iddlmZddlmZmZmZddlmZmZddl m Z m Z ddl m Z ddlmZddlmZmZesdgZGd d e Zy ) )Sequence)AnyOptionalUnion)Tensortensor)_word_info_lost_compute_word_info_lost_update)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEzWordInfoLost.plotceZdZUdZdZeed<dZeed<dZeed<dZ e ed<dZ e ed <e ed <e ed <e ed <d e ddffd ZdeeeefdeeeefddfdZde fdZ ddeee ee fdeedefdZxZS) 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_totalkwargsreturnNc t|di||jdtdd|jdtdd|jdtddy)Nrrsum)dist_reduce_fxrr)super__init__ add_stater)selfr __class__s j/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/torchmetrics/text/wil.pyr!zWordInfoLost.__init__LsW "6" xUC ~vc{5I }fSk%Hpredstargetct||\}}}|xj|z c_|xj|z c_|xj|z c_y)z*Update state with predictions and targets.N)r rrr)r#r'r(rrrs r%updatezWordInfoLost.updateUsE,B5&,Q) k v  \) K'r&cXt|j|j|jS)z$Calculate the Word Information Lost.)r rrr)r#s r%computezWordInfoLost.compute\s!&t{{D4E4EtGWGWXXr&valaxc&|j||S)aPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> # Example plotting a single value >>> from torchmetrics.text import WordInfoLost >>> metric = WordInfoLost() >>> preds = ["this is the prediction", "there is an other sample"] >>> target = ["this is the reference", "there is another one"] >>> metric.update(preds, target) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> from torchmetrics.text import WordInfoLost >>> metric = WordInfoLost() >>> preds = ["this is the prediction", "there is an other sample"] >>> target = ["this is the reference", "there is another one"] >>> values = [ ] >>> for _ in range(10): ... values.append(metric(preds, target)) >>> fig_, ax_ = metric.plot(values) )_plot)r#r-r.s r%plotzWordInfoLost.plot`sTzz#r""r&)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrrr!rstrlistr*r,rrr rr1 __classcell__)r$s@r%rrs#J$t#"d"#t#!e!!e! NII I(E#tCy.1(5d3i;P(UY(YY _c*#E&(6*:":;<*#IQRZI[*# *#r&rN)collections.abcrtypingrrrtorchrr torchmetrics.functional.text.wilr r torchmetrics.metricr torchmetrics.utilities.importsr torchmetrics.utilities.plotr r__doctest_skip__rrr&r%rDs9%'' \&@@+,n#6n#r&