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%global _empty_manifest_terminate_build 0
Name: python-g2p-en
Version: 2.1.0
Release: 1
Summary: A Simple Python Module for English Grapheme To Phoneme Conversion
License: Apache Software License
URL: https://github.com/Kyubyong/g2p
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/5f/22/2c7acbe6164ed6cfd4301e9ad2dbde69c68d22268a0f9b5b0ee6052ed3ab/g2p_en-2.1.0.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-nltk
Requires: python3-inflect
Requires: python3-distance
%description
[Update] * We removed TensorFlow from the dependencies. After all, it changes its APIs quite often, and we don't expect you to have a GPU. Instead, NumPy is used for inference.
This module is designed to convert English graphemes (spelling) to
phonemes (pronunciation). It is considered essential in several tasks
such as speech synthesis. Unlike many languages like Spanish or German
where pronunciation of a word can be inferred from its spelling, English
words are often far from people's expectations. Therefore, it will be
the best idea to consult a dictionary if we want to know the
pronunciation of some word. However, there are at least two tentative
issues in this approach. First, you can't disambiguate the pronunciation
of homographs, words which have multiple pronunciations. (See ``a``
below.) Second, you can't check if the word is not in the dictionary.
(See ``b`` below.)
-
\a. I refuse to collect the refuse around here. (rɪ\|fju:z as verb vs. \|refju:s as noun)
-
\b. I am an activationist. (activationist: newly coined word which means ``n. A person who designs and implements programs of treatment or therapy that use recreation and activities to help people whose functional abilities are affected by illness or disability.`` from `WORD SPY <https://wordspy.com/index.php?word=activationist>`__
For the first homograph issue, fortunately many homographs can be
disambiguated using their part-of-speech, if not all. When it comes to
the words not in the dictionary, however, we should make our best guess
using our knowledge. In this project, we employ a deep learning seq2seq
framework based on TensorFlow.
%package -n python3-g2p-en
Summary: A Simple Python Module for English Grapheme To Phoneme Conversion
Provides: python-g2p-en
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-g2p-en
[Update] * We removed TensorFlow from the dependencies. After all, it changes its APIs quite often, and we don't expect you to have a GPU. Instead, NumPy is used for inference.
This module is designed to convert English graphemes (spelling) to
phonemes (pronunciation). It is considered essential in several tasks
such as speech synthesis. Unlike many languages like Spanish or German
where pronunciation of a word can be inferred from its spelling, English
words are often far from people's expectations. Therefore, it will be
the best idea to consult a dictionary if we want to know the
pronunciation of some word. However, there are at least two tentative
issues in this approach. First, you can't disambiguate the pronunciation
of homographs, words which have multiple pronunciations. (See ``a``
below.) Second, you can't check if the word is not in the dictionary.
(See ``b`` below.)
-
\a. I refuse to collect the refuse around here. (rɪ\|fju:z as verb vs. \|refju:s as noun)
-
\b. I am an activationist. (activationist: newly coined word which means ``n. A person who designs and implements programs of treatment or therapy that use recreation and activities to help people whose functional abilities are affected by illness or disability.`` from `WORD SPY <https://wordspy.com/index.php?word=activationist>`__
For the first homograph issue, fortunately many homographs can be
disambiguated using their part-of-speech, if not all. When it comes to
the words not in the dictionary, however, we should make our best guess
using our knowledge. In this project, we employ a deep learning seq2seq
framework based on TensorFlow.
%package help
Summary: Development documents and examples for g2p-en
Provides: python3-g2p-en-doc
%description help
[Update] * We removed TensorFlow from the dependencies. After all, it changes its APIs quite often, and we don't expect you to have a GPU. Instead, NumPy is used for inference.
This module is designed to convert English graphemes (spelling) to
phonemes (pronunciation). It is considered essential in several tasks
such as speech synthesis. Unlike many languages like Spanish or German
where pronunciation of a word can be inferred from its spelling, English
words are often far from people's expectations. Therefore, it will be
the best idea to consult a dictionary if we want to know the
pronunciation of some word. However, there are at least two tentative
issues in this approach. First, you can't disambiguate the pronunciation
of homographs, words which have multiple pronunciations. (See ``a``
below.) Second, you can't check if the word is not in the dictionary.
(See ``b`` below.)
-
\a. I refuse to collect the refuse around here. (rɪ\|fju:z as verb vs. \|refju:s as noun)
-
\b. I am an activationist. (activationist: newly coined word which means ``n. A person who designs and implements programs of treatment or therapy that use recreation and activities to help people whose functional abilities are affected by illness or disability.`` from `WORD SPY <https://wordspy.com/index.php?word=activationist>`__
For the first homograph issue, fortunately many homographs can be
disambiguated using their part-of-speech, if not all. When it comes to
the words not in the dictionary, however, we should make our best guess
using our knowledge. In this project, we employ a deep learning seq2seq
framework based on TensorFlow.
%prep
%autosetup -n g2p-en-2.1.0
%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-g2p-en -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Sun Apr 23 2023 Python_Bot <Python_Bot@openeuler.org> - 2.1.0-1
- Package Spec generated
|
