mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
143 lines
4.5 KiB
Python
143 lines
4.5 KiB
Python
import argparse
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from funasr.models.ctc import CTC
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from funasr.utils.get_default_kwargs import get_default_kwargs
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from funasr.utils.nested_dict_action import NestedDictAction
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from funasr.utils.types import int_or_none
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from funasr.utils.types import str2bool
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from funasr.utils.types import str_or_none
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def build_args(args):
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parser = argparse.ArgumentParser("Task related config")
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if args.task_name == "asr":
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from funasr.build_utils.build_asr_model import class_choices_list
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for class_choices in class_choices_list:
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# Append --<name> and --<name>_conf.
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# e.g. --encoder and --encoder_conf
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class_choices.add_arguments(parser)
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parser.add_argument(
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"--token_list",
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type=str_or_none,
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default=None,
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help="A text mapping int-id to token",
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)
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parser.add_argument(
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"--split_with_space",
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type=str2bool,
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default=True,
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help="whether to split text using <space>",
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)
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parser.add_argument(
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"--seg_dict_file",
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type=str,
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default=None,
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help="seg_dict_file for text processing",
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)
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parser.add_argument(
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"--init",
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type=lambda x: str_or_none(x.lower()),
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default=None,
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help="The initialization method",
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choices=[
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"chainer",
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"xavier_uniform",
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"xavier_normal",
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"kaiming_uniform",
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"kaiming_normal",
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None,
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],
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)
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parser.add_argument(
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"--input_size",
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type=int_or_none,
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default=None,
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help="The number of input dimension of the feature",
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)
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parser.add_argument(
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"--ctc_conf",
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action=NestedDictAction,
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default=get_default_kwargs(CTC),
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help="The keyword arguments for CTC class.",
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)
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parser.add_argument(
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"--token_type",
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type=str,
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default="bpe",
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choices=["bpe", "char", "word", "phn"],
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help="The text will be tokenized " "in the specified level token",
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)
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parser.add_argument(
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"--bpemodel",
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type=str_or_none,
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default=None,
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help="The model file of sentencepiece",
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)
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parser.add_argument(
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"--cmvn_file",
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type=str_or_none,
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default=None,
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help="The file path of noise scp file.",
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)
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elif args.task_name == "pretrain":
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from funasr.build_utils.build_pretrain_model import class_choices_list
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for class_choices in class_choices_list:
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# Append --<name> and --<name>_conf.
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# e.g. --encoder and --encoder_conf
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class_choices.add_arguments(parser)
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parser.add_argument(
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"--init",
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type=lambda x: str_or_none(x.lower()),
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default=None,
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help="The initialization method",
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choices=[
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"chainer",
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"xavier_uniform",
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"xavier_normal",
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"kaiming_uniform",
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"kaiming_normal",
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None,
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],
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)
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parser.add_argument(
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"--input_size",
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type=int_or_none,
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default=None,
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help="The number of input dimension of the feature",
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)
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parser.add_argument(
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"--feats_type",
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type=str,
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default='fbank',
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help="feats type, e.g. fbank, wav, ark_wav(needed to be scale normalization)",
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)
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parser.add_argument(
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"--noise_db_range",
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type=str,
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default="13_15",
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help="The range of noise decibel level.",
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)
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parser.add_argument(
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"--pred_masked_weight",
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type=float,
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default=1.0,
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help="weight for predictive loss for masked frames",
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)
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parser.add_argument(
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"--pred_nomask_weight",
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type=float,
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default=0.0,
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help="weight for predictive loss for unmasked frames",
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)
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parser.add_argument(
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"--loss_weights",
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type=float,
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default=0.0,
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help="weights for additional loss terms (not first one)",
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)
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else:
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raise NotImplementedError("Not supported task: {}".format(args.task_name))
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args = parser.parse_args()
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return args
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