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

automatic-speech-recognition, evaluation-metrics, python3, speech-to-text, wer, word-error-rate
pip install jiwer==3.0.3



JiWER is a simple and fast python package to evaluate an automatic speech recognition system. It supports the following measures:

  1. word error rate (WER)
  2. match error rate (MER)
  3. word information lost (WIL)
  4. word information preserved (WIP)
  5. character error rate (CER)

These measures are computed with the use of the minimum-edit distance between one or more reference and hypothesis sentences. The minimum-edit distance is calculated using RapidFuzz, which uses C++ under the hood, and is therefore faster than a pure python implementation.


For further info, see the documentation at jitsi.github.io/jiwer.


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 word error rate between two strings:

from jiwer import wer

reference = "hello world"
hypothesis = "hello duck"

error = wer(reference, hypothesis)


The jiwer package is released under the Apache License, Version 2.0 licence by 8x8.

For further information, see LICENCE.


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.