class_docstr: An abstract class representing a distance metric.            Every distance is normalized to be defined in [0, 1].
class_docstr_tok: ["An","abstract","class","representing","a","distance","metric",".","Every","distance","is","normalized","to","be","defined","in","[","0",",","1","]","."]
docstr: Return the distance between truth and hypothesis.
  
  :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 distance value between `truth` and `hypothesis`.
docstr_tok: ["Return","the","distance","between","truth","and","hypothesis",".",":","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","distance","value","between","`","truth","`","and","`","hypothesis","`","."]
code: def __call__(
      self,
      truth: Union[str, List[str]],
      hypothesis: Union[str, List[str]],
  ) -> float:
      """
      Return the distance between truth and hypothesis.
  
      :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 distance value between `truth` and `hypothesis`.
      """
      raise NotImplementedError
  
file_path: espnet/egs2/slurp_entity/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 Distance obj's underlying member method code with example usage: obj.__call__(truth, hypothesis) without return types