class_docstr: Word Information Preserved (`WIP`_) is a metric of the performance of an automatic speech recognition system.
  
  This value indicates the percentage of words that were correctly predicted between a set of ground-
  truth sentences and a set of hypothesis sentences. The higher the value, the better the performance of the ASR
  system with a WordInfoPreserved of 1 being a perfect score. Word Information Preserved rate can then be
  computed as:
  
  .. math::
      wip = \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:
  
  - ``wip`` (:class:`~torch.Tensor`): A tensor with the Word Information Preserved score
  
  Args:
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Examples:
      >>> from torchmetrics.text import WordInfoPreserved
      >>> preds = ["this is the prediction", "there is an other sample"]
      >>> target = ["this is the reference", "there is another one"]
      >>> wip = WordInfoPreserved()
      >>> wip(preds, target)
      tensor(0.3472)
docstr: Update state with predictions and targets.
code: def update(
      self, preds: Union[str, List[str]], target: Union[str, List[str]]
  ) -> None:
      """Update state with predictions and targets."""
      errors, target_total, preds_total = _wip_update(preds, target)
      self.errors += errors
      self.target_total += target_total
      self.preds_total += preds_total
  
file_path: torchmetrics/src/torchmetrics/text/wip.py
imports: from typing import Any, List, Optional, Sequence, Union
  from torch import Tensor, tensor
  from torchmetrics.functional.text.wip import _wip_compute, _wip_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 2 for calling the WordInfoPreserved obj's underlying member method code with example usage: obj.update(preds, target) without return types