code: def word_error_rate(
      preds: Union[str, List[str]], target: Union[str, List[str]]
  ) -> Tensor:
      """Word error rate (WordErrorRate_) is a common metric of performance of an automatic speech recognition system.
  
      This value indicates the percentage of words that were incorrectly predicted. The lower the value, the better the
      performance of the ASR system with a WER of 0 being a perfect score.
  
      Args:
          preds: Transcription(s) to score as a string or list of strings
          target: Reference(s) for each speech input as a string or list of strings
  
      Returns:
          Word error rate score
  
      Examples:
          >>> preds = ["this is the prediction", "there is an other sample"]
          >>> target = ["this is the reference", "there is another one"]
          >>> word_error_rate(preds=preds, target=target)
          tensor(0.5000)
  
      """
      errors, total = _wer_update(preds, target)
      return _wer_compute(errors, total)
  
imports: from typing import List, Tuple, Union
  import torch
  from torch import Tensor, tensor
  from torchmetrics.functional.text.helper import _edit_distance
label_desc: Use image node_id 3 for calling a global function with example usage: word_error_rate(preds, target) and returns: _wer_compute