vits-simple-api

Simply call the vits api

English|中文文档

Feature

Online Demo

Thanks to Hugging Face!

Please note that different IDs may support different languages.speakers

https://artrajz-vits-simple-api.hf.space/voice/vits?text=你好,こんにちは&id=164
https://artrajz-vits-simple-api.hf.space/voice/vits?text=Difficult the first time, easy the second.&id=4
excited:https://artrajz-vits-simple-api.hf.space/voice/w2v2-vits?text=こんにちは&id=3&emotion=111
whispered:https://artrajz-vits-simple-api.hf.space/w2v2-vits?text=こんにちは&id=3&emotion=2077

ssml.mov

Deployment

There are two deployment options to choose from. Regardless of the option you select, you'll need to import the model after deployment to use the application.

Docker Deployment (Recommended for Linux)

Step 1: Pull the Docker Image

Run the following command to pull the Docker image. Follow the prompts in the script to choose the necessary files to download and pull the image:

bash -c "$(wget -O- https://raw.githubusercontent.com/Artrajz/vits-simple-api/main/vits-simple-api-installer-latest.sh)"

The default paths for project configuration files and model folders are /usr/local/vits-simple-api/.

Step 2: Start

Run the following command to start the container:

docker-compose up -d

Image Update

To update the image, run the following commands:

docker-compose pull

Then, restart the container:

docker-compose up -d

Virtual Environment Deployment

Step 1: Clone the Project

Clone the project repository using the following command:

git clone https://github.com/Artrajz/vits-simple-api.git

Step 2: Install Python Dependencies

It is recommended to use a virtual environment with Python version 3.10 for this project. Run the following command to install the Python dependencies required for the project:

If you encounter issues installing certain dependencies, please refer to the common problems outlined below.

pip install -r requirements.txt

Step 3: Start

Run the following command to start the program:

python app.py

Windows Quick Deployment Package

Step 1: Download and Extract the Deployment Package

Go to the releases page and download the latest deployment package. Extract the downloaded files.

Step 2: Start

Run start.bat to launch the program.

Model Loading

Step 1: Download VITS Models

Download the VITS model files and place them in the data/models folder.

Step 2: Loading Models

Automatic Model Loading

Starting from version 0.6.6, it is default behavior to automatically load all models in the data/models folder, making it easier for beginners to use.

Manual Model Loading

After the initial startup, a config.yaml configuration file will be generated. You need to change tts_config.auto_load to false in order to enable manual loading mode.

You can modify the tts_config.models in the config.yaml or make modifications in the admin panel in the browser.

Note: After version 0.6.6, the model loading path has been modified. Please follow the steps below to configure the model path again!

The path can be an absolute path or a relative path. If it's a relative path, it starts from the data/models folder in the project root directory.

For example, if the data/models folder has the following files:

├─model1
│  │─G_1000.pth
│  └─config.json
└─model2
   │─G_1000.pth
   └─config.json

Fill in the configuration like this in the YAML file:

tts_config:
  auto_load: false
  models:
  - config_path: model1/config.json
    model_path: model1/G_1000.pth
  - config_path: model2/config.json
    model_path: model2/G_1000.pth
	# GPT-SoVITS
  - sovits_path: gpt_sovits1/model1_e8_s11536.pth
    gpt_path: gpt_sovits1/model1-e15.ckpt
  - sovits_path: gpt_sovits2/model2_e8_s11536.pth
    gpt_path: gpt_sovits2/model2-e15.ckpt

Loading models through the admin panel is convenient, but if you want to load models outside the data/models folder, you can only do so by modifying the config.yaml configuration file. The method is to directly provide the absolute path.

Absolute path example:

tts_config:
  auto_load: false
  models:
  - config_path: D://model3/config.json
    model_path: D://model3/G_1000.pth

models_path: This is the models folder relative to the data directory, with the default value being "models". When auto_load is set to true, all models in the models_path directory will be loaded.

Other Models

After downloading the BERT model and emotion model, place them in the data/bert and data/emotional folders respectively. Find the corresponding names and insert them accordingly.

GPU accelerated

Windows

Install CUDA

Check the highest version of CUDA supported by your graphics card:

nvidia-smi

Taking CUDA 11.7 as an example, download it from the official website

Install GPU version of PyTorch

1.13.1+cu117 is recommended, other versions may have memory instability issues.

pip install torch==1.13.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117

Linux

The installation process is similar, but I don't have the environment to test it.

WebUI

Inference Frontend

http://127.0.0.1:23456

*Port is modifiable under the default setting of port 23456.

Admin Backend

The default address is http://127.0.0.1:23456/admin.

The initial username and password can be found by searching for 'admin' in the config.yaml file after the first startup.

Function Options Explanation

Disable the Admin Backend

The admin backend allows loading and unloading models, and while it has login authentication, for added security, you can disable the admin backend in the config.yaml:

'IS_ADMIN_ENABLED': !!bool 'false'

This extra measure helps ensure absolute security when making the admin backend inaccessible to the public network.

Bert-VITS2 Configuration and Language/Bert Model Usage

Starting from Bert-VITS2 v2.0, a model requires loading three different language Bert models. If you only need to use one or two languages, you can add the lang parameter in the config.json file of the model's data section. The value ['zh'] indicates that the model only uses Chinese and will load Chinese Bert models. The value ['zh', 'ja'] indicates the usage of both Chinese and Japanese bilingual models, and only Chinese and Japanese Bert models will be loaded. Similarly, this pattern continues for other language combinations.

Example:

"data": {
  "lang": ["zh", "ja"],
  "training_files": "filelists/train.list",
  "validation_files": "filelists/val.list",
  "max_wav_value": 32768.0,
  ...

Custom Chinese Polyphonic Dictionary

If you encounter issues with incorrect pronunciation of polyphonic characters, you can try resolving it using the following method.

Create and open phrases_dict.txt in the project's root directory to add polyphonic words.

{
"一骑当千": [["yí"], ["jì"], ["dāng"], ["qiān"]],
}

GPT-SoVITS Reference Audio Presets

Find the configuration for GPT-SoVITS in the config.yaml file. Add presets under the presets section. Multiple presets can be added, with keys serving as preset names. Below are two default presets, default and default2:

gpt_sovits_config:
  hz: 50
  is_half: false
  id: 0
  lang: auto
  format: wav
  segment_size: 50
  presets:
    default:
      refer_wav_path: null
      prompt_text: null
      prompt_lang: auto
    default2:
      refer_wav_path: null
      prompt_text: null
      prompt_lang: auto

Frequently Asked Questions

Installation Issues with fastText Dependency

Fasttext may not be installed on windows, you can install it with the following command,or download wheels here

# For Python 3.10 on win_amd64
pip install https://github.com/Artrajz/archived/raw/main/fasttext/fasttext-0.9.2-cp310-cp310-win_amd64.whl

or

pip install fasttext -i https://pypi.artrajz.cn/simple

Installation Issues with pyopenjtalk Dependency

Since pypi.org does not provide a wheel file for pyopenjtalk, you often need to install it from the source code. This process might be cumbersome for some users, so you can also install it using a pre-built wheel as follows:

pip install pyopenjtalk -i https://pypi.artrajz.cn/simple

Bert-VITS2 Version Compatibility

To ensure compatibility with the Bert-VITS2 model, modify the config.json file by adding a version parameter "version": "x.x.x". For instance, if the model version is 1.0.1, the configuration file should be written as:

{
  "version": "1.0.1",
  "train": {
    "log_interval": 10,
    "eval_interval": 100,
    "seed": 52,
    ...

Please note that for the Chinese extra version, the version should be changed to extra or zh-clap, and for the extra fix version, the version should be 2.4 or extra-fix.

API

GET

speakers list

GET http://127.0.0.1:23456/voice/speakers

Returns the mapping table of role IDs to speaker names.

voice vits

GET http://127.0.0.1:23456/voice/vits?text=text

Default values are used when other parameters are not specified.
GET http://127.0.0.1:23456/voice/vits?text=[ZH]text[ZH][JA]text[JA]&lang=mix

When lang=mix, the text needs to be annotated.
GET http://127.0.0.1:23456/voice/vits?text=text&id=142&format=wav&lang=zh&length=1.4

The text is "text", the role ID is 142, the audio format is wav, the text language is zh, the speech length is 1.4, and the other parameters are default.

check

GET http://127.0.0.1:23456/voice/check?id=0&model=vits

POST

See api_test.py

API KEY

Set api_key_enabled: true in config.yaml to enable API key authentication. The API key is api_key: api-key. After enabling it, you need to add the api_key parameter in GET requests and add the X-API-KEY parameter in the header for POST requests.

Parameter

VITS

Name	Parameter	Is must	Default	Type	Instruction
Synthesized text	text	true		str	Text needed for voice synthesis.
Speaker ID	id	false	From `config.yaml`	int	The speaker ID.
Audio format	format	false	From `config.yaml`	str	Support for wav,ogg,silk,mp3,flac
Text language	lang	false	From `config.yaml`	str	The language of the text to be synthesized. Available options include auto, zh, ja, and mix. When lang=mix, the text should be wrapped in [ZH] or [JA].The default mode is auto, which automatically detects the language of the text
Audio length	length	false	From `config.yaml`	float	Adjusts the length of the synthesized speech, which is equivalent to adjusting the speed of the speech. The larger the value, the slower the speed.
Noise	noise	false	From `config.yaml`	float	Sample noise, controlling the randomness of the synthesis.
SDP noise	noisew	false	From `config.yaml`	float	Stochastic Duration Predictor noise, controlling the length of phoneme pronunciation.
Segment Size	segment_size	false	From `config.yaml`	int	Divide the text into paragraphs based on punctuation marks, and combine them into one paragraph when the length exceeds segment_size. If segment_size<=0, the text will not be divided into paragraphs.
Streaming response	streaming	false	false	bool	Streamed synthesized speech with faster initial response.

VITS voice conversion

Name	Parameter	Is must	Type	Instruction
Uploaded Audio	upload	true	file	The audio file to be uploaded. It should be in wav or ogg
Source Role ID	original_id	true	int	The ID of the role used to upload the audio file.
Target Role ID	target_id	true	int	The ID of the target role to convert the audio to.

HuBert-VITS

Name	Parameter	Is must	Type	Instruction
Uploaded Audio	upload	true	file	The audio file to be uploaded. It should be in wav or ogg format.
Target speaker ID	id	true	int	The target speaker ID.
Audio format	format	true	str	wav,ogg,silk
Audio length	length	true	float	Adjusts the length of the synthesized speech, which is equivalent to adjusting the speed of the speech. The larger the value, the slower the speed.
Noise	noise	true	float	Sample noise, controlling the randomness of the synthesis.
sdp noise	noisew	true	float	Stochastic Duration Predictor noise, controlling the length of phoneme pronunciation.

W2V2-VITS

Name	Parameter	Is must	Default	Type	Instruction
Synthesized text	text	true		str	Text needed for voice synthesis.
Speaker ID	id	false	From `config.yaml`	int	The speaker ID.
Audio format	format	false	From `config.yaml`	str	Support for wav,ogg,silk,mp3,flac
Text language	lang	false	From `config.yaml`	str	The language of the text to be synthesized. Available options include auto, zh, ja, and mix. When lang=mix, the text should be wrapped in [ZH] or [JA].The default mode is auto, which automatically detects the language of the text
Audio length	length	false	From `config.yaml`	float	Adjusts the length of the synthesized speech, which is equivalent to adjusting the speed of the speech. The larger the value, the slower the speed.
Noise	noise	false	From `config.yaml`	float	Sample noise, controlling the randomness of the synthesis.
SDP noise	noisew	false	From `config.yaml`	float	Stochastic Duration Predictor noise, controlling the length of phoneme pronunciation.
Segment Size	segment_size	false	From `config.yaml`	int	Divide the text into paragraphs based on punctuation marks, and combine them into one paragraph when the length exceeds segment_size. If segment_size<=0, the text will not be divided into paragraphs.
Dimensional emotion	emotion	false	0	int	The range depends on the emotion reference file in npy format, such as the range of the innnky's model all_emotions.npy, which is 0-5457.

Dimensional emotion

Name	Parameter	Is must	Default	Type	Instruction
Uploaded Audio	upload	true		file	Return the npy file that stores the dimensional emotion vectors.

Bert-VITS2

Name	Parameter	Is must	Default	Type	Instruction
Synthesized text	text	true		str	Text needed for voice synthesis.
Speaker ID	id	false	From `config.yaml`	int	The speaker ID.
Audio format	format	false	From `config.yaml`	str	Support for wav,ogg,silk,mp3,flac
Text language	lang	false	From `config.yaml`	str	"Auto" is a mode for automatic language detection and is also the default mode. However, it currently only supports detecting the language of an entire text passage and cannot distinguish languages on a per-sentence basis. The other available language options are "zh" and "ja".
Audio length	length	false	From `config.yaml`	float	Adjusts the length of the synthesized speech, which is equivalent to adjusting the speed of the speech. The larger the value, the slower the speed.
Noise	noise	false	From `config.yaml`	float	Sample noise, controlling the randomness of the synthesis.
SDP noise	noisew	false	From `config.yaml`	float	Stochastic Duration Predictor noise, controlling the length of phoneme pronunciation.
Segment Size	segment_size	false	From `config.yaml`	int	Divide the text into paragraphs based on punctuation marks, and combine them into one paragraph when the length exceeds segment_size. If segment_size<=0, the text will not be divided into paragraphs.
SDP/DP mix ratio	sdp_ratio	false	From `config.yaml`	int	The theoretical proportion of SDP during synthesis, the higher the ratio, the larger the variance in synthesized voice tone.
Emotion	emotion	false	From `config.yaml`	int	Available for Bert-VITS2 v2.1, ranging from 0 to 9
Emotion reference Audio	reference_audio	false	None		Bert-VITS2 v2.1 uses reference audio to control the synthesized audio's emotion
Text Prompt	text_prompt	false	From `config.yaml`	str	Bert-VITS2 v2.2 text prompt used for emotion control
Style Text	style_text	false	From `config.yaml`	str	Bert-VITS2 v2.3 text prompt used for emotion control
Style Text Weight	style_weight	false	From `config.yaml`	float	Bert-VITS2 v2.3 text prompt weight used for prompt weighting
Streaming response	streaming	false	false	bool	Streamed synthesized speech with faster initial response.

GPT-SoVITS Speech Synthesis

Name	Parameter	Is must	Default	Type	Instruction
Synthesized text	text	true		str	Text needed for voice synthesis.
Speaker ID	id	false	From `config.yaml`	int	Speaker ID. In GPT-SoVITS, each model serves as a Speaker ID, and the voice is switched by reference audio presets.
Audio format	format	false	From `config.yaml`	str	Support for wav, ogg, silk, mp3, flac
Text language	lang	false	From `config.yaml`	str	"auto" is the automatic language detection mode, which is also the default mode. However, it currently only supports recognizing the language of the entire text passage, and cannot distinguish each sentence.
Reference Audio	reference_audio	false	None		reference_audio is required, but it can be replaced by preset.
Reference Audio Text	prompt_text	false	From `config.yaml`	float	Need to be consistent with the actual text of the reference audio.
Reference Audio Language	prompt_lang	false	From `config.yaml`	str	Defaults to auto for automatic text language recognition. If recognition fails, manually fill in, zh for Chinese, ja for Japanese, en for English.
Reference Audio Preset	preset	false	default	str	Replace the reference audio with pre-set presets, multiple presets can be set.

SSML (Speech Synthesis Markup Language)

Supported Elements and Attributes

speak Element

Attribute	Instruction	Is must
id	Default value is retrieved From `config.yaml`	false
lang	Default value is retrieved From `config.yaml`	false
length	Default value is retrieved From `config.yaml`	false
noise	Default value is retrieved From `config.yaml`	false
noisew	Default value is retrieved From `config.yaml`	false
segment_size	Splits text into segments based on punctuation marks. When the sum of segment lengths exceeds `segment_size`, it is treated as one segment. `segment_size<=0` means no segmentation. The default value is 0.	false
model_type	Default is VITS. Options: W2V2-VITS, BERT-VITS2	false
emotion	Only effective when using W2V2-VITS . The range depends on the npy emotion reference file.	false
sdp_ratio	Only effective when using BERT-VITS2 .	false

voice Element

Higher priority than speak.

Attribute	Instruction	Is must
id	Default value is retrieved From `config.yaml`	false
lang	Default value is retrieved From `config.yaml`	false
length	Default value is retrieved From `config.yaml`	false
noise	Default value is retrieved From `config.yaml`	false
noisew	Default value is retrieved From `config.yaml`	false
segment_size	Splits text into segments based on punctuation marks. When the sum of segment lengths exceeds `segment_size`, it is treated as one segment. `segment_size<=0` means no segmentation. The default value is 0.	false
model_type	Default is VITS. Options: W2V2-VITS, BERT-VITS2	false
emotion	Only effective when using W2V2-VITS . The range depends on the npy emotion reference file.	false
sdp_ratio	Only effective when using BERT-VITS2 .	false

break Element

Attribute	Instruction	Is must
strength	x-weak, weak, medium (default), strong, x-strong	false
time	The absolute duration of a pause in seconds (such as `2s`) or milliseconds (such as `500ms`). Valid values range from 0 to 5000 milliseconds. If you set a value greater than the supported maximum, the service will use `5000ms`. If the `time` attribute is set, the `strength` attribute is ignored.	false

Strength	Relative Duration
x-weak	250 ms
weak	500 ms
medium	750 ms
strong	1000 ms
x-strong	1250 ms

Example

See api_test.py

Communication

Learning and communication,now there is only Chinese QQ group

Acknowledgements

vits:https://github.com/jaywalnut310/vits
MoeGoe:https://github.com/CjangCjengh/MoeGoe
emotional-vits:https://github.com/innnky/emotional-vits
vits-uma-genshin-honkai:https://huggingface.co/spaces/zomehwh/vits-uma-genshin-honkai
vits_chinese:https://github.com/PlayVoice/vits_chinese
Bert_VITS2:https://github.com/fishaudio/Bert-VITS2
GPT-SoVITS:https://github.com/RVC-Boss/GPT-SoVITS

Thank You to All Contributors

Name		Name	Last commit message	Last commit date
Latest commit History 459 Commits
.github		.github
bert_vits2		bert_vits2
data		data
gpt_sovits		gpt_sovits
manager		manager
tts_app		tts_app
utils		utils
vits		vits
.gitignore		.gitignore
Dockerfile		Dockerfile
Dockerfile_GPU		Dockerfile_GPU
LICENSE		LICENSE
README.md		README.md
README_zh.md		README_zh.md
api_test.py		api_test.py
app.py		app.py
config.py		config.py
contants.py		contants.py
docker-compose-gpu.yaml		docker-compose-gpu.yaml
docker-compose.yaml		docker-compose.yaml
gunicorn_config.py		gunicorn_config.py
logger.py		logger.py
requirements.txt		requirements.txt
run.ps1		run.ps1
vits-simple-api-installer-latest.sh		vits-simple-api-installer-latest.sh

License

zwssunny/vits-simple-api

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vits-simple-api

Feature

Online Demo

Deployment

Docker Deployment (Recommended for Linux)

Step 1: Pull the Docker Image

Step 2: Start

Image Update

Virtual Environment Deployment

Step 1: Clone the Project

Step 2: Install Python Dependencies

Step 3: Start

Windows Quick Deployment Package

Step 1: Download and Extract the Deployment Package

Step 2: Start

Model Loading

Step 1: Download VITS Models

Step 2: Loading Models

Automatic Model Loading

Manual Model Loading

Other Models

GPU accelerated

Windows

Install CUDA

Install GPU version of PyTorch

Linux

WebUI

Inference Frontend

Admin Backend

Function Options Explanation

Disable the Admin Backend

Bert-VITS2 Configuration and Language/Bert Model Usage

Custom Chinese Polyphonic Dictionary

GPT-SoVITS Reference Audio Presets

Frequently Asked Questions

Installation Issues with fastText Dependency

Installation Issues with pyopenjtalk Dependency

Bert-VITS2 Version Compatibility

API

GET

speakers list

voice vits

check

POST

API KEY

Parameter

VITS

VITS voice conversion

HuBert-VITS

W2V2-VITS

Dimensional emotion

Bert-VITS2

GPT-SoVITS Speech Synthesis

SSML (Speech Synthesis Markup Language)

Communication

Acknowledgements

Thank You to All Contributors

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