class_docstr: Orthography scheme used for text normalization and tokenization.
  
  Args:
      do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`False`):
          Whether or not to accept lowercase input and lowercase the output when decoding.
      vocab_file (:obj:`str`, `optional`):
          File containing the vocabulary.
      word_delimiter_token (:obj:`str`, `optional`, defaults to :obj:`"|"`):
          The token used for delimiting words; it needs to be in the vocabulary.
      translation_table (:obj:`Dict[str, str]`, `optional`, defaults to :obj:`{}`):
          Table to use with `str.translate()` when preprocessing text (e.g., "-" -> " ").
      words_to_remove (:obj:`Set[str]`, `optional`, defaults to :obj:`set()`):
          Words to remove when preprocessing text (e.g., "sil").
      untransliterator (:obj:`Callable[[str], str]`, `optional`):
          Function that untransliterates text back into native writing system.
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code: def from_name(cls, name: str):
      if name == "librispeech":
          return cls()
      if name == "timit":
          return cls(
              do_lower_case=True,
              # break compounds like "quarter-century-old" and replace pauses "--"
              translation_table=str.maketrans({"-": " "}),
          )
      if name == "buckwalter":
          translation_table = {
              "-": " ",  # sometimes used to represent pauses
              "^": "v",  # fixing "tha" in arabic_speech_corpus dataset
          }
          return cls(
              vocab_file=pathlib.Path(__file__).parent.joinpath(
                  "vocab/buckwalter.json"
              ),
              word_delimiter_token="/",  # "|" is Arabic letter alef with madda above
              translation_table=str.maketrans(translation_table),
              words_to_remove={
                  "sil"
              },  # fixing "sil" in arabic_speech_corpus dataset
              untransliterator=arabic.buckwalter.untransliterate,
          )
      raise ValueError(f"Unsupported orthography: '{name}'.")
  
imports: import logging
  import pathlib
  import re
  import sys
  from dataclasses import dataclass, field
  from typing import Any, Callable, Dict, List, Optional, Set, Union
  import datasets
  import numpy
  import torch
  from packaging import version
  from torch import nn
  import librosa
  from lang_trans import arabic
  from transformers import HfArgumentParser, Trainer, TrainingArguments, Wav2Vec2CTCTokenizer, Wav2Vec2FeatureExtractor, Wav2Vec2ForCTC, Wav2Vec2Processor, is_apex_available, trainer_utils
label_desc: Use image node_id 1 for calling the Orthography obj's underlying member method code with example usage: obj.from_name(cls, name) and returns: cls, cls, cls