classes)}: {name}"
    #             )
    #         scheduler = cls_(optim, **conf)
    #     else:
    #         scheduler = None
    #
    #     schedulers.append(scheduler)