class_docstr: Match Error Rate (`MER`_) is a common metric of the performance of an automatic speech recognition system.
  
  This value indicates the percentage of words that were incorrectly predicted and inserted.
  The lower the value, the better the performance of the ASR system with a MatchErrorRate of 0 being a perfect score.
  Match error rate can then be computed as:
  
  .. math::
      mer = \frac{S + D + I}{N + I} = \frac{S + D + I}{S + D + C + I}
  
  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`).
  
  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:
  
  - ``mer`` (:class:`~torch.Tensor`): A tensor with the match error rate
  
  Args:
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Examples:
      >>> from torchmetrics.text import MatchErrorRate
      >>> preds = ["this is the prediction", "there is an other sample"]
      >>> target = ["this is the reference", "there is another one"]
      >>> mer = MatchErrorRate()
      >>> mer(preds, target)
      tensor(0.4444)
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docstr: Calculate the Match error rate.
docstr_tok: ["Calculate","the","Match","error","rate","."]
code: def compute(self) -> Tensor:
      """Calculate the Match error rate."""
      return _mer_compute(self.errors, self.total)
  
file_path: torchmetrics/src/torchmetrics/text/mer.py
imports: from typing import Any, List, Optional, Sequence, Union
  import torch
  from torch import Tensor, tensor
  from torchmetrics.functional.text.mer import _mer_compute, _mer_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 MatchErrorRate obj's underlying member method code with example usage: obj.compute() and returns: _mer_compute