Word Info. Lost¶
Module Interface¶
- class torchmetrics.WordInfoLost(compute_on_step=None, **kwargs)[source]
Word 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:
where:
is the number of correct words,
is the number of words in the reference
is the number of words in the prediction
- Parameters
Examples
>>> from torchmetrics import WordInfoLost >>> preds = ["this is the prediction", "there is an other sample"] >>> target = ["this is the reference", "there is another one"] >>> metric = WordInfoLost() >>> metric(preds, target) tensor(0.6528)
Initializes internal Module state, shared by both nn.Module and ScriptModule.
- compute()[source]
Calculate the Word Information Lost.
- Return type
- Returns
Word Information Lost score
- update(preds, target)[source]
Store predictions/references for computing Word Information Lost scores.
Functional Interface¶
- torchmetrics.functional.word_information_lost(preds, target)[source]
Word 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.
- Parameters
- Return type
- 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)