g2pM

This is the official repository of our paper A Neural Grapheme-to-Phoneme Conversion Package for MandarinChinese Based on a New Open Benchmark Dataset.

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

>>> 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)
['ran2', 'er2', '，', 'ta1', 'hong2', 'le5', '20', '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 = {arXiv preprint arXiv:2004.03136},
 url = {https://arxiv.org/abs/2004.03136},
 year = {2020}
}

yes7rose/g2pM