automatic import of python-g2pm

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+/g2pM-0.1.2.5.tar.gz
diff --git a/python-g2pm.spec b/python-g2pm.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-g2pM
+Version:	0.1.2.5
+Release:	1
+Summary:	g2pM: A Neural Grapheme-to-Phoneme Conversion Package for MandarinChinese
+License:	Apache License 2.0
+URL:		https://github.com/kakaobrain/g2pM
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/2e/d6/06b20ffa5ea2e2a6c55ada6bf9503c1ee7bae2c64b3f6aa6107396a0a657/g2pM-0.1.2.5.tar.gz
+BuildArch:	noarch
+
+
+%description
+
+# g2pM
+[![Release](https://img.shields.io/badge/release-v0.1.2.4-green)](https://pypi.org/project/g2pM/)
+[![Downloads](https://pepy.tech/badge/g2pm)](https://pepy.tech/project/g2pm)
+[![license](https://img.shields.io/badge/license-Apache%202.0-red)](https://github.com/kakaobrain/g2pM/blob/master/LICENSE)
+
+This is the official repository of our paper [A Neural Grapheme-to-Phoneme Conversion Package for MandarinChinese Based on a New Open Benchmark Dataset](https://arxiv.org/abs/2004.03136) (**Interspeech 2020**).
+
+## Install
+```
+pip install g2pM
+```
+
+## The CPP Dataset
+In data folder, there are [train/dev/test].sent files and [train/dev/test].lb files. In *.sent file, each lines corresponds to one sentence and a special symbol ▁ (U+2581) is added to the left and right of polyphonic character. The pronunciation of the corresponding character is at the same line from *.lb file. For each sentence, there could be several polyphonic characters, but we randomly choose only one polyphonic character to annotate.
+
+## Requirements
+* python >= 3.6
+* numpy
+
+## Usage
+If you want to remove all the digits which denote the tones, set tone=False. Default setting is tone=True. <br />
+If you want to split all the non Chinese characters (e.g. digit), set char_split=True. Default setting is char_split=False. <br />
+
+```
+>>> from g2pM import G2pM
+>>> model = G2pM()
+>>> sentence = "然而，他红了20年以后，他竟退出了大家的视线。"
+>>> model(sentence, tone=True, char_split=False)
+['ran2', 'er2', '，', 'ta1', 'hong2', 'le5', '20', 'nian2', 'yi3', 'hou4', '，', 'ta1', 'jing4', 'tui4', 'chu1', 'le5', 'da4', 'jia1', 'de5', 'shi4', 'xian4', '。']
+>>> model(sentence, tone=False, char_split=False)
+['ran', 'er', '，', 'ta', 'hong', 'le', '2', '0', 'nian', 'yi', 'hou', '，', 'ta', 'jing', 'tui', 'chu', 'le', 'da', 'jia', 'de', 'shi', 'xian', '。']
+>>> model(sentence, tone=True, char_split=True)
+['ran2', 'er2', '，', 'ta1', 'hong2', 'le5', '2', '0', 'nian2', 'yi3', 'hou4', '，', 'ta1', 'jing4', 'tui4', 'chu1', 'le5', 'da4', 'jia1', 'de5', 'shi4', 'xian4', '。']
+
+```
+
+## Model Size
+| Layer                 | Size    |
+|-----------------------|---------|
+| Embedding             | 64      |
+| LSTM x1               | 64      |
+| Fully-Connected x2    | 64      |
+| Total # of parameters | 477,228 |
+| Model size            | 1.7MB   |
+| Package size          | 2.1MB   |
+
+## Evaluation Result
+
+| Model            | Dev.            | Test         |
+| :--------------| --------------: |:--------------:|
+| g2pC                 | 84.84                | 84.45           |
+| xpinyin(0.5.6)       | 78.74                | 78.56           |
+| pypinyin(0.36.0)     | 85.44                | 86.13           |
+| Majority Vote        | 92.15                | 92.08           |
+| Chinese Bert         | **97.95**            | **97.85**       |
+| Ours                 | 97.36                | 97.31           |
+
+
+## Reference
+To cite the code/data/paper, please use this BibTex
+```bibtex
+@article{park2020g2pm,
+ author={Park, Kyubyong and Lee, Seanie},
+ title = {A Neural Grapheme-to-Phoneme Conversion Package for MandarinChinese Based on a New Open Benchmark Dataset
+},
+ journal={Proc. Interspeech 2020},
+ url = {https://arxiv.org/abs/2004.03136},
+ year = {2020}
+}
+```
+
+
+
+
+%package -n python3-g2pM
+Summary:	g2pM: A Neural Grapheme-to-Phoneme Conversion Package for MandarinChinese
+Provides:	python-g2pM
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-g2pM
+
+# g2pM
+[![Release](https://img.shields.io/badge/release-v0.1.2.4-green)](https://pypi.org/project/g2pM/)
+[![Downloads](https://pepy.tech/badge/g2pm)](https://pepy.tech/project/g2pm)
+[![license](https://img.shields.io/badge/license-Apache%202.0-red)](https://github.com/kakaobrain/g2pM/blob/master/LICENSE)
+
+This is the official repository of our paper [A Neural Grapheme-to-Phoneme Conversion Package for MandarinChinese Based on a New Open Benchmark Dataset](https://arxiv.org/abs/2004.03136) (**Interspeech 2020**).
+
+## Install
+```
+pip install g2pM
+```
+
+## The CPP Dataset
+In data folder, there are [train/dev/test].sent files and [train/dev/test].lb files. In *.sent file, each lines corresponds to one sentence and a special symbol ▁ (U+2581) is added to the left and right of polyphonic character. The pronunciation of the corresponding character is at the same line from *.lb file. For each sentence, there could be several polyphonic characters, but we randomly choose only one polyphonic character to annotate.
+
+## Requirements
+* python >= 3.6
+* numpy
+
+## Usage
+If you want to remove all the digits which denote the tones, set tone=False. Default setting is tone=True. <br />
+If you want to split all the non Chinese characters (e.g. digit), set char_split=True. Default setting is char_split=False. <br />
+
+```
+>>> from g2pM import G2pM
+>>> model = G2pM()
+>>> sentence = "然而，他红了20年以后，他竟退出了大家的视线。"
+>>> model(sentence, tone=True, char_split=False)
+['ran2', 'er2', '，', 'ta1', 'hong2', 'le5', '20', 'nian2', 'yi3', 'hou4', '，', 'ta1', 'jing4', 'tui4', 'chu1', 'le5', 'da4', 'jia1', 'de5', 'shi4', 'xian4', '。']
+>>> model(sentence, tone=False, char_split=False)
+['ran', 'er', '，', 'ta', 'hong', 'le', '2', '0', 'nian', 'yi', 'hou', '，', 'ta', 'jing', 'tui', 'chu', 'le', 'da', 'jia', 'de', 'shi', 'xian', '。']
+>>> model(sentence, tone=True, char_split=True)
+['ran2', 'er2', '，', 'ta1', 'hong2', 'le5', '2', '0', 'nian2', 'yi3', 'hou4', '，', 'ta1', 'jing4', 'tui4', 'chu1', 'le5', 'da4', 'jia1', 'de5', 'shi4', 'xian4', '。']
+
+```
+
+## Model Size
+| Layer                 | Size    |
+|-----------------------|---------|
+| Embedding             | 64      |
+| LSTM x1               | 64      |
+| Fully-Connected x2    | 64      |
+| Total # of parameters | 477,228 |
+| Model size            | 1.7MB   |
+| Package size          | 2.1MB   |
+
+## Evaluation Result
+
+| Model            | Dev.            | Test         |
+| :--------------| --------------: |:--------------:|
+| g2pC                 | 84.84                | 84.45           |
+| xpinyin(0.5.6)       | 78.74                | 78.56           |
+| pypinyin(0.36.0)     | 85.44                | 86.13           |
+| Majority Vote        | 92.15                | 92.08           |
+| Chinese Bert         | **97.95**            | **97.85**       |
+| Ours                 | 97.36                | 97.31           |
+
+
+## Reference
+To cite the code/data/paper, please use this BibTex
+```bibtex
+@article{park2020g2pm,
+ author={Park, Kyubyong and Lee, Seanie},
+ title = {A Neural Grapheme-to-Phoneme Conversion Package for MandarinChinese Based on a New Open Benchmark Dataset
+},
+ journal={Proc. Interspeech 2020},
+ url = {https://arxiv.org/abs/2004.03136},
+ year = {2020}
+}
+```
+
+
+
+
+%package help
+Summary:	Development documents and examples for g2pM
+Provides:	python3-g2pM-doc
+%description help
+
+# g2pM
+[![Release](https://img.shields.io/badge/release-v0.1.2.4-green)](https://pypi.org/project/g2pM/)
+[![Downloads](https://pepy.tech/badge/g2pm)](https://pepy.tech/project/g2pm)
+[![license](https://img.shields.io/badge/license-Apache%202.0-red)](https://github.com/kakaobrain/g2pM/blob/master/LICENSE)
+
+This is the official repository of our paper [A Neural Grapheme-to-Phoneme Conversion Package for MandarinChinese Based on a New Open Benchmark Dataset](https://arxiv.org/abs/2004.03136) (**Interspeech 2020**).
+
+## Install
+```
+pip install g2pM
+```
+
+## The CPP Dataset
+In data folder, there are [train/dev/test].sent files and [train/dev/test].lb files. In *.sent file, each lines corresponds to one sentence and a special symbol ▁ (U+2581) is added to the left and right of polyphonic character. The pronunciation of the corresponding character is at the same line from *.lb file. For each sentence, there could be several polyphonic characters, but we randomly choose only one polyphonic character to annotate.
+
+## Requirements
+* python >= 3.6
+* numpy
+
+## Usage
+If you want to remove all the digits which denote the tones, set tone=False. Default setting is tone=True. <br />
+If you want to split all the non Chinese characters (e.g. digit), set char_split=True. Default setting is char_split=False. <br />
+
+```
+>>> from g2pM import G2pM
+>>> model = G2pM()
+>>> sentence = "然而，他红了20年以后，他竟退出了大家的视线。"
+>>> model(sentence, tone=True, char_split=False)
+['ran2', 'er2', '，', 'ta1', 'hong2', 'le5', '20', 'nian2', 'yi3', 'hou4', '，', 'ta1', 'jing4', 'tui4', 'chu1', 'le5', 'da4', 'jia1', 'de5', 'shi4', 'xian4', '。']
+>>> model(sentence, tone=False, char_split=False)
+['ran', 'er', '，', 'ta', 'hong', 'le', '2', '0', 'nian', 'yi', 'hou', '，', 'ta', 'jing', 'tui', 'chu', 'le', 'da', 'jia', 'de', 'shi', 'xian', '。']
+>>> model(sentence, tone=True, char_split=True)
+['ran2', 'er2', '，', 'ta1', 'hong2', 'le5', '2', '0', 'nian2', 'yi3', 'hou4', '，', 'ta1', 'jing4', 'tui4', 'chu1', 'le5', 'da4', 'jia1', 'de5', 'shi4', 'xian4', '。']
+
+```
+
+## Model Size
+| Layer                 | Size    |
+|-----------------------|---------|
+| Embedding             | 64      |
+| LSTM x1               | 64      |
+| Fully-Connected x2    | 64      |
+| Total # of parameters | 477,228 |
+| Model size            | 1.7MB   |
+| Package size          | 2.1MB   |
+
+## Evaluation Result
+
+| Model            | Dev.            | Test         |
+| :--------------| --------------: |:--------------:|
+| g2pC                 | 84.84                | 84.45           |
+| xpinyin(0.5.6)       | 78.74                | 78.56           |
+| pypinyin(0.36.0)     | 85.44                | 86.13           |
+| Majority Vote        | 92.15                | 92.08           |
+| Chinese Bert         | **97.95**            | **97.85**       |
+| Ours                 | 97.36                | 97.31           |
+
+
+## Reference
+To cite the code/data/paper, please use this BibTex
+```bibtex
+@article{park2020g2pm,
+ author={Park, Kyubyong and Lee, Seanie},
+ title = {A Neural Grapheme-to-Phoneme Conversion Package for MandarinChinese Based on a New Open Benchmark Dataset
+},
+ journal={Proc. Interspeech 2020},
+ url = {https://arxiv.org/abs/2004.03136},
+ year = {2020}
+}
+```
+
+
+
+
+%prep
+%autosetup -n g2pM-0.1.2.5
+
+%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-g2pM -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Thu May 18 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.2.5-1
+- Package Spec generated
diff --git a/sources b/sources
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+581c17efe8db335122e56750054e0abc  g2pM-0.1.2.5.tar.gz