This repository contains a simple python package to approximate the Word Error Rate (WER), Match Error Rate (MER), Word Information Lost (WIL) and Word Information Preserved (WIP) of a transcript. It computes the minimum-edit distance between the ground-truth sentence and the hypothesis sentence of a speech-to-text API. The minimum-edit distance is calculated using the python C module python-Levenshtein.
For a comparison between WER, MER and WIL, see:
Morris, Andrew & Maier, Viktoria & Green, Phil. (2004). From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.
You should be able to install this package using pip if you're using Python >= 3.5:
$ pip install jiwerThe most simple use-case is computing the edit distance between two strings:
from jiwer import wer
ground_truth = "hello world"
hypothesis = "hello duck"
error = wer(ground_truth, hypothesis)Similarly, to get other measures:
import jiwer
ground_truth = "hello world"
hypothesis = "hello duck"
wer = jiwer.wer(ground_truth, hypothesis)
mer = jiwer.mer(ground_truth, hypothesis)
wil = jiwer.wil(ground_truth, hypothesis)
# faster, because `compute_measures` only needs to perform the heavy lifting once:
measures = jiwer.compute_measures(ground_truth, hypothesis)
wer = measures['wer']
mer = measures['mer']
wil = measures['wil']You can also compute the WER over multiple sentences:
from jiwer import wer
ground_truth = ["hello world", "i like monthy python"]
hypothesis = ["hello duck", "i like python"]
error = wer(ground_truth, hypothesis)When the amount of ground-truth sentences and hypothesis sentences differ, a minimum alignment is done over the merged sentence:
ground_truth = ["i like monthy python", "what do you mean, african or european swallow"]
hypothesis = ["i like", "python", "what you mean" , "or swallow"]
# is equivalent to
ground_truth = "i like monthy python what do you mean african or european swallow"
hypothesis = "i like python what you mean or swallow"It might be necessary to apply some pre-processing steps on either the hypothesis or ground truth text. This is possible with the transformation API:
import jiwer
ground_truth = "I like python!"
hypothesis = "i like Python?\n"
transformation = jiwer.Compose([
jiwer.ToLowerCase(),
jiwer.RemoveMultipleSpaces(),
jiwer.RemoveWhiteSpace(replace_by_space=False),
jiwer.SentencesToListOfWords(word_delimiter=" ")
])
jiwer.wer(
ground_truth,
hypothesis,
truth_transform=transformation,
hypothesis_transform=transformation
)By default, the following transformation is applied to both the ground truth and the hypothesis. Note that is simply to get it into the right format to calculate the WER.
default_transformation = jiwer.Compose([
jiwer.RemoveMultipleSpaces(),
jiwer.Strip(),
jiwer.SentencesToListOfWords(),
jiwer.RemoveEmptyStrings()
])jiwer.Compose(transformations: List[Transform]) can be used to combine multiple transformations.
Example:
jiwer.Compose([
jiwer.RemoveMultipleSpaces(),
jiwer.SentencesToListOfWords()
])jiwer.SentencesToListOfWords(word_delimiter=" ") can be used to transform one or more sentences into a
list of words. The sentences can be given as a string (one sentence) or a list of strings (one or more sentences).
Example:
sentences = ["hi", "this is an example"]
print(jiwer.SentencesToListOfWords()(sentences))
# prints: ['hi', 'this', 'is', 'an, 'example']jiwer.RemoveSpecificWords(words_to_remove: List[str]) can be used to filter out certain words.
Example:
sentences = ["yhe awesome", "the apple is not a pear", "yhe"]
print(jiwer.RemoveSpecificWords(["yhe", "the", "a"])(sentences))
# prints: ["awesome", "apple is pear", ""]jiwer.RemoveWhiteSpace(replace_by_space=False) can be used to filter out white space.
The whitespace characters are , \t, \n, \r, \x0b and \x0c.
Note that by default space ( ) is also removed, which will make it impossible to split a sentence into words by using SentencesToListOfWords.
This can be prevented by replacing all whitespace with the space character.
Example:
sentences = ["this is an example", "hello\tworld\n\r"]
print(jiwer.RemoveWhiteSpace()(sentences))
# prints: ["thisisanexample", "helloworld"]
print(jiwer.RemoveWhiteSpace(replace_by_space=True)(sentences))
# prints: ["this is an example", "hello world "]
# note the trailing spacesjiwer.RemovePunctuation() can be used to filter out punctuation. The punctuation characters are:
'!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~'
Example:
sentences = ["this is an example!", "hello. goodbye"]
print(jiwer.RemovePunctuation()(sentences))
# prints: ['this is an example', "hello goodbye"]jiwer.RemoveMultipleSpaces() can be used to filter out multiple spaces between words.
Example:
sentences = ["this is an example ", " hello goodbye ", " "]
print(jiwer.RemoveMultipleSpaces()(sentences))
# prints: ['this is an example ', " hello goodbye ", " "]
# note that there are still trailing spacesjiwer.Strip() can be used to remove all leading and trailing spaces.
Example:
sentences = [" this is an example ", " hello goodbye ", " "]
print(jiwer.Strip()(sentences))
# prints: ['this is an example', "hello goodbye", ""]
# note that there is an empty string left behind which might need to be cleaned upjiwer.RemoveEmptyStrings() can be used to remove empty strings.
Example:
sentences = ["", "this is an example", " ", " "]
print(jiwer.RemoveEmptyStrings()(sentences))
# prints: ['this is an example']jiwer.ExpandCommonEnglishContractions() can be used to replace common contractions such as let's to let us.
Currently, this method will perform the following replacements. Note that ␣ is used to indicate a space ( ) to get
around markdown rendering constrains.
| Contraction | transformed into |
|---|---|
won't |
␣will not |
can't |
␣can not |
let's |
␣let us |
n't |
␣not |
're |
␣are |
's |
␣is |
'd |
␣would |
'll |
␣will |
't |
␣not |
've |
␣have |
'm |
␣am |
Example:
sentences = ["she'll make sure you can't make it", "let's party!"]
print(jiwer.ExpandCommonEnglishContractions()(sentences))
# prints: ["she will make sure you can not make it", "let us party!"]jiwer.SubstituteWords(dictionary: Mapping[str, str]) can be used to replace a word into another word. Note that
the whole word is matched. If the word you're attempting to substitute is a substring of another word it will
not be affected.
For example, if you're substituting foo into bar, the word foobar will NOT be substituted into barbar.
Example:
sentences = ["you're pretty", "your book", "foobar"]
print(jiwer.SubstituteWords({"pretty": "awesome", "you": "i", "'re": " am", 'foo': 'bar'})(sentences))
# prints: ["i am awesome", "your book", "foobar"]jiwer.SubstituteRegexes(dictionary: Mapping[str, str]) can be used to replace a substring matching a regex
expression into another substring.
Example:
sentences = ["is the world doomed or loved?", "edibles are allegedly cultivated"]
# note: the regex string "\b(\w+)ed\b", matches every word ending in 'ed',
# and "\1" stands for the first group ('\w+). It therefore removes 'ed' in every match.
print(jiwer.SubstituteRegexes({r"doom": r"sacr", r"\b(\w+)ed\b": r"\1"}))
# prints: ["is the world sacr or lov?", "edibles are allegedly cultivat"]jiwer.ToLowerCase() can be used to convert every character into lowercase.
Example:
sentences = ["You're PRETTY"]
print(jiwer.ToLowerCase()(sentences))
# prints: ["you're pretty"]jiwer.ToLowerCase() can be used to replace every character into uppercase.
Example:
sentences = ["You're amazing"]
print(jiwer.ToUpperCase()(sentences))
# prints: ["YOU'RE AMAZING"]jiwer.RemoveKaldiNonWords() can be used to remove any word between [] and <>. This can be useful when working
with hypotheses from the Kaldi project, which can output non-words such as [laugh] and <unk>.
Example:
sentences = ["you <unk> like [laugh]"]
print(jiwer.RemoveKaldiNonWords()(sentences))
# prints: ["you like "]
# note the extra spaces