jiwer 1.3.1

Approximate the WER of an ASR transcript

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 Wagner-Fisher algorithm. Because this algorithm computes the character-level minimum-edit distance, every word in a sentence is assigned a unique integer, and the edit-distance is computed over a string of integers.

Installation

You should be able to install this package using pip:

$ 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 = ["hello world", "i like monthy python", "what do you mean, african or european swallow?"]
hypothesis = ["hello", "i like", "python", "what you mean swallow"]

# is equivelent to

ground_truth = "hello world i like monhty python what do you mean african or european swallow"
hypothesis = "hello i like python what you mean swallow"

Additional preprocessing

Some additional preprocessing can be done on the input. By default, whitespace is removed, everything is set to lower-case, . and , are removed, everything between [] and <> (common for Kaldi models) is removed and each word is tokenized by splitting by one or more spaces. Additionally, common abbreviations, such as won't, let's,n't will be expanded if standardize=True is passed along the wer method.

from jiwer import wer

ground_truth = "he's my neminis"
hypothesis = "he is my <unk> [laughter]"

wer(ground_truth, hypothesis, standardize=True)

# is equivelent to 

ground_truth = "he is my neminis"
hypothesis = "he is my"

wer(ground_truth, hypothesis)

Also, there is an option give a list of words to remove from the transcription, such as "yhe", or "so".

from jiwer import wer

ground_truth = "yhe about that bug"
hypothesis = "yeah about that bug"

wer(ground_truth, hypothesis, words_to_filter=["yhe", "yeah"])

# is equivelent to 

ground_truth = "about that bug"
hypothesis = "about that bug"

wer(ground_truth, hypothesis)