• 20221108更新
• VITS 原神语音合成V2
• VITS 原神语音合成V1
• VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
• • • Jaehyeon Kim, Jungil Kong, and Juhee Son
  • Pre-requisites
  • Training Exmaple
  • Inference Example

20221108更新

模型和API均不再提供，大家可以使用我整理的数据集：点击这里

VITS 原神语音合成V2

本repo包含了我用于训练原神VITS模型对源代码做出的修改，以及新的config文件。

请注意，下方的API均已经停止运行。

感谢星尘以及国家超级计算广州中心提供的算力支持，感谢VITS模型作者Jaehyeon Kim, Jungil Kong, and Juhee Son，感谢ContentVEC作者 Kaizhi Qian.
本模型训练时使用的所有音频文件版权属于米哈游科技（上海）有限公司。

支持的说话者：
['派蒙', '凯亚', '安柏', '丽莎', '琴', '香菱', '枫原万叶',
'迪卢克', '温迪', '可莉', '早柚', '托马', '芭芭拉', '优菈',
'云堇', '钟离', '魈', '凝光', '雷电将军', '北斗',
'甘雨', '七七', '刻晴', '神里绫华', '戴因斯雷布', '雷泽',
'神里绫人', '罗莎莉亚', '阿贝多', '八重神子', '宵宫',
'荒泷一斗', '九条裟罗', '夜兰', '珊瑚宫心海', '五郎',
'散兵', '女士', '达达利亚', '莫娜', '班尼特', '申鹤',
'行秋', '烟绯', '久岐忍', '辛焱', '砂糖', '胡桃', '重云',
'菲谢尔', '诺艾尔', '迪奥娜', '鹿野院平藏']

Query String 参数：

参数	类型	值
text	字符串	生成的文本，支持常见标点符号。英文可能无法正常生成，数字请转换为对应的汉字再进行生成。
speaker	字符串	说话者名称。必须是上面的名称之一。
noise	浮点数	生成时使用的 noise_factor，可用于控制感情等变化程度。默认为0.667。
format	字符串	生成语音的格式，必须为mp3或者wav。默认为mp3。

示例：http://233366.proxy.nscc-gz.cn:8888/?text=你好&speaker=枫原万叶

VITS 原神语音合成V1

此外，也可以尝试使用公开的api：http://233366.proxy.nscc-gz.cn:8888/ 来进行尝试，此API可用于二创等用途，但禁止用于任何商业用途。
请注意多次生成的效果不会一致，可以多次尝试来选择一次较好的效果。
同时支持可视化合成：http://150.158.164.18:9069/
感谢星尘以及国家超级计算广州中心提供的算力支持，感谢VITS模型作者Jaehyeon Kim, Jungil Kong, and Juhee Son，本模型训练时使用的所有音频文件版权属于米哈游科技（上海）有限公司。

Query String 参数：

参数	类型	值
text	字符串	生成的文本，支持常见标点符号。英文可能无法正常生成，数字请转换为对应的汉字再进行生成。
speaker	字符串	说话者名称。必须是上面的名称之一。
noise	浮点数	生成时使用的 noise_factor，可用于控制感情等变化程度。默认为0.667。
noisew	浮点数	生成时使用的 noise_factor_w，可用于控制音素发音长度变化程度。默认为0.8。
length	浮点数	生成时使用的 length_factor，可用于控制整体语速。默认为1.2。
format	字符串	生成语音的格式，必须为mp3或者wav。默认为mp3。

示例：http://233366.proxy.nscc-gz.cn:8888/?text=你好&speaker=派蒙

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