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https://github.com/rotten-work/vits-mandarin-windows
VITS for Mandarin. Support Windows and Linux, low-end and high-end hardwares
https://github.com/rotten-work/vits-mandarin-windows
chinese linux mandarin pytorch vits windows-desktop
Last synced: about 2 months ago
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VITS for Mandarin. Support Windows and Linux, low-end and high-end hardwares
- Host: GitHub
- URL: https://github.com/rotten-work/vits-mandarin-windows
- Owner: rotten-work
- License: mit
- Fork: true (jaywalnut310/vits)
- Created: 2022-10-20T13:53:36.000Z (almost 2 years ago)
- Default Branch: mandarin
- Last Pushed: 2023-05-06T10:29:48.000Z (over 1 year ago)
- Last Synced: 2024-04-06T05:34:26.849Z (6 months ago)
- Topics: chinese, linux, mandarin, pytorch, vits, windows-desktop
- Language: Jupyter Notebook
- Homepage: https://colab.research.google.com/drive/1uFUnZDbHMqKWBUQDZKih56Vkj2ixTN9B#scrollTo=p16LO6nXVDzP
- Size: 5.69 MB
- Stars: 107
- Watchers: 0
- Forks: 12
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-vits - rotten\-work/vits\-mandarin\-windows: VITS for Mandarin\. Support Windows and Linux, low\-end and high\-end hardwares
README
# VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
### Jaehyeon Kim, Jungil Kong, and Juhee Son
In our recent [paper](https://arxiv.org/abs/2106.06103), we propose VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech.
Several recent end-to-end text-to-speech (TTS) models enabling single-stage training and parallel sampling have been proposed, but their sample quality does not match that of two-stage TTS systems. In this work, we present a parallel end-to-end TTS method that generates more natural sounding audio than current two-stage models. Our method adopts variational inference augmented with normalizing flows and an adversarial training process, which improves the expressive power of generative modeling. We also propose a stochastic duration predictor to synthesize speech with diverse rhythms from input text. With the uncertainty modeling over latent variables and the stochastic duration predictor, our method expresses the natural one-to-many relationship in which a text input can be spoken in multiple ways with different pitches and rhythms. A subjective human evaluation (mean opinion score, or MOS) on the LJ Speech, a single speaker dataset, shows that our method outperforms the best publicly available TTS systems and achieves a MOS comparable to ground truth.
Visit our [demo](https://jaywalnut310.github.io/vits-demo/index.html) for audio samples.
We also provide the [pretrained models](https://drive.google.com/drive/folders/1ksarh-cJf3F5eKJjLVWY0X1j1qsQqiS2?usp=sharing).
** Update note: Thanks to [Rishikesh (ऋषिकेश)](https://github.com/jaywalnut310/vits/issues/1), our interactive TTS demo is now available on [Colab Notebook](https://colab.research.google.com/drive/1CO61pZizDj7en71NQG_aqqKdGaA_SaBf?usp=sharing).
VITS at training
VITS at inference
## Pre-requisites
0. Python >= 3.6
0. Clone this repository
0. Install python requirements. Please refer [requirements.txt](requirements.txt)
1. You may need to install espeak first: `apt-get install espeak`
0. Download datasets
1. Download and extract the LJ Speech dataset, then rename or create a link to the dataset folder: `ln -s /path/to/LJSpeech-1.1/wavs DUMMY1`
1. For mult-speaker setting, download and extract the VCTK dataset, and downsample wav files to 22050 Hz. Then rename or create a link to the dataset folder: `ln -s /path/to/VCTK-Corpus/downsampled_wavs DUMMY2`
0. Build Monotonic Alignment Search and run preprocessing if you use your own datasets.
```sh
# Cython-version Monotonoic Alignment Search
cd monotonic_align
python setup.py build_ext --inplace
# Preprocessing (g2p) for your own datasets. Preprocessed phonemes for LJ Speech and VCTK have been already provided.
# python preprocess.py --text_index 1 --filelists filelists/ljs_audio_text_train_filelist.txt filelists/ljs_audio_text_val_filelist.txt filelists/ljs_audio_text_test_filelist.txt
# python preprocess.py --text_index 2 --filelists filelists/vctk_audio_sid_text_train_filelist.txt filelists/vctk_audio_sid_text_val_filelist.txt filelists/vctk_audio_sid_text_test_filelist.txt
```
## Training Exmaple
```sh
# LJ Speech
python train.py -c configs/ljs_base.json -m ljs_base
# VCTK
python train_ms.py -c configs/vctk_base.json -m vctk_base
```
## Inference Example
See [inference.ipynb](inference.ipynb)
## 补充说明
### 项目特点
- 支持Windows和Linux，两个平台上都可以进行训练和推断
- 兼容最新版本的各个依赖库
- Windows平台所需特殊环境配置和操作说明
- 支持中文和英文
- 本项目添加了一个简易的面向对象风格的[推断脚本](inference.py)。
- [这里](https://colab.research.google.com/drive/1uFUnZDbHMqKWBUQDZKih56Vkj2ixTN9B)是一个简单的Colab notebook，展示了如何使用该项目进行训练和推断的步骤。
- [这里](https://colab.research.google.com/drive/1VWBOp3PDGNO77_xOm20yRtc4CSmsbqtb)是一个简单的Colab notebook，展示了如何使用预训练权重进行迁移训练（精调）
- 预处理好的几套音频数据集以方便大家学习实验
### Windows平台环境配置
#### 安装PyTorch的GPU版本
在Windows平台，pip install -r requirements.txt 安装的是CPU版本的PyTorch。所以需要去[PyTorch官网](https://pytorch.org)挑选并运行合适的GPU版本PyTorch安装命令。下面命令仅供参考：
```
conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
```
#### eSpeak的配置
- 在Windows平台上用英文做训练或推断的话，需要安装[eSpeak Ng](https://github.com/espeak-ng/espeak-ng)库。[这里](https://github.com/espeak-ng/espeak-ng/releases)是下载页面，推荐使用.msi安装。
- 安装eSpeak Ng后，请添加环境变量PHONEMIZER_ESPEAK_LIBRARY，并将变量值设置为{INSTALLDIR}\libespeak-ng.dll。如图所示：
#### 构建Monotonoic Alignment Search扩展模块
请先下载安装Visual Studio。到[这里](https://visualstudio.microsoft.com/#vs-section)下载。
### 数据集
标贝中文标准女声音库（处理后）16-bit PCM WAV，22050 Hz
链接：https://pan.baidu.com/s/1oihti9-aoJ447l54kdjChQ
提取码：vits
LJSpeech数据集16-bit PCM WAV，22050 Hz
链接：https://pan.baidu.com/s/1q2A38znFmxn3zCn587ZKkw
提取码：vits
标贝中文标准女声音库官网
https://www.data-baker.com/data/index/TNtts/
LJSpeech数据集官网
https://keithito.com/LJ-Speech-Dataset/
### 预训练权重
标贝中文标准女声音库预训练权重
链接：https://pan.baidu.com/s/1pN-wL_5wB9gYMAr2Mh7Jvg
提取码：vits
注：各预训练权重文件包括生成网络权重（G开头），鉴别器网络权重（D开头），还有训练时使用的cleaners与symbols（方便与其他VITS仓库的代码或工具兼容）
## 效果展示
### [Gallery](gallery/Gallery.md)
## 参考与鸣谢
### 大佬们的VITS语音合成GitHub仓库
* https://github.com/jaywalnut310/vits
* https://github.com/CjangCjengh/vits
* https://github.com/AlexandaJerry/vits-mandarin-biaobei
* https://github.com/JOETtheIV/VITS-Paimon
* https://github.com/w4123/vits
* https://github.com/xiaoyou-bilibili/tts_vits
* https://github.com/wind4000/vits.git
### 参考B站链接
* 【CV失业计划】基于VITS神经网络模型的近乎完美派蒙中文语音合成：\
https://www.bilibili.com/video/BV1rB4y157fd
* 【原神】派蒙Vtuber出道计划——基于AI深度学习VITS和VSeeFace的派蒙语音合成/套皮：\
https://www.bilibili.com/video/BV16G4y1B7Ey
* 【深度学习】基于vits的语音合成：\
https://www.bilibili.com/video/BV1Fe4y1r737
* 零基础炼丹 - vits版补充：\
https://www.bilibili.com/read/cv18357171
## 恰饭
生活不易，喵喵叹气。。。如果您喜欢该项目，请对该项目star一下表示支持吧~
