code: def get_parser():
      """Get default arguments."""
      parser = configargparse.ArgumentParser(
          description="Align text to audio using CTC segmentation."
          "using a pre-trained speech recognition model.",
          config_file_parser_class=configargparse.YAMLConfigFileParser,
          formatter_class=configargparse.ArgumentDefaultsHelpFormatter,
      )
      # general configuration
      parser.add(
          "--config",
          is_config_file=True,
          help="Decoding config file path.",
      )
      parser.add_argument(
          "--ngpu",
          type=int,
          default=0,
          help="Number of GPUs (max. 1 is supported)",
      )
      parser.add_argument(
          "--dtype",
          choices=("float16", "float32", "float64"),
          default="float32",
          help="Float precision (only available in --api v2)",
      )
      parser.add_argument(
          "--backend",
          type=str,
          default="pytorch",
          choices=["pytorch"],
          help="Backend library",
      )
      parser.add_argument(
          "--debugmode", type=int, default=1, help="Debugmode"
      )
      parser.add_argument(
          "--verbose", "-V", type=int, default=1, help="Verbose option"
      )
      parser.add_argument(
          "--preprocess-conf",
          type=str,
          default=None,
          help="The configuration file for the pre-processing",
      )
      # task related
      parser.add_argument(
          "--data-json",
          type=str,
          help="Json of recognition data for audio and text",
      )
      parser.add_argument(
          "--utt-text", type=str, help="Text separated into utterances"
      )
      # model (parameter) related
      parser.add_argument(
          "--model",
          type=str,
          required=True,
          help="Model file parameters to read",
      )
      parser.add_argument(
          "--model-conf",
          type=str,
          default=None,
          help="Model config file",
      )
      parser.add_argument(
          "--num-encs",
          default=1,
          type=int,
          help="Number of encoders in the model.",
      )
      # ctc-segmentation related
      parser.add_argument(
          "--subsampling-factor",
          type=int,
          default=None,
          help="Subsampling factor."
          " If the encoder sub-samples its input, the number of frames at the CTC layer"
          " is reduced by this factor. For example, a BLSTMP with subsampling 1_2_2_1_1"
          " has a subsampling factor of 4.",
      )
      parser.add_argument(
          "--frame-duration",
          type=int,
          default=None,
          help="Non-overlapping duration of a single frame in milliseconds.",
      )
      parser.add_argument(
          "--min-window-size",
          type=int,
          default=None,
          help="Minimum window size considered for utterance.",
      )
      parser.add_argument(
          "--max-window-size",
          type=int,
          default=None,
          help="Maximum window size considered for utterance.",
      )
      parser.add_argument(
          "--use-dict-blank",
          type=int,
          default=None,
          help="DEPRECATED.",
      )
      parser.add_argument(
          "--set-blank",
          type=int,
          default=None,
          help="Index of model dictionary for blank token (default: 0).",
      )
      parser.add_argument(
          "--gratis-blank",
          type=int,
          default=None,
          help="Set the transition cost of the blank token to zero. Audio sections"
          " labeled with blank tokens can then be skipped without penalty. Useful"
          " if there are unrelated audio segments between utterances.",
      )
      parser.add_argument(
          "--replace-spaces-with-blanks",
          type=int,
          default=None,
          help="Fill blanks in between words to better model pauses between words."
          " Segments can be misaligned if this option is combined with --gratis-blank."
          " May increase length of ground truth.",
      )
      parser.add_argument(
          "--scoring-length",
          type=int,
          default=None,
          help="Changes partitioning length L for calculation of the confidence score.",
      )
      parser.add_argument(
          "--output",
          type=configargparse.FileType("w"),
          required=True,
          help="Output segments file",
      )
      return parser
  
file_path: espnet/espnet/bin/asr_align.py
imports: import json
  import logging
  import os
  import sys
  import configargparse
  import torch
  from ctc_segmentation import CtcSegmentationParameters, ctc_segmentation, determine_utterance_segments, prepare_text
  from espnet.asr.pytorch_backend.asr_init import load_trained_model
  from espnet.nets.asr_interface import ASRInterface
  from espnet.utils.io_utils import LoadInputsAndTargets
label_desc: Use image node_id 1 for calling a global function with example usage: get_parser() and returns: parser