| Name | jiwer JSON |
| Version |
3.0.4
JSON |
| download |
| home_page | https://github.com/jitsi/jiwer |
| Summary | Evaluate your speech-to-text system with similarity measures such as word error rate (WER) |
| upload_time | 2024-05-05 08:14:48 |
| maintainer | None |
| docs_url | None |
| author | Nik Vaessen |
| requires_python | <4.0,>=3.7 |
| license | Apache-2.0 |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
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# JiWER
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](https://github.com/maxbachmann/RapidFuzz), which uses C++ under the hood, and is therefore faster than a pure python implementation.
## Documentation
For further info, see the documentation at [jitsi.github.io/jiwer](https://jitsi.github.io/jiwer).
## Installation
You should be able to install this package using [poetry](https://python-poetry.org/docs/):
```
$ poetry add jiwer
```
Or, if you prefer old-fashioned pip and you're using Python >= `3.7`:
```bash
$ pip install jiwer
```
## Usage
The most simple use-case is computing the word error rate between two strings:
```python
from jiwer import wer
reference = "hello world"
hypothesis = "hello duck"
error = wer(reference, hypothesis)
```
## Licence
The jiwer package is released under the `Apache License, Version 2.0` licence by [8x8](https://www.8x8.com/).
For further information, see [`LICENCE`](./LICENSE).
## Reference
_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.](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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"description": "# JiWER\n\nJiWER is a simple and fast python package to evaluate an automatic speech recognition system.\nIt supports the following measures:\n\n1. word error rate (WER)\n2. match error rate (MER)\n3. word information lost (WIL) \n4. word information preserved (WIP) \n5. character error rate (CER)\n\nThese measures are computed with the use of the minimum-edit distance between one or more reference and hypothesis sentences.\nThe 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.\n\n## Documentation\n\nFor further info, see the documentation at [jitsi.github.io/jiwer](https://jitsi.github.io/jiwer).\n\n## Installation\n\nYou should be able to install this package using [poetry](https://python-poetry.org/docs/): \n\n```\n$ poetry add jiwer\n```\n\nOr, if you prefer old-fashioned pip and you're using Python >= `3.7`:\n\n```bash\n$ pip install jiwer\n```\n\n## Usage\n\nThe most simple use-case is computing the word error rate between two strings:\n\n```python\nfrom jiwer import wer\n\nreference = \"hello world\"\nhypothesis = \"hello duck\"\n\nerror = wer(reference, hypothesis)\n```\n\n## Licence\n\nThe jiwer package is released under the `Apache License, Version 2.0` licence by [8x8](https://www.8x8.com/).\n\nFor further information, see [`LICENCE`](./LICENSE).\n\n## Reference\n\n_For a comparison between WER, MER and WIL, see: \\\nMorris, 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)_\n",
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