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g2pM: A Neural Grapheme-to-Phoneme Conversion Package for MandarinChinese

Project description

g2pM

Release Downloads 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 (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

@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}
}

Project details


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