jiwer 2.2.0

Evaluate your speech-to-text system with similarity measures such as word error rate (WER)

JiWER: Similarity measures for automatic speech recognition evaluation

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.

Installation

You should be able to install this package using pip if you're using Python >= 3.5:

$ pip install jiwer

Usage

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)

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"

pre-processing

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()
])

Transformations

Compose

jiwer.Compose(transformations: List[Transform]) can be used to combine multiple transformations.

Example:

jiwer.Compose([
    jiwer.RemoveMultipleSpaces(),
    jiwer.SentencesToListOfWords()
])

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']

RemoveSpecificWords

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", ""]

RemoveWhiteSpace

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

RemovePunctuation

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"]

RemoveMultipleSpaces

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

Strip

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

RemoveEmptyStrings

jiwer.RemoveEmptyStrings() can be used to remove empty strings.

Example:

sentences = ["", "this is an example", " ",  "                "]

print(jiwer.RemoveEmptyStrings()(sentences))
# prints: ['this is an example']

ExpandCommonEnglishContractions

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!"]

SubstituteWords

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"]

SubstituteRegexes

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"]

ToLowerCase

jiwer.ToLowerCase() can be used to convert every character into lowercase.

Example:

sentences = ["You're PRETTY"]

print(jiwer.ToLowerCase()(sentences))

# prints: ["you're pretty"]

ToUpperCase

jiwer.ToLowerCase() can be used to replace every character into uppercase.

Example:

sentences = ["You're amazing"]

print(jiwer.ToUpperCase()(sentences))

# prints: ["YOU'RE AMAZING"]

RemoveKaldiNonWords

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