How to use the jiwer.transforms.RemoveSpecificWords function in jiwer

The optional `transforms` arguments can be used to apply pre-processing to
    respectively the ground truth and hypotheses input. Note that the transform
    should ALWAYS include `SentencesToListOfWords`, as that is the expected input.

    :param truth: the ground-truth sentence(s) as a string or list of strings
    :param hypothesis: the hypothesis sentence(s) as a string or list of strings
    :param truth_transform: the transformation to apply on the truths input
    :param hypothesis_transform: the transformation to apply on the hypothesis input
    :return: the WER as a floating number between 0 and 1
    """
    # deal with old API
    if "standardize" in kwargs:
        truth = _standardize_transform(truth)
        hypothesis = _standardize_transform(hypothesis)
    if "words_to_filter" in kwargs:
        t = tr.RemoveSpecificWords(kwargs["words_to_filter"])
        truth = t(truth)
        hypothesis = t(hypothesis)

    # Apply transforms. By default, it collapses input to a list of words
    truth = truth_transform(truth)
    hypothesis = hypothesis_transform(hypothesis)

    # raise an error if the ground truth is empty
    if len(truth) == 0:
        raise ValueError("the ground truth cannot be an empty")

    # tokenize each word into an integer
    vocabulary = set(truth + hypothesis)
    word2char = dict(zip(vocabulary, range(len(vocabulary))))

    truth_chars = [chr(word2char[w]) for w in truth]