automatic import of python-g2p-en

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+/g2p_en-2.1.0.tar.gz
diff --git a/python-g2p-en.spec b/python-g2p-en.spec
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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
+* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 2.1.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..0ef21a2
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+a2472d72e09d266a3d725a3bf839e5b6  g2p_en-2.1.0.tar.gz