jiwer 2.6.0

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 RapidFuzz, which uses C++ under the hood.

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 poetry:

$ poetry add jiwer

Or, if you prefer old-fashioned pip and you're using Python >= 3.7:

$ 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)

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)

Alignment

With jiwer.compute_measures, you also get the alignment between the ground-truth and hypothesis.

We provide the alignment as a tuple of (op, truth_idx_start, truth_idx_end, hyp_idx_start, hyp_idx_end), where op is one of equal, replace, delete, or insert.

This looks like the following:

import jiwer

out = jiwer.compute_measures("short one here", "shoe order one")
print(out['ops'])
# [[('insert', 0, 0, 0, 1), ('replace', 0, 1, 1, 2), ('equal', 1, 2, 2, 3), ('delete', 2, 3, 3, 3)]]

To visualize the alignment, you can use jiwer.visualize_measures()

For example:

import jiwer

out = jiwer.compute_measures(
    ["short one here", "quite a bit of longer sentence"],
    ["shoe order one", "quite bit of an even longest sentence here"],
)

print(jiwer.visualize_measures(out))

Gives the following output

sentence 1
REF:    # short one here 
HYP: shoe order one    * 
        I     S        D 

sentence 2
REF: quite a bit of  #    #  longer sentence    # 
HYP: quite * bit of an even longest sentence here 
           D         I    I       S             I 

number of sentences: 2
substitutions=2 deletions=2 insertions=4 hits=5	

mer=61.54%
wil=74.75%
wip=25.25%
wer=88.89%

Note that you can print the CER alignment with jiwer.cer(return_dict=True) and jiwer.visualize_measures(visualize_cer=True).

Command-line interface

JiWER provides a simple CLI, which should be available after installation.

For details, see jiwer --help.

$ jiwer --help
Usage: jiwer [OPTIONS]

  JiWER is a python tool and API for computing the word-error-rate of ASR systems. To
  use this CLI, store the ground-truth and hypothesis sentences in a text
  file, where each sentence is delimited by a new-line character. The text
  files are expected to have an equal number of lines, unless the `-j` flag is
  used. The `-j` flag joins computation of the WER by doing a global
  alignment.

Options:
  --gt PATH     Path to new-line delimited text file of ground-truth
                sentences.  [required]
  --hp PATH     Path to new-line delimited text file of hypothesis sentences.
                [required]
  -c, --cer     Compute CER instead of WER.
  -a, --align   Print alignment of each sentence.
  -g, --global  Apply a global alignment between ground-truth and hypothesis
                sentences before computing the WER.
  --help        Show this message and exit.

Note that the CLI does not support a custom pre-processing (as described below). Any pre-processing should be done on the text files manually before calling JiWER when using the CLI.

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.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(),
])

transforms

We provide some predefined transforms. See jiwer.transformations.

Compose

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. `

ReduceToListOfListOfWords

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']]

ReduceToSingleSentence

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']

RemoveSpecificWords

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

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 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

RemovePunctuation

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"]

RemoveMultipleSpaces

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

Strip

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

RemoveEmptyStrings

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']

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:

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!"]

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:

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"]

SubstituteRegexes

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"]

ToLowerCase

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"]

ToUpperCase

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"]

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:

import jiwer 

sentences = ["you <unk> like [laugh]"]

print(jiwer.RemoveKaldiNonWords()(sentences))

# prints: ["you  like "]
# note the extra spaces