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---
title: WER
emoji: 🤗
colorFrom: blue
colorTo: red
sdk: gradio
sdk_version: 3.19.1
app_file: app.py
pinned: false
tags:
- evaluate
- metric
description: >-
Word error rate (WER) is a common metric of the performance of an automatic speech recognition system.
The general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.
This problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.
Word error rate can then be computed as:
WER = (S + D + I) / N = (S + D + I) / (S + D + C)
where
S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct words,
N is the number of words in the reference (N=S+D+C).
This value indicates the average number of errors per reference word. The lower the value, the better the
performance of the ASR system with a WER of 0 being a perfect score.
---
## Word Error Rate (WER)
Word error rate (WER) is a common metric of the performance of an automatic speech recognition (ASR) system.
The general difficulty of measuring the performance of ASR systems lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the [Levenshtein distance](https://en.wikipedia.org/wiki/Levenshtein_distance), working at the word level.
This problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between [perplexity](https://huggingface.co/metrics/perplexity) and word error rate (see [this article](https://www.cs.cmu.edu/~roni/papers/eval-metrics-bntuw-9802.pdf) for further information).
Word error rate can then be computed as:
`WER = (S + D + I) / N = (S + D + I) / (S + D + C)`
where
`S` is the number of substitutions,
`D` is the number of deletions,
`I` is the number of insertions,
`C` is the number of correct words,
`N` is the number of words in the reference (`N=S+D+C`).
## How to use
The metric takes two inputs: references (a list of references for each speech input) and predictions (a list of transcriptions to score).
The **lower** the value, the **better** the performance of the ASR system, with a WER of 0 being a perfect score.
## Limitations and bias
WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.
## Further References
- [Word Error Rate -- Wikipedia](https://en.wikipedia.org/wiki/Word_error_rate)
- [Hugging Face Tasks -- Automatic Speech Recognition](https://huggingface.co/tasks/automatic-speech-recognition)
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