update

嘉渊

2023-04-25 7436acc5ddca0ebb7458a0c4c483079346e10715

update

 egs/aishell/paraformer/run.sh

@@ -13,7 +13,7 @@
infer_cmd=utils/run.pl

# general configuration
feats_dir="/nfs/wangjiaming.wjm/Funasr_data/aishell-1-fix-cmvn" #feature output dictionary
feats_dir="/nfs/wangjiaming.wjm/Funasr_data_test/aishell" #feature output dictionary
exp_dir="."
lang=zh
dumpdir=dump/fbank
@@ -167,14 +167,10 @@
                --use_preprocessor true \
                --token_type char \
                --token_list $token_list \
                --train_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${train_set}/${scp},speech,${type} \
                --train_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${train_set}/text,text,text \
                --train_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${train_set}/speech_shape \
                --train_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${train_set}/text_shape.char \
                --valid_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${valid_set}/${scp},speech,${type} \
                --valid_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${valid_set}/text,text,text \
                --valid_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${valid_set}/speech_shape \
                --valid_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${valid_set}/text_shape.char  \
                --data_dir ${feats_dir}/data \
                --train_set ${train_set} \
                --valid_set ${valid_set} \
                --cmvn_file ${feats_dir}/cmvn/cmvn.mvn \
                --resume true \
                --output_dir ${exp_dir}/exp/${model_dir} \
                --config $asr_config \

 funasr/bin/train.py

@@ -23,7 +23,6 @@
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

@@ -316,42 +315,24 @@
        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=None,
        help="valid_list for large dataset",
        default="train",
        help="train 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",
        "--valid_set",
        type=str,
        action="append",
        default=[],
        default="validation",
        help="dev dataset",
    )
    parser.add_argument(
        "--valid_shape_file",
        type=str,
        action="append",
        default=[],
    )

    parser.add_argument(
        "--use_preprocessor",
        type=str2bool,

 funasr/utils/prepare_data.py

@@ -36,10 +36,8 @@
                f_text.write(sample_name + " " + text_dict[sample_name] + "\n")
            else:
                filter_count += 1
    logging.info(
        "{}/{} samples in {} are filtered because of the mismatch between wav.scp and text".format(len(wav_lines),
                                                                                                   filter_count,
                                                                                                   dataset))
    logging.info("{}/{} samples in {} are filtered because of the mismatch between wav.scp and text".
                 format(filter_count, len(wav_lines), dataset))


def wav2num_frame(wav_path, frontend_conf):
@@ -157,30 +155,34 @@


def prepare_data(args, distributed_option):
    if args.dataset_type == "small" and args.train_data_path_and_name_and_type is not None:
        return
    if args.dataset_type == "large" and args.train_data_file is not None:
        return
    distributed = distributed_option.distributed
    if not hasattr(args, "train_set"):
        args.train_set = "train"
    if not hasattr(args, "dev_set"):
        args.dev_set = "validation"
    if not distributed or distributed_option.dist_rank == 0:
        filter_wav_text(args.data_dir, args.train_set)
        filter_wav_text(args.data_dir, args.dev_set)
        filter_wav_text(args.data_dir, args.valid_set)

        if args.dataset_type == "small" and args.train_shape_file is None:
            calc_shape(args, args.train_set)
            calc_shape(args, args.dev_set)
            calc_shape(args, args.valid_set)

        if args.dataset_type == "large" and args.train_data_file is None:
            generate_data_list(args.data_dir, args.train_set)
            generate_data_list(args.data_dir, args.dev_set)
            generate_data_list(args.data_dir, args.valid_set)

    args.train_shape_file = [os.path.join(args.data_dir, args.train_set, "speech_shape")]
    args.valid_shape_file = [os.path.join(args.data_dir, args.dev_set, "speech_shape")]
    args.train_data_file = os.path.join(args.data_dir, args.train_set, "data.list")
    args.valid_data_file = os.path.join(args.data_dir, args.dev_set, "data.list")
    if args.dataset_type == "small":
        args.train_shape_file = [os.path.join(args.data_dir, args.train_set, "speech_shape")]
        args.valid_shape_file = [os.path.join(args.data_dir, args.valid_set, "speech_shape")]
        data_names = args.dataset_conf.get("data_names", "speech,text").split(",")
        data_types = args.dataset_conf.get("data_types", "sound,text").split(",")
        args.train_data_path_and_name_and_type = [
            ["{}/{}/wav.scp".format(args.data_dir, args.train_set), data_names[0], data_types[0]],
            ["{}/{}/text".format(args.data_dir, args.train_set), data_names[1], data_types[1]]
        ]
        args.valid_data_path_and_name_and_type = [
            ["{}/{}/wav.scp".format(args.data_dir, args.valid_set), data_names[0], data_types[0]],
            ["{}/{}/text".format(args.data_dir, args.valid_set), data_names[1], data_types[1]]
        ]
    else:
        args.train_data_file = os.path.join(args.data_dir, args.train_set, "data.list")
        args.valid_data_file = os.path.join(args.data_dir, args.valid_set, "data.list")
    if distributed:
        dist.barrier()