class_docstr: Word error rate (`WordErrorRate`_) is a common metric of the performance of an automatic speech recognition.
  
  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. Word error rate can then be
  computed as:
  
  .. math::
      WER = \frac{S + D + I}{N} = \frac{S + D + I}{S + D + C}
  
  where:
  - :math:`S` is the number of substitutions,
  - :math:`D` is the number of deletions,
  - :math:`I` is the number of insertions,
  - :math:`C` is the number of correct words,
  - :math:`N` is the number of words in the reference (:math:`N=S+D+C`).
  
  Compute WER score of transcribed segments against references.
  
  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:
  
  -  ``wer`` (:class:`~torch.Tensor`): A tensor with the Word Error Rate score
  
  Args:
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Examples:
      >>> from torchmetrics.text import WordErrorRate
      >>> preds = ["this is the prediction", "there is an other sample"]
      >>> target = ["this is the reference", "there is another one"]
      >>> wer = WordErrorRate()
      >>> wer(preds, target)
      tensor(0.5000)
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code: def __init__(
      self,
      **kwargs: Any,
  ) -> None:
      super().__init__(**kwargs)
      self.add_state(
          "errors", tensor(0, dtype=torch.float), dist_reduce_fx="sum"
      )
      self.add_state(
          "total", tensor(0, dtype=torch.float), dist_reduce_fx="sum"
      )
  
file_path: torchmetrics/src/torchmetrics/text/wer.py
imports: from typing import Any, List, Optional, Sequence, Union
  import torch
  from torch import Tensor, tensor
  from torchmetrics.functional.text.wer import _wer_compute, _wer_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 1 to create a new WordErrorRate object from inherited base classes: Metric with example: obj = WordErrorRate()