o yi @s\ddlmZmZmZddlZddlmZmZddlmZm Z ddl m Z Gddde Z dS))AnyListUnionN)Tensortensor) _mer_compute _mer_update)MetriccseZdZUdZdZeed<dZeed<dZeed<e ed<e ed<de ffd d Z d e e ee fd e e ee fd dfddZd e fddZZS)MatchErrorRateaMatch 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: >>> 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) Fis_differentiablehigher_is_betterfull_state_updateerrortotalkwargsc sJtjdi||jdtdtjddd|jdtdtjddddS)Nerrorsr)dtypesum)dist_reduce_fxr)super__init__ add_statertorchfloat)selfr __class__rI/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/text/mer.pyrCszMatchErrorRate.__init__predstargetreturnNcCs.t||\}}|j|7_|j|7_dS)z*Update state with predictions and targets.N)rrr)rrr rrrrrupdateKs zMatchErrorRate.updatecCst|j|jS)zCalculate the Match error rate.)rrr)rrrrcomputeXszMatchErrorRate.compute)__name__ __module__ __qualname____doc__r bool__annotations__r r rrrrstrrr"r# __classcell__rrrrr s$  #    r ) typingrrrrrr torchmetrics.functional.text.merrrtorchmetrics.metricr r rrrrs