o yi @sddlmZmZmZddlZddlmZmZddlmZdee ee fdee ee fdeeeffdd Z d ed edefd d Z dee ee fdee ee fdefddZ dS))ListTupleUnionN)Tensortensor)_edit_distancepredstargetreturncCst|tr|g}t|tr|g}tdtjd}tdtjd}t||D]\}}|}|}|tt|t|7}|t|7}q#||fS)aUpdate the cer 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 character overall references r)dtype) isinstancestrrtorchfloatziprlistlen)rr errorstotalpredtgt pred_tokens tgt_tokensrT/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/functional/text/cer.py _cer_updates  rrrcCs||S)a Compute the Character error rate. Args: errors: Number of edit operations to get from the reference to the prediction, summed over all samples total: Number of characters over all references Returns: Character error rate score r)rrrrr _cer_compute3s rcCst||\}}t||S)acharacter error rate is a common 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 CER 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: Character error rate score Examples: >>> preds = ["this is the prediction", "there is an other sample"] >>> target = ["this is the reference", "there is another one"] >>> char_error_rate(preds=preds, target=target) tensor(0.3415) )rr)rr rrrrrchar_error_rate@s r) typingrrrrrr#torchmetrics.functional.text.helperrr rrrrrrrs   2