vits-simple-api

Simply call the vits api

Feature

VITS text-to-speech
HuBert-soft VITS
VITS voice conversion
Support for loading multiple models
Automatic language recognition and processing,support for custom language type range
Customize default parameters
Long text batch processing
GPU accelerated inference

Update Logs


2023.4.17
Added the feature that the cleaner for a single language needs to be annotated to clean, and added GPU acceleration for inference, but the GPU inference environment needs to be manually installed.
2023.4.12
Renamed the project from MoeGoe-Simple-API to vits-simple-api, added support for batch processing of long texts, and added a segment threshold "max" for long texts.
2023.4.7
Added a configuration file to customize default parameters. This update requires manually updating config.py. See config.py for specific usage.
2023.4.6
Added the "auto" option for automatically recognizing the language of the text. Modified the default value of the "lang" parameter to "auto". Automatic recognition still has some defects, please choose manually.
Unified the POST request type as multipart/form-data.

demo：https://api.artrajz.cn/py/voice?text=你好,こんにちは&id=142

Deploy

Docker

Docker image pull script

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

The image size is 5GB, and it will be 8GB after decompression. Please prepare enough disk space.
After a successful pull, the vits model needs to be imported before use. Please follow the steps below to import the model.

Download VITS model

Put the model into /usr/local/vits-simple-api/Model

Folder structure


├─g
│      config.json
│      G_953000.pth
│
├─louise
│      360_epochs.pth
│      config.json
│      hubert-soft-0d54a1f4.pt
│
├─Nene_Nanami_Rong_Tang
│      1374_epochs.pth
│      config.json
│
└─Zero_no_tsukaima
        1158_epochs.pth
        config.json

Modify model path

Modify in /usr/local/vits-simple-api/config.py

config.py


For each model, the filling method is as follows 模型列表中每个模型的填写方法如下
example 示例:
MODEL_LIST = [
    #VITS
    [ABS_PATH+"/Model/Nene_Nanami_Rong_Tang/1374_epochs.pth", ABS_PATH+"/Model/Nene_Nanami_Rong_Tang/config.json"],
    [ABS_PATH+"/Model/Zero_no_tsukaima/1158_epochs.pth", ABS_PATH+"/Model/Zero_no_tsukaima/config.json"],
    [ABS_PATH+"/Model/g/G_953000.pth", ABS_PATH+"/Model/g/config.json"],
    #HuBert-VITS
    [ABS_PATH+"/Model/louise/360_epochs.pth", ABS_PATH+"/Model/louise/config.json", ABS_PATH+"/Model/louise/hubert-soft-0d54a1f4.pt"],
]

Startup

docker compose up -d

Or execute the pull script again

Image update

Run the docker image pull script again

Virtual environment deployment

Clone

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

Download python dependencies

A python virtual environment is recommended，use python >= 3.9

pip install -r requirements.txt

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

#python3.10 win_amd64
pip install https://github.com/Artrajz/archived/raw/main/fasttext/fasttext-0.9.2-cp310-cp310-win_amd64.whl
#python3.9 win_amd64
pip install https://github.com/Artrajz/archived/raw/main/fasttext/fasttext-0.9.2-cp39-cp39-win_amd64.whl

Download VITS model

Put the model into /path/to/vits-simple-api/Model

Folder structure


├─g
│      config.json
│      G_953000.pth
│
├─louise
│      360_epochs.pth
│      config.json
│      hubert-soft-0d54a1f4.pt
│
├─Nene_Nanami_Rong_Tang
│      1374_epochs.pth
│      config.json
│
└─Zero_no_tsukaima
        1158_epochs.pth
        config.json

Modify model path

Modify in /path/to/vits-simple-api/config.py

config.py


For each model, the filling method is as follows 模型列表中每个模型的填写方法如下
example 示例:
MODEL_LIST = [
    #VITS
    [ABS_PATH+"/Model/Nene_Nanami_Rong_Tang/1374_epochs.pth", ABS_PATH+"/Model/Nene_Nanami_Rong_Tang/config.json"],
    [ABS_PATH+"/Model/Zero_no_tsukaima/1158_epochs.pth", ABS_PATH+"/Model/Zero_no_tsukaima/config.json"],
    [ABS_PATH+"/Model/g/G_953000.pth", ABS_PATH+"/Model/g/config.json"],
    #HuBert-VITS
    [ABS_PATH+"/Model/louise/360_epochs.pth", ABS_PATH+"/Model/louise/config.json", ABS_PATH+"/Model/louise/hubert-soft-0d54a1f4.pt"],
]

Startup

python app.py

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: https://developer.nvidia.com/cuda-11-7-0-download-archive?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exe_local

Install GPU version of PyTorch

pip3 install torch torchvision torchaudio --index-url ![img](file:///C:\Users\Administrator\AppData\Roaming\Tencent\QQTempSys\%W@GJ$ACOF(TYDYECOKVDYB.png)https://download.pytorch.org/whl/cu117

You can find the corresponding command for the version you need on the official website: https://pytorch.org/get-started/locally/

Linux

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

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/voice?text=text

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

When lang=mix, the text needs to be annotated.
GET http://127.0.0.1/voice?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

python

import re
import requests
import json
import os
import random
import string
from requests_toolbelt.multipart.encoder import MultipartEncoder

abs_path = os.path.dirname(__file__)
base = "http://127.0.0.1:23456"

#speakers
def voice_speakers():
    url = f"{base}/voice/speakers"

    res = requests.post(url=url)
    json = res.json()
    for i in json:
        print(i)
        for j in json[i]:
            print(j)

#voice vits
def voice_vits(text):
    fields = {
        "text":text,
        "id":"0",
        "format":"wav",
        "lang":"auto",
        "length":"1",
        "noise":"0.667",
        "noisew":"0.8",
        "max": "50"
    }
    boundary = '----VoiceConversionFormBoundary' \
               + ''.join(random.sample(string.ascii_letters + string.digits, 16))
    
    m = MultipartEncoder(fields=fields, boundary=boundary)
    headers = {"Content-Type": m.content_type}
    url = f"{base}/voice"

    res = requests.post(url=url,data=m,headers=headers)
    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
    path = f"{abs_path}/{fname}"
    
    with open(path, "wb") as f:
        f.write(res.content)
    print(path)

#hubert-vits
def voice_hubert_vits(upload_path):
    upload_name = os.path.basename(upload_path)
    upload_type = f'audio/{upload_name.split(".")[1]}' #wav,ogg
    
    with open(upload_path,'rb') as upload_file:
        fields = {
            "upload": (upload_name, upload_file,upload_type),
            "target_id":"0",
            "format":"wav",
            "length":"1",
            "noise":"0.1",
            "noisew":"0.1",
        }
        boundary = '----VoiceConversionFormBoundary' \
                   + ''.join(random.sample(string.ascii_letters + string.digits, 16))
        
        m = MultipartEncoder(fields=fields, boundary=boundary)
        headers = {"Content-Type": m.content_type}
        url = f"{base}/voice/hubert-vits"

        res = requests.post(url=url,data=m,headers=headers)
    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
    path = f"{abs_path}/{fname}"
    
    with open(path, "wb") as f:
        f.write(res.content)
    print(path)

#voice conversion in the same VITS model
def voice_conversion(upload_path):
    upload_name = os.path.basename(upload_path)
    upload_type = f'audio/{upload_name.split(".")[1]}' #wav,ogg

    with open(upload_path,'rb') as upload_file:
        fields = {
            "upload": (upload_name, upload_file,upload_type),
            "original_id": "3",
            "target_id": "0",
        }
        boundary = '----VoiceConversionFormBoundary' \
                   + ''.join(random.sample(string.ascii_letters + string.digits, 16))
        m = MultipartEncoder(fields=fields, boundary=boundary)
        
        headers = {"Content-Type": m.content_type}
        url = f"{base}/voice/conversion"

        res = requests.post(url=url,data=m,headers=headers)
        
    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
    path = f"{abs_path}/{fname}"
    
    with open(path, "wb") as f:
        f.write(res.content)
    print(path)

Parameter

voice vits

Name	Parameter	Is must	Default	Type	Instruction
Synthesized text	text	true		str
Role ID	id	false	0	int
Audio format	format	false	wav	str	Support for wav,ogg,silk
Text language	lang	false	auto	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	1.0	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	0.667	float
Noise Weight	noisew	false	0.8	float
Segmentation threshold	max	false	50	int	Divide the text into paragraphs based on punctuation marks, and combine them into one paragraph when the length exceeds max. If max<=0, the text will not be divided into paragraphs.

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	he audio file to be uploaded. It should be in wav or ogg format.
Target Role ID	target_id	true	int
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
Noise Weight	noisew	true	float

Communication

Learning and communication,now there is only Chinese QQ group

Acknowledgements

vits:https://github.com/jaywalnut310/vits
MoeGoe:https://github.com/CjangCjengh/MoeGoe

Files

README.md

Latest commit

History

README.md

File metadata and controls

vits-simple-api

Feature

2023.4.17

2023.4.12

2023.4.7

2023.4.6

Deploy

Docker

Docker image pull script

Download VITS model

Modify model path

Startup

Image update

Virtual environment deployment

Clone

Download python dependencies

Download VITS model

Modify model path

Startup

GPU accelerated

Windows

Install CUDA

Install GPU version of PyTorch

Linux

API

GET

speakers list

voice vits

check

POST

Parameter

voice vits

voice conversion

HuBert-VITS

Communication

Acknowledgements