o y“©i“ ã @s´ddlmZmZmZddlmZmZddlmZdee ee fdee ee fdeeeeffdd„Z d ed ed edefd d „Z dee ee fdee ee fdefdd„Z dS)é)ÚListÚTupleÚUnion)ÚTensorÚtensor)Ú_edit_distanceÚpredsÚtargetÚreturnc Cs®t|tƒr|g}t|tƒr|g}tdƒ}tdƒ}tdƒ}tdƒ}t||ƒD]*\}}| ¡}| ¡} |t|| ƒ7}|t| ƒ7}|t|ƒ7}|tt| ƒt|ƒƒ7}q%||||fS)a¾Update the wil score with the current set of references and predictions. Args: preds: Transcription(s) to score as a string or list of strings target: Reference(s) for each speech input as a string or list of strings Returns: Number of edit operations to get from the reference to the prediction, summed over all samples Number of words overall references Number of words overall predictions g)Ú isinstanceÚstrrÚzipÚsplitrÚlenÚmax) rr ÚtotalÚerrorsÚ target_totalÚ preds_totalÚpredÚtgtÚ pred_tokensÚ target_tokens©rúT/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/functional/text/wil.pyÚ _wil_updates    rrrrcCsd||||S)aDCompute the Word Information Lost. Args: errors: Number of edit operations to get from the reference to the prediction, summed over all samples target_total: Number of words overall references preds_total: Number of words overall prediction Returns: Word Information Lost score ér)rrrrrrÚ _wil_compute8s rcCst||ƒ\}}}t|||ƒS)aIWord Information Lost rate is a metric of the performance of an automatic speech recognition 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 Word Information Lost rate of 0 being a perfect score. Args: preds: Transcription(s) to score as a string or list of strings target: Reference(s) for each speech input as a string or list of strings Returns: Word Information Lost rate Examples: >>> from torchmetrics.functional import word_information_lost >>> preds = ["this is the prediction", "there is an other sample"] >>> target = ["this is the reference", "there is another one"] >>> word_information_lost(preds, target) tensor(0.6528) )rr)rr rrrrrrÚword_information_lostFs rN) ÚtypingrrrÚtorchrrÚ#torchmetrics.functional.text.helperrr rrrrrrrÚs$ ÿþ  ý"ÿþý