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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语音合成
- [x] VITS语音转换
- [x] HuBert-soft VITS模型
- [x] W2V2 VITS / emotional-vits维度情感模型
- [x] 加载多模型
- [x] 自动识别语言并处理，根据模型的cleaner设置语言类型识别的范围，支持自定义语言类型范围
- [x] 自定义默认参数
- [x] 长文本批处理
- [x] GPU加速推理
- [x] SSML语音合成标记语言（完善中...）
<details><summary>Update Logs</summary><pre><code>
<h2>2023.6.5</h2>
<p>更换音频编码使用的库，增加flac格式，增加中文对读简单数学公式的支持</p>
<h2>2023.5.24</h2>
<p>添加dimensional_emotion api,从文件夹加载多个npy文件,Docker添加了Linux/ARM64和Linux/ARM64/v8平台</p>
<h2>2023.5.15</h2>
<p>增加english_cleaner，需要额外安装espeak才能使用</p>
<h2>2023.5.12</h2>
<p>增加ssml支持，但仍需完善。重构部分功能，hubert_vits中的speaker_id改为id</p>
<h2>2023.5.2</h2>
<p>增加w2v2-vits/emotional-vits模型支持，修改了speakers映射表并添加了对应模型支持的语言</p>
<h2>2023.4.23</h2>
<p>增加api key鉴权，默认禁用，需要在config.py中启用</p>
<h2>2023.4.17</h2>
<p>修改单语言的cleaner需要标注才会clean，增加GPU加速推理，但需要手动安装gpu推理环境</p>
<h2>2023.4.12</h2>
<p>项目由MoeGoe-Simple-API更名为vits-simple-api，支持长文本批处理，增加长文本分段阈值max</p>
<h2>2023.4.7</h2>
<p>增加配置文件可自定义默认参数，本次更新需要手动更新config.py，具体使用方法见config.py</p>
<h2>2023.4.6</h2>
<p>加入自动识别语种选项auto，lang参数默认修改为auto，自动识别仍有一定缺陷，请自行选择</p>
<p>统一POST请求类型为multipart/form-data</p>
</code></pre></details>
## demo
[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/Artrajz/vits-simple-api)
注意不同的id支持的语言可能有所不同。[speakers](https://artrajz-vits-simple-api.hf.space/voice/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=你知道1+1=几吗？我觉得1+1≠3&id=164&lang=zh`
- `https://artrajz-vits-simple-api.hf.space/voice/vits?text=Difficult the first time, easy the second.&id=4`
- 激动：`https://artrajz-vits-simple-api.hf.space/voice/w2v2-vits?text=こんにちは&id=3&emotion=111`
- 小声：`https://artrajz-vits-simple-api.hf.space/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
# 部署
## Docker部署
### 镜像拉取脚本
```
bash -c "$(wget -O- https://raw.githubusercontent.com/Artrajz/vits-simple-api/main/vits-simple-api-installer-latest.sh)"
```
- 目前docker镜像支持的平台`linux/amd64,linux/arm64`
- 在拉取完成后，需要导入VITS模型才能使用，请根据以下步骤导入模型。
### 下载VITS模型
将模型放入`/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>
### 修改模型路径
Modify in `/usr/local/vits-simple-api/config.py`
<details><summary>config.py</summary><pre><code>
# 在此填写模型路径
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 编码器
HUBERT_SOFT_MODEL = ABS_PATH + "/Model/hubert-soft-0d54a1f4.pt"
# w2v2-vits: Dimensional emotion npy file
# 加载单独的npy: ABS_PATH+"/all_emotions.npy
# 加载多个npy: [ABS_PATH + "/emotions1.npy", ABS_PATH + "/emotions2.npy"]
# 从文件夹里加载npy: ABS_PATH + "/Model/npy"
DIMENSIONAL_EMOTION_NPY = ABS_PATH + "/Model/npy"
# w2v2-vits: 需要在同一路径下有model.onnx和model.yaml
DIMENSIONAL_EMOTION_MODEL = ABS_PATH + "/Model/model.yaml"
</code></pre></details>
### 启动
`docker compose up -d`
或者重新执行拉取脚本
### 镜像更新
重新执行docker镜像拉取脚本即可
## 虚拟环境部署
### Clone
`git clone https://github.com/Artrajz/vits-simple-api.git`
### 下载python依赖
推荐使用python的虚拟环境，python版本 >= 3.9
`pip install -r requirements.txt`
windows下可能安装不了fasttext,可以用以下命令安装，附[wheels下载地址](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
```
### 下载VITS模型
将模型放入 `/path/to/vits-simple-api/Model`
<details><summary>文件夹结构</summary><pre><code>
├─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
</code></pre></details>
### 修改模型路径
在 `/path/to/vits-simple-api/config.py` 修改
<details><summary>config.py</summary><pre><code>
# 在此填写模型路径
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 编码器
HUBERT_SOFT_MODEL = ABS_PATH + "/Model/hubert-soft-0d54a1f4.pt"
# w2v2-vits: Dimensional emotion npy file
# 加载单独的npy: ABS_PATH+"/all_emotions.npy
# 加载多个npy: [ABS_PATH + "/emotions1.npy", ABS_PATH + "/emotions2.npy"]
# 从文件夹里加载npy: ABS_PATH + "/Model/npy"
DIMENSIONAL_EMOTION_NPY = ABS_PATH + "/Model/npy"
# w2v2-vits: 需要在同一路径下有model.onnx和model.yaml
DIMENSIONAL_EMOTION_MODEL = ABS_PATH + "/Model/model.yaml"
</code></pre></details>
### 启动
`python app.py`
# GPU 加速
## windows
### 安装CUDA
查看显卡最高支持CUDA的版本
```
nvidia-smi
```
以CUDA11.7为例，[官网](https://developer.nvidia.com/cuda-11-7-0-download-archive?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exe_local)
### 安装GPU版pytorch
CUDA11.7对应的pytorch是用这个命令安装
```
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117
```
对应版本的命令可以在[官网](https://pytorch.org/get-started/locally/)找到
## Linux
安装过程类似，但我没有相应的环境所以没办法测试
# Openjtalk安装问题
如果你是arm64架构的平台，由于pypi官网上没有arm64对应的whl，可能安装会出现一些问题，你可以使用我构建的whl来安装
```
pip install openjtalk==0.3.0.dev2 --index-url https://pypi.artrajz.cn/simple
```
或者是自己手动构建一个whl，可以根据[教程](https://artrajz.cn/index.php/archives/167/)来构建
# API
## GET
#### speakers list
- GET http://127.0.0.1:23456/voice/speakers
返回id对应角色的映射表
#### voice vits
- GET http://127.0.0.1:23456/voice/vits?text=text
其他参数不指定时均为默认值
- GET http://127.0.0.1:23456/voice/vits?text=[ZH]text[ZH][JA]text[JA]&lang=mix
lang=mix时文本要标注
- GET http://127.0.0.1:23456/voice/vits?text=text&id=142&format=wav&lang=zh&length=1.4
文本为text，角色id为142，音频格式为wav，文本语言为zh，语音长度为1.4，其余参数默认
#### 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
在config.py中设置`API_KEY_ENABLED = True`以启用，api key填写：`API_KEY = "api-key"`。
启用后，GET请求中使用需要增加参数api_key，POST请求中使用需要在header中添加参数`X-API-KEY`。
# Parameter
## VITS语音合成
| Name | Parameter | Is must | Default | Type | Instruction |
| ------------- | --------- | ------- | ------- | ----- | ------------------------------------------------------------ |
| 合成文本 | text | true | | str | |
| 角色id | id | false | 0 | int | |
| 音频格式 | format | false | wav | str | 支持wav,ogg,silk,mp3,flac |
| 文本语言 | lang | false | auto | str | auto为自动识别语言模式，也是默认模式。lang=mix时，文本应该用[ZH] 或 [JA] 包裹。方言无法自动识别。 |
| 语音长度/语速 | length | false | 1.0 | float | 调节语音长度，相当于调节语速，该数值越大语速越慢 |
| 噪声 | noise | false | 0.667 | float | |
| 噪声偏差 | noisew | false | 0.8 | float | |
| 分段阈值 | max | false | 50 | int | 按标点符号分段，加起来大于max时为一段文本。max<=0表示不分段。 |
## VITS 语音转换
| Name | Parameter | Is must | Default | Type | Instruction |
| ---------- | ----------- | ------- | ------- | ---- | ---------------------- |
| 上传音频 | upload | true | | file | wav or ogg |
| 源角色id | original_id | true | | int | 上传文件所使用的角色id |
| 目标角色id | target_id | true | | int | 要转换的目标角色id |
## HuBert-VITS 语音转换
| Name | Parameter | Is must | Default | Type | Instruction |
| ------------- | --------- | ------- | ------- | ----- | ------------------------------------------------ |
| 上传音频 | upload | true | | file | |
| 目标角色id | id | true | | int | |
| 音频格式 | format | true | | str | wav,ogg,silk |
| 语音长度/语速 | length | true | | float | 调节语音长度，相当于调节语速，该数值越大语速越慢 |
| 噪声 | noise | true | | float | |
| 噪声偏差 | noisew | true | | float | |
## Dimensional emotion
| Name | Parameter | Is must | Default | Type | Instruction |
| -------- | --------- | ------- | ------- | ---- | ----------------------------- |
| 上传音频 | upload | true | | file | 返回存储维度情感向量的npy文件 |
## W2V2-VITS
| Name | Parameter | Is must | Default | Type | Instruction |
| ------------- | --------- | ------- | ------- | ----- | ------------------------------------------------------------ |
| 合成文本 | text | true | | str | |
| 角色id | id | false | 0 | int | |
| 音频格式 | format | false | wav | str | 支持wav,ogg,silk,mp3,flac |
| 文本语言 | lang | false | auto | str | auto为自动识别语言模式，也是默认模式。lang=mix时，文本应该用[ZH] 或 [JA] 包裹。方言无法自动识别。 |
| 语音长度/语速 | length | false | 1.0 | float | 调节语音长度，相当于调节语速，该数值越大语速越慢 |
| 噪声 | noise | false | 0.667 | float | |
| 噪声偏差 | noisew | false | 0.8 | float | |
| 分段阈值 | max | false | 50 | int | 按标点符号分段，加起来大于max时为一段文本。max<=0表示不分段。 |
| 维度情感 | emotion | false | 0 | int | 范围取决于npy情感参考文件，如[innnky](https://huggingface.co/spaces/innnky/nene-emotion/tree/main)的all_emotions.npy模型范围是0-5457 |
## SSML语音合成标记语言
目前支持的元素与属性
`speak`元素
| Attribute | Description | Is must |
| --------- | ------------------------------------------------------------ | ------- |
| id | 默认值从`config.py`中读取 | false |
| lang | 默认值从`config.py`中读取 | false |
| length | 默认值从`config.py`中读取 | false |
| noise | 默认值从`config.py`中读取 | false |
| noisew | 默认值从`config.py`中读取 | false |
| max | 按标点符号分段，加起来大于max时为一段文本。max<=0表示不分段，这里默认为0。 | false |
| model | 默认为vits，可选`w2v2-vits`，`emotion-vits` | false |
| emotion | 只有用`w2v2-vits`或`emotion-vits`时`emotion`才生效，范围取决于npy情感参考文件 | false |
`voice`元素
优先级大于`speak`
| Attribute | Description | Is must |
| --------- | ------------------------------------------------------------ | ------- |
| id | 默认值从`config.py`中读取 | false |
| lang | 默认值从`config.py`中读取 | false |
| length | 默认值从`config.py`中读取 | false |
| noise | 默认值从`config.py`中读取 | false |
| noisew | 默认值从`config.py`中读取 | false |
| max | 按标点符号分段，加起来大于max时为一段文本。max<=0表示不分段，这里默认为0。 | false |
| model | 默认为vits，可选`w2v2-vits`，`emotion-vits` | false |
| emotion | 只有用`w2v2-vits`或`emotion-vits`时`emotion`才会生效 | false |
`break`元素
| Attribute | Description | Is must |
| --------- | ------------------------------------------------------------ | ------- |
| strength | x-weak,weak,medium（默认值）,strong,x-strong | false |
| time | 暂停的绝对持续时间，以秒为单位（例如 `2s`）或以毫秒为单位（例如 `500ms`）。 有效值的范围为 0 到 5000 毫秒。 如果设置的值大于支持的最大值，则服务将使用 `5000ms`。 如果设置了 `time` 属性，则会忽略 `strength` 属性。 | false |
| Strength | Relative Duration |
| :------- | :---------------- |
| x-weak | 250 毫秒 |
| weak | 500 毫秒 |
| Medium | 750 毫秒 |
| Strong | 1000 毫秒 |
| x-strong | 1250 毫秒 |
示例
```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>
```
# 交流平台
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# 鸣谢
- 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