class_docstr: Character Error Rate (`CER`_) is a metric of the performance of an automatic speech recognition (ASR) 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 CharErrorRate of 0 being
  a perfect score.
  Character error rate can then be computed as:
  
  .. math::
      CharErrorRate = \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 characters,
      - :math:`N` is the number of characters in the reference (N=S+D+C).
  
  Compute CharErrorRate score of transcribed segments against references.
  
  As input to ``forward`` and ``update`` the metric accepts the following input:
  
  - ``preds`` (:class:`~str`): Transcription(s) to score as a string or list of strings
  - ``target`` (:class:`~str`): 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:
  
  -  ``cer`` (:class:`~torch.Tensor`): A tensor with the Character Error Rate score
  
  Args:
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Examples:
      >>> from torchmetrics.text import CharErrorRate
      >>> preds = ["this is the prediction", "there is an other sample"]
      >>> target = ["this is the reference", "there is another one"]
      >>> cer = CharErrorRate()
      >>> cer(preds, target)
      tensor(0.3415)
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docstr: Update state with predictions and targets.
docstr_tok: ["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, total = _cer_update(preds, target)
      self.errors += errors
      self.total += total
  
file_path: torchmetrics/src/torchmetrics/text/cer.py
imports: from typing import Any, List, Optional, Sequence, Union
  import torch
  from torch import Tensor, tensor
  from torchmetrics.functional.text.cer import _cer_compute, _cer_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 CharErrorRate obj's underlying member method code with example usage: obj.update(preds, target) without return types