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 |
| Ours | 97.36 | 97.31 |
Reference
To cite the code/data/paper, please use this BibTex
@article{Kyubyong2020g2pM,
author = {Kyubyong Park, Seanie Lee},
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}
}