From 85280905b6b095eda325befbf1016d770197a79b Mon Sep 17 00:00:00 2001 From: CoprDistGit Date: Thu, 18 May 2023 07:36:28 +0000 Subject: automatic import of python-g2pm --- python-g2pm.spec | 291 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 291 insertions(+) create mode 100644 python-g2pm.spec (limited to 'python-g2pm.spec') diff --git a/python-g2pm.spec b/python-g2pm.spec new file mode 100644 index 0000000..895fecf --- /dev/null +++ b/python-g2pm.spec @@ -0,0 +1,291 @@ +%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.
+If you want to split all the non Chinese characters (e.g. digit), set char_split=True. Default setting is char_split=False.
+ +``` +>>> 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.
+If you want to split all the non Chinese characters (e.g. digit), set char_split=True. Default setting is char_split=False.
+ +``` +>>> 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.
+If you want to split all the non Chinese characters (e.g. digit), set char_split=True. Default setting is char_split=False.
+ +``` +>>> 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 - 0.1.2.5-1 +- Package Spec generated -- cgit v1.2.3