def th_accuracy(
    pad_outputs: torch.Tensor, pad_targets: torch.Tensor, ignore_label: int
) -> float:
    """Calculate accuracy.

    Args:
        pad_outputs (Tensor): Prediction tensors (B * Lmax, D).
        pad_targets (LongTensor): Target label tensors (B, Lmax).
        ignore_label (int): Ignore label id.

    Returns:
        float: Accuracy value (0.0 - 1.0).

    """
    pad_pred = pad_outputs.view(
        pad_targets.size(0), pad_targets.size(1), pad_outputs.size(1)
    ).argmax(2)
    mask = pad_targets != ignore_label
    numerator = torch.sum(
        pad_pred.masked_select(mask) == pad_targets.masked_select(mask)
    )
    denominator = torch.sum(mask)
    return float(numerator) / float(denominator)
Calculate accuracy.

Args:
pad_outputs (Tensor): Prediction tensors (B * Lmax, D).
pad_targets (LongTensor): Target label tensors (B, Lmax).
ignore_label (int): Ignore label id.

Returns:
float: Accuracy value (0.0 - 1.0).
modules/wenet_extractor/utils/common.py