%global _empty_manifest_terminate_build 0 Name: python-jiwer Version: 3.0.1 Release: 1 Summary: Evaluate your speech-to-text system with similarity measures such as word error rate (WER) License: Apache-2.0 URL: https://github.com/jitsi/jiwer Source0: https://mirrors.nju.edu.cn/pypi/web/packages/96/b1/ebacdf7d82d92d3ab93bdaf9f0657782e59cf734ad336e331a8806f2bbf7/jiwer-3.0.1.tar.gz BuildArch: noarch Requires: python3-rapidfuzz Requires: python3-click %description # 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)_ %package -n python3-jiwer Summary: Evaluate your speech-to-text system with similarity measures such as word error rate (WER) Provides: python-jiwer BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-jiwer # 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)_ %package help Summary: Development documents and examples for jiwer Provides: python3-jiwer-doc %description help # 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)_ %prep %autosetup -n jiwer-3.0.1 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-jiwer -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Sun Apr 23 2023 Python_Bot - 3.0.1-1 - Package Spec generated