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 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 poetry:
$ poetry add jiwer
Or, if you prefer old-fashioned pip and you're using Python >= 3.7
:
$ 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)
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)
We also provide the character error rate:
from jiwer import cer
ground_truth = ["i can spell", "i hope"]
hypothesis = ["i kan cpell", "i hop"]
error = cer(ground_truth, hypothesis)
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.RemoveWhiteSpace(replace_by_space=True),
jiwer.RemoveMultipleSpaces(),
jiwer.ReduceToListOfListOfWords(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.
import jiwer
wer_default = jiwer.Compose([
jiwer.RemoveMultipleSpaces(),
jiwer.Strip(),
jiwer.ReduceToListOfListOfWords(),
])
We provide some predefined transforms. See jiwer.transformations
.
jiwer.Compose(transformations: List[Transform])
can be used to combine multiple transformations.
Example:
import jiwer
jiwer.Compose([
jiwer.RemoveMultipleSpaces(),
jiwer.ReduceToListOfListOfWords()
])
Note that each transformation needs to end with jiwer.ReduceToListOfListOfWords()
, as the library internally computes the word error rate
based on a double list of words.
`
jiwer.ReduceToListOfListOfWords(word_delimiter=" ")
can be used to transform one or more sentences into a list of lists of words.
The sentences can be given as a string (one sentence) or a list of strings (one or more sentences). This operation should be the final step
of any transformation pipeline as the library internally computes the word error rate
based on a double list of words.
Example:
import jiwer
sentences = ["hi", "this is an example"]
print(jiwer.ReduceToListOfListOfWords()(sentences))
# prints: [['hi'], ['this', 'is', 'an, 'example']]
jiwer.ReduceToSingleSentence(word_delimiter=" ")
can be used to transform multiple sentences into a single sentence.
The sentences can be given as a string (one sentence) or a list of strings (one or more sentences).
This operation can be useful when the number of
ground truth sentences and hypothesis sentences differ, and you want to do a minimal
alignment over these lists. Note that this creates an invariance: wer([a, b], [a, b])
might not
be equal to wer([b, a], [b, a])
.
Example:
import jiwer
sentences = ["hi", "this is an example"]
print(jiwer.ReduceToSingleSentence()(sentences))
# prints: ['hi this is an example']
jiwer.RemoveSpecificWords(words_to_remove: List[str])
can be used to filter out certain words.
As words are replaced with a
character, make sure to that jiwer.RemoveMultipleSpaces
,
jiwer.Strip()
and jiwer.RemoveEmptyStrings
are present in the composition after jiwer.RemoveSpecificWords
.
Example:
import jiwer
sentences = ["yhe awesome", "the apple is not a pear", "yhe"]
print(jiwer.RemoveSpecificWords(["yhe", "the", "a"])(sentences))
# prints: [' awesome', ' apple is not pear', ' ']
# note the extra spaces
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 a list of words by using ReduceToListOfListOfWords
or ReduceToSingleSentence
.
This can be prevented by replacing all whitespace with the space character.
If so, make sure that jiwer.RemoveMultipleSpaces
,
jiwer.Strip()
and jiwer.RemoveEmptyStrings
are present in the composition after jiwer.RemoveWhiteSpace
.
Example:
import jiwer
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 defined as
all unicode characters whose catogary name starts with P
. See https://www.unicode.org/reports/tr44/#General_Category_Values.
Example:
import jiwer
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:
import jiwer
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:
import jiwer
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:
import jiwer
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:
import jiwer
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:
import jiwer
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:
import jiwer
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"})(sentences))
# prints: ["is the world sacr or lov?", "edibles are allegedly cultivat"]
jiwer.ToLowerCase()
can be used to convert every character into lowercase.
Example:
import jiwer
sentences = ["You're PRETTY"]
print(jiwer.ToLowerCase()(sentences))
# prints: ["you're pretty"]
jiwer.ToUpperCase()
can be used to replace every character into uppercase.
Example:
import jiwer
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:
import jiwer
sentences = ["you <unk> like [laugh]"]
print(jiwer.RemoveKaldiNonWords()(sentences))
# prints: ["you like "]
# note the extra spaces