o .wi@sddlmZddlmZmZmZddlZddlmZmZddl m Z m Z ddl m Z ddlmZddlmZmZes=d gZGd d d e ZdS) )Sequence)AnyOptionalUnionN)Tensortensor) _cer_compute _cer_update)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPECharErrorRate.plotcseZdZUdZdZeed<dZeed<dZeed<dZ e ed<dZ e ed <e ed <e ed <d e d dffdd Zdeeeefdeeeefd dfddZd e fddZ ddeee ee fdeed efddZZS) CharErrorRateaCharacter Error Rate (`CER`_) is a metric of the performance of an automatic speech recognition (ASR) system. This value indicates the percentage of characters that were incorrectly predicted. The lower the value, the better the performance of the ASR system with a CharErrorRate of 0 being a perfect score. Character error rate can then be computed as: .. math:: CharErrorRate = \frac{S + D + I}{N} = \frac{S + D + I}{S + D + C} 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 characters, - :math:`N` is the number of characters in the reference (N=S+D+C). Compute CharErrorRate score of transcribed segments against references. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~str`): Transcription(s) to score as a string or list of strings - ``target`` (:class:`~str`): 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: - ``cer`` (:class:`~torch.Tensor`): A tensor with the Character Error Rate score Args: kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Examples: >>> from torchmetrics.text import CharErrorRate >>> preds = ["this is the prediction", "there is an other sample"] >>> target = ["this is the reference", "there is another one"] >>> cer = CharErrorRate() >>> cer(preds, target) tensor(0.3415) Fis_differentiablehigher_is_betterfull_state_updategplot_lower_boundg?plot_upper_bounderrorstotalkwargsreturnNc sJtjdi||jdtdtjddd|jdtdtjddddS)Nrr)dtypesum)dist_reduce_fxr)super__init__ add_statertorchfloat)selfr __class__rR/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/text/cer.pyrPszCharErrorRate.__init__predstargetcCs.t||\}}|j|7_|j|7_dS)z*Update state with predictions and targets.N)r rr)r"r&r'rrrrr%updateXszCharErrorRate.updatecCst|j|jS)z#Calculate the character error rate.)rrr)r"rrr%compute^szCharErrorRate.computevalaxcCs |||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 CharErrorRate >>> metric = CharErrorRate() >>> 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 CharErrorRate >>> metric = CharErrorRate() >>> 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+rrr%plotbs *r)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrr!rrrrrstrlistr(r)rrr r r- __classcell__rrr#r%rs0  )    .r)collections.abcrtypingrrrr rr torchmetrics.functional.text.cerrr torchmetrics.metricr torchmetrics.utilities.importsr torchmetrics.utilities.plotr r __doctest_skip__rrrrr%s