vits-simple-api

Simply call the vits api

# 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...）

Update Logs


2023.6.5
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
2023.5.24
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
2023.5.15
增加english_cleaner，需要额外安装espeak才能使用
2023.5.12
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
2023.5.2
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
2023.4.23
增加api key鉴权，默认禁用，需要在config.py中启用
2023.4.17
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
2023.4.12
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
2023.4.7
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
2023.4.6
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
统一POST请求类型为multipart/form-data

## 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`

Folder structure


│  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

### 修改模型路径 Modify in `/usr/local/vits-simple-api/config.py`

config.py


# 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"

### 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`

文件夹结构


├─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

### 修改模型路径在 `/path/to/vits-simple-api/config.py` 修改

config.py


# 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"

### 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 这几天心里颇不宁静。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。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；妻在屋里拍着闰儿，迷迷糊糊地哼着眠歌。我悄悄地披了大衫，带上门出去。沿着荷塘，是一条曲折的小煤屑路。这是一条幽僻的路；白天也少人走，夜晚更加寂寞。荷塘四面，长着许多树，蓊蓊郁郁的。路的一旁，是些杨柳，和一些不知道名字的树。没有月光的晚上，这路上阴森森的，有些怕人。今晚却很好，虽然月光也还是淡淡的。路上只我一个人，背着手踱着。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。我爱热闹，也爱冷静；爱群居，也爱独处。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。白天里一定要做的事，一定要说的话，现在都可不理。这是独处的妙处，我且受用这无边的荷香月色好了。 ``` # 交流平台现在只有 [Q群](https://qm.qq.com/cgi-bin/qm/qr?k=-1GknIe4uXrkmbDKBGKa1aAUteq40qs_&jump_from=webapi&authKey=x5YYt6Dggs1ZqWxvZqvj3fV8VUnxRyXm5S5Kzntc78+Nv3iXOIawplGip9LWuNR/) # 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