a  d@sddlmZmZmZmZmZddlmZmZddl m Z m Z ddl m Z ddlmZddlmZmZesndgZGdd d e Zd S) )AnyListOptionalSequenceUnion)Tensortensor) _wil_compute _wil_update)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEWordInfoLost.plotcseZdZUdZdZeed<dZeed<dZeed<dZ e ed<dZ e ed <e ed <e ed <e ed <e d dfdd Zeeeefeeeefd dddZe dddZdeee ee feeedddZZS) WordInfoLostaWord Information Lost (`WIL`_) is a metric of the performance of an automatic speech recognition system. This value indicates the percentage of words that were incorrectly predicted between a set of ground-truth sentences and a set of hypothesis sentences. The lower the value, the better the performance of the ASR system with a WordInfoLost of 0 being a perfect score. Word Information Lost rate can then be computed as: .. math:: wil = 1 - \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: - ``wil`` (:class:`~torch.Tensor`): A tensor with the Word Information Lost score Args: kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Examples: >>> from torchmetrics.text import WordInfoLost >>> preds = ["this is the prediction", "there is an other sample"] >>> target = ["this is the reference", "there is another one"] >>> wil = WordInfoLost() >>> wil(preds, target) tensor(0.6528) Fis_differentiablehigher_is_betterfull_state_updateplot_lower_boundg?plot_upper_bounderrors target_total preds_totalN)kwargsreturnc sRtjfi||jdtddd|jdtddd|jdtddddS)Nrrsum)Zdist_reduce_fxrr)super__init__Z add_stater)selfr __class__^/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/text/wil.pyrJszWordInfoLost.__init__)predstargetrcCs>t||\}}}|j|7_|j|7_|j|7_dS)z*Update state with predictions and targets.N)r rrr)rr$r%rrrr"r"r#updateSszWordInfoLost.update)rcCst|j|j|jS)z$Calculate the Word Information Lost.)r rrr)rr"r"r#computeZszWordInfoLost.compute)valaxrcCs |||S)aPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> # Example plotting a single value >>> from torchmetrics.text import WordInfoLost >>> metric = WordInfoLost() >>> preds = ["this is the prediction", "there is an other sample"] >>> target = ["this is the reference", "there is another one"] >>> metric.update(preds, target) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> from torchmetrics.text import WordInfoLost >>> metric = WordInfoLost() >>> preds = ["this is the prediction", "there is an other sample"] >>> target = ["this is the reference", "there is another one"] >>> values = [ ] >>> for _ in range(10): ... values.append(metric(preds, target)) >>> fig_, ax_ = metric.plot(values) )Z_plot)rr(r)r"r"r#plot^s*r)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrrrrstrrr&r'rrr rr* __classcell__r"r"r r#rs& $      *rN)typingrrrrrZtorchrrZ torchmetrics.functional.text.wilr r Ztorchmetrics.metricr Ztorchmetrics.utilities.importsr Ztorchmetrics.utilities.plotr rZ__doctest_skip__rr"r"r"r#s