%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 `__ 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 `__ 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 `__ 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 - 2.1.0-1 - Package Spec generated