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|
%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
* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 3.0.1-1
- Package Spec generated
|
