Char Error Rate¶
Module Interface¶
- class torchmetrics.CharErrorRate(**kwargs)[source]
Character 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:
- where:
is the number of substitutions,
is the number of deletions,
is the number of insertions,
is the number of correct characters,
is the number of characters in the reference (N=S+D+C).
Compute CharErrorRate score of transcribed segments against references.
As input to
forward
andupdate
the metric accepts the following input:preds
(str
): Transcription(s) to score as a string or list of stringstarget
(str
): Reference(s) for each speech input as a string or list of strings
As output of
forward
andcompute
the metric returns the following output:cer
(Tensor
): A tensor with the Character Error Rate score
- Parameters
kwargs¶ (
Any
) – Additional keyword arguments, see Advanced metric settings for more info.
Examples
>>> 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)
Initializes internal Module state, shared by both nn.Module and ScriptModule.
Functional Interface¶
- torchmetrics.functional.char_error_rate(preds, target)[source]
character 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.
- Parameters
- Return type
- 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)