本项目已停止维护，已相当老旧

推荐：

• https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend
• https://github.com/thuhcsi/Crystal

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A Demo of MTTS Mandarin/Chinese Text to Speech FrontEnd

Mandarin/Chinese Text to Speech based on statistical parametric speech synthesis using merlin toolkit

这只是一个语音合成前端的Demo，没有提供文本正则化，韵律预测功能，文字转拼音使用pypinyin，分词使用结巴分词，这两者的准确度也达不到商用水平。

其他语音合成项目传送门，端到端是不错的方向，自然度要优于merlin。

This is only a demo of mandarin frontend which is lack of some parts like "text normalization" and "prosody prediction", and the phone set && Question Set this project use havn't fully tested yet.

一个粗略的文档：A draft documentation written in Mandarin

Data

There is no open-source mandarin speech synthesis dataset on the internet, this proj used thchs30 dataset to demostrate speech synthesis

UPDATE

open-source mandarin speech synthesis data from data-banker company, 开源的中文语音合成数据，感谢标贝公司

【数据下载】https://weixinxcxdb.oss-cn-beijing.aliyuncs.com/gwYinPinKu/BZNSYP.rar 【数据说明】http://www.data-baker.com/open_source.html

Generated Samples

Listen to https://jackiexiao.github.io/MTTS/

How To Reproduce

1. First, you need data contain wav and txt (prosody mark is optional)
2. Second, generate HTS label using this project
3. Using merlin/egs/mandarin_voice to train and generate Mandarin Voice

Context related annotation & Question Set

• Context related annotation上下文相关标注
• Question Set问题集
• Rules to design a Question Set问题集设计规则

Install

Python : python3.6
System: linux(tested on ubuntu16.04)

pip install jieba pypinyin
sudo apt-get install libatlas3-base

Run bash tools/install_mtts.sh
Or download file by yourself

• Download montreal-forced-aligner and unzip to directory tools/
• Download acoustic_model thchs30.zip and copy to directory misc/

Run Demo

bash run_demo.sh

Usage

1. Generate HTS Label by wav and text

• Usage: Run python src/mtts.py txtfile wav_directory_path output_directory_path (Absolute path or relative path) Then you will get HTS label, if you have your own acoustic model trained by monthreal-forced-aligner, add-a your_acoustic_model.zip, otherwise, this project use thchs30.zip acoustic model as default
• Attention: Currently only support Chinese Character, txt should not have any Arabia number or English alphabet(不可包含阿拉伯数字和英文字符)

txtfile example

A_01 这是一段文本
A_02 这是第二段文本

wav_directory example(Sampleing Rate should larger than 16khz)

A_01.wav  
A_02.wav  

2. Generate HTS Label by text with or without alignment file

• Usage: Run python src/mandarin_frontend.py txtfile output_directory_path
• or import mandarin_frontend

from mandarin_frontend import txt2label

result = txt2label('向香港特别行政区同胞澳门和台湾同胞海外侨胞')
[print(line) for line in result]

# with prosody mark and alignment file (sfs file)
# result = txt2label('向#1香港#2特别#1行政区#1同胞#4澳门#2和#1台湾#1同胞#4海外#1侨胞',
            sfsfile='example_file/example.sfs')

see source code for more information, but pay attention to the alignment file(sfs file), the format is endtime phone_type not start_time, phone_type(which is different from speech ocean's data)

3. Forced-alignment

This project use Montreal-Forced-Aligner to do forced alignment, if you want to get a better alignment, use your data to train a alignment-model, see mfa: algin-using-only-the-dataset

1. We trained the acoustic model using thchs30 dataset, see misc/thchs30.zip, the dictionary we use mandarin_mtts.lexicon. If you use larger dataset than thchs30, you may get better alignment.
2. If you want to use mfa's (montreal-forced-aligner) pre-trained mandarin model, this is the dictionary you need mandarin-for-montreal-forced-aligner-pre-trained-model.lexicon

Prosody Mark

You can generate HTS Label without prosody mark. we assume that word segment is smaller than prosodic word(which is adjusted in code)

"#0","#1", "#2","#3" and "#4" are the prosody labeling symbols.

• #0 stands for word segment
• #1 stands for prosodic word
• #2 stands for stressful word (actually in this project we regrad it as #1)
• #3 stands for prosodic phrase
• #4 stands for intonational phrase

Improvement to be done in future

• Text Normalization
• Better Chinese word segment
• G2P: Polyphone Problem
• Better Label format and Question Set
• Improvement of prosody analyse
• Better alignment

Contributor

• Jackiexiao
• willian56