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, 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 for audio samples.

We also provide the pretrained models.

** Update note: Thanks to Rishikesh (ऋषिकेश), our interactive TTS demo is now available on Colab Notebook.

VITS at training	VITS at inference

Pre-requisites

0. Python >= 3.6
1. Clone this repository
2. Install python requirements. Please refer requirements.txt
   1. You may need to install espeak first: apt-get install espeak
3. 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
   2. 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
4. Build Monotonic Alignment Search and run preprocessing if you use your own datasets.

# 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

# 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

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。
• 这里是一个简单的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库。这里是下载页面，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

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

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