class_docstr: Word Information Lost (`WIL`_) is a metric of the performance of an automatic speech recognition system.
  
  This value indicates the percentage of words that were incorrectly predicted between a set of ground-truth
  sentences and a set of hypothesis sentences. The lower the value, the better the performance of the ASR system
  with a WordInfoLost of 0 being a perfect score. Word Information Lost rate can then be computed as:
  
  .. math::
      wil = 1 - \frac{C}{N} + \frac{C}{P}
  
  where:
  
      - :math:`C` is the number of correct words,
      - :math:`N` is the number of words in the reference
      - :math:`P` is the number of words in the prediction
  
  As input to ``forward`` and ``update`` the metric accepts the following input:
  
  - ``preds`` (:class:`~List`): Transcription(s) to score as a string or list of strings
  - ``target`` (:class:`~List`): Reference(s) for each speech input as a string or list of strings
  
  As output of ``forward`` and ``compute`` the metric returns the following output:
  
  - ``wil`` (:class:`~torch.Tensor`): A tensor with the Word Information Lost score
  
  Args:
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Examples:
      >>> from torchmetrics.text import WordInfoLost
      >>> preds = ["this is the prediction", "there is an other sample"]
      >>> target = ["this is the reference", "there is another one"]
      >>> wil = WordInfoLost()
      >>> wil(preds, target)
      tensor(0.6528)
docstr: Calculate the Word Information Lost.
code: def compute(self) -> Tensor:
      """Calculate the Word Information Lost."""
      return _word_info_lost_compute(
          self.errors, self.target_total, self.preds_total
      )
  
file_path: torchmetrics/src/torchmetrics/text/wil.py
imports: from typing import Any, List, Optional, Sequence, Union
  from torch import Tensor, tensor
  from torchmetrics.functional.text.wil import _word_info_lost_compute, _word_info_lost_update
  from torchmetrics.metric import Metric
  from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
  from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
label_desc: Use image node_id 3 for calling the WordInfoLost obj's underlying member method code with example usage: obj.compute() and returns: _word_info_lost_compute