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# JiWER |
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JiWER is a simple and fast python package to evaluate an automatic speech recognition system. |
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It supports the following measures: |
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1. word error rate (WER) |
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2. match error rate (MER) |
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3. word information lost (WIL) |
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4. word information preserved (WIP) |
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5. character error rate (CER) |
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These measures are computed with the use of the minimum-edit distance between one or more reference and hypothesis sentences. |
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The minimum-edit distance is calculated using [RapidFuzz](https://github.com/maxbachmann/RapidFuzz), which uses C++ under the hood, and is therefore faster than a pure python implementation. |
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## Documentation |
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For further info, see the documentation at [jitsi.github.io/jiwer](https://jitsi.github.io/jiwer). |
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## Installation |
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You should be able to install this package using [poetry](https://python-poetry.org/docs/): |
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``` |
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$ poetry add jiwer |
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``` |
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Or, if you prefer old-fashioned pip and you're using Python >= `3.7`: |
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```bash |
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$ pip install jiwer |
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``` |
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## Usage |
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The most simple use-case is computing the word error rate between two strings: |
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```python |
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from jiwer import wer |
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reference = "hello world" |
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hypothesis = "hello duck" |
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error = wer(reference, hypothesis) |
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``` |
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## Licence |
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The jiwer package is released under the `Apache License, Version 2.0` licence by [8x8](https://www.8x8.com/). |
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For further information, see [`LICENCE`](./LICENSE). |
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## Reference |
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_For a comparison between WER, MER and WIL, see: \ |
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Morris, Andrew & Maier, Viktoria & Green, Phil. (2004). [From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.](https://www.researchgate.net/publication/221478089_From_WER_and_RIL_to_MER_and_WIL_improved_evaluation_measures_for_connected_speech_recognition)_ |
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