o yi$ @sTddlmZmZmZddlmZmZddlmZm Z ddl m Z Gddde Z dS))AnyListUnion)Tensortensor) _wip_compute _wip_update)MetriccseZdZUdZdZeed<dZeed<dZeed<e ed<e ed<e ed<d e ffd d Z d e e ee fd e e ee fddfddZde fddZZS)WordInfoPreservedaWord 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 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) Fis_differentiablehigher_is_betterfull_state_updateerrors preds_total target_totalkwargsc sRtjdi||jdtddd|jdtddd|jdtddddS)Nrgsum)dist_reduce_fxrr)super__init__ add_stater)selfr __class__rI/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/text/wip.pyrCszWordInfoPreserved.__init__predstargetreturnNcCs>t||\}}}|j|7_|j|7_|j|7_dS)z*Update state with predictions and targets.N)rrrr)rrrrrrrrrupdateLszWordInfoPreserved.updatecCst|j|j|jS)z)Calculate the Word Information Preserved.)rrrr)rrrrcomputeSszWordInfoPreserved.compute)__name__ __module__ __qualname____doc__r bool__annotations__r r rrrrstrrrr __classcell__rrrrr s  #  . r N) typingrrrtorchrr torchmetrics.functional.text.wiprrtorchmetrics.metricr r rrrrs