Skip to main content

Approximate the WER of an ASR transcript

Project description

Word Error Rate for automatic speech recognition

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.

Installation

You should be able to install this package using pip if your python version is >= 3:

$ 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)

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for jiwer, version 2.0.1
Filename, size File type Python version Upload date Hashes
Filename, size jiwer-2.0.1-py3-none-any.whl (12.1 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size jiwer-2.0.1.tar.gz (7.3 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page