This repository contains a simple python package to approximate the WER 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.
You should be able to install this package using pip if your python version is >= 3:
$ pip install jiwer
The 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)
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 spaces
jiwer.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 spaces
jiwer.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 up
jiwer.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