class_docstr: The Character-level distance, implemented through            the normalised Levenshtein distance.
class_docstr_tok: ["The","Character-level","distance",",","implemented","through","the","normalised","Levenshtein","distance","."]
docstr: Evaluates the character-level distance
  
  :param truth: The ground-truth sentence as a string or list of words.
  :param hypothesis: The hypothesis sentence as a string or list of words.
  :return: The character-level distance.
docstr_tok: ["Evaluates","the","character-level","distance",":","param","truth",":","The","ground-truth","sentence","as","a","string","or","list","of","words",".",":","param","hypothesis",":","The","hypothesis","sentence","as","a","string","or","list","of","words",".",":","return",":","The","character-level","distance","."]
code: def __call__(
      self,
      truth: Union[str, List[str]],
      hypothesis: Union[str, List[str]],
  ) -> float:
      """
      Evaluates the character-level distance
  
      :param truth: The ground-truth sentence as a string or list of words.
      :param hypothesis: The hypothesis sentence as a string or list of words.
      :return: The character-level distance.
      """
      return levenshtein.normalized_distance(truth, hypothesis)
  
file_path: espnet/egs2/uslu14/asr1/local/evaluation/metrics/distance.py
imports: from typing import List, Union
  from jiwer import wer
  from textdistance.algorithms.edit_based import levenshtein
label_desc: Use image node_id 1 for calling the CharDistance obj's underlying member method code with example usage: obj.__call__(truth, hypothesis) and returns: levenshtein