code: def word_information_preserved(
      preds: Union[str, List[str]], target: Union[str, List[str]]
  ) -> Tensor:
      """Word Information Preserved rate is a metric of the performance of an automatic speech recognition system.
  
      This value indicates the percentage of characters that were incorrectly predicted. The lower the value, the
      better the performance of the ASR system with a Word Information preserved rate 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 Information preserved rate
  
      Examples:
          >>> from torchmetrics.functional.text import word_information_preserved
          >>> preds = ["this is the prediction", "there is an other sample"]
          >>> target = ["this is the reference", "there is another one"]
          >>> word_information_preserved(preds, target)
          tensor(0.3472)
  
      """
      errors, reference_total, prediction_total = _wip_update(
          preds, target
      )
      return _wip_compute(errors, reference_total, prediction_total)
  
imports: from typing import List, Tuple, Union
  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_information_preserved(preds, target) and returns: _wip_compute