class_docstr: Construct an Speech2Text tokenizer.
  
  This tokenizer inherits from [`PreTrainedTokenizer`] which contains some of the main methods. Users should refer to
  the superclass for more information regarding such methods.
  
  Args:
      vocab_file (`str`):
          File containing the vocabulary.
      spm_file (`str`):
          Path to the [SentencePiece](https://github.com/google/sentencepiece) model file
      bos_token (`str`, *optional*, defaults to `"<s>"`):
          The beginning of sentence token.
      eos_token (`str`, *optional*, defaults to `"</s>"`):
          The end of sentence token.
      unk_token (`str`, *optional*, defaults to `"<unk>"`):
          The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
          token instead.
      pad_token (`str`, *optional*, defaults to `"<pad>"`):
          The token used for padding, for example when batching sequences of different lengths.
      do_upper_case (`bool`, *optional*, defaults to `False`):
         Whether or not to uppercase the output when decoding.
      do_lower_case (`bool`, *optional*, defaults to `False`):
          Whether or not to lowercase the input when tokenizing.
      tgt_lang (`str`, *optional*):
          A string representing the target language.
      sp_model_kwargs (`dict`, *optional*):
          Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
          SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
          to set:
  
          - `enable_sampling`: Enable subword regularization.
          - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
  
            - `nbest_size = {0,1}`: No sampling is performed.
            - `nbest_size > 1`: samples from the nbest_size results.
            - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
              using forward-filtering-and-backward-sampling algorithm.
  
          - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
            BPE-dropout.
  
      **kwargs
          Additional keyword arguments passed along to [`PreTrainedTokenizer`]
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code: def _convert_token_to_id(self, token):
      return self.encoder.get(token, self.encoder[self.unk_token])
  
imports: import json
  import os
  from pathlib import Path
  from shutil import copyfile
  from typing import Any, Dict, List, Optional, Tuple, Union
  import sentencepiece
  from ...tokenization_utils import PreTrainedTokenizer
  from ...utils import logging
label_desc: Use image node_id 8 for calling the Speech2TextTokenizer obj's underlying member method code with example usage: obj._convert_token_to_id(token) and returns: self