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<div class="title" align=center>
<h1>vits-simple-api</h1>
<div>Simply call the vits api</div>
<br/>
<br/>
<p>
<img src="https://img.shields.io/github/license/Artrajz/vits-simple-api">
<img src="https://img.shields.io/badge/python-3.9%7C3.10-green">
<a href="https://hub.docker.com/r/artrajz/vits-simple-api">
<img src="https://img.shields.io/docker/pulls/artrajz/vits-simple-api"></a>
</p>
<a href="https://github.com/Artrajz/vits-simple-api/blob/main/README.md">English</a>|<a href="https://github.com/Artrajz/vits-simple-api/blob/main/README_zh.md">中文文档</a>
<br/>
</div>
# Feature
- [x] VITS text-to-speech
- [x] VITS voice conversion
- [x] HuBert-soft VITS
- [x] W2V2 VITS / emotional-vits dimensional emotion model
- [x] Support for loading multiple models
- [x] Automatic language recognition and processing,set the scope of language type recognition according to model's cleaner,support for custom language type range
- [x] Customize default parameters
- [x] Long text batch processing
- [x] GPU accelerated inference
- [x] SSML (Speech Synthesis Markup Language) work in progress...
<details><summary>Update Logs</summary><pre><code>
<h2>2023.5.24</h2>
<p>Added api dimensional_emotion,load mutiple npy from folder.Docker add linux/arm64 and linux/arm64/v8 platforms</p>
<h2>2023.5.15</h2>
<p>Added english_cleaner. To use it, you need to install espeak separately.</p>
<h2>2023.5.12</h2>
<p>Added support for SSML, but still needs improvement. Refactored some functions and changed "speaker_id" to "id" in hubert_vits.</p>
<h2>2023.5.2</h2>
<p>Added support for the w2v2-vits/emotional-vits model, updated the speakers mapping table, and added support for the languages corresponding to the model.</p>
<h2>2023.4.23</h2>
<p>Add API Key authentication, disabled by default, needs to be enabled in config.py.</p>
<h2>2023.4.17</h2>
<p>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.</p>
<h2>2023.4.12</h2>
<p>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.</p>
<h2>2023.4.7</h2>
<p>Added a configuration file to customize default parameters. This update requires manually updating config.py. See config.py for specific usage.</p>
<h2>2023.4.6</h2>
<p>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.</p>
<p>Unified the POST request type as multipart/form-data.</p>
</code></pre></details>
## demo
- `https://api.artrajz.cn/py/voice/vits?text=你好,こんにちは&id=142`
- excited:`https://api.artrajz.cn/py/voice/w2v2-vits?text=こんにちは&id=3&emotion=111`
- whispered:`https://api.artrajz.cn/py/voice/w2v2-vits?text=こんにちは&id=3&emotion=2077`
https://user-images.githubusercontent.com/73542220/237995061-c1f25b4e-dd86-438a-9363-4bb1fe65b425.mov
The demo server is unstable due to its relatively low configuration.
# 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 platforms currently supported by Docker images are `linux/amd64` and `linux/arm64`.
- 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`
<details><summary>Folder structure</summary><pre><code>
│ hubert-soft-0d54a1f4.pt
│ model.onnx
│ model.yaml
│
├─g
│ config.json
│ G_953000.pth
│
├─louise
│ 360_epochs.pth
│ config.json
│
├─Nene_Nanami_Rong_Tang
│ 1374_epochs.pth
│ config.json
│
├─Zero_no_tsukaima
│ 1158_epochs.pth
│ config.json
│
└─npy
25ecb3f6-f968-11ed-b094-e0d4e84af078.npy
all_emotions.npy
</code></pre></details>
### Modify model path
Modify in `/usr/local/vits-simple-api/config.py`
<details><summary>config.py</summary><pre><code>
# Fill in the model path here
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 (Need to configure HUBERT_SOFT_MODEL)
[ABS_PATH + "/Model/louise/360_epochs.pth", ABS_PATH + "/Model/louise/config.json"],
# W2V2-VITS (Need to configure DIMENSIONAL_EMOTION_NPY)
[ABS_PATH + "/Model/w2v2-vits/1026_epochs.pth", ABS_PATH + "/Model/w2v2-vits/config.json"],
]
# hubert-vits: hubert soft model
HUBERT_SOFT_MODEL = ABS_PATH + "/Model/hubert-soft-0d54a1f4.pt"
# w2v2-vits: Dimensional emotion npy file
# load single npy: ABS_PATH+"/all_emotions.npy
# load mutiple npy: [ABS_PATH + "/emotions1.npy", ABS_PATH + "/emotions2.npy"]
# load mutiple npy from folder: ABS_PATH + "/Model/npy"
DIMENSIONAL_EMOTION_NPY = ABS_PATH + "/Model/npy"
# w2v2-vits: Need to have both `model.onnx` and `model.yaml` files in the same path.
DIMENSIONAL_EMOTION_MODEL = ABS_PATH + "/Model/model.yaml"
</code></pre></details>
### 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](https://www.lfd.uci.edu/~gohlke/pythonlibs/#fasttext)
```
#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`
<details><summary>Folder structure</summary><pre><code>
│ hubert-soft-0d54a1f4.pt
│ model.onnx
│ model.yaml
│
├─g
│ config.json
│ G_953000.pth
│
├─louise
│ 360_epochs.pth
│ config.json
│
├─Nene_Nanami_Rong_Tang
│ 1374_epochs.pth
│ config.json
│
├─Zero_no_tsukaima
│ 1158_epochs.pth
│ config.json
│
└─npy
25ecb3f6-f968-11ed-b094-e0d4e84af078.npy
all_emotions.npy
</code></pre></details>
### Modify model path
Modify in `/path/to/vits-simple-api/config.py`
<details><summary>config.py</summary><pre><code>
# Fill in the model path here
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 (Need to configure HUBERT_SOFT_MODEL)
[ABS_PATH + "/Model/louise/360_epochs.pth", ABS_PATH + "/Model/louise/config.json"],
# W2V2-VITS (Need to configure DIMENSIONAL_EMOTION_NPY)
[ABS_PATH + "/Model/w2v2-vits/1026_epochs.pth", ABS_PATH + "/Model/w2v2-vits/config.json"],
]
# hubert-vits: hubert soft model
HUBERT_SOFT_MODEL = ABS_PATH + "/Model/hubert-soft-0d54a1f4.pt"
# w2v2-vits: Dimensional emotion npy file
# load single npy: ABS_PATH+"/all_emotions.npy
# load mutiple npy: [ABS_PATH + "/emotions1.npy", ABS_PATH + "/emotions2.npy"]
# load mutiple npy from folder: ABS_PATH + "/Model/npy"
DIMENSIONAL_EMOTION_NPY = ABS_PATH + "/Model/npy"
# w2v2-vits: Need to have both `model.onnx` and `model.yaml` files in the same path.
DIMENSIONAL_EMOTION_MODEL = ABS_PATH + "/Model/model.yaml"
</code></pre></details>
### 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&amp;target_arch=x86_64&amp;target_version=10&amp;target_type=exe_local)
### Install GPU version of PyTorch
```
pip3 install torch torchvision torchaudio --index-url 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.
# Openjtalk Installation Issue
If you are using an arm64 architecture platform, you may encounter some issues during installation due to the lack of arm64-compatible whl files on the official PyPI website. In such cases, you can use the whl file I have built to install Openjtalk.
```
pip install openjtalk==0.3.0.dev2 --index-url https://pypi.artrajz.cn/simple
```
Alternatively, you can manually build a whl file by following the instructions in this [tutorial](https://artrajz.cn/index.php/archives/167/).
# 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
```python
import re
import requests
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"
# 映射表
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)
return json
# 语音合成 voice vits
def voice_vits(text, id=0, format="wav", lang="auto", length=1, noise=0.667, noisew=0.8, max=50):
fields = {
"text": text,
"id": str(id),
"format": format,
"lang": lang,
"length": str(length),
"noise": str(noise),
"noisew": str(noisew),
"max": str(max)
}
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)
return path
# 语音转换 hubert-vits
def voice_hubert_vits(upload_path, id, format="wav", length=1, noise=0.667, noisew=0.8):
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),
"id": str(id),
"format": format,
"length": str(length),
"noise": str(noise),
"noisew": str(noisew),
}
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)
return path
# 维度情感模型 w2v2-vits
def voice_w2v2_vits(text, id=0, format="wav", lang="auto", length=1, noise=0.667, noisew=0.8, max=50, emotion=0):
fields = {
"text": text,
"id": str(id),
"format": format,
"lang": lang,
"length": str(length),
"noise": str(noise),
"noisew": str(noisew),
"max": str(max),
"emotion": str(emotion)
}
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/w2v2-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)
return path
# 语音转换 同VITS模型内角色之间的音色转换
def voice_conversion(upload_path, original_id, target_id):
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": str(original_id),
"target_id": str(target_id),
}
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)
return path
def voice_ssml(ssml):
fields = {
"ssml": ssml,
}
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/ssml"
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)
return path
def voice_dimensional_emotion(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),
}
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/dimension-emotion"
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)
return path
```
## API KEY
Set `API_KEY_ENABLED = True` in `config.py` 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 | |
| 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. |
## VITS voice conversion
| Name | Parameter | Is must | Default | 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 | Default | Type | Instruction |
| -------------- | --------- | ------- | ------- | ----- | ------------------------------------------------------------ |
| Uploaded Audio | upload | true | | file | he audio file to be uploaded. It should be in wav or ogg format. |
| Target Role ID | 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 | |
## W2V2-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. |
| Dimensional emotion | emotion | false | 0 | int | The range depends on the emotion reference file in npy format, such as the range of the [innnky](https://huggingface.co/spaces/innnky/nene-emotion/tree/main)'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. |
## SSML (Speech Synthesis Markup Language)
Supported Elements and Attributes
`speak` Element
| Attribute | Instruction | Is must |
| --------- | ------------------------------------------------------------ | ------- |
| id | Default value is retrieved from `config.py` | false |
| lang | Default value is retrieved from `config.py` | false |
| length | Default value is retrieved from `config.py` | false |
| noise | Default value is retrieved from `config.py` | false |
| noisew | Default value is retrieved from `config.py` | false |
| max | Splits text into segments based on punctuation marks. When the sum of segment lengths exceeds `max`, it is treated as one segment. `max<=0` means no segmentation. The default value is 0. | false |
| model | Default is `vits`. Options: `w2v2-vits`, `emotion-vits` | false |
| emotion | Only effective when using `w2v2-vits` or `emotion-vits`. The range depends on the npy emotion reference file. | false |
`voice` Element
Higher priority than `speak`.
| Attribute | Instruction | Is must |
| --------- | ------------------------------------------------------------ | ------- |
| id | Default value is retrieved from `config.py` | false |
| lang | Default value is retrieved from `config.py` | false |
| length | Default value is retrieved from `config.py` | false |
| noise | Default value is retrieved from `config.py` | false |
| noisew | Default value is retrieved from `config.py` | false |
| max | Splits text into segments based on punctuation marks. When the sum of segment lengths exceeds `max`, it is treated as one segment. `max<=0` means no segmentation. The default value is 0. | false |
| model | Default is `vits`. Options: `w2v2-vits`, `emotion-vits` | false |
| emotion | Only effective when using `w2v2-vits` or `emotion-vits` | 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
```xml
<speak lang="zh" format="mp3" length="1.2">
<voice id="92" >这几天心里颇不宁静。</voice>
<voice id="125">今晚在院子里坐着乘凉，忽然想起日日走过的荷塘，在这满月的光里，总该另有一番样子吧。</voice>
<voice id="142">月亮渐渐地升高了，墙外马路上孩子们的欢笑，已经听不见了；</voice>
<voice id="98">妻在屋里拍着闰儿，迷迷糊糊地哼着眠歌。</voice>
<voice id="120">我悄悄地披了大衫，带上门出去。</voice><break time="2s"/>
<voice id="121">沿着荷塘，是一条曲折的小煤屑路。</voice>
<voice id="122">这是一条幽僻的路；白天也少人走，夜晚更加寂寞。</voice>
<voice id="123">荷塘四面，长着许多树，蓊蓊郁郁的。</voice>
<voice id="124">路的一旁，是些杨柳，和一些不知道名字的树。</voice>
<voice id="125">没有月光的晚上，这路上阴森森的，有些怕人。</voice>
<voice id="126">今晚却很好，虽然月光也还是淡淡的。</voice><break time="2s"/>
<voice id="127">路上只我一个人，背着手踱着。</voice>
<voice id="128">这一片天地好像是我的；我也像超出了平常的自己，到了另一个世界里。</voice>
<voice id="129">我爱热闹，也爱冷静；<break strength="x-weak"/>爱群居，也爱独处。</voice>
<voice id="130">像今晚上，一个人在这苍茫的月下，什么都可以想，什么都可以不想，便觉是个自由的人。</voice>
<voice id="131">白天里一定要做的事，一定要说的话，现在都可不理。</voice>
<voice id="132">这是独处的妙处，我且受用这无边的荷香月色好了。</voice>
</speak>
```
# Communication
Learning and communication,now there is only Chinese [QQ group](https://qm.qq.com/cgi-bin/qm/qr?k=-1GknIe4uXrkmbDKBGKa1aAUteq40qs_&jump_from=webapi&authKey=x5YYt6Dggs1ZqWxvZqvj3fV8VUnxRyXm5S5Kzntc78+Nv3iXOIawplGip9LWuNR/)
# 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