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2023-11-16 4ace5a95b052d338947fc88809a440ccd55cf6b4

 funasr/bin/train.py

@@ -1,4 +1,6 @@
#!/usr/bin/env python3
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
#  MIT License  (https://opensource.org/licenses/MIT)

import argparse
import logging
@@ -16,6 +18,7 @@
from funasr.build_utils.build_scheduler import build_scheduler
from funasr.build_utils.build_trainer import build_trainer
from funasr.text.phoneme_tokenizer import g2p_choices
from funasr.torch_utils.load_pretrained_model import load_pretrained_model
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
@@ -23,9 +26,9 @@
from funasr.utils.prepare_data import prepare_data
from funasr.utils.types import int_or_none
from funasr.utils.types import str2bool
from funasr.utils.types import str2triple_str
from funasr.utils.types import str_or_none
from funasr.utils.yaml_no_alias_safe_dump import yaml_no_alias_safe_dump
from funasr.modules.lora.utils import mark_only_lora_as_trainable


def get_parser():
@@ -161,6 +164,7 @@
    )
    parser.add_argument(
        "--patience",
        type=int_or_none,
        default=None,
        help="Number of epochs to wait without improvement "
             "before stopping the training",
@@ -271,8 +275,8 @@
    parser.add_argument(
        "--init_param",
        type=str,
        action="append",
        default=[],
        nargs="*",
        help="Specify the file path used for initialization of parameters. "
             "The format is '<file_path>:<src_key>:<dst_key>:<exclude_keys>', "
             "where file_path is the model file path, "
@@ -298,7 +302,7 @@
        "--freeze_param",
        type=str,
        default=[],
        nargs="*",
        action="append",
        help="Freeze parameters",
    )

@@ -316,41 +320,35 @@
        help=f"The keyword arguments for dataset",
    )
    parser.add_argument(
        "--train_data_file",
        "--data_dir",
        type=str,
        default=None,
        help="train_list for large dataset",
        help="root path of data",
    )
    parser.add_argument(
        "--valid_data_file",
        "--train_set",
        type=str,
        default="train",
        help="train dataset",
    )
    parser.add_argument(
        "--valid_set",
        type=str,
        default="validation",
        help="dev dataset",
    )
    parser.add_argument(
        "--data_file_names",
        type=str,
        default="wav.scp,text",
        help="input data files",
    )
    parser.add_argument(
        "--speed_perturb",
        type=float,
        nargs="+",
        default=None,
        help="valid_list for large dataset",
    )
    parser.add_argument(
        "--train_data_path_and_name_and_type",
        type=str2triple_str,
        action="append",
        default=[],
        help="e.g. '--train_data_path_and_name_and_type some/path/a.scp,foo,sound'. ",
    )
    parser.add_argument(
        "--valid_data_path_and_name_and_type",
        type=str2triple_str,
        action="append",
        default=[],
    )
    parser.add_argument(
        "--train_shape_file",
        type=str,
        action="append",
        default=[],
    )
    parser.add_argument(
        "--valid_shape_file",
        type=str,
        action="append",
        default=[],
        help="speed perturb",
    )
    parser.add_argument(
        "--use_preprocessor",
@@ -481,6 +479,18 @@
        default=None,
        help="oss bucket.",
    )
    parser.add_argument(
        "--enable_lora",
        type=str2bool,
        default=False,
        help="Apply lora for finetuning.",
    )
    parser.add_argument(
        "--lora_bias",
        type=str,
        default="none",
        help="lora bias.",
    )

    return parser

@@ -520,6 +530,18 @@
    prepare_data(args, distributed_option)

    model = build_model(args)
    model = model.to(
        dtype=getattr(torch, args.train_dtype),
        device="cuda" if args.ngpu > 0 else "cpu",
    )
    if args.enable_lora:
        mark_only_lora_as_trainable(model, args.lora_bias)
    for t in args.freeze_param:
        for k, p in model.named_parameters():
            if k.startswith(t + ".") or k == t:
                logging.info(f"Setting {k}.requires_grad = False")
                p.requires_grad = False

    optimizers = build_optimizer(args, model=model)
    schedulers = build_scheduler(args, optimizers)

@@ -544,6 +566,18 @@
            else:
                yaml_no_alias_safe_dump(vars(args), f, indent=4, sort_keys=False)

    for p in args.init_param:
        logging.info(f"Loading pretrained params from {p}")
        load_pretrained_model(
            model=model,
            init_param=p,
            ignore_init_mismatch=args.ignore_init_mismatch,
            map_location=f"cuda:{torch.cuda.current_device()}"
            if args.ngpu > 0
            else "cpu",
            oss_bucket=args.oss_bucket,
        )

    # dataloader for training/validation
    train_dataloader, valid_dataloader = build_dataloader(args)