init

.gitignore

@@ -0,0 +1,5 @@
+**/__pycache__
+/Model/
+/logs/
+/cache/
+/upload/

Dockerfile

@@ -0,0 +1,38 @@
+FROM artrajz/pytorch:2.2.1-cpu-py3.10.11-ubuntu22.04
+
+RUN mkdir -p /app
+WORKDIR /app
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+
+RUN apt-get update && \
+    apt-get install -yq build-essential espeak-ng cmake wget ca-certificates tzdata&& \
+    update-ca-certificates && \
+    apt-get clean && \
+    apt-get purge -y --auto-remove -o APT::AutoRemove::RecommendsImportant=false && \
+    rm -rf /var/lib/apt/lists/* 
+
+# Install jemalloc
+RUN wget https://github.com/jemalloc/jemalloc/releases/download/5.3.0/jemalloc-5.3.0.tar.bz2 && \
+    tar -xvf jemalloc-5.3.0.tar.bz2 && \
+    cd jemalloc-5.3.0 && \
+    ./configure && \
+    make -j$(nproc) && \
+    make install && \
+    cd .. && \
+    rm -rf jemalloc-5.3.0* && \
+    ldconfig
+
+ENV LD_PRELOAD=/usr/local/lib/libjemalloc.so
+
+COPY requirements.txt /app/
+RUN pip install gunicorn --no-cache-dir && \
+    pip install -r requirements.txt --no-cache-dir&& \
+    rm -rf /root/.cache/pip/*
+
+COPY . /app
+
+EXPOSE 23456
+
+CMD ["gunicorn", "-c", "gunicorn_config.py", "app:app"]

Dockerfile_GPU

@@ -0,0 +1,38 @@
+FROM artrajz/pytorch:2.2.1-cu118-py3.10.11-ubuntu22.04
+
+RUN mkdir -p /app
+WORKDIR /app
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt-get update && \
+    apt-get install -yq build-essential espeak-ng cmake wget ca-certificates tzdata&& \
+    update-ca-certificates && \
+    apt-get clean && \
+    apt-get purge -y --auto-remove -o APT::AutoRemove::RecommendsImportant=false && \
+    rm -rf /var/lib/apt/lists/* 
+
+
+# Install jemalloc
+RUN wget https://github.com/jemalloc/jemalloc/releases/download/5.3.0/jemalloc-5.3.0.tar.bz2 && \
+    tar -xvf jemalloc-5.3.0.tar.bz2 && \
+    cd jemalloc-5.3.0 && \
+    ./configure && \
+    make -j$(nproc) && \
+    make install && \
+    cd .. && \
+    rm -rf jemalloc-5.3.0* && \
+    ldconfig
+
+ENV LD_PRELOAD=/usr/local/lib/libjemalloc.so
+
+COPY requirements.txt /app/
+RUN pip install gunicorn --no-cache-dir && \
+    pip install -r requirements.txt --no-cache-dir&& \
+    rm -rf /root/.cache/pip/*
+
+COPY . /app
+
+EXPOSE 23456
+
+CMD ["gunicorn", "-c", "gunicorn_config.py", "app:app"]

LICENSE

@@ -0,0 +1,661 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
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README.md

@@ -1,12 +1,9 @@
 ---
-title: Vits Simple Api Bv2
-emoji: 🏃
-colorFrom: yellow
-colorTo: gray
+license: mit
+title: vits-simple-api-gsv
 sdk: gradio
-sdk_version: 4.21.0
+pinned: true
+python_version: 3.10.11
+emoji: 👀
 app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---

README_zh.md

@@ -0,0 +1,584 @@
+<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-3.10-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，语音转换
+- [x] HuBert-soft VITS模型
+- [x] W2V2 VITS / [emotional-vits](https://github.com/innnky/emotional-vits)维度情感模型
+- [x] [vits_chinese](https://github.com/PlayVoice/vits_chinese)
+- [x] [Bert-VITS2](https://github.com/Stardust-minus/Bert-VITS2)
+- [x] [GPT-SoVITS](https://github.com/RVC-Boss/GPT-SoVITS)
+- [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...）
+
+
+## 在线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) 感谢hugging face喵
+
+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=我觉得1%2B1≠3&id=164&lang=zh`（get中一些字符需要转义不然会被过滤掉）
+- `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
+
+有两种部署方式可供选择。不论你选择哪一种，完成部署后都需要导入模型才能使用。
+
+## Docker部署（Linux推荐）
+
+### 步骤1: 镜像拉取
+
+运行以下命令以拉取 Docker 镜像，根据脚本中的提示选择需要下载的必要文件和拉取镜像：
+
+```bash
+bash -c "$(wget -O- https://raw.githubusercontent.com/Artrajz/vits-simple-api/main/vits-simple-api-installer-latest.sh)"
+```
+
+项目配置文件以及模型文件夹的默认路径为`/usr/local/vits-simple-api/`
+
+### 步骤2: 启动
+
+运行以下命令启动容器：
+
+```bash
+docker-compose up -d
+```
+
+### 镜像更新
+
+运行以下命令更新镜像：
+
+```bash
+docker-compose pull
+```
+
+重新启动容器：
+
+```bash
+docker-compose up -d
+```
+
+## 虚拟环境部署
+
+### 步骤1: 克隆项目
+
+使用以下命令克隆项目仓库：
+
+```bash
+git clone https://github.com/Artrajz/vits-simple-api.git
+```
+
+### 步骤2: 下载 Python 依赖
+
+推荐使用 Python  3.10版本的虚拟环境。运行以下命令安装项目所需的 Python 依赖：
+
+如果遇到某些无法安装的依赖，请看下面的常见问题。
+
+```bash
+pip install -r requirements.txt
+```
+
+### 步骤3: 启动
+
+运行以下命令启动程序：
+
+```bash
+python app.py
+```
+
+## Windows快速部署包
+
+### 步骤1：下载并解压部署包
+
+进入[releases页面](https://github.com/Artrajz/vits-simple-api/releases)下载并解压最新的部署包
+
+### 步骤2：启动
+
+运行start.bat启动程序
+
+## 模型加载
+
+### 步骤1: 下载 VITS 模型
+
+将 VITS 模型文件下载并放入 `data/models`文件夹。
+
+### 步骤2: 加载模型
+
+#### 自动加载模型
+
+v0.6.6版本之后默认会**自动加载**`data/models`文件夹下的所有模型，方便新手使用。
+
+#### 手动加载模型
+
+首次启动之后会生成一个config.yaml配置文件，需要将`tts_config.auto_load`改为`false`以启用手动加载模式。
+
+可以修改配置文件中的`tts_config.models`或者在浏览器中进入管理员后台进行修改。
+
+**注意：v0.6.6版本之后已修改模型读取路径，请重新按照以下步骤配置模型路径！**
+
+路径可填绝对路径或相对路径，相对路径则是从项目根目录中的`data/models`文件夹开始。
+
+比如`data/models`文件夹中有如下文件
+
+```
+├─model1
+│  │─G_1000.pth
+│  └─config.json
+└─model2
+   │─G_1000.pth
+   └─config.json
+```
+
+填写
+
+```yaml
+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则为
+  - 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
+
+```
+
+在管理员后台加载模型比较方便，但如果想加载`data/models`文件夹之外的模型，则只能通过修改config.yaml配置文件来加载，方法是直接填写绝对路径。
+
+绝对路径填写：
+
+```yaml
+tts_config:
+  auto_load: false
+  models:
+  - config_path: D://model3/config.json
+    model_path: D://model3/G_1000.pth
+```
+
+- models_path:是相对于data目录下的模型文件夹，默认为models，auto_load为true时将会加载models_path目录下的所有模型。
+
+#### 其他模型
+
+bert模型以及情感模型下载之后放在`data/bert`文件夹和`data/emotional`文件夹中，找到对应名字放入即可。
+
+# 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，推荐使用1.13.1+cu117，其他版本可能存在内存不稳定的问题。
+
+```
+pip install torch==1.13.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117
+```
+
+## Linux
+
+安装过程类似，可以查阅网上的安装资料。也可以直接使用docker部署脚本中的gpu版本。
+
+# WebUI
+
+## 推理前端 
+
+http://127.0.0.1:23456
+
+*在默认端口为23456的情况下，端口可修改
+
+## 管理员后台
+
+默认为http://127.0.0.1:23456/admin
+
+**初始账号密码在初次启动后，在config.yaml中搜索admin可找到。**
+
+# 功能选项说明
+
+## 关闭管理员后台
+
+由于管理员后台可以对模型进行加载和卸载操作，虽然有登录验证的保障，为了绝对安全，当对公网开放时，可以在`config.yaml`中关闭管理员后台：
+
+```yaml
+'IS_ADMIN_ENABLED': !!bool 'false'
+```
+
+## Bert-VITS2配置使用语言/Bert模型
+
+在Bert-VITS2 v2.0以后，一个模型需要加载三个不同语言的Bert模型。如果只需要使用其中一或两种语言，可以在模型的config.json的data中，添加`lang`参数，值为`['zh']`，表示该模型只使用中文，同时也只会加载中文的Bert模型。值为`['zh','ja']`表示只使用中日双语，同时也只会加载中文和日文的Bert模型。以此类推。
+
+示例：
+
+```json
+"data": {
+  "lang": ["zh","ja"],
+  "training_files": "filelists/train.list",
+  "validation_files": "filelists/val.list",
+  "max_wav_value": 32768.0,
+  ...
+```
+
+## 自定义中文多音字词典
+
+如果遇到多音字发音不正确，可以尝试用这种办法解决。
+
+在data目录创建并打开phrases_dict.txt添加多音字词。
+
+```python
+{
+"一骑当千": [["yí"], ["jì"], ["dāng"], ["qiān"]],
+}
+```
+
+## GPT-SoVITS参考音频预设
+
+在config.yaml中找到gpt_sovits的配置，在presets下添加预设，预设可添加多个，其中key作为预设名称，如下有两个默认的预设default和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
+```
+
+## 阅读API
+
+在[开源阅读](https://gedoor.github.io/)中测试
+
+可使用多种模型朗读，包括VITS，Bert-VITS2，GPT-SoVITS，`in`开头的参数配置的是对话即引号中的文本的说话人，`nr`开头的参数配置的是旁白。
+
+使用GPT-SoVITS需要提前在`config.yaml`配置好`presets`里的参考音频，并修改下方url中的preset
+
+url中的IP可在API启动后找到，一般使用192.168开头的局域网IP。
+
+修改好后，选择朗读引擎-添加朗读引擎-粘贴源，并启用该朗读引擎。
+
+```js
+{
+  "concurrentRate": "1",
+  "contentType": "audio/wav",
+  "enabledCookieJar": false,
+  "header": "",
+  "id": 1709643305070,
+  "lastUpdateTime": 1709821070082,
+  "loginCheckJs": "",
+  "loginUi": "",
+  "loginUrl": "",
+  "name": "vits-simple-api",
+  "url": "http://192.168.xxx.xxx:23456/voice/reading?text={{java.encodeURI(speakText)}}&in_model_type=GPT-SOVITS&in_id=0&in_preset=default&nr_model_type=BERT-VITS2&nr_id=0&nr_preset=default&format=wav&lang=zh"
+}
+```
+
+
+
+# 常见问题
+
+## fasttext依赖安装问题
+
+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
+```
+
+或者
+
+```
+pip install fasttext -i https://pypi.artrajz.cn/simple
+```
+
+## pyopenjtalk依赖安装问题
+
+由于pypi.org没有pyopenjtalk的whl文件，通常需要从源代��来安装，这一过程对于一些人来说可能比较麻烦，所以你也可以使用我构建的whl来安装。
+
+```
+pip install pyopenjtalk -i https://pypi.artrajz.cn/simple
+```
+
+## Bert-VITS2版本兼容
+
+修改Bert-VITS2模型的config.json，加入版本号参数`"version": "x.x.x"`，比如模型版本为1.0.1时，配置文件应该写成：
+
+```
+{
+  "version": "1.0.1",
+  "train": {
+    "log_interval": 10,
+    "eval_interval": 100,
+    "seed": 52,
+    ...
+```
+
+需要注意的是，中文特化版的版本号应改为`extra`或`zh-clap`，特化修复版的版本号为`2.4`或`extra-fix`。
+
+# 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
+
+- 见`api_test.py`
+
+
+
+## API KEY
+
+在config.yaml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   | 需要合成语音的文本。                                         |
+| 角色id        | id           | false   | 从`config.yaml`中获取 | int   | 即说话人id。                                                 |
+| 音频格式      | format       | false   | 从`config.yaml`中获取 | str   | 支持wav,ogg,silk,mp3,flac                                    |
+| 文本语言      | lang         | false   | 从`config.yaml`中获取 | 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   | 从`config.yaml`中获取 | 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   | 从`config.yaml`中获取 | float | 样本噪声，控制合成的随机性。                                 |
+| sdp噪声       | noisew       | false   | 从`config.yaml`中获取 | float | 随机时长预测器噪声，控制音素发音长度。                       |
+| 分段阈值      | segment_size | false   | 从`config.yaml`中获取 | int   | 按标点符号分段，加起来大于segment_size时为一段文本。segment_size<=0表示不分段。 |
+| 流式响应      | streaming    | false   | false                | bool  | 流式合成语音，更快的首包响应。                               |
+
+## 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  | 需要转换说话人的音频文件。                       |
+| 目标角色id    | id        | true    |         | int   | 目标说话人id。                                   |
+| 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 | 样本噪声，控制合成的随机性。                     |
+| sdp噪声       | noisew    | true    |         | float | 随机时长预测器噪声，控制音素发音长度。           |
+
+## W2V2-VITS
+
+| Name          | Parameter    | Is must | Default              | Type  | Instruction                                                  |
+| ------------- | ------------ | ------- | -------------------- | ----- | ------------------------------------------------------------ |
+| 合成文本      | text         | true    |                      | str   | 需要合成语音的文本。                                         |
+| 角色id        | id           | false   | 从`config.yaml`中获取 | int   | 即说话人id。                                                 |
+| 音频格式      | format       | false   | 从`config.yaml`中获取 | str   | 支持wav,ogg,silk,mp3,flac                                    |
+| 文本语言      | lang         | false   | 从`config.yaml`中获取 | 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   | 从`config.yaml`中获取 | 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   | 从`config.yaml`中获取 | float | 样本噪声，控制合成的随机性。                                 |
+| sdp噪声       | noisew       | false   | 从`config.yaml`中获取 | float | 随机时长预测器噪声，控制音素发音长度。                       |
+| 分段阈值      | segment_size | false   | 从`config.yaml`中获取 | int   | 按标点符号分段，加起来大于segment_size时为一段文本。segment_size<=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 |
+
+## 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 |
+
+## Bert-VITS2语音合成
+
+| Name           | Parameter       | Is must | Default              | Type  | Instruction                                                  |
+| -------------- | --------------- | ------- | -------------------- | ----- | ------------------------------------------------------------ |
+| 合成文本       | text            | true    |                      | str   | 需要合成语音的文本。                                         |
+| 角色id         | id              | false   | 从`config.yaml`中获取 | int   | 即说话人id。                                                 |
+| 音频格式       | format          | false   | 从`config.yaml`中获取 | str   | 支持wav,ogg,silk,mp3,flac                                    |
+| 文本语言       | lang            | false   | 从`config.yaml`中获取 | str   | auto为自动识别语言模式，也是默认模式，但目前只支持识别整段文本的语言，无法细分到每个句子。其余可选语言zh和ja。 |
+| 语音长度/语速  | length          | false   | 从`config.yaml`中获取 | 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   | 从`config.yaml`中获取 | float | 样本噪声，控制合成的随机性。                                 |
+| sdp噪声        | noisew          | false   | 从`config.yaml`中获取 | float | 随机时长预测器噪声，控制音素发音长度。                       |
+| 分段阈值       | segment_size    | false   | 从`config.yaml`中获取 | int   | 按标点符号分段，加起来大于segment_size时为一段文本。segment_size<=0表示不分段。 |
+| SDP/DP混合比   | sdp_ratio       | false   | 从`config.yaml`中获取 | int   | SDP在合成时的占比，理论上此比率越高，合成的语音语调方差越大。 |
+| 情感控制       | emotion         | false   | 从`config.yaml`中获取 | int   | Bert-VITS2 v2.1可用，范围为0-9                               |
+| 情感参考音频   | reference_audio | false   | None                 |       | Bert-VITS2 v2.1 使用参考音频来控制合成音频的情感             |
+| 文本提示词     | text_prompt     | false   | 从`config.yaml`中获取 | str   | Bert-VITS2 v2.2 文本提示词，用于控制情感                     |
+| 文本提示词     | style_text      | false   | 从`config.yaml`中获取 | str   | Bert-VITS2 v2.3 文本提示词，用于控制情感                     |
+| 文本提示词权重 | style_weight    | false   | 从`config.yaml`中获取 | float | Bert-VITS2 v2.3 文本提示词，用于提示词权重                   |
+| 流式响应      | streaming    | false   | false                | bool  | 流式合成语音，更快的首包响应。                               |
+
+## GPT-SoVITS语音合成
+
+| Name         | Parameter       | Is must | Default               | Type  | Instruction                                                  |
+| ------------ | --------------- | ------- | --------------------- | ----- | ------------------------------------------------------------ |
+| 合成文本     | text            | true    |                       | str   | 需要合成语音的文本。                                         |
+| 角色id       | id              | false   | 从`config.yaml`中获取 | int   | 即说话人id。在GPT-SoVITS中，每一个模型作为一个角色id，音色通过参考音频预设来切换。 |
+| 音频格式     | format          | false   | 从`config.yaml`中获取 | str   | 支持wav,ogg,silk,mp3,flac                                    |
+| 文本语言     | lang            | false   | 从`config.yaml`中获取 | str   | auto为自动识别语言模式，也是默认模式，但目前只支持识别整段文本的语言，无法细分到每个句子。 |
+| 参考音频     | reference_audio | false   | None                  |       | 参考音频是必须的，但是可以被预设代替                         |
+| 参考音频文本 | prompt_text     | false   | 从`config.yaml`中获取 | float | 需要和参考音频实际文本保持一致。                             |
+| 参考音频语言 | prompt_lang     | false   | 从`config.yaml`中获取 | str   | 默认为auto，自动识别文本语言。如果识别失败则手动填写，中文就是zh，日文是ja，英文是en。 |
+| 参考音频预设 | preset          | false   | default               | str   | 通过提前设置好的预设代替参考音频，可设置多个预设。           |
+
+
+## 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.yaml`中读取                                   | false   |
+| lang         | 默认值从`config.yaml`中读取                                   | false   |
+| length       | 默认值从`config.yaml`中读取                                   | false   |
+| noise        | 默认值从`config.yaml`中读取                                   | false   |
+| noisew       | 默认值从`config.yaml`中读取                                   | false   |
+| segment_size | 按标点符号分段，加起来大于segment_size时为一段文本。segment_size<=0表示不分段，这里默认为0。 | false   |
+| model_type   | 默认为VITS，可选W2V2-VITS，BERT-VITS2                        | false   |
+| emotion      | 只有用W2V2-VITS时`emotion`才会生效，范围取决于npy情感参考文件 | false   |
+| sdp_ratio    | 只有用BERT-VITS2时`sdp_ratio`才会生效                        | false   |
+
+`voice`元素
+
+优先级大于`speak`
+
+| Attribute    | Description                                                  | Is must |
+| ------------ | ------------------------------------------------------------ | ------- |
+| id           | 默认值从`config.yaml`中读取                                   | false   |
+| lang         | 默认值从`config.yaml`中读取                                   | false   |
+| length       | 默认值从`config.yaml`中读取                                   | false   |
+| noise        | 默认值从`config.yaml`中读取                                   | false   |
+| noisew       | 默认值从`config.yaml`中读取                                   | false   |
+| segment_size | 按标点符号分段，加起来大于segment_size时为一段文本。segment_size<=0表示不分段，这里默认为0。 | false   |
+| model_type   | 默认为VITS，可选W2V2-VITS，BERT-VITS2                        | false   |
+| emotion      | 只有用W2V2-VITS时`emotion`才会生效，范围取决于npy情感参考文件 | false   |
+| sdp_ratio    | 只有用BERT-VITS2时`sdp_ratio`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         |
+
+## 阅读
+
+| Name                 | Parameter     | Is must | Default               | Type | Instruction                                                  |
+| -------------------- | ------------- | ------- | --------------------- | ---- | ------------------------------------------------------------ |
+| 合成文本             | text          | true    |                       | str  | 需要合成语音的文本。                                         |
+| 对话角色模型类型     | in_model_type | false   | 从`config.yaml`中获取 | str  |                                                              |
+| 对话角色id           | in_id         | false   | 从`config.yaml`中获取 | int  |                                                              |
+| 对话角色参考音频预设 | in_preset     | false   | default               | str  | 通过提前设置好的预设代替参考音频，可设置多个预设。           |
+| 旁白角色模型类型     | nr_model_type | false   | 从`config.yaml`中获取 | str  |                                                              |
+| 旁白角色id           | nr_id         | false   | 从`config.yaml`中获取 | int  |                                                              |
+| 旁白角色参考音频预设 | nr_preset     | false   | default               | str  | 通过提前设置好的预设代替参考音频，可设置多个预设。           |
+| 音频格式             | format        | false   | 从`config.yaml`中获取 | str  | 支持wav,ogg,silk,mp3,flac                                    |
+| 文本语言             | lang          | false   | 从`config.yaml`中获取 | str  | auto为自动识别语言模式，也是默认模式，但目前只支持识别整段文本的语言，无法细分到每个句子。 |
+
+模型的其他参数将使用config.yaml文件中对应模型的默认参数。
+
+
+
+## 示例
+
+见`api_test.py`
+
+# 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
+- 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
+
+# 感谢所有的贡献者
+
+<a href="https://github.com/artrajz/vits-simple-api/graphs/contributors" target="_blank">
+  <img src="https://contrib.rocks/image?repo=artrajz/vits-simple-api"/></a>

api_test.py

@@ -0,0 +1,575 @@
+import json
+import re
+import requests
+import os
+import time
+import random
+import string
+from requests_toolbelt.multipart.encoder import MultipartEncoder
+
+absolute_path = os.path.dirname(__file__)
+base_url = "http://127.0.0.1:23456"
+
+
+# 映射表
+def voice_speakers():
+    url = f"{base_url}/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, segment_size=50,
+               save_audio=True,
+               save_path=None):
+    fields = {
+        "text": text,
+        "id": str(id),
+        "format": format,
+        "lang": lang,
+        "length": str(length),
+        "noise": str(noise),
+        "noisew": str(noisew),
+        "segment_size": str(segment_size)
+    }
+    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_url}/voice/vits"
+
+    res = requests.post(url=url, data=m, headers=headers)
+    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
+    if save_path is not None:
+        path = os.path.join(save_path, fname)
+    else:
+        path = os.path.join(absolute_path, fname)
+    if save_audio:
+        with open(path, "wb") as f:
+            f.write(res.content)
+        print(path)
+        return path
+    return None
+
+
+def voice_vits_streaming(text, id=0, format="wav", lang="auto", length=1, noise=0.667, noisew=0.8, segment_size=50,
+                         save_audio=True, save_path=None):
+    fields = {
+        "text": text,
+        "id": str(id),
+        "format": format,
+        "lang": lang,
+        "length": str(length),
+        "noise": str(noise),
+        "noisew": str(noisew),
+        "segment_size": str(segment_size),
+        "streaming": 'True'
+    }
+    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_url}/voice"
+
+    res = requests.post(url=url, data=m, headers=headers)
+    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
+    if save_path is not None:
+        path = os.path.join(save_path, fname)
+    else:
+        path = os.path.join(absolute_path, fname)
+    if save_audio:
+        with open(path, "wb") as f:
+            f.write(res.content)
+        print(path)
+        return path
+    return None
+
+
+def voice_vits_streaming(text, id=0, format="wav", lang="auto", length=1, noise=0.667, noisew=0.8, segment_size=50,
+                         save_path=None):
+    fields = {
+        "text": text,
+        "id": str(id),
+        "format": format,
+        "lang": lang,
+        "length": str(length),
+        "noise": str(noise),
+        "noisew": str(noisew),
+        "segment_size": str(segment_size),
+        "streaming": 'True'
+    }
+    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_url}/voice"
+
+    res = requests.post(url=url, data=m, headers=headers, stream=True)
+    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
+    if save_path is not None:
+        path = os.path.join(save_path, fname)
+    else:
+        path = os.path.join(absolute_path, fname)
+    audio = res.content
+
+    def get_file_size_from_bytes(byte_data):
+        file_size_offset = 4
+        file_size_length = 4
+
+        try:
+            file_size_bytes = byte_data[file_size_offset:file_size_offset + file_size_length]
+            file_size = int.from_bytes(file_size_bytes, byteorder='little')
+            return file_size + 8
+        except IndexError:
+            return None
+
+    audio = None
+    p = 0
+    audio_size = None
+    audios = []
+
+    for chunk in res.iter_content(chunk_size=1024):
+        if audio is None:
+            audio = chunk
+        else:
+            audio += chunk
+
+        p += len(chunk)
+        if audio_size is not None:
+            if p >= audio_size:
+                p = p - audio_size
+                audios.append(audio[:audio_size])
+                audio = audio[audio_size:]
+                audio_size = get_file_size_from_bytes(audio)
+        else:
+            audio_size = get_file_size_from_bytes(audio)
+    for i, audio in enumerate(audios):
+        with open(f"{path[:-4]}-{i}.wav", "wb") as f:
+            f.write(audio)
+
+        print(f"{path[:-4]}-{i}.wav")
+    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, save_audio=True,
+                      save_path=None):
+    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_url}/voice/hubert-vits"
+
+        res = requests.post(url=url, data=m, headers=headers)
+    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
+    if save_path is not None:
+        path = os.path.join(save_path, fname)
+    else:
+        path = os.path.join(absolute_path, fname)
+    if save_audio:
+        with open(path, "wb") as f:
+            f.write(res.content)
+        print(path)
+        return path
+    return None
+
+
+# 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, segment_size=50,
+                    emotion=0,
+                    save_audio=True, save_path=None):
+    fields = {
+        "text": text,
+        "id": str(id),
+        "format": format,
+        "lang": lang,
+        "length": str(length),
+        "noise": str(noise),
+        "noisew": str(noisew),
+        "segment_size": str(segment_size),
+        "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_url}/voice/w2v2-vits"
+
+    res = requests.post(url=url, data=m, headers=headers)
+    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
+    if save_path is not None:
+        path = os.path.join(save_path, fname)
+    else:
+        path = os.path.join(absolute_path, fname)
+    if save_audio:
+        with open(path, "wb") as f:
+            f.write(res.content)
+        print(path)
+        return path
+    return None
+
+
+# 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, save_audio=True, save_path=None):
+    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_url}/voice/conversion"
+
+        res = requests.post(url=url, data=m, headers=headers)
+
+    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
+    if save_path is not None:
+        path = os.path.join(save_path, fname)
+    else:
+        path = os.path.join(absolute_path, fname)
+
+    if save_audio:
+        with open(path, "wb") as f:
+            f.write(res.content)
+        print(path)
+        return path
+    return None
+
+
+def voice_ssml(ssml, save_audio=True, save_path=None):
+    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_url}/voice/ssml"
+
+    res = requests.post(url=url, data=m, headers=headers)
+    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
+    if save_path is not None:
+        path = os.path.join(save_path, fname)
+    else:
+        path = os.path.join(absolute_path, fname)
+
+    if save_audio:
+        with open(path, "wb") as f:
+            f.write(res.content)
+        print(path)
+        return path
+    return None
+
+
+def voice_dimensional_emotion(upload_path, save_audio=True,
+                              save_path=None):
+    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_url}/voice/dimension-emotion"
+
+        res = requests.post(url=url, data=m, headers=headers)
+    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
+    if save_path is not None:
+        path = os.path.join(save_path, fname)
+    else:
+        path = os.path.join(absolute_path, fname)
+    if save_audio:
+        with open(path, "wb") as f:
+            f.write(res.content)
+        print(path)
+        return path
+    return None
+
+
+def vits_json(text, id=0, format="wav", lang="auto", length=1, noise=0.667, noisew=0.8, segment_size=50,
+              save_audio=True, save_path=None):
+    fields = {
+        "text": text,
+        "id": str(id),
+        "format": format,
+        "lang": lang,
+        "length": str(length),
+        "noise": str(noise),
+        "noisew": str(noisew),
+        "segment_size": str(segment_size)
+    }
+    f = json.dumps(fields)
+    url = f"{base_url}/voice"
+    header = {"Content-Type": 'application/json'}
+    res = requests.post(url=url, data=f, headers=header)
+
+    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
+    if save_path is not None:
+        path = os.path.join(save_path, fname)
+    else:
+        path = os.path.join(absolute_path, fname)
+
+    with open(path, "wb") as f:
+        f.write(res.content)
+
+    if save_audio:
+        with open(path, "wb") as f:
+            f.write(res.content)
+        print(path)
+        return path
+    return None
+
+
+# Bert_vits2
+def voice_bert_vits2(text, id=0, format="wav", lang="auto", length=1, noise=0.667, noisew=0.8, segment_size=50,
+                     sdp_ratio=0.2, save_audio=True, save_path=None):
+    fields = {
+        "text": text,
+        "id": str(id),
+        "format": format,
+        "lang": lang,
+        "length": str(length),
+        "noise": str(noise),
+        "noisew": str(noisew),
+        "segment_size": str(segment_size),
+        "sdp_ratio": str(sdp_ratio)
+    }
+    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_url}/voice/bert-vits2"
+
+    res = requests.post(url=url, data=m, headers=headers)
+    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
+    if save_path is not None:
+        path = os.path.join(save_path, fname)
+    else:
+        path = os.path.join(absolute_path, fname)
+    if save_audio:
+        with open(path, "wb") as f:
+            f.write(res.content)
+        print(path)
+        return path
+    return None
+
+
+# gpt_sovits
+def voice_gpt_sovits(text, id=0, format="wav", lang="auto", preset=None, prompt_text=None, prompt_lang="auto",
+                     segment_size=50, reference_audio=None, save_audio=True, save_path=None):
+    upload_name, upload_type, upload_file = None, None, None
+    if reference_audio is not None:
+        upload_name = os.path.basename(reference_audio)
+        upload_type = f'audio/{upload_name.split(".")[1]}'
+        with open(reference_audio, 'rb') as f:
+            upload_file = f.read()
+
+    fields = {
+        "text": text,
+        "id": str(id),
+        "format": format,
+        "lang": lang,
+        "segment_size": str(segment_size),
+        "preset": preset,
+        "reference_audio": (upload_name, upload_file, upload_type) if reference_audio else None,
+        "prompt_text": prompt_text,
+        "prompt_lang": prompt_lang
+    }
+    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_url}/voice/gpt-sovits"
+
+    res = requests.post(url=url, data=m, headers=headers)
+    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
+    if save_path is not None:
+        path = os.path.join(save_path, fname)
+    else:
+        path = os.path.join(absolute_path, fname)
+    if save_audio:
+        with open(path, "wb") as f:
+            f.write(res.content)
+        print(path)
+        return path
+    return None
+
+
+# Reading
+def voice_reading_get(text, in_model_type, in_id, nr_model_type, nr_id, format="wav", lang="auto", preset=None,
+                      save_audio=True, save_path=None):
+    res = requests.get(
+        url=f"{base_url}/voice/reading?text={text}&in_model_type={in_model_type}&in_id={in_id}&preset={preset}&nr_model_type={nr_model_type}&nr_id={nr_id}&lang={lang}&format={format}")
+
+    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
+    if save_path is not None:
+        path = os.path.join(save_path, fname)
+    else:
+        path = os.path.join(absolute_path, fname)
+
+    with open(path, "wb") as f:
+        f.write(res.content)
+
+    if save_audio:
+        with open(path, "wb") as f:
+            f.write(res.content)
+        print(path)
+        return path
+    return None
+
+
+# Reading
+def voice_reading_json(text, in_model_type, in_id, nr_model_type, nr_id, format="wav", lang="auto", preset=None,
+                       save_audio=True, save_path=None):
+    fields = {
+        "text": text,
+        "in_model_type": in_model_type,
+        "in_id": str(in_id),
+        "nr_model_type": nr_model_type,
+        "nr_id": str(nr_id),
+        "format": format,
+        "lang": lang,
+    }
+    f = json.dumps(fields)
+    url = f"{base_url}/voice/reading"
+    header = {"Content-Type": 'application/json'}
+    res = requests.post(url=url, data=f, headers=header)
+
+    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
+    if save_path is not None:
+        path = os.path.join(save_path, fname)
+    else:
+        path = os.path.join(absolute_path, fname)
+
+    with open(path, "wb") as f:
+        f.write(res.content)
+
+    if save_audio:
+        with open(path, "wb") as f:
+            f.write(res.content)
+        print(path)
+        return path
+    return None
+
+
+# Reading
+def voice_reading(text, in_model_type, in_id, nr_model_type, nr_id, format="wav", lang="auto", preset=None,
+                  save_audio=True, save_path=None):
+    fields = {
+        "text": text,
+        "in_model_type": in_model_type,
+        "in_id": str(in_id),
+        "nr_model_type": nr_model_type,
+        "nr_id": str(nr_id),
+        "format": format,
+        "lang": lang,
+    }
+    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_url}/voice/reading"
+
+    res = requests.post(url=url, data=m, headers=headers)
+    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
+    if save_path is not None:
+        path = os.path.join(save_path, fname)
+    else:
+        path = os.path.join(absolute_path, fname)
+    if save_audio:
+        with open(path, "wb") as f:
+            f.write(res.content)
+        print(path)
+        return path
+    return None
+
+
+def test_interface(text):
+    error_num = 0
+    for i in range(100):
+        try:
+            time.sleep(1)
+            t1 = time.time()
+            voice_vits(text, format="wav", lang="zh", save_audio=False)
+            t2 = time.time()
+            print(f"{i}:len:{len(text)}耗时:{t2 - t1}")
+        except Exception as e:
+            error_num += 1
+            print(e)
+    print(f"error_num={error_num}")
+
+
+if __name__ == '__main__':
+    cache_path = os.path.join(os.path.curdir, "cache")
+
+    text = "你好,こんにちは"
+
+    ssml = """
+    <speak lang="zh" format="mp3" length="1.2">
+            <voice id="0" model_type="GPT-SOVITS" preset="default">这几天心里颇不宁静。</voice>
+            <voice id="0" model_type="Bert-VITS2">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="0" model_type="Bert-VITS2">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>
+    """
+
+    # path = voice_vits(text, save_path=cache_path)
+    # path =voice_vits_streaming(text, save_path=cache_path)
+    # path = voice_w2v2_vits(text, save_path=cache_path)
+    # path = voice_conversion(path, 1, 3, save_path=cache_path)
+    # path = voice_hubert_vits(path, 0, save_path=cache_path)
+    # path = voice_dimensional_emotion(path, save_path=cache_path)
+    # path = voice_ssml(ssml, save_path=cache_path)
+    # path = voice_bert_vits2("你好", lang="zh", save_path=cache_path)
+    # path = voice_bert_vits2("こんにちは", lang="ja", save_path=cache_path)
+    # path = voice_gpt_sovits(text=text, id=2, preset="wz")
+    # path = voice_gpt_sovits(text=text, id=2, reference_audio=r"H:\git\vits-simple-api\data\reference_audio\wz_10068.wav",prompt_text="……嗯……大概、快上课的时候开始的。到这个程度的话，……半个小时吧？")
+
+    # os.system(path)
+
+    # text = "你好“你的修炼速度有些出乎我的意料”"
+    # path = voice_reading_json(text=text, in_model_type="GPT-SOVITS", preset="wz", in_id=2, nr_model_type="BERT-VITS2",
+    #                           nr_id=0)
+
+    # os.system(path)

app.py

@@ -0,0 +1,74 @@
+import os.path
+
+from flask import Flask
+from flask_apscheduler import APScheduler
+from flask_login import LoginManager
+from flask_wtf import CSRFProtect
+
+from utils.data_utils import clean_folder
+from utils.phrases_dict import phrases_dict_init
+from tts_app.frontend.views import frontend
+from tts_app.voice_api.views import voice_api
+from tts_app.auth.views import auth
+from tts_app.admin.views import admin
+
+from contants import config
+
+app = Flask(__name__, template_folder=os.path.join(os.path.dirname(__file__), 'tts_app', 'templates'),
+            static_folder=os.path.join(os.path.dirname(__file__), 'tts_app', 'static'))
+
+app.config.from_pyfile("config.py")
+# app.config.update(config)
+
+phrases_dict_init()
+
+csrf = CSRFProtect(app)
+# 禁用tts api请求的CSRF防护
+csrf.exempt(voice_api)
+
+if config.system.is_admin_enabled:
+    login_manager = LoginManager()
+    login_manager.init_app(app)
+    login_manager.login_view = 'auth.login'
+
+
+    @login_manager.user_loader
+    def load_user(user_id):
+        admin = config.admin
+        if admin.get_id() == user_id:
+            return admin
+        return None
+
+# Initialize scheduler
+scheduler = APScheduler()
+scheduler.init_app(app)
+if config.system.clean_interval_seconds > 0:
+    scheduler.start()
+
+app.register_blueprint(frontend, url_prefix='/')
+app.register_blueprint(voice_api, url_prefix='/voice')
+if config.system.is_admin_enabled:
+    app.register_blueprint(auth, url_prefix=config.system.admin_route)
+    app.register_blueprint(admin, url_prefix=config.system.admin_route)
+
+
+def create_folders(paths):
+    for path in paths:
+        if not os.path.exists(path):
+            os.makedirs(path, exist_ok=True)
+
+
+create_folders([os.path.join(config.abs_path, config.system.upload_folder),
+                os.path.join(config.abs_path, config.system.cache_path), ])
+
+
+# regular cleaning
+@scheduler.task('interval', id='clean_task', seconds=config.system.clean_interval_seconds,
+                misfire_grace_time=900)
+def clean_task():
+    clean_folder(os.path.join(config.abs_path, config.system.upload_folder))
+    clean_folder(os.path.join(config.abs_path, config.system.cache_path))
+
+
+if __name__ == '__main__':
+    app.run(host=config.http_service.host, port=config.http_service.port, debug=config.http_service.debug)

bert_vits2/LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU General Public License is a free, copyleft license for
+software and other kinds of works.
+
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+the GNU General Public License is intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.  We, the Free Software Foundation, use the
+GNU General Public License for most of our software; it applies also to
+any other work released this way by its authors.  You can apply it to
+your programs, too.
+
+  When we speak of free software, we are referring to freedom, not
+price.  Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+them if you wish), that you receive source code or can get it if you
+want it, that you can change the software or use pieces of it in new
+free programs, and that you know you can do these things.
+
+  To protect your rights, we need to prevent others from denying you
+these rights or asking you to surrender the rights.  Therefore, you have
+certain responsibilities if you distribute copies of the software, or if
+you modify it: responsibilities to respect the freedom of others.
+
+  For example, if you distribute copies of such a program, whether
+gratis or for a fee, you must pass on to the recipients the same
+freedoms that you received.  You must make sure that they, too, receive
+or can get the source code.  And you must show them these terms so they
+know their rights.
+
+  Developers that use the GNU GPL protect your rights with two steps:
+(1) assert copyright on the software, and (2) offer you this License
+giving you legal permission to copy, distribute and/or modify it.
+
+  For the developers' and authors' protection, the GPL clearly explains
+that there is no warranty for this free software.  For both users' and
+authors' sake, the GPL requires that modified versions be marked as
+changed, so that their problems will not be attributed erroneously to
+authors of previous versions.
+
+  Some devices are designed to deny users access to install or run
+modified versions of the software inside them, although the manufacturer
+can do so.  This is fundamentally incompatible with the aim of
+protecting users' freedom to change the software.  The systematic
+pattern of such abuse occurs in the area of products for individuals to
+use, which is precisely where it is most unacceptable.  Therefore, we
+have designed this version of the GPL to prohibit the practice for those
+products.  If such problems arise substantially in other domains, we
+stand ready to extend this provision to those domains in future versions
+of the GPL, as needed to protect the freedom of users.
+
+  Finally, every program is threatened constantly by software patents.
+States should not allow patents to restrict development and use of
+software on general-purpose computers, but in those that do, we wish to
+avoid the special danger that patents applied to a free program could
+make it effectively proprietary.  To prevent this, the GPL assures that
+patents cannot be used to render the program non-free.
+
+  The precise terms and conditions for copying, distribution and
+modification follow.
+
+                       TERMS AND CONDITIONS
+
+  0. Definitions.
+
+  "This License" refers to version 3 of the GNU General Public License.
+
+  "Copyright" also means copyright-like laws that apply to other kinds of
+works, such as semiconductor masks.
+
+  "The Program" refers to any copyrightable work licensed under this
+License.  Each licensee is addressed as "you".  "Licensees" and
+"recipients" may be individuals or organizations.
+
+  To "modify" a work means to copy from or adapt all or part of the work
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+
+  A "covered work" means either the unmodified Program or a work based
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+
+  To "propagate" a work means to do anything with it that, without
+permission, would make you directly or secondarily liable for
+infringement under applicable copyright law, except executing it on a
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+
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+
+  1. Source Code.
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bert_vits2/README.md

@@ -0,0 +1,5 @@
+# Bert-VITS2
+
+VITS2 Backbone with bert
+## 成熟的旅行者/开拓者/舰长/博士/sensei/猎魔人/喵喵露/V应该参阅代码自己学习如何训练。
+### 严禁将此项目用于一切违反《中华人民共和国宪法》，《中华人民共和国刑法》，《中华人民共和国治安管理处罚法》和《中华人民共和国民法典》之用途。

bert_vits2/__init__.py

@@ -0,0 +1,2 @@
+from bert_vits2.bert_vits2 import Bert_VITS2
+from bert_vits2 import text

bert_vits2/attentions.py

@@ -0,0 +1,352 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+from bert_vits2 import commons
+from torch.nn.utils import weight_norm, remove_weight_norm
+
+
+class LayerNorm(nn.Module):
+    def __init__(self, channels, eps=1e-5):
+        super().__init__()
+        self.channels = channels
+        self.eps = eps
+
+        self.gamma = nn.Parameter(torch.ones(channels))
+        self.beta = nn.Parameter(torch.zeros(channels))
+
+    def forward(self, x):
+        x = x.transpose(1, -1)
+        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+        return x.transpose(1, -1)
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+    n_channels_int = n_channels[0]
+    in_act = input_a + input_b
+    t_act = torch.tanh(in_act[:, :n_channels_int, :])
+    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+    acts = t_act * s_act
+    return acts
+
+
+class Encoder(nn.Module):
+    def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4,
+                 isflow=True, **kwargs):
+        super().__init__()
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+        # if isflow:
+        #     cond_layer = torch.nn.Conv1d(256, 2 * hidden_channels * n_layers, 1)
+        #     self.cond_pre = torch.nn.Conv1d(hidden_channels, 2 * hidden_channels, 1)
+        #     self.cond_layer = weight_norm(cond_layer, name='weight')
+        #     self.gin_channels = 256
+        self.cond_layer_idx = self.n_layers
+        if 'gin_channels' in kwargs:
+            self.gin_channels = kwargs['gin_channels']
+            if self.gin_channels != 0:
+                self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels)
+                # vits2 says 3rd block, so idx is 2 by default
+                self.cond_layer_idx = kwargs['cond_layer_idx'] if 'cond_layer_idx' in kwargs else 2
+                # print(self.gin_channels, self.cond_layer_idx)
+                assert self.cond_layer_idx < self.n_layers, 'cond_layer_idx should be less than n_layers'
+        self.drop = nn.Dropout(p_dropout)
+        self.attn_layers = nn.ModuleList()
+        self.norm_layers_1 = nn.ModuleList()
+        self.ffn_layers = nn.ModuleList()
+        self.norm_layers_2 = nn.ModuleList()
+        for i in range(self.n_layers):
+            self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout,
+                                                       window_size=window_size))
+            self.norm_layers_1.append(LayerNorm(hidden_channels))
+            self.ffn_layers.append(
+                FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
+            self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+    def forward(self, x, x_mask, g=None):
+        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+        x = x * x_mask
+        for i in range(self.n_layers):
+            if i == self.cond_layer_idx and g is not None:
+                g = self.spk_emb_linear(g.transpose(1, 2))
+                g = g.transpose(1, 2)
+                x = x + g
+                x = x * x_mask
+            y = self.attn_layers[i](x, x, attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_1[i](x + y)
+
+            y = self.ffn_layers[i](x, x_mask)
+            y = self.drop(y)
+            x = self.norm_layers_2[i](x + y)
+        x = x * x_mask
+        return x
+
+
+class Decoder(nn.Module):
+    def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0.,
+                 proximal_bias=False, proximal_init=True, **kwargs):
+        super().__init__()
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.proximal_bias = proximal_bias
+        self.proximal_init = proximal_init
+
+        self.drop = nn.Dropout(p_dropout)
+        self.self_attn_layers = nn.ModuleList()
+        self.norm_layers_0 = nn.ModuleList()
+        self.encdec_attn_layers = nn.ModuleList()
+        self.norm_layers_1 = nn.ModuleList()
+        self.ffn_layers = nn.ModuleList()
+        self.norm_layers_2 = nn.ModuleList()
+        for i in range(self.n_layers):
+            self.self_attn_layers.append(
+                MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout,
+                                   proximal_bias=proximal_bias, proximal_init=proximal_init))
+            self.norm_layers_0.append(LayerNorm(hidden_channels))
+            self.encdec_attn_layers.append(
+                MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
+            self.norm_layers_1.append(LayerNorm(hidden_channels))
+            self.ffn_layers.append(
+                FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
+            self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+    def forward(self, x, x_mask, h, h_mask):
+        """
+        x: decoder input
+        h: encoder output
+        """
+        self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
+        encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+        x = x * x_mask
+        for i in range(self.n_layers):
+            y = self.self_attn_layers[i](x, x, self_attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_0[i](x + y)
+
+            y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_1[i](x + y)
+
+            y = self.ffn_layers[i](x, x_mask)
+            y = self.drop(y)
+            x = self.norm_layers_2[i](x + y)
+        x = x * x_mask
+        return x
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True,
+                 block_length=None, proximal_bias=False, proximal_init=False):
+        super().__init__()
+        assert channels % n_heads == 0
+
+        self.channels = channels
+        self.out_channels = out_channels
+        self.n_heads = n_heads
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+        self.heads_share = heads_share
+        self.block_length = block_length
+        self.proximal_bias = proximal_bias
+        self.proximal_init = proximal_init
+        self.attn = None
+
+        self.k_channels = channels // n_heads
+        self.conv_q = nn.Conv1d(channels, channels, 1)
+        self.conv_k = nn.Conv1d(channels, channels, 1)
+        self.conv_v = nn.Conv1d(channels, channels, 1)
+        self.conv_o = nn.Conv1d(channels, out_channels, 1)
+        self.drop = nn.Dropout(p_dropout)
+
+        if window_size is not None:
+            n_heads_rel = 1 if heads_share else n_heads
+            rel_stddev = self.k_channels ** -0.5
+            self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
+            self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
+
+        nn.init.xavier_uniform_(self.conv_q.weight)
+        nn.init.xavier_uniform_(self.conv_k.weight)
+        nn.init.xavier_uniform_(self.conv_v.weight)
+        if proximal_init:
+            with torch.no_grad():
+                self.conv_k.weight.copy_(self.conv_q.weight)
+                self.conv_k.bias.copy_(self.conv_q.bias)
+
+    def forward(self, x, c, attn_mask=None):
+        q = self.conv_q(x)
+        k = self.conv_k(c)
+        v = self.conv_v(c)
+
+        x, self.attn = self.attention(q, k, v, mask=attn_mask)
+
+        x = self.conv_o(x)
+        return x
+
+    def attention(self, query, key, value, mask=None):
+        # reshape [b, d, t] -> [b, n_h, t, d_k]
+        b, d, t_s, t_t = (*key.size(), query.size(2))
+        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
+        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+
+        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
+        if self.window_size is not None:
+            assert t_s == t_t, "Relative attention is only available for self-attention."
+            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
+            rel_logits = self._matmul_with_relative_keys(query / math.sqrt(self.k_channels), key_relative_embeddings)
+            scores_local = self._relative_position_to_absolute_position(rel_logits)
+            scores = scores + scores_local
+        if self.proximal_bias:
+            assert t_s == t_t, "Proximal bias is only available for self-attention."
+            scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
+        if mask is not None:
+            scores = scores.masked_fill(mask == 0, -1e4)
+            if self.block_length is not None:
+                assert t_s == t_t, "Local attention is only available for self-attention."
+                block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
+                scores = scores.masked_fill(block_mask == 0, -1e4)
+        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
+        p_attn = self.drop(p_attn)
+        output = torch.matmul(p_attn, value)
+        if self.window_size is not None:
+            relative_weights = self._absolute_position_to_relative_position(p_attn)
+            value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
+            output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
+        output = output.transpose(2, 3).contiguous().view(b, d, t_t)  # [b, n_h, t_t, d_k] -> [b, d, t_t]
+        return output, p_attn
+
+    def _matmul_with_relative_values(self, x, y):
+        """
+        x: [b, h, l, m]
+        y: [h or 1, m, d]
+        ret: [b, h, l, d]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0))
+        return ret
+
+    def _matmul_with_relative_keys(self, x, y):
+        """
+        x: [b, h, l, d]
+        y: [h or 1, m, d]
+        ret: [b, h, l, m]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
+        return ret
+
+    def _get_relative_embeddings(self, relative_embeddings, length):
+        max_relative_position = 2 * self.window_size + 1
+        # Pad first before slice to avoid using cond ops.
+        pad_length = max(length - (self.window_size + 1), 0)
+        slice_start_position = max((self.window_size + 1) - length, 0)
+        slice_end_position = slice_start_position + 2 * length - 1
+        if pad_length > 0:
+            padded_relative_embeddings = F.pad(
+                relative_embeddings,
+                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
+        else:
+            padded_relative_embeddings = relative_embeddings
+        used_relative_embeddings = padded_relative_embeddings[:, slice_start_position:slice_end_position]
+        return used_relative_embeddings
+
+    def _relative_position_to_absolute_position(self, x):
+        """
+        x: [b, h, l, 2*l-1]
+        ret: [b, h, l, l]
+        """
+        batch, heads, length, _ = x.size()
+        # Concat columns of pad to shift from relative to absolute indexing.
+        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
+
+        # Concat extra elements so to add up to shape (len+1, 2*len-1).
+        x_flat = x.view([batch, heads, length * 2 * length])
+        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]]))
+
+        # Reshape and slice out the padded elements.
+        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[:, :, :length, length - 1:]
+        return x_final
+
+    def _absolute_position_to_relative_position(self, x):
+        """
+        x: [b, h, l, l]
+        ret: [b, h, l, 2*l-1]
+        """
+        batch, heads, length, _ = x.size()
+        # padd along column
+        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]]))
+        x_flat = x.view([batch, heads, length ** 2 + length * (length - 1)])
+        # add 0's in the beginning that will skew the elements after reshape
+        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
+        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
+        return x_final
+
+    def _attention_bias_proximal(self, length):
+        """Bias for self-attention to encourage attention to close positions.
+        Args:
+          length: an integer scalar.
+        Returns:
+          a Tensor with shape [1, 1, length, length]
+        """
+        r = torch.arange(length, dtype=torch.float32)
+        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
+        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
+
+
+class FFN(nn.Module):
+    def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None,
+                 causal=False):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.activation = activation
+        self.causal = causal
+
+        if causal:
+            self.padding = self._causal_padding
+        else:
+            self.padding = self._same_padding
+
+        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
+        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
+        self.drop = nn.Dropout(p_dropout)
+
+    def forward(self, x, x_mask):
+        x = self.conv_1(self.padding(x * x_mask))
+        if self.activation == "gelu":
+            x = x * torch.sigmoid(1.702 * x)
+        else:
+            x = torch.relu(x)
+        x = self.drop(x)
+        x = self.conv_2(self.padding(x * x_mask))
+        return x * x_mask
+
+    def _causal_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = self.kernel_size - 1
+        pad_r = 0
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, commons.convert_pad_shape(padding))
+        return x
+
+    def _same_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = (self.kernel_size - 1) // 2
+        pad_r = self.kernel_size // 2
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, commons.convert_pad_shape(padding))
+        return x

bert_vits2/bert_vits2.py

@@ -0,0 +1,403 @@
+import logging
+
+import numpy as np
+import torch
+
+from bert_vits2 import commons
+from bert_vits2 import utils as bert_vits2_utils
+from bert_vits2.clap_wrapper import get_clap_audio_feature, get_clap_text_feature
+from bert_vits2.get_emo import get_emo
+from bert_vits2.models import SynthesizerTrn
+from bert_vits2.models_v230 import SynthesizerTrn as SynthesizerTrn_v230
+from bert_vits2.models_ja_extra import SynthesizerTrn as SynthesizerTrn_ja_extra
+from bert_vits2.text import *
+from bert_vits2.text.cleaner import clean_text
+from bert_vits2.utils import process_legacy_versions
+from contants import config
+from utils import get_hparams_from_file
+from utils.sentence import split_languages
+
+
+class Bert_VITS2:
+    def __init__(self, model_path, config, device=torch.device("cpu"), **kwargs):
+        self.model_path = model_path
+        self.hps_ms = get_hparams_from_file(config) if isinstance(config, str) else config
+        self.n_speakers = getattr(self.hps_ms.data, 'n_speakers', 0)
+        self.speakers = [item[0] for item in
+                         sorted(list(getattr(self.hps_ms.data, 'spk2id', {'0': 0}).items()), key=lambda x: x[1])]
+        self.symbols = symbols
+        self.sampling_rate = self.hps_ms.data.sampling_rate
+
+        self.bert_model_names = {}
+        self.zh_bert_extra = False
+        self.ja_bert_extra = False
+        self.ja_bert_dim = 1024
+        self.num_tones = num_tones
+        self.pinyinPlus = None
+
+        # Compatible with legacy versions
+        self.version = process_legacy_versions(self.hps_ms).lower().replace("-", "_")
+        self.text_extra_str_map = {"zh": "", "ja": "", "en": ""}
+        self.bert_extra_str_map = {"zh": "", "ja": "", "en": ""}
+        self.hps_ms.model.emotion_embedding = None
+        if self.version in ["1.0", "1.0.0", "1.0.1"]:
+            """
+            chinese-roberta-wwm-ext-large
+            """
+            self.version = "1.0"
+            self.symbols = symbols_legacy
+            self.hps_ms.model.n_layers_trans_flow = 3
+            self.lang = getattr(self.hps_ms.data, "lang", ["zh"])
+            self.ja_bert_dim = 768
+            self.num_tones = num_tones_v111
+            self.text_extra_str_map.update({"zh": "_v100"})
+
+        elif self.version in ["1.1.0-transition"]:
+            """
+            chinese-roberta-wwm-ext-large
+            """
+            self.version = "1.1.0-transition"
+            self.hps_ms.model.n_layers_trans_flow = 3
+            self.lang = getattr(self.hps_ms.data, "lang", ["zh", "ja"])
+            self.ja_bert_dim = 768
+            self.num_tones = num_tones_v111
+            if "ja" in self.lang: self.bert_model_names.update({"ja": "BERT_BASE_JAPANESE_V3"})
+            self.text_extra_str_map.update({"zh": "_v100", "ja": "_v111"})
+            self.bert_extra_str_map.update({"ja": "_v111"})
+
+        elif self.version in ["1.1", "1.1.0", "1.1.1"]:
+            """
+            chinese-roberta-wwm-ext-large
+            bert-base-japanese-v3
+            """
+            self.version = "1.1"
+            self.hps_ms.model.n_layers_trans_flow = 6
+            self.lang = getattr(self.hps_ms.data, "lang", ["zh", "ja"])
+            self.ja_bert_dim = 768
+            self.num_tones = num_tones_v111
+            if "ja" in self.lang: self.bert_model_names.update({"ja": "BERT_BASE_JAPANESE_V3"})
+            self.text_extra_str_map.update({"zh": "_v100", "ja": "_v111"})
+            self.bert_extra_str_map.update({"ja": "_v111"})
+
+        elif self.version in ["2.0", "2.0.0", "2.0.1", "2.0.2"]:
+            """
+            chinese-roberta-wwm-ext-large
+            deberta-v2-large-japanese
+            deberta-v3-large
+            """
+            self.version = "2.0"
+            self.hps_ms.model.n_layers_trans_flow = 4
+            self.lang = getattr(self.hps_ms.data, "lang", ["zh", "ja", "en"])
+            self.num_tones = num_tones
+            if "ja" in self.lang: self.bert_model_names.update({"ja": "DEBERTA_V2_LARGE_JAPANESE"})
+            if "en" in self.lang: self.bert_model_names.update({"en": "DEBERTA_V3_LARGE"})
+            self.text_extra_str_map.update({"zh": "_v100", "ja": "_v200", "en": "_v200"})
+            self.bert_extra_str_map.update({"ja": "_v200", "en": "_v200"})
+
+        elif self.version in ["2.1", "2.1.0"]:
+            """
+            chinese-roberta-wwm-ext-large
+            deberta-v2-large-japanese-char-wwm
+            deberta-v3-large
+            wav2vec2-large-robust-12-ft-emotion-msp-dim
+            """
+            self.version = "2.1"
+            self.hps_ms.model.n_layers_trans_flow = 4
+            self.hps_ms.model.emotion_embedding = 1
+            self.lang = getattr(self.hps_ms.data, "lang", ["zh", "ja", "en"])
+            self.num_tones = num_tones
+            if "ja" in self.lang: self.bert_model_names.update({"ja": "DEBERTA_V2_LARGE_JAPANESE_CHAR_WWM"})
+            if "en" in self.lang: self.bert_model_names.update({"en": "DEBERTA_V3_LARGE"})
+
+        elif self.version in ["2.2", "2.2.0"]:
+            """
+            chinese-roberta-wwm-ext-large
+            deberta-v2-large-japanese-char-wwm
+            deberta-v3-large
+            clap-htsat-fused
+            """
+            self.version = "2.2"
+            self.hps_ms.model.n_layers_trans_flow = 4
+            self.hps_ms.model.emotion_embedding = 2
+            self.lang = getattr(self.hps_ms.data, "lang", ["zh", "ja", "en"])
+            self.num_tones = num_tones
+            if "ja" in self.lang: self.bert_model_names.update({"ja": "DEBERTA_V2_LARGE_JAPANESE_CHAR_WWM"})
+            if "en" in self.lang: self.bert_model_names.update({"en": "DEBERTA_V3_LARGE"})
+
+        elif self.version in ["2.3", "2.3.0"]:
+            """
+            chinese-roberta-wwm-ext-large
+            deberta-v2-large-japanese-char-wwm
+            deberta-v3-large
+            """
+            self.version = "2.3"
+            self.lang = getattr(self.hps_ms.data, "lang", ["zh", "ja", "en"])
+            self.num_tones = num_tones
+            self.text_extra_str_map.update({"en": "_v230"})
+            if "ja" in self.lang: self.bert_model_names.update({"ja": "DEBERTA_V2_LARGE_JAPANESE_CHAR_WWM"})
+            if "en" in self.lang: self.bert_model_names.update({"en": "DEBERTA_V3_LARGE"})
+
+        elif self.version is not None and self.version in ["extra", "zh_clap"]:
+            """
+            Erlangshen-MegatronBert-1.3B-Chinese
+            clap-htsat-fused
+            """
+            self.version = "extra"
+            self.hps_ms.model.emotion_embedding = 2
+            self.hps_ms.model.n_layers_trans_flow = 6
+            self.lang = ["zh"]
+            self.num_tones = num_tones
+            self.zh_bert_extra = True
+            self.bert_model_names.update({"zh": "Erlangshen_MegatronBert_1.3B_Chinese"})
+            self.bert_extra_str_map.update({"zh": "_extra"})
+
+        elif self.version is not None and self.version in ["extra_fix", "2.4", "2.4.0"]:
+            """
+            Erlangshen-MegatronBert-1.3B-Chinese
+            clap-htsat-fused
+            """
+            self.version = "2.4"
+            self.hps_ms.model.emotion_embedding = 2
+            self.hps_ms.model.n_layers_trans_flow = 6
+            self.lang = ["zh"]
+            self.num_tones = num_tones
+            self.zh_bert_extra = True
+            self.bert_model_names.update({"zh": "Erlangshen_MegatronBert_1.3B_Chinese"})
+            self.bert_extra_str_map.update({"zh": "_extra"})
+            self.text_extra_str_map.update({"zh": "_v240"})
+
+        elif self.version is not None and self.version in ["ja_extra"]:
+            """
+            deberta-v2-large-japanese-char-wwm
+            """
+            self.version = "ja_extra"
+            self.hps_ms.model.emotion_embedding = 2
+            self.hps_ms.model.n_layers_trans_flow = 6
+            self.lang = ["ja"]
+            self.num_tones = num_tones
+            self.ja_bert_extra = True
+            self.bert_model_names.update({"ja": "DEBERTA_V2_LARGE_JAPANESE_CHAR_WWM"})
+            self.bert_extra_str_map.update({"ja": "_extra"})
+            self.text_extra_str_map.update({"ja": "_extra"})
+
+        else:
+            logging.debug("Version information not found. Loaded as the newest version: v2.3.")
+            self.version = "2.3"
+            self.lang = getattr(self.hps_ms.data, "lang", ["zh", "ja", "en"])
+            self.num_tones = num_tones
+            self.text_extra_str_map.update({"en": "_v230"})
+            if "ja" in self.lang: self.bert_model_names.update({"ja": "DEBERTA_V2_LARGE_JAPANESE_CHAR_WWM"})
+            if "en" in self.lang: self.bert_model_names.update({"en": "DEBERTA_V3_LARGE"})
+
+        if "zh" in self.lang and "zh" not in self.bert_model_names.keys():
+            self.bert_model_names.update({"zh": "CHINESE_ROBERTA_WWM_EXT_LARGE"})
+
+        self._symbol_to_id = {s: i for i, s in enumerate(self.symbols)}
+
+        self.device = device
+
+    def load_model(self, model_handler):
+        self.model_handler = model_handler
+
+        if self.version in ["2.3", "extra", "2.4"]:
+            Synthesizer = SynthesizerTrn_v230
+        elif self.version == "ja_extra":
+            Synthesizer = SynthesizerTrn_ja_extra
+        else:
+            Synthesizer = SynthesizerTrn
+
+        if self.version == "2.4":
+            self.pinyinPlus = self.model_handler.get_pinyinPlus()
+        self.net_g = Synthesizer(
+            len(self.symbols),
+            self.hps_ms.data.filter_length // 2 + 1,
+            self.hps_ms.train.segment_size // self.hps_ms.data.hop_length,
+            n_speakers=self.hps_ms.data.n_speakers,
+            symbols=self.symbols,
+            ja_bert_dim=self.ja_bert_dim,
+            num_tones=self.num_tones,
+            zh_bert_extra=self.zh_bert_extra,
+            **self.hps_ms.model).to(self.device)
+        _ = self.net_g.eval()
+        bert_vits2_utils.load_checkpoint(self.model_path, self.net_g, None, skip_optimizer=True, version=self.version)
+
+    def get_speakers(self):
+        return self.speakers
+
+    def get_text(self, text, language_str, hps, style_text=None, style_weight=0.7):
+        clean_text_lang_str = language_str + self.text_extra_str_map.get(language_str, "")
+        bert_feature_lang_str = language_str + self.bert_extra_str_map.get(language_str, "")
+
+        tokenizer, _ = self.model_handler.get_bert_model(self.bert_model_names[language_str])
+
+        norm_text, phone, tone, word2ph = clean_text(text, clean_text_lang_str, tokenizer, self.pinyinPlus)
+
+        phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str, self._symbol_to_id)
+
+        if hps.data.add_blank:
+            phone = commons.intersperse(phone, 0)
+            tone = commons.intersperse(tone, 0)
+            language = commons.intersperse(language, 0)
+            for i in range(len(word2ph)):
+                word2ph[i] = word2ph[i] * 2
+            word2ph[0] += 1
+
+        if style_text == "" or self.zh_bert_extra:
+            style_text = None
+
+        bert = self.model_handler.get_bert_feature(norm_text, word2ph, bert_feature_lang_str,
+                                                   self.bert_model_names[language_str], style_text, style_weight)
+        del word2ph
+        assert bert.shape[-1] == len(phone), phone
+
+        if self.zh_bert_extra:
+            zh_bert = bert
+            ja_bert, en_bert = None, None
+        elif self.ja_bert_extra:
+            ja_bert = bert
+            zh_bert, en_bert = None, None
+        elif language_str == "zh":
+            zh_bert = bert
+            ja_bert = torch.zeros(self.ja_bert_dim, len(phone))
+            en_bert = torch.zeros(1024, len(phone))
+        elif language_str == "ja":
+            zh_bert = torch.zeros(1024, len(phone))
+            ja_bert = bert
+            en_bert = torch.zeros(1024, len(phone))
+        elif language_str == "en":
+            zh_bert = torch.zeros(1024, len(phone))
+            ja_bert = torch.zeros(self.ja_bert_dim, len(phone))
+            en_bert = bert
+        else:
+            zh_bert = torch.zeros(1024, len(phone))
+            ja_bert = torch.zeros(self.ja_bert_dim, len(phone))
+            en_bert = torch.zeros(1024, len(phone))
+        assert bert.shape[-1] == len(
+            phone
+        ), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
+        phone = torch.LongTensor(phone)
+        tone = torch.LongTensor(tone)
+        language = torch.LongTensor(language)
+        return zh_bert, ja_bert, en_bert, phone, tone, language
+
+    def _get_emo(self, reference_audio, emotion):
+        if reference_audio:
+            emo = torch.from_numpy(
+                get_emo(reference_audio, self.model_handler.emotion_model,
+                        self.model_handler.emotion_processor))
+        else:
+            if emotion is None: emotion = 0
+            emo = torch.Tensor([emotion])
+
+        return emo
+
+    def _get_clap(self, reference_audio, text_prompt):
+        if isinstance(reference_audio, np.ndarray):
+            emo = get_clap_audio_feature(reference_audio, self.model_handler.clap_model,
+                                         self.model_handler.clap_processor, self.device)
+        else:
+            if text_prompt is None: text_prompt = config.bert_vits2_config.text_prompt
+            emo = get_clap_text_feature(text_prompt, self.model_handler.clap_model,
+                                        self.model_handler.clap_processor, self.device)
+        emo = torch.squeeze(emo, dim=1).unsqueeze(0)
+        return emo
+
+    def _infer(self, id, phones, tones, lang_ids, zh_bert, ja_bert, en_bert, sdp_ratio, noise, noisew, length,
+               emo=None):
+        with torch.no_grad():
+            x_tst = phones.to(self.device).unsqueeze(0)
+            tones = tones.to(self.device).unsqueeze(0)
+            lang_ids = lang_ids.to(self.device).unsqueeze(0)
+            if self.zh_bert_extra:
+                zh_bert = zh_bert.to(self.device).unsqueeze(0)
+            elif self.ja_bert_extra:
+                ja_bert = ja_bert.to(self.device).unsqueeze(0)
+            else:
+                zh_bert = zh_bert.to(self.device).unsqueeze(0)
+                ja_bert = ja_bert.to(self.device).unsqueeze(0)
+                en_bert = en_bert.to(self.device).unsqueeze(0)
+            x_tst_lengths = torch.LongTensor([phones.size(0)]).to(self.device)
+            speakers = torch.LongTensor([int(id)]).to(self.device)
+            audio = self.net_g.infer(x_tst,
+                                     x_tst_lengths,
+                                     speakers,
+                                     tones,
+                                     lang_ids,
+                                     zh_bert=zh_bert,
+                                     ja_bert=ja_bert,
+                                     en_bert=en_bert,
+                                     sdp_ratio=sdp_ratio,
+                                     noise_scale=noise,
+                                     noise_scale_w=noisew,
+                                     length_scale=length,
+                                     emo=emo
+                                     )[0][0, 0].data.cpu().float().numpy()
+
+        torch.cuda.empty_cache()
+        return audio
+
+    def infer(self, text, id, lang, sdp_ratio, noise, noisew, length, reference_audio=None, emotion=None,
+              text_prompt=None, style_text=None, style_weigth=0.7, **kwargs):
+        zh_bert, ja_bert, en_bert, phones, tones, lang_ids = self.get_text(text, lang, self.hps_ms, style_text,
+                                                                           style_weigth)
+
+        emo = None
+        if self.hps_ms.model.emotion_embedding == 1:
+            emo = self._get_emo(reference_audio, emotion).to(self.device).unsqueeze(0)
+        elif self.hps_ms.model.emotion_embedding == 2:
+            emo = self._get_clap(reference_audio, text_prompt)
+
+        return self._infer(id, phones, tones, lang_ids, zh_bert, ja_bert, en_bert, sdp_ratio, noise, noisew, length,
+                           emo)
+
+    def infer_multilang(self, text, id, lang, sdp_ratio, noise, noisew, length, reference_audio=None, emotion=None,
+                        text_prompt=None, style_text=None, style_weigth=0.7, **kwargs):
+        sentences_list = split_languages(text, self.lang, expand_abbreviations=True, expand_hyphens=True)
+
+        emo = None
+        if self.hps_ms.model.emotion_embedding == 1:
+            emo = self._get_emo(reference_audio, emotion).to(self.device).unsqueeze(0)
+        elif self.hps_ms.model.emotion_embedding == 2:
+            emo = self._get_clap(reference_audio, text_prompt)
+
+        phones, tones, lang_ids, zh_bert, ja_bert, en_bert = [], [], [], [], [], []
+
+        for idx, (_text, lang) in enumerate(sentences_list):
+            skip_start = idx != 0
+            skip_end = idx != len(sentences_list) - 1
+            _zh_bert, _ja_bert, _en_bert, _phones, _tones, _lang_ids = self.get_text(_text, lang, self.hps_ms,
+                                                                                     style_text, style_weigth)
+
+            if skip_start:
+                _phones = _phones[3:]
+                _tones = _tones[3:]
+                _lang_ids = _lang_ids[3:]
+                _zh_bert = _zh_bert[:, 3:]
+                _ja_bert = _ja_bert[:, 3:]
+                _en_bert = _en_bert[:, 3:]
+            if skip_end:
+                _phones = _phones[:-2]
+                _tones = _tones[:-2]
+                _lang_ids = _lang_ids[:-2]
+                _zh_bert = _zh_bert[:, :-2]
+                _ja_bert = _ja_bert[:, :-2]
+                _en_bert = _en_bert[:, :-2]
+
+            phones.append(_phones)
+            tones.append(_tones)
+            lang_ids.append(_lang_ids)
+            zh_bert.append(_zh_bert)
+            ja_bert.append(_ja_bert)
+            en_bert.append(_en_bert)
+
+        zh_bert = torch.cat(zh_bert, dim=1)
+        ja_bert = torch.cat(ja_bert, dim=1)
+        en_bert = torch.cat(en_bert, dim=1)
+        phones = torch.cat(phones, dim=0)
+        tones = torch.cat(tones, dim=0)
+        lang_ids = torch.cat(lang_ids, dim=0)
+
+        audio = self._infer(id, phones, tones, lang_ids, zh_bert, ja_bert, en_bert, sdp_ratio, noise,
+                            noisew, length, emo)
+
+        return audio

bert_vits2/clap_wrapper.py

@@ -0,0 +1,17 @@
+import torch
+
+
+def get_clap_audio_feature(audio_data, clap_model, processor, device):
+    with torch.no_grad():
+        inputs = processor(
+            audios=audio_data, return_tensors="pt", sampling_rate=48000
+        ).to(device)
+        emb = clap_model.get_audio_features(**inputs).float()
+    return emb.T
+
+
+def get_clap_text_feature(text, clap_model, processor, device):
+    with torch.no_grad():
+        inputs = processor(text=text, return_tensors="pt").to(device)
+        emb = clap_model.get_text_features(**inputs).float()
+    return emb.T

bert_vits2/commons.py

@@ -0,0 +1,158 @@
+import math
+import torch
+from torch.nn import functional as F
+
+
+def init_weights(m, mean=0.0, std=0.01):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        m.weight.data.normal_(mean, std)
+
+
+def get_padding(kernel_size, dilation=1):
+    return int((kernel_size * dilation - dilation) / 2)
+
+
+def convert_pad_shape(pad_shape):
+    layer = pad_shape[::-1]
+    pad_shape = [item for sublist in layer for item in sublist]
+    return pad_shape
+
+
+def intersperse(lst, item):
+    result = [item] * (len(lst) * 2 + 1)
+    result[1::2] = lst
+    return result
+
+
+def kl_divergence(m_p, logs_p, m_q, logs_q):
+    """KL(P||Q)"""
+    kl = (logs_q - logs_p) - 0.5
+    kl += (
+            0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
+    )
+    return kl
+
+
+def rand_gumbel(shape):
+    """Sample from the Gumbel distribution, protect from overflows."""
+    uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
+    return -torch.log(-torch.log(uniform_samples))
+
+
+def rand_gumbel_like(x):
+    g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
+    return g
+
+
+def slice_segments(x, ids_str, segment_size=4):
+    gather_indices = ids_str.view(x.size(0), 1, 1).repeat(
+        1, x.size(1), 1
+    ) + torch.arange(segment_size, device=x.device)
+    return torch.gather(x, 2, gather_indices)
+
+
+def rand_slice_segments(x, x_lengths=None, segment_size=4):
+    b, d, t = x.size()
+    if x_lengths is None:
+        x_lengths = t
+    ids_str_max = torch.clamp(x_lengths - segment_size + 1, min=0)
+    ids_str = (torch.rand([b], device=x.device) * ids_str_max).to(dtype=torch.long)
+    ret = slice_segments(x, ids_str, segment_size)
+    return ret, ids_str
+
+
+def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4):
+    position = torch.arange(length, dtype=torch.float)
+    num_timescales = channels // 2
+    log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / (
+            num_timescales - 1
+    )
+    inv_timescales = min_timescale * torch.exp(
+        torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment
+    )
+    scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
+    signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
+    signal = F.pad(signal, [0, 0, 0, channels % 2])
+    signal = signal.view(1, channels, length)
+    return signal
+
+
+def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
+    b, channels, length = x.size()
+    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+    return x + signal.to(dtype=x.dtype, device=x.device)
+
+
+def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
+    b, channels, length = x.size()
+    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+    return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
+
+
+def subsequent_mask(length):
+    mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
+    return mask
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+    n_channels_int = n_channels[0]
+    in_act = input_a + input_b
+    t_act = torch.tanh(in_act[:, :n_channels_int, :])
+    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+    acts = t_act * s_act
+    return acts
+
+
+def convert_pad_shape(pad_shape):
+    layer = pad_shape[::-1]
+    pad_shape = [item for sublist in layer for item in sublist]
+    return pad_shape
+
+
+def shift_1d(x):
+    x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
+    return x
+
+
+def sequence_mask(length, max_length=None):
+    if max_length is None:
+        max_length = length.max()
+    x = torch.arange(max_length, dtype=length.dtype, device=length.device)
+    return x.unsqueeze(0) < length.unsqueeze(1)
+
+
+def generate_path(duration, mask):
+    """
+    duration: [b, 1, t_x]
+    mask: [b, 1, t_y, t_x]
+    """
+
+    b, _, t_y, t_x = mask.shape
+    cum_duration = torch.cumsum(duration, -1)
+
+    cum_duration_flat = cum_duration.view(b * t_x)
+    path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
+    path = path.view(b, t_x, t_y)
+    path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
+    path = path.unsqueeze(1).transpose(2, 3) * mask
+    return path
+
+
+def clip_grad_value_(parameters, clip_value, norm_type=2):
+    if isinstance(parameters, torch.Tensor):
+        parameters = [parameters]
+    parameters = list(filter(lambda p: p.grad is not None, parameters))
+    norm_type = float(norm_type)
+    if clip_value is not None:
+        clip_value = float(clip_value)
+
+    total_norm = 0
+    for p in parameters:
+        param_norm = p.grad.data.norm(norm_type)
+        total_norm += param_norm.item() ** norm_type
+        if clip_value is not None:
+            p.grad.data.clamp_(min=-clip_value, max=clip_value)
+    total_norm = total_norm ** (1.0 / norm_type)
+    return total_norm

bert_vits2/g2pW/pypinyin_G2pW_bv2/__init__.py

@@ -0,0 +1,5 @@
+# -*- coding: utf-8 -*-
+
+from .g2pw import G2PWPinyin
+
+__all__ = ["G2PWPinyin"]

bert_vits2/g2pW/pypinyin_G2pW_bv2/g2pw.py

@@ -0,0 +1,121 @@
+# -*- coding: utf-8 -*-
+
+from pypinyin.constants import RE_HANS
+from pypinyin.core import Pinyin, Style
+from pypinyin.seg.simpleseg import simple_seg
+from pypinyin.converter import UltimateConverter
+from pypinyin.contrib.tone_convert import to_tone
+from .g2pw1.onnx_api import G2PWOnnxConverter
+
+class G2PWPinyin(Pinyin):
+    def __init__(
+        self,
+        model_dir="G2PWModel/",
+        model_source=None,
+        num_workers=None,
+        batch_size=None,
+        turnoff_tqdm=True,
+        enable_non_tradional_chinese=True,
+        v_to_u=False,
+        neutral_tone_with_five=False,
+        tone_sandhi=False,
+        **kwargs
+    ):
+        self._g2pw = G2PWOnnxConverter(
+            model_dir=model_dir,
+            style="pinyin",
+            model_source=model_source,
+            enable_non_tradional_chinese=enable_non_tradional_chinese,
+        )
+        self._converter = Converter(
+            self._g2pw,
+            v_to_u=v_to_u,
+            neutral_tone_with_five=neutral_tone_with_five,
+            tone_sandhi=tone_sandhi,
+        )
+
+    def get_seg(self, **kwargs):
+        return simple_seg
+
+
+class Converter(UltimateConverter):
+    def __init__(
+        self,
+        g2pw_instance,
+        v_to_u=False,
+        neutral_tone_with_five=False,
+        tone_sandhi=False,
+        **kwargs
+    ):
+        super(Converter, self).__init__(
+            v_to_u=v_to_u,
+            neutral_tone_with_five=neutral_tone_with_five,
+            tone_sandhi=tone_sandhi,
+            **kwargs
+        )
+
+        self._g2pw = g2pw_instance
+
+    def convert(self, words, style, heteronym, errors, strict, **kwargs):
+        pys = []
+        if RE_HANS.match(words):
+            pys = self._to_pinyin(
+                words, style=style, heteronym=heteronym, errors=errors, strict=strict
+            )
+            post_data = self.post_pinyin(words, heteronym, pys)
+            if post_data is not None:
+                pys = post_data
+
+            pys = self.convert_styles(pys, words, style, heteronym, errors, strict)
+
+        else:
+            py = self.handle_nopinyin(
+                words, style=style, errors=errors, heteronym=heteronym, strict=strict
+            )
+            if py:
+                pys.extend(py)
+
+        return _remove_dup_and_empty(pys)
+
+    def _to_pinyin(self, han, style, heteronym, errors, strict, **kwargs):
+        g2pw_pinyin = self._g2pw(han)
+
+        if not g2pw_pinyin:  # g2pw 不支持的汉字改为使用 pypinyin 原有逻辑
+            return super(Converter, self).convert(
+                han, Style.TONE, heteronym, errors, strict, **kwargs
+            )
+
+        pinyins = []
+
+        for i, item in enumerate(g2pw_pinyin[0]):
+            if item is None:  # g2pw 不支持的汉字改为使用 pypinyin 原有逻辑
+                py = super(Converter, self).convert(
+                    han[i], Style.TONE, heteronym, errors, strict, **kwargs
+                )
+                pinyins.extend(py)
+            else:
+                pinyins.append([to_tone(item)])
+
+        return pinyins
+
+
+def _remove_dup_items(lst, remove_empty=False):
+    new_lst = []
+    for item in lst:
+        if remove_empty and not item:
+            continue
+        if item not in new_lst:
+            new_lst.append(item)
+    return new_lst
+
+
+def _remove_dup_and_empty(lst_list):
+    new_lst_list = []
+    for lst in lst_list:
+        lst = _remove_dup_items(lst, remove_empty=True)
+        if lst:
+            new_lst_list.append(lst)
+        else:
+            new_lst_list.append([""])
+
+    return new_lst_list

bert_vits2/g2pW/pypinyin_G2pW_bv2/g2pw1/bopomofo_to_pinyin_wo_tune_dict.json

@@ -0,0 +1 @@
+{"ㄌㄧㄥ": "ling", "ㄩㄢ": "yuan", "ㄒㄧㄥ": "xing", "ㄑㄧㄡ": "qiu", "ㄊㄧㄢ": "tian", "ㄎㄨㄚ": "kua", "ㄨ": "wu", "ㄧㄣ": "yin", "ㄧ": "yi", "ㄒㄧㄝ": "xie", "ㄔㄡ": "chou", "ㄋㄨㄛ": "nuo", "ㄉㄢ": "dan", "ㄒㄩ": "xu", "ㄒㄩㄥ": "xiong", "ㄌㄧㄡ": "liu", "ㄌㄧㄣ": "lin", "ㄒㄧㄤ": "xiang", "ㄩㄥ": "yong", "ㄒㄧㄣ": "xin", "ㄓㄣ": "zhen", "ㄉㄞ": "dai", "ㄆㄢ": "pan", "ㄖㄨ": "ru", "ㄇㄚ": "ma", "ㄑㄧㄢ": "qian", "ㄘ": "ci", "ㄓㄨㄥ": "zhong", "ㄋㄟ": "nei", "ㄔㄥ": "cheng", "ㄈㄥ": "feng", "ㄓㄨㄛ": "zhuo", "ㄈㄤ": "fang", "ㄠ": "ao", "ㄗㄨㄛ": "zuo", "ㄓㄡ": "zhou", "ㄉㄨㄥ": "dong", "ㄙㄨ": "su", "ㄑㄩㄥ": "qiong", "ㄎㄨㄤ": "kuang", "ㄨㄤ": "wang", "ㄌㄟ": "lei", "ㄋㄠ": "nao", "ㄓㄨ": "zhu", "ㄕㄨ": "shu", "ㄕㄣ": "shen", "ㄐㄧㄝ": "jie", "ㄉㄧㄝ": "die", "ㄔ": "chi", "ㄌㄨㄥ": "long", "ㄧㄥ": "ying", "ㄅㄥ": "beng", "ㄌㄢ": "lan", "ㄇㄧㄠ": "miao", "ㄌㄧ": "li", "ㄐㄧ": "ji", "ㄩ": "yu", "ㄌㄨㄛ": "luo", "ㄔㄞ": "chai", "ㄏㄨㄣ": "hun", "ㄏㄨㄟ": "hui", "ㄖㄠ": "rao", "ㄏㄢ": "han", "ㄒㄧ": "xi", "ㄊㄞ": "tai", "ㄧㄠ": "yao", "ㄐㄩㄣ": "jun", "ㄌㄩㄝ": "lve", "ㄊㄤ": "tang", "ㄓㄠ": "zhao", "ㄓㄞ": "zhai", "ㄓㄚ": "zha", "ㄦ": "er", "ㄖㄢ": "ran", "ㄑㄧ": "qi", "ㄙㄜ": "se", "ㄙ": "si", "ㄙㄚ": "sa", "ㄎㄨㄟ": "kui", "ㄆㄨ": "pu", "ㄊㄚ": "ta", "ㄉㄨ": "du", "ㄊㄨ": "tu", "ㄧㄤ": "yang", "ㄡ": "ou", "ㄇㄧㄢ": "mian", "ㄨㄣ": "wen", "ㄉㄧㄠ": "diao", "ㄇㄧㄝ": "mie", "ㄨㄚ": "wa", "ㄋㄧㄠ": "niao", "ㄧㄡ": "you", "ㄔㄜ": "che", "ㄑㄩㄢ": "quan", "ㄘㄞ": "cai", "ㄌㄧㄤ": "liang", "ㄍㄨ": "gu", "ㄇㄠ": "mao", "ㄍㄨㄚ": "gua", "ㄙㄨㄟ": "sui", "ㄇㄢ": "man", "ㄕ": "shi", "ㄎㄡ": "kou", "ㄊㄧㄥ": "ting", "ㄅㄧㄥ": "bing", "ㄏㄨㄛ": "huo", "ㄍㄨㄥ": "gong", "ㄑㄧㄣ": "qin", "ㄐㄩㄥ": "jiong", "ㄌㄨ": "lu", "ㄋㄢ": "nan", "ㄅㄧ": "bi", "ㄑㄧㄚ": "qia", "ㄆㄧ": "pi", "ㄉㄧㄢ": "dian", "ㄈㄨ": "fu", "ㄍㄜ": "ge", "ㄅㄞ": "bai", "ㄍㄢ": "gan", "ㄒㄩㄢ": "xuan", "ㄌㄤ": "lang", "ㄕㄜ": "she", "ㄏㄨㄚ": "hua", "ㄊㄡ": "tou", "ㄆㄧㄢ": "pian", "ㄉㄧ": "di", "ㄖㄨㄢ": "ruan", "ㄜ": "e", "ㄑㄧㄝ": "qie", "ㄉㄡ": "dou", "ㄖㄨㄟ": "rui", "ㄘㄨㄟ": "cui", "ㄐㄧㄢ": "jian", "ㄔㄨㄥ": "chong", "ㄉㄥ": "deng", "ㄐㄩㄝ": "jue", "ㄒㄩㄝ": "xue", "ㄒㄧㄠ": "xiao", "ㄗㄢ": "zan", "ㄓㄢ": "zhan", "ㄗㄡ": "zou", "ㄘㄡ": "cou", "ㄔㄨㄚ": "chua", "ㄈㄟ": "fei", "ㄅㄟ": "bei", "ㄔㄨ": "chu", "ㄅㄚ": "ba", "ㄎㄨㄞ": "kuai", "ㄒㄧㄚ": "xia", "ㄏㄜ": "he", "ㄅㄧㄝ": "bie", "ㄌㄩ": "lv", "ㄙㄨㄢ": "suan", "ㄏㄥ": "heng", "ㄍㄨㄟ": "gui", "ㄌㄡ": "lou", "ㄊㄧ": "ti", "ㄌㄜ": "le", "ㄙㄨㄣ": "sun", "ㄒㄧㄢ": "xian", "ㄑㄩㄝ": "que", "ㄓ": "zhi", "ㄐㄧㄚ": "jia", "ㄏㄨ": "hu", "ㄌㄚ": "la", "ㄎㄜ": "ke", "ㄞ": "ai", "ㄨㄟ": "wei", "ㄏㄨㄢ": "huan", "ㄕㄨㄚ": "shua", "ㄕㄨㄤ": "shuang", "ㄍㄞ": "gai", "ㄏㄞ": "hai", "ㄧㄢ": "yan", "ㄈㄢ": "fan", "ㄆㄤ": "pang", "ㄙㄨㄥ": "song", "ㄋㄜ": "ne", "ㄔㄣ": "chen", "ㄍㄨㄛ": "guo", "ㄣ": "en", "ㄋㄍ": "ng", "ㄆㄚ": "pa", "ㄈㄚ": "fa", "ㄆㄡ": "pou", "ㄏㄡ": "hou", "ㄑㄩ": "qu", "ㄒㄩㄣ": "xun", "ㄋㄧㄝ": "nie", "ㄏㄨㄥ": "hong", "ㄊㄨㄣ": "tun", "ㄨㄞ": "wai", "ㄕㄡ": "shou", "ㄧㄝ": "ye", "ㄐㄩ": "ju", "ㄙㄡ": "sou", "ㄌㄨㄣ": "lun", "ㄋㄧㄚ": "nia", "ㄆㄣ": "pen", "ㄈㄣ": "fen", "ㄔㄨㄣ": "chun", "ㄋㄧㄡ": "niu", "ㄖㄡ": "rou", "ㄉㄨㄛ": "duo", "ㄗㄜ": "ze", "ㄕㄥ": "sheng", "ㄎㄨ": "ku", "ㄧㄚ": "ya", "ㄓㄨㄟ": "zhui", "ㄍㄡ": "gou", "ㄅㄛ": "bo", "ㄋㄚ": "na", "ㄒㄧㄡ": "xiu", "ㄘㄨ": "cu", "ㄎㄨㄛ": "kuo", "ㄌㄠ": "lao", "ㄘㄨㄥ": "cong", "ㄉㄚ": "da", "ㄆㄛ": "po", "ㄙㄞ": "sai", "ㄌㄥ": "leng", "ㄖㄨㄥ": "rong", "ㄋㄧ": "ni", "ㄆㄠ": "pao", "ㄎㄢ": "kan", "ㄨㄥ": "weng", "ㄨㄢ": "wan", "ㄏㄠ": "hao", "ㄐㄧㄥ": "jing", "ㄊㄢ": "tan", "ㄅㄨ": "bu", "ㄗㄤ": "zang", "ㄐㄧㄡ": "jiu", "ㄇㄟ": "mei", "ㄇㄨ": "mu", "ㄉㄨㄟ": "dui", "ㄅㄤ": "bang", "ㄅㄠ": "bao", "ㄔㄤ": "chang", "ㄓㄤ": "zhang", "ㄗㄨㄥ": "zong", "ㄍㄨㄣ": "gun", "ㄌㄧㄠ": "liao", "ㄔㄢ": "chan", "ㄓㄜ": "zhe", "ㄇㄥ": "meng", "ㄑㄧㄠ": "qiao", "ㄋㄤ": "nang", "ㄩㄣ": "yun", "ㄎㄞ": "kai", "ㄍㄠ": "gao", "ㄊㄠ": "tao", "ㄕㄢ": "shan", "ㄌㄞ": "lai", "ㄅㄢ": "ban", "ㄎㄨㄥ": "kong", "ㄔㄨㄛ": "chuo", "ㄋㄨ": "nu", "ㄆㄟ": "pei", "ㄆㄥ": "peng", "ㄘㄢ": "can", "ㄙㄨㄛ": "suo", "ㄊㄨㄥ": "tong", "ㄑㄧㄤ": "qiang", "ㄙㄠ": "sao", "ㄓㄨㄢ": "zhuan", "ㄢ": "an", "ㄔㄚ": "cha", "ㄕㄚ": "sha", "ㄌㄧㄢ": "lian", "ㄇㄧ": "mi", "ㄋㄡ": "nou", "ㄘㄠ": "cao", "ㄙㄣ": "sen", "ㄋㄣ": "nen", "ㄋㄧㄢ": "nian", "ㄇㄞ": "mai", "ㄩㄝ": "yue", "ㄋㄞ": "nai", "ㄏㄨㄞ": "huai", "ㄗ": "zi", "ㄌㄨㄢ": "luan", "ㄉ��ㄥ": "ding", "ㄇㄤ": "mang", "ㄋㄧㄥ": "ning", "ㄇㄧㄥ": "ming", "ㄗㄨㄟ": "zui", "ㄎㄤ": "kang", "ㄉㄜ": "de", "ㄅㄧㄢ": "bian", "ㄐㄧㄣ": "jin", "ㄔㄨㄟ": "chui", "ㄊㄨㄟ": "tui", "ㄗㄚ": "za", "ㄘㄣ": "cen", "ㄇㄧㄣ": "min", "ㄏㄨㄤ": "huang", "ㄗㄨ": "zu", "ㄘㄨㄛ": "cuo", "ㄊㄨㄛ": "tuo", "ㄑㄩㄣ": "qun", "ㄅㄧㄣ": "bin", "ㄊㄧㄠ": "tiao", "ㄍㄤ": "gang", "ㄉㄨㄢ": "duan", "ㄅㄧㄠ": "biao", "ㄉㄠ": "dao", "ㄖㄨㄣ": "run", "ㄐㄧㄠ": "jiao", "ㄨㄛ": "wo", "ㄘㄨㄢ": "cuan", "ㄖㄣ": "ren", "ㄇㄣ": "men", "ㄓㄨㄣ": "zhun", "ㄎㄨㄣ": "kun", "ㄔㄨㄤ": "chuang", "ㄗㄠ": "zao", "ㄓㄥ": "zheng", "ㄆㄧㄣ": "pin", "ㄅㄣ": "ben", "ㄐㄧㄤ": "jiang", "ㄐㄩㄢ": "juan", "ㄘㄥ": "ceng", "ㄏㄤ": "hang", "ㄋㄧㄣ": "nin", "ㄌㄧㄝ": "lie", "ㄍㄨㄤ": "guang", "ㄙㄢ": "san", "ㄊㄜ": "te", "ㄕㄨㄣ": "shun", "ㄕㄨㄟ": "shui", "ㄔㄠ": "chao", "ㄘㄜ": "ce", "ㄍㄨㄞ": "guai", "ㄎㄥ": "keng", "ㄕㄞ": "shai", "ㄉㄣ": "den", "ㄊㄨㄢ": "tuan", "ㄆㄧㄠ": "piao", "ㄑㄧㄥ": "qing", "ㄍㄥ": "geng", "ㄔㄨㄞ": "chuai", "ㄕㄠ": "shao", "ㄍㄣ": "gen", "ㄋㄨㄢ": "nuan", "ㄖㄥ": "reng", "ㄇㄡ": "mou", "ㄆㄞ": "pai", "ㄤ": "ang", "ㄎㄚ": "ka", "ㄍㄨㄢ": "guan", "ㄕㄨㄛ": "shuo", "ㄏㄣ": "hen", "ㄔㄨㄢ": "chuan", "ㄎㄨㄢ": "kuan", "ㄏㄟ": "hei", "ㄇㄛ": "mo", "ㄗㄞ": "zai", "ㄋㄥ": "neng", "ㄕㄨㄞ": "shuai", "ㄖㄜ": "re", "ㄋㄩ": "nv", "ㄆㄧㄥ": "ping", "ㄘㄤ": "cang", "ㄋㄨㄥ": "nong", "ㄎㄠ": "kao", "ㄗㄨㄢ": "zuan", "ㄎㄣ": "ken", "ㄍㄚ": "ga", "ㄗㄣ": "zen", "ㄉㄤ": "dang", "ㄗㄥ": "zeng", "ㄉㄨㄣ": "dun", "ㄘㄚ": "ca", "ㄖㄤ": "rang", "ㄘㄨㄣ": "cun", "ㄖㄨㄛ": "ruo", "ㄊㄧㄝ": "tie", "ㄊㄥ": "teng", "ㄙㄥ": "seng", "ㄖ": "ri", "ㄗㄨㄣ": "zun", "ㄋㄧㄤ": "niang", "ㄋㄩㄝ": "nve", "ㄙㄤ": "sang", "ㄓㄨㄤ": "zhuang", "ㄕㄤ": "shang", "ㄆㄧㄝ": "pie", "ㄕㄨㄢ": "shuan", "ㄈㄡ": "fou", "ㄉㄧㄡ": "diu", "ㄇㄜ": "me", "ㄈㄛ": "fo", "ㄌㄧㄚ": "lia", "ㄎㄟ": "kei", "ㄏㄚ": "ha", "ㄚ": "a", "ㄌㄛ": "lo", "ㄧㄛ": "yo", "ㄛ": "o", "ㄏㄋㄍ": "hng", "ㄋ": "n", "ㄌㄣ": "len", "ㄉㄧㄚ": "dia", "ㄇㄧㄡ": "miu", "ㄉㄟ": "dei", "ㄏㄇ": "hm", "ㄋㄨㄣ": "nun", "ㄓㄨㄞ": "zhuai", "ㄊㄟ": "tei", "ㄗㄟ": "zei", "ㄓㄨㄚ": "zhua", "ㄖㄨㄚ": "rua", "ê": "ê", "ㄟ": "ei", "ㄍㄟ": "gei", "ㄈㄧㄠ": "fiao", "ㄕㄟ": "shei", "ㄓㄟ": "zhei", "ㄥ": "eng", "ㄘㄟ": "cei", "ㄉㄧㄣ": "din", "ㄅㄧㄤ": "biang", "ㄧㄞ": "yai"}

bert_vits2/g2pW/pypinyin_G2pW_bv2/g2pw1/char_convert.py

@@ -0,0 +1,44 @@
+# coding=utf-8
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Traditional and simplified Chinese conversion, a simplified character may correspond to multiple traditional characters.
+"""
+simplified_charcters = 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+
+traditional_characters = 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鐃鐄鐇鐏鐒鐓鐔鐗馗鐙鐝鐠鐡鐦鐨鐩鐫鐬鐱鐳鐶鐻鐽鐿鑀鑅鑌鑐鑕鑚鑛鑢鑤鑥鑪鑭鑯鑱鑴鑵鑷钁钃镻閆閈閌閎閒閔閗閟閡関閤閤閧閬閲閹閺閻閼閽閿闇闉闋闐闑闒闓闘闚闞闟闠闤闥阞阢阤阨阬阯阹阼阽陁陑陔陛陜陡陥陬騭陴険陼陾隂隃隈隒隗隞隠隣隤隩隮隰顴隳隷隹雂雈雉雊雎雑雒雗雘雚雝雟雩雰雱驛霂霅霈霊霑霒霓霙霝霢霣霤霨霩霪霫霮靁靆靉靑靚靣靦靪靮靰靳靷靸靺靼靿鞀鞃鞄鞌鞗鞙鞚鞝鞞鞡鞣鞨鞫鞬鞮鞶鞹鞾韃韅韉馱韍韎韔韖韘韝韞韡韣韭韮韱韹韺頀颳頄頇頊頍頎頏頒頖頞頠頫頬顱頯頲頴頼顇顋顑顒顓顔顕顚顜顢顣顬顳颭颮颱颶颸颺颻颽颾颿飀飂飈飌飜飡飣飤飥飩飫飮飱飶餀餂餄餎餇餈餑餔餕餖餗餚餛餜餟餠餤餧餩餪餫餬餮餱餲餳餺餻餼餽餿饁饅饇饉饊饍饎饐饘饟饢馘馥馝馡馣騮騾馵馹駃駄駅駆駉駋駑駓駔駗駘駙駜駡駢駪駬駰駴駸駹駽駾騂騄騅騆騉騋騍騏驎騑騒験騕騖騠騢騣騤騧驤騵騶騸騺驀驂驃驄驆驈驊驌驍驎驏驒驔驖驙驦驩驫骺鯁骫骭骯骱骴骶骷髏骾髁髂髄髆髈髐髑髕髖髙髝髞髟髡髣髧髪髫髭髯髲髳髹髺髽髾鬁鬃鬅鬈鬋鬎鬏鬐鬑鬒鬖鬗鬘鬙鬠鬣鬪鬫鬬鬮鬯鬰鬲鬵鬷魆魈魊魋魍魎魑魖鰾魛魟魣魦魨魬魴魵魸鮀鮁鮆鮌鮎鮑鮒鮓鮚鮞鮟鱇鮠鮦鮨鮪鮭鮶鮸鮿鯀鯄鯆鯇鯈鯔鯕鯖鯗鯙鯠鯤鯥鯫鯰鯷鯸鯿鰂鰆鶼鰉鰋鰐鰒鰕鰛鰜鰣鰤鰥鰦鰨鰩鰮鰳鰶鰷鱺鰼鰽鱀鱄鱅鱆鱈鱎鱐鱓鱔鱖鱘鱟鱠鱣鱨鱭鱮鱲鱵鱻鲅鳦鳧鳯鳲鳷鳻鴂鴃鴄鴆鴈鴎鴒鴔鴗鴛鴦鴝鵒鴟鴠鴢鴣鴥鴯鶓鴳鴴鴷鴽鵀鵁鵂鵓鵖鵙鵜鶘鵞鵟鵩鵪鵫鵵鵷鵻鵾鶂鶊鶏鶒鶖鶗鶡鶤鶦鶬鶱鶲鶵鶸鶹鶺鶿鷀鷁鷃鷄鷇鷈鷉鷊鷏鷓鷕鷖鷙鷞鷟鷥鷦鷯鷩鷫鷭鷳鷴鷽鷾鷿鸂鸇鸊鸏鸑鸒鸓鸕鸛鸜鸝鹸鹹鹺麀麂麃麄麇麋麌麐麑麒麚麛麝麤麩麪麫麮麯麰麺麾黁黈黌黢黒黓黕黙黝黟黥黦黧黮黰黱黲黶黹黻黼黽黿鼂鼃鼅鼈鼉鼏鼐鼒鼕鼖鼙鼚鼛鼡鼩鼱鼪鼫鼯鼷鼽齁齆齇齈齉齌齎齏齔齕齗齙齚齜齞齟齬齠齢齣齧齩齮齯齰齱齵齾龎龑龒龔龖龘龝龡龢龤"
+
+assert len(simplified_charcters) == len(simplified_charcters)
+
+s2t_dict = {}
+t2s_dict = {}
+for i, item in enumerate(simplified_charcters):
+    s2t_dict[item] = traditional_characters[i]
+    t2s_dict[traditional_characters[i]] = item
+
+
+def tranditional_to_simplified(text: str) -> str:
+    return "".join([t2s_dict[item] if item in t2s_dict else item for item in text])
+
+
+def simplified_to_traditional(text: str) -> str:
+    return "".join([s2t_dict[item] if item in s2t_dict else item for item in text])
+
+
+if __name__ == "__main__":
+    text = "一般是指存取一個應用程式啟動時始終顯示在網站或網頁瀏覽器中的一個或多個初始網頁等畫面存在的站點"
+    print(text)
+    text_simple = tranditional_to_simplified(text)
+    print(text_simple)
+    text_traditional = simplified_to_traditional(text_simple)
+    print(text_traditional)

bert_vits2/g2pW/pypinyin_G2pW_bv2/g2pw1/dataset.py

@@ -0,0 +1,181 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Credits
+    This code is modified from https://github.com/GitYCC/g2pW
+"""
+from typing import Dict
+from typing import List
+from typing import Tuple
+
+import numpy as np
+
+from .utils import tokenize_and_map
+
+ANCHOR_CHAR = "▁"
+
+
+def prepare_onnx_input(
+    tokenizer,
+    labels: List[str],
+    char2phonemes: Dict[str, List[int]],
+    chars: List[str],
+    texts: List[str],
+    query_ids: List[int],
+    use_mask: bool = False,
+    window_size: int = None,
+    max_len: int = 512,
+) -> Dict[str, np.array]:
+    if window_size is not None:
+        truncated_texts, truncated_query_ids = _truncate_texts(
+            window_size=window_size, texts=texts, query_ids=query_ids
+        )
+    input_ids = []
+    token_type_ids = []
+    attention_masks = []
+    phoneme_masks = []
+    char_ids = []
+    position_ids = []
+
+    for idx in range(len(texts)):
+        text = (truncated_texts if window_size else texts)[idx].lower()
+        query_id = (truncated_query_ids if window_size else query_ids)[idx]
+
+        try:
+            tokens, text2token, token2text = tokenize_and_map(
+                tokenizer=tokenizer, text=text
+            )
+        except Exception:
+            print(f'warning: text "{text}" is invalid')
+            return {}
+
+        text, query_id, tokens, text2token, token2text = _truncate(
+            max_len=max_len,
+            text=text,
+            query_id=query_id,
+            tokens=tokens,
+            text2token=text2token,
+            token2text=token2text,
+        )
+
+        processed_tokens = ["[CLS]"] + tokens + ["[SEP]"]
+
+        input_id = list(np.array(tokenizer.convert_tokens_to_ids(processed_tokens)))
+        token_type_id = list(np.zeros((len(processed_tokens),), dtype=int))
+        attention_mask = list(np.ones((len(processed_tokens),), dtype=int))
+
+        query_char = text[query_id]
+        phoneme_mask = (
+            [1 if i in char2phonemes[query_char] else 0 for i in range(len(labels))]
+            if use_mask
+            else [1] * len(labels)
+        )
+        char_id = chars.index(query_char)
+        position_id = text2token[query_id] + 1  # [CLS] token locate at first place
+
+        input_ids.append(input_id)
+        token_type_ids.append(token_type_id)
+        attention_masks.append(attention_mask)
+        phoneme_masks.append(phoneme_mask)
+        char_ids.append(char_id)
+        position_ids.append(position_id)
+
+    outputs = {
+        "input_ids": np.array(input_ids).astype(np.int64),
+        "token_type_ids": np.array(token_type_ids).astype(np.int64),
+        "attention_masks": np.array(attention_masks).astype(np.int64),
+        "phoneme_masks": np.array(phoneme_masks).astype(np.float32),
+        "char_ids": np.array(char_ids).astype(np.int64),
+        "position_ids": np.array(position_ids).astype(np.int64),
+    }
+    return outputs
+
+
+def _truncate_texts(
+    window_size: int, texts: List[str], query_ids: List[int]
+) -> Tuple[List[str], List[int]]:
+    truncated_texts = []
+    truncated_query_ids = []
+    for text, query_id in zip(texts, query_ids):
+        start = max(0, query_id - window_size // 2)
+        end = min(len(text), query_id + window_size // 2)
+        truncated_text = text[start:end]
+        truncated_texts.append(truncated_text)
+
+        truncated_query_id = query_id - start
+        truncated_query_ids.append(truncated_query_id)
+    return truncated_texts, truncated_query_ids
+
+
+def _truncate(
+    max_len: int,
+    text: str,
+    query_id: int,
+    tokens: List[str],
+    text2token: List[int],
+    token2text: List[Tuple[int]],
+):
+    truncate_len = max_len - 2
+    if len(tokens) <= truncate_len:
+        return (text, query_id, tokens, text2token, token2text)
+
+    token_position = text2token[query_id]
+
+    token_start = token_position - truncate_len // 2
+    token_end = token_start + truncate_len
+    font_exceed_dist = -token_start
+    back_exceed_dist = token_end - len(tokens)
+    if font_exceed_dist > 0:
+        token_start += font_exceed_dist
+        token_end += font_exceed_dist
+    elif back_exceed_dist > 0:
+        token_start -= back_exceed_dist
+        token_end -= back_exceed_dist
+
+    start = token2text[token_start][0]
+    end = token2text[token_end - 1][1]
+
+    return (
+        text[start:end],
+        query_id - start,
+        tokens[token_start:token_end],
+        [i - token_start if i is not None else None for i in text2token[start:end]],
+        [(s - start, e - start) for s, e in token2text[token_start:token_end]],
+    )
+
+
+def get_phoneme_labels(
+    polyphonic_chars: List[List[str]],
+) -> Tuple[List[str], Dict[str, List[int]]]:
+    labels = sorted(list(set([phoneme for char, phoneme in polyphonic_chars])))
+    char2phonemes = {}
+    for char, phoneme in polyphonic_chars:
+        if char not in char2phonemes:
+            char2phonemes[char] = []
+        char2phonemes[char].append(labels.index(phoneme))
+    return labels, char2phonemes
+
+
+def get_char_phoneme_labels(
+    polyphonic_chars: List[List[str]],
+) -> Tuple[List[str], Dict[str, List[int]]]:
+    labels = sorted(
+        list(set([f"{char} {phoneme}" for char, phoneme in polyphonic_chars]))
+    )
+    char2phonemes = {}
+    for char, phoneme in polyphonic_chars:
+        if char not in char2phonemes:
+            char2phonemes[char] = []
+        char2phonemes[char].append(labels.index(f"{char} {phoneme}"))
+    return labels, char2phonemes

bert_vits2/g2pW/pypinyin_G2pW_bv2/g2pw1/onnx_api.py

@@ -0,0 +1,273 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Credits
+    This code is modified from https://github.com/GitYCC/g2pW
+"""
+import json
+import os
+from typing import Any
+from typing import Dict
+from typing import List
+from typing import Tuple
+import numpy as np
+import onnxruntime
+from opencc import OpenCC
+from transformers import BertTokenizer
+from pypinyin import pinyin
+from pypinyin import Style
+
+from .dataset import get_char_phoneme_labels
+from .dataset import get_phoneme_labels
+from .dataset import prepare_onnx_input
+from .utils import load_config
+from .char_convert import tranditional_to_simplified
+
+model_version = "1.1"
+
+
+def predict(
+    session, onnx_input: Dict[str, Any], labels: List[str]
+) -> Tuple[List[str], List[float]]:
+    all_preds = []
+    all_confidences = []
+    probs = session.run(
+        [],
+        {
+            "input_ids": onnx_input["input_ids"],
+            "token_type_ids": onnx_input["token_type_ids"],
+            "attention_mask": onnx_input["attention_masks"],
+            "phoneme_mask": onnx_input["phoneme_masks"],
+            "char_ids": onnx_input["char_ids"],
+            "position_ids": onnx_input["position_ids"],
+        },
+    )[0]
+
+    preds = np.argmax(probs, axis=1).tolist()
+    max_probs = []
+    for index, arr in zip(preds, probs.tolist()):
+        max_probs.append(arr[index])
+    all_preds += [labels[pred] for pred in preds]
+    all_confidences += max_probs
+
+    return all_preds, all_confidences
+
+
+class G2PWOnnxConverter:
+    def __init__(
+        self,
+        model_dir: None,
+        model_source=None,
+        style: str = "bopomofo",
+        enable_non_tradional_chinese: bool = False,
+    ):
+        sess_options = onnxruntime.SessionOptions()
+        sess_options.graph_optimization_level = (
+            onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
+        )
+        sess_options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL
+        sess_options.intra_op_num_threads = os.cpu_count() - 1
+        try:
+            self.session_g2pw = onnxruntime.InferenceSession(
+                os.path.join(model_dir, "g2pW.onnx"),
+                sess_options=sess_options,
+                providers=["CUDAExecutionProvider"],
+            )
+        except:
+            self.session_g2pw = onnxruntime.InferenceSession(
+                os.path.join(model_dir, "g2pW.onnx"), sess_options=sess_options
+            )
+        self.config = load_config(
+            os.path.join(model_dir, "config.py"), use_default=True
+        )
+
+        self.model_source = (
+            os.path.join(os.path.abspath(os.curdir), model_source)
+            if model_source
+            else os.path.join(os.path.abspath(os.curdir), self.config.model_source)
+        )
+        self.enable_opencc = enable_non_tradional_chinese
+
+        self.tokenizer = (
+            BertTokenizer.from_pretrained(self.model_source)
+            if model_source
+            else BertTokenizer.from_pretrained(self.config.model_source)
+        )
+        polyphonic_chars_path = os.path.join(model_dir, "POLYPHONIC_CHARS.txt")
+        monophonic_chars_path = os.path.join(model_dir, "MONOPHONIC_CHARS.txt")
+
+        self.polyphonic_chars = [
+            line.split("\t")
+            for line in open(polyphonic_chars_path, encoding="utf-8")
+            .read()
+            .strip()
+            .split("\n")
+        ]
+        self.non_polyphonic = {
+            "一",
+            "不",
+            "和",
+            "咋",
+            "嗲",
+            "剖",
+            "差",
+            "攢",
+            "倒",
+            "難",
+            "奔",
+            "勁",
+            "拗",
+            "肖",
+            "瘙",
+            "誒",
+            "泊",
+            "听",
+            "噢",
+        }
+        self.non_monophonic = {"似", "攢"}
+        self.monophonic_chars = [
+            line.split("\t")
+            for line in open(monophonic_chars_path, encoding="utf-8")
+            .read()
+            .strip()
+            .split("\n")
+        ]
+        self.labels, self.char2phonemes = (
+            get_char_phoneme_labels(polyphonic_chars=self.polyphonic_chars)
+            if self.config.use_char_phoneme
+            else get_phoneme_labels(polyphonic_chars=self.polyphonic_chars)
+        )
+
+        self.chars = sorted(list(self.char2phonemes.keys()))
+
+        self.polyphonic_chars_new = set(self.chars)
+        for char in self.non_polyphonic:
+            if char in self.polyphonic_chars_new:
+                self.polyphonic_chars_new.remove(char)
+
+        self.monophonic_chars_dict = {
+            char: phoneme for char, phoneme in self.monophonic_chars
+        }
+        for char in self.non_monophonic:
+            if char in self.monophonic_chars_dict:
+                self.monophonic_chars_dict.pop(char)
+
+        self.pos_tags = ["UNK", "A", "C", "D", "I", "N", "P", "T", "V", "DE", "SHI"]
+
+        with open(
+            os.path.join(
+                os.path.dirname(os.path.abspath(__file__)),
+                "bopomofo_to_pinyin_wo_tune_dict.json",
+            ),
+            "r",
+            encoding="utf-8",
+        ) as fr:
+            self.bopomofo_convert_dict = json.load(fr)
+        self.style_convert_func = {
+            "bopomofo": lambda x: x,
+            "pinyin": self._convert_bopomofo_to_pinyin,
+        }[style]
+
+        with open(
+            os.path.join(
+                os.path.dirname(os.path.abspath(__file__)), "char_bopomofo_dict.json"
+            ),
+            "r",
+            encoding="utf-8",
+        ) as fr:
+            self.char_bopomofo_dict = json.load(fr)
+
+        if self.enable_opencc:
+            self.cc = OpenCC("s2tw")
+
+    def _convert_bopomofo_to_pinyin(self, bopomofo: str) -> str:
+        tone = bopomofo[-1]
+        assert tone in "12345"
+        component = self.bopomofo_convert_dict.get(bopomofo[:-1])
+        if component:
+            return component + tone
+        else:
+            print(f'Warning: "{bopomofo}" cannot convert to pinyin')
+            return None
+
+    def __call__(self, sentences: List[str]) -> List[List[str]]:
+        if isinstance(sentences, str):
+            sentences = [sentences]
+
+        if self.enable_opencc:
+            translated_sentences = []
+            for sent in sentences:
+                translated_sent = self.cc.convert(sent)
+                assert len(translated_sent) == len(sent)
+                translated_sentences.append(translated_sent)
+            sentences = translated_sentences
+
+        texts, query_ids, sent_ids, partial_results = self._prepare_data(
+            sentences=sentences
+        )
+        if len(texts) == 0:
+            # sentences no polyphonic words
+            return partial_results
+
+        onnx_input = prepare_onnx_input(
+            tokenizer=self.tokenizer,
+            labels=self.labels,
+            char2phonemes=self.char2phonemes,
+            chars=self.chars,
+            texts=texts,
+            query_ids=query_ids,
+            use_mask=self.config.use_mask,
+            window_size=None,
+        )
+
+        preds, confidences = predict(
+            session=self.session_g2pw, onnx_input=onnx_input, labels=self.labels
+        )
+        if self.config.use_char_phoneme:
+            preds = [pred.split(" ")[1] for pred in preds]
+
+        results = partial_results
+        for sent_id, query_id, pred in zip(sent_ids, query_ids, preds):
+            results[sent_id][query_id] = self.style_convert_func(pred)
+
+        return results
+
+    def _prepare_data(
+        self, sentences: List[str]
+    ) -> Tuple[List[str], List[int], List[int], List[List[str]]]:
+        texts, query_ids, sent_ids, partial_results = [], [], [], []
+        for sent_id, sent in enumerate(sentences):
+            # pypinyin works well for Simplified Chinese than Traditional Chinese
+            sent_s = tranditional_to_simplified(sent)
+            pypinyin_result = pinyin(
+                sent_s, neutral_tone_with_five=True, style=Style.TONE3
+            )
+            partial_result = [None] * len(sent)
+            for i, char in enumerate(sent):
+                if char in self.polyphonic_chars_new:
+                    texts.append(sent)
+                    query_ids.append(i)
+                    sent_ids.append(sent_id)
+                elif char in self.monophonic_chars_dict:
+                    partial_result[i] = self.style_convert_func(
+                        self.monophonic_chars_dict[char]
+                    )
+                elif char in self.char_bopomofo_dict:
+                    partial_result[i] = pypinyin_result[i][0]
+                    # partial_result[i] =  self.style_convert_func(self.char_bopomofo_dict[char][0])
+                else:
+                    partial_result[i] = pypinyin_result[i][0]
+
+            partial_results.append(partial_result)
+        return texts, query_ids, sent_ids, partial_results

bert_vits2/g2pW/pypinyin_G2pW_bv2/g2pw1/utils.py

@@ -0,0 +1,144 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Credits
+    This code is modified from https://github.com/GitYCC/g2pW
+"""
+import os
+import re
+
+
+def wordize_and_map(text: str):
+    words = []
+    index_map_from_text_to_word = []
+    index_map_from_word_to_text = []
+    while len(text) > 0:
+        match_space = re.match(r"^ +", text)
+        if match_space:
+            space_str = match_space.group(0)
+            index_map_from_text_to_word += [None] * len(space_str)
+            text = text[len(space_str) :]
+            continue
+
+        match_en = re.match(r"^[a-zA-Z0-9]+", text)
+        if match_en:
+            en_word = match_en.group(0)
+
+            word_start_pos = len(index_map_from_text_to_word)
+            word_end_pos = word_start_pos + len(en_word)
+            index_map_from_word_to_text.append((word_start_pos, word_end_pos))
+
+            index_map_from_text_to_word += [len(words)] * len(en_word)
+
+            words.append(en_word)
+            text = text[len(en_word) :]
+        else:
+            word_start_pos = len(index_map_from_text_to_word)
+            word_end_pos = word_start_pos + 1
+            index_map_from_word_to_text.append((word_start_pos, word_end_pos))
+
+            index_map_from_text_to_word += [len(words)]
+
+            words.append(text[0])
+            text = text[1:]
+    return words, index_map_from_text_to_word, index_map_from_word_to_text
+
+
+def tokenize_and_map(tokenizer, text: str):
+    words, text2word, word2text = wordize_and_map(text=text)
+
+    tokens = []
+    index_map_from_token_to_text = []
+    for word, (word_start, word_end) in zip(words, word2text):
+        word_tokens = tokenizer.tokenize(word)
+
+        if len(word_tokens) == 0 or word_tokens == ["[UNK]"]:
+            index_map_from_token_to_text.append((word_start, word_end))
+            tokens.append("[UNK]")
+        else:
+            current_word_start = word_start
+            for word_token in word_tokens:
+                word_token_len = len(re.sub(r"^##", "", word_token))
+                index_map_from_token_to_text.append(
+                    (current_word_start, current_word_start + word_token_len)
+                )
+                current_word_start = current_word_start + word_token_len
+                tokens.append(word_token)
+
+    index_map_from_text_to_token = text2word
+    for i, (token_start, token_end) in enumerate(index_map_from_token_to_text):
+        for token_pos in range(token_start, token_end):
+            index_map_from_text_to_token[token_pos] = i
+
+    return tokens, index_map_from_text_to_token, index_map_from_token_to_text
+
+
+def _load_config(config_path: os.PathLike):
+    import importlib.util
+
+    spec = importlib.util.spec_from_file_location("__init__", config_path)
+    config = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(config)
+    return config
+
+
+default_config_dict = {
+    "manual_seed": 1313,
+    "model_source": "bert-base-chinese",
+    "window_size": 32,
+    "num_workers": 2,
+    "use_mask": True,
+    "use_char_phoneme": False,
+    "use_conditional": True,
+    "param_conditional": {
+        "affect_location": "softmax",
+        "bias": True,
+        "char-linear": True,
+        "pos-linear": False,
+        "char+pos-second": True,
+        "char+pos-second_lowrank": False,
+        "lowrank_size": 0,
+        "char+pos-second_fm": False,
+        "fm_size": 0,
+        "fix_mode": None,
+        "count_json": "train.count.json",
+    },
+    "lr": 5e-5,
+    "val_interval": 200,
+    "num_iter": 10000,
+    "use_focal": False,
+    "param_focal": {"alpha": 0.0, "gamma": 0.7},
+    "use_pos": True,
+    "param_pos ": {
+        "weight": 0.1,
+        "pos_joint_training": True,
+        "train_pos_path": "train.pos",
+        "valid_pos_path": "dev.pos",
+        "test_pos_path": "test.pos",
+    },
+}
+
+
+def load_config(config_path: os.PathLike, use_default: bool = False):
+    config = _load_config(config_path)
+    if use_default:
+        for attr, val in default_config_dict.items():
+            if not hasattr(config, attr):
+                setattr(config, attr, val)
+            elif isinstance(val, dict):
+                d = getattr(config, attr)
+                for dict_k, dict_v in val.items():
+                    if dict_k not in d:
+                        d[dict_k] = dict_v
+    return config

bert_vits2/get_emo.py

@@ -0,0 +1,92 @@
+import librosa
+import numpy as np
+import torch
+import torch.nn as nn
+from transformers import Wav2Vec2Processor
+from transformers.models.wav2vec2.modeling_wav2vec2 import (
+    Wav2Vec2Model,
+    Wav2Vec2PreTrainedModel,
+)
+
+from contants import config
+
+
+class RegressionHead(nn.Module):
+    r"""Classification head."""
+
+    def __init__(self, config):
+        super().__init__()
+
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.dropout = nn.Dropout(config.final_dropout)
+        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
+
+    def forward(self, features, **kwargs):
+        x = features
+        x = self.dropout(x)
+        x = self.dense(x)
+        x = torch.tanh(x)
+        x = self.dropout(x)
+        x = self.out_proj(x)
+
+        return x
+
+
+class EmotionModel(Wav2Vec2PreTrainedModel):
+    r"""Speech emotion classifier."""
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.config = config
+        self.wav2vec2 = Wav2Vec2Model(config)
+        self.classifier = RegressionHead(config)
+        self.init_weights()
+
+    def forward(
+            self,
+            input_values,
+    ):
+        outputs = self.wav2vec2(input_values)
+        hidden_states = outputs[0]
+        hidden_states = torch.mean(hidden_states, dim=1)
+        logits = self.classifier(hidden_states)
+
+        return hidden_states, logits
+
+
+def process_func(
+        x: np.ndarray,
+        sampling_rate: int,
+        model: EmotionModel,
+        processor: Wav2Vec2Processor,
+        device: str,
+        embeddings: bool = False,
+) -> np.ndarray:
+    r"""Predict emotions or extract embeddings from raw audio signal."""
+    model = model.to(device)
+    y = processor(x, sampling_rate=sampling_rate)
+    y = y["input_values"][0]
+    y = torch.from_numpy(y).unsqueeze(0).to(device)
+
+    # run through model
+    with torch.no_grad():
+        y = model(y)[0 if embeddings else 1]
+
+    # convert to numpy
+    y = y.detach().cpu().numpy()
+
+    return y
+
+
+def get_emo(audio, emotion_model, processor):
+    wav, sr = librosa.load(audio, 16000)
+    device = config.system.device
+    return process_func(
+        np.expand_dims(wav, 0).astype(np.float),
+        sr,
+        emotion_model,
+        processor,
+        device,
+        embeddings=True,
+    ).squeeze(0)

bert_vits2/models.py

@@ -0,0 +1,799 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from bert_vits2 import commons
+from bert_vits2 import modules
+from bert_vits2 import attentions
+
+from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+from vector_quantize_pytorch import VectorQuantize
+
+from bert_vits2.commons import init_weights, get_padding
+from bert_vits2.text import num_languages
+
+
+class DurationDiscriminator(nn.Module):  # vits2
+    def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+
+        self.drop = nn.Dropout(p_dropout)
+        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
+        self.norm_1 = modules.LayerNorm(filter_channels)
+        self.conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_2 = modules.LayerNorm(filter_channels)
+        self.dur_proj = nn.Conv1d(1, filter_channels, 1)
+
+        self.pre_out_conv_1 = nn.Conv1d(2 * filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
+        self.pre_out_norm_1 = modules.LayerNorm(filter_channels)
+        self.pre_out_conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
+        self.pre_out_norm_2 = modules.LayerNorm(filter_channels)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+        self.output_layer = nn.Sequential(
+            nn.Linear(filter_channels, 1),
+            nn.Sigmoid()
+        )
+
+    def forward_probability(self, x, x_mask, dur, g=None):
+        dur = self.dur_proj(dur)
+        x = torch.cat([x, dur], dim=1)
+        x = self.pre_out_conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.pre_out_norm_1(x)
+        x = self.drop(x)
+        x = self.pre_out_conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.pre_out_norm_2(x)
+        x = self.drop(x)
+        x = x * x_mask
+        x = x.transpose(1, 2)
+        output_prob = self.output_layer(x)
+        return output_prob
+
+    def forward(self, x, x_mask, dur_r, dur_hat, g=None):
+        x = torch.detach(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_2(x)
+        x = self.drop(x)
+
+        output_probs = []
+        for dur in [dur_r, dur_hat]:
+            output_prob = self.forward_probability(x, x_mask, dur, g)
+            output_probs.append(output_prob)
+
+        return output_probs
+
+
+class Block(nn.Module):
+    def __init__(self, in_dim, hidden_dim) -> None:
+        super().__init__()
+        self.norm = nn.LayerNorm(in_dim)
+        self.mlp = MLP(in_dim, hidden_dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x + self.mlp(self.norm(x))
+        return x
+
+
+class MLP(nn.Module):
+    def __init__(self, in_dim, hidden_dim):
+        super().__init__()
+        self.c_fc1 = nn.Linear(in_dim, hidden_dim, bias=False)
+        self.c_fc2 = nn.Linear(in_dim, hidden_dim, bias=False)
+        self.c_proj = nn.Linear(hidden_dim, in_dim, bias=False)
+
+    def forward(self, x: torch.Tensor):
+        x = F.silu(self.c_fc1(x)) * self.c_fc2(x)
+        x = self.c_proj(x)
+        return x
+
+
+class TransformerCouplingBlock(nn.Module):
+    def __init__(self,
+                 channels,
+                 hidden_channels,
+                 filter_channels,
+                 n_heads,
+                 n_layers,
+                 kernel_size,
+                 p_dropout,
+                 n_flows=4,
+                 gin_channels=0,
+                 share_parameter=False
+                 ):
+
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+
+        self.wn = attentions.FFT(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout,
+                                 isflow=True, gin_channels=self.gin_channels) if share_parameter else None
+
+        for i in range(n_flows):
+            self.flows.append(
+                modules.TransformerCouplingLayer(channels, hidden_channels, kernel_size, n_layers, n_heads, p_dropout,
+                                                 filter_channels, mean_only=True, wn_sharing_parameter=self.wn,
+                                                 gin_channels=self.gin_channels))
+            self.flows.append(modules.Flip())
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        if not reverse:
+            for flow in self.flows:
+                x, _ = flow(x, x_mask, g=g, reverse=reverse)
+        else:
+            for flow in reversed(self.flows):
+                x = flow(x, x_mask, g=g, reverse=reverse)
+        return x
+
+
+class StochasticDurationPredictor(nn.Module):
+    def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
+        super().__init__()
+        filter_channels = in_channels  # it needs to be removed from future version.
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.log_flow = modules.Log()
+        self.flows = nn.ModuleList()
+        self.flows.append(modules.ElementwiseAffine(2))
+        for i in range(n_flows):
+            self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
+            self.flows.append(modules.Flip())
+
+        self.post_pre = nn.Conv1d(1, filter_channels, 1)
+        self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
+        self.post_flows = nn.ModuleList()
+        self.post_flows.append(modules.ElementwiseAffine(2))
+        for i in range(4):
+            self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
+            self.post_flows.append(modules.Flip())
+
+        self.pre = nn.Conv1d(in_channels, filter_channels, 1)
+        self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
+
+    def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
+        x = torch.detach(x)
+        x = self.pre(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.convs(x, x_mask)
+        x = self.proj(x) * x_mask
+
+        if not reverse:
+            flows = self.flows
+            assert w is not None
+
+            logdet_tot_q = 0
+            h_w = self.post_pre(w)
+            h_w = self.post_convs(h_w, x_mask)
+            h_w = self.post_proj(h_w) * x_mask
+            e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
+            z_q = e_q
+            for flow in self.post_flows:
+                z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
+                logdet_tot_q += logdet_q
+            z_u, z1 = torch.split(z_q, [1, 1], 1)
+            u = torch.sigmoid(z_u) * x_mask
+            z0 = (w - u) * x_mask
+            logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2])
+            logq = torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q ** 2)) * x_mask, [1, 2]) - logdet_tot_q
+
+            logdet_tot = 0
+            z0, logdet = self.log_flow(z0, x_mask)
+            logdet_tot += logdet
+            z = torch.cat([z0, z1], 1)
+            for flow in flows:
+                z, logdet = flow(z, x_mask, g=x, reverse=reverse)
+                logdet_tot = logdet_tot + logdet
+            nll = torch.sum(0.5 * (math.log(2 * math.pi) + (z ** 2)) * x_mask, [1, 2]) - logdet_tot
+            return nll + logq  # [b]
+        else:
+            flows = list(reversed(self.flows))
+            flows = flows[:-2] + [flows[-1]]  # remove a useless vflow
+            z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
+            for flow in flows:
+                z = flow(z, x_mask, g=x, reverse=reverse)
+            z0, z1 = torch.split(z, [1, 1], 1)
+            logw = z0
+            return logw
+
+
+class DurationPredictor(nn.Module):
+    def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+
+        self.drop = nn.Dropout(p_dropout)
+        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
+        self.norm_1 = modules.LayerNorm(filter_channels)
+        self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
+        self.norm_2 = modules.LayerNorm(filter_channels)
+        self.proj = nn.Conv1d(filter_channels, 1, 1)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+    def forward(self, x, x_mask, g=None):
+        x = torch.detach(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_2(x)
+        x = self.drop(x)
+        x = self.proj(x * x_mask)
+        return x * x_mask
+
+
+class TextEncoder(nn.Module):
+    def __init__(self,
+                 n_vocab,
+                 out_channels,
+                 hidden_channels,
+                 filter_channels,
+                 n_heads,
+                 n_layers,
+                 kernel_size,
+                 p_dropout,
+                 n_speakers,
+                 gin_channels=0,
+                 symbols=None,
+                 ja_bert_dim=1024,
+                 num_tones=None,
+                 emotion_embedding=1,
+                 zh_bert_extra=False,
+                 ):
+        super().__init__()
+        self.n_vocab = n_vocab
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+        self.emb = nn.Embedding(len(symbols), hidden_channels)
+        nn.init.normal_(self.emb.weight, 0.0, hidden_channels ** -0.5)
+        self.tone_emb = nn.Embedding(num_tones, hidden_channels)
+        nn.init.normal_(self.tone_emb.weight, 0.0, hidden_channels ** -0.5)
+        self.language_emb = nn.Embedding(num_languages, hidden_channels)
+        nn.init.normal_(self.language_emb.weight, 0.0, hidden_channels ** -0.5)
+        self.bert_proj = nn.Conv1d(1024, hidden_channels, 1)
+        self.zh_bert_extra = zh_bert_extra
+        if self.zh_bert_extra:
+            self.bert_pre_proj = nn.Conv1d(2048, 1024, 1)
+        self.ja_bert_proj = nn.Conv1d(ja_bert_dim, hidden_channels, 1)
+        self.en_bert_proj = nn.Conv1d(1024, hidden_channels, 1)
+        self.emotion_embedding = emotion_embedding
+
+        if self.emotion_embedding == 1:
+            self.emo_proj = nn.Linear(1024, 1024)
+            self.emo_quantizer = VectorQuantize(
+                dim=1024,
+                codebook_size=10,
+                decay=0.8,
+                commitment_weight=1.0,
+                learnable_codebook=True,
+                ema_update=False,
+            )
+            self.emo_q_proj = nn.Linear(1024, hidden_channels)
+        elif self.emotion_embedding == 2:
+            self.in_feature_net = nn.Sequential(
+                # input is assumed to an already normalized embedding
+                nn.Linear(512, 1028, bias=False),
+                nn.GELU(),
+                nn.LayerNorm(1028),
+                *[Block(1028, 512) for _ in range(1)],
+                nn.Linear(1028, 512, bias=False),
+                # normalize before passing to VQ?
+                # nn.GELU(),
+                # nn.LayerNorm(512),
+            )
+            self.emo_vq = VectorQuantize(
+                dim=512,
+                codebook_size=64,
+                codebook_dim=32,
+                commitment_weight=0.1,
+                decay=0.85,
+                heads=32,
+                kmeans_iters=20,
+                separate_codebook_per_head=True,
+                stochastic_sample_codes=True,
+                threshold_ema_dead_code=2,
+            )
+            self.out_feature_net = nn.Linear(512, hidden_channels)
+
+        self.encoder = attentions.Encoder(
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            gin_channels=self.gin_channels)
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, x, x_lengths, tone, language, zh_bert, ja_bert, en_bert, emo=None, sid=None, g=None):
+        x = self.emb(x) + self.tone_emb(tone) + self.language_emb(language)
+
+        if self.zh_bert_extra:
+            zh_bert = self.bert_pre_proj(zh_bert)
+        x += self.bert_proj(zh_bert).transpose(1, 2)
+        x += self.ja_bert_proj(ja_bert).transpose(1, 2)
+        x += self.en_bert_proj(en_bert).transpose(1, 2)
+
+        x *= math.sqrt(self.hidden_channels)  # [b, t, h]
+        if self.emotion_embedding == 1:
+            # emo = emo.to(zh_bert_emb.device)
+            if emo.size(-1) == 1024:
+                emo_emb = self.emo_proj(emo.unsqueeze(1))
+                emo_emb_ = []
+                for i in range(emo_emb.size(0)):
+                    temp_emo_emb, _, _ = self.emo_quantizer(
+                        emo_emb[i].unsqueeze(0).to(emo.device)
+                    )
+                    emo_emb_.append(temp_emo_emb)
+                emo_emb = torch.cat(emo_emb_, dim=0).to(emo_emb.device)
+            else:
+                emo_emb = (
+                    self.emo_quantizer.get_output_from_indices(emo.to(torch.long))
+                        .unsqueeze(0)
+                        .to(emo.device)
+                )
+
+            x += self.emo_q_proj(emo_emb)
+        elif self.emotion_embedding == 2:
+            emo_emb = self.in_feature_net(emo)
+            emo_emb, _, _ = self.emo_vq(emo_emb.unsqueeze(1))
+            emo_emb = self.out_feature_net(emo_emb)
+            x += emo_emb
+
+        x *= math.sqrt(self.hidden_channels)  # [b, t, h]
+        x = torch.transpose(x, 1, -1)  # [b, h, t]
+        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
+
+        x = self.encoder(x * x_mask, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        return x, m, logs, x_mask
+
+
+class ResidualCouplingBlock(nn.Module):
+    def __init__(self,
+                 channels,
+                 hidden_channels,
+                 kernel_size,
+                 dilation_rate,
+                 n_layers,
+                 n_flows=4,
+                 gin_channels=0):
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+        for i in range(n_flows):
+            self.flows.append(
+                modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers,
+                                              gin_channels=gin_channels, mean_only=True))
+            self.flows.append(modules.Flip())
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        if not reverse:
+            for flow in self.flows:
+                x, _ = flow(x, x_mask, g=g, reverse=reverse)
+        else:
+            for flow in reversed(self.flows):
+                x = flow(x, x_mask, g=g, reverse=reverse)
+        return x
+
+
+class PosteriorEncoder(nn.Module):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 hidden_channels,
+                 kernel_size,
+                 dilation_rate,
+                 n_layers,
+                 gin_channels=0):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.gin_channels = gin_channels
+
+        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+        self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, x, x_lengths, g=None):
+        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
+        x = self.pre(x) * x_mask
+        x = self.enc(x, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
+        return z, m, logs, x_mask
+
+
+class Generator(torch.nn.Module):
+    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
+                 upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
+        super(Generator, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
+        resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(weight_norm(
+                ConvTranspose1d(upsample_initial_channel // (2 ** i), upsample_initial_channel // (2 ** (i + 1)),
+                                k, u, padding=(k - u) // 2)))
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel // (2 ** (i + 1))
+            for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
+                self.resblocks.append(resblock(ch, k, d))
+
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+    def forward(self, x, g=None):
+        x = self.conv_pre(x)
+        if g is not None:
+            x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        print('Removing weight norm...')
+        for l in self.ups:
+            remove_weight_norm(l)
+        for l in self.resblocks:
+            l.remove_weight_norm()
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        self.use_spectral_norm = use_spectral_norm
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList([
+            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
+        ])
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x):
+        fmap = []
+
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0:  # pad first
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList([
+            norm_f(Conv1d(1, 16, 15, 1, padding=7)),
+            norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
+            norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
+            norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
+            norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
+            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
+        ])
+        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        fmap = []
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(MultiPeriodDiscriminator, self).__init__()
+        periods = [2, 3, 5, 7, 11]
+
+        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
+        self.discriminators = nn.ModuleList(discs)
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            y_d_gs.append(y_d_g)
+            fmap_rs.append(fmap_r)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+class ReferenceEncoder(nn.Module):
+    '''
+    inputs --- [N, Ty/r, n_mels*r]  mels
+    outputs --- [N, ref_enc_gru_size]
+    '''
+
+    def __init__(self, spec_channels, gin_channels=0):
+
+        super().__init__()
+        self.spec_channels = spec_channels
+        ref_enc_filters = [32, 32, 64, 64, 128, 128]
+        K = len(ref_enc_filters)
+        filters = [1] + ref_enc_filters
+        convs = [weight_norm(nn.Conv2d(in_channels=filters[i],
+                                       out_channels=filters[i + 1],
+                                       kernel_size=(3, 3),
+                                       stride=(2, 2),
+                                       padding=(1, 1))) for i in range(K)]
+        self.convs = nn.ModuleList(convs)
+        # self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)])
+
+        out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K)
+        self.gru = nn.GRU(input_size=ref_enc_filters[-1] * out_channels,
+                          hidden_size=256 // 2,
+                          batch_first=True)
+        self.proj = nn.Linear(128, gin_channels)
+
+    def forward(self, inputs, mask=None):
+        N = inputs.size(0)
+        out = inputs.view(N, 1, -1, self.spec_channels)  # [N, 1, Ty, n_freqs]
+        for conv in self.convs:
+            out = conv(out)
+            # out = wn(out)
+            out = F.relu(out)  # [N, 128, Ty//2^K, n_mels//2^K]
+
+        out = out.transpose(1, 2)  # [N, Ty//2^K, 128, n_mels//2^K]
+        T = out.size(1)
+        N = out.size(0)
+        out = out.contiguous().view(N, T, -1)  # [N, Ty//2^K, 128*n_mels//2^K]
+
+        self.gru.flatten_parameters()
+        memory, out = self.gru(out)  # out --- [1, N, 128]
+
+        return self.proj(out.squeeze(0))
+
+    def calculate_channels(self, L, kernel_size, stride, pad, n_convs):
+        for i in range(n_convs):
+            L = (L - kernel_size + 2 * pad) // stride + 1
+        return L
+
+
+class SynthesizerTrn(nn.Module):
+    """
+      Synthesizer for Training
+      """
+
+    def __init__(self,
+                 n_vocab,
+                 spec_channels,
+                 segment_size,
+                 inter_channels,
+                 hidden_channels,
+                 filter_channels,
+                 n_heads,
+                 n_layers,
+                 kernel_size,
+                 p_dropout,
+                 resblock,
+                 resblock_kernel_sizes,
+                 resblock_dilation_sizes,
+                 upsample_rates,
+                 upsample_initial_channel,
+                 upsample_kernel_sizes,
+                 n_speakers=256,
+                 gin_channels=256,
+                 use_sdp=True,
+                 n_flow_layer=4,
+                 n_layers_trans_flow=6,
+                 flow_share_parameter=False,
+                 use_transformer_flow=True,
+                 symbols=None,
+                 ja_bert_dim=1024,
+                 num_tones=None,
+                 emotion_embedding=False,
+                 zh_bert_extra=False,
+                 **kwargs):
+
+        super().__init__()
+        self.n_vocab = n_vocab
+        self.spec_channels = spec_channels
+        self.inter_channels = inter_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.resblock = resblock
+        self.resblock_kernel_sizes = resblock_kernel_sizes
+        self.resblock_dilation_sizes = resblock_dilation_sizes
+        self.upsample_rates = upsample_rates
+        self.upsample_initial_channel = upsample_initial_channel
+        self.upsample_kernel_sizes = upsample_kernel_sizes
+        self.segment_size = segment_size
+        self.n_speakers = n_speakers
+        self.gin_channels = gin_channels
+        self.n_layers_trans_flow = n_layers_trans_flow
+        self.use_spk_conditioned_encoder = kwargs.get("use_spk_conditioned_encoder", True)
+        self.use_sdp = use_sdp
+        self.use_noise_scaled_mas = kwargs.get("use_noise_scaled_mas", False)
+        self.mas_noise_scale_initial = kwargs.get("mas_noise_scale_initial", 0.01)
+        self.noise_scale_delta = kwargs.get("noise_scale_delta", 2e-6)
+        self.current_mas_noise_scale = self.mas_noise_scale_initial
+        if self.use_spk_conditioned_encoder and gin_channels > 0:
+            self.enc_gin_channels = gin_channels
+        self.emotion_embedding = emotion_embedding
+        self.enc_p = TextEncoder(n_vocab,
+                                 inter_channels,
+                                 hidden_channels,
+                                 filter_channels,
+                                 n_heads,
+                                 n_layers,
+                                 kernel_size,
+                                 p_dropout,
+                                 self.n_speakers,
+                                 gin_channels=self.enc_gin_channels,
+                                 symbols=symbols,
+                                 ja_bert_dim=ja_bert_dim,
+                                 num_tones=num_tones,
+                                 emotion_embedding=self.emotion_embedding,
+                                 zh_bert_extra=zh_bert_extra,
+                                 )
+        self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
+                             upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
+        self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16,
+                                      gin_channels=gin_channels)
+        if use_transformer_flow:
+            self.flow = TransformerCouplingBlock(inter_channels, hidden_channels, filter_channels, n_heads,
+                                                 n_layers_trans_flow, 5, p_dropout, n_flow_layer,
+                                                 gin_channels=gin_channels, share_parameter=flow_share_parameter)
+        else:
+            self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, n_flow_layer,
+                                              gin_channels=gin_channels)
+        self.sdp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
+        self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
+
+        if self.n_speakers > 0:
+            self.emb_g = nn.Embedding(self.n_speakers, gin_channels)
+        else:
+            self.ref_enc = ReferenceEncoder(spec_channels, gin_channels)
+
+    def infer(self, x, x_lengths, sid, tone, language, zh_bert, ja_bert, en_bert, noise_scale=.667, length_scale=1,
+              noise_scale_w=0.8, max_len=None, sdp_ratio=0, y=None, emo=None):
+        # x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert)
+        # g = self.gst(y)
+        if self.n_speakers > 0:
+            g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
+        else:
+            g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1)
+        x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, zh_bert, ja_bert, en_bert, emo, sid, g=g)
+        logw = self.sdp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) * (sdp_ratio) + self.dp(x, x_mask,
+                                                                                                             g=g) * (
+                               1 - sdp_ratio)
+        w = torch.exp(logw) * x_mask * length_scale
+        w_ceil = torch.ceil(w)
+        y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
+        y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
+        attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
+        attn = commons.generate_path(w_ceil, attn_mask)
+
+        m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)  # [b, t', t], [b, t, d] -> [b, d, t']
+        logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1,
+                                                                                 2)  # [b, t', t], [b, t, d] -> [b, d, t']
+
+        z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
+        z = self.flow(z_p, y_mask, g=g, reverse=True)
+        o = self.dec((z * y_mask)[:, :, :max_len], g=g)
+        return o, attn, y_mask, (z, z_p, m_p, logs_p)

bert_vits2/models_ja_extra.py

@@ -0,0 +1,1016 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from bert_vits2 import commons
+from bert_vits2 import modules
+from bert_vits2 import attentions
+
+from torch.nn import Conv1d, ConvTranspose1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+
+from bert_vits2.commons import init_weights, get_padding
+from bert_vits2.text import symbols, num_tones, num_languages
+
+from vector_quantize_pytorch import VectorQuantize
+
+
+class DurationDiscriminator(nn.Module):  # vits2
+    def __init__(
+            self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+
+        self.drop = nn.Dropout(p_dropout)
+        self.conv_1 = nn.Conv1d(
+            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_1 = modules.LayerNorm(filter_channels)
+        self.conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_2 = modules.LayerNorm(filter_channels)
+        self.dur_proj = nn.Conv1d(1, filter_channels, 1)
+
+        self.LSTM = nn.LSTM(
+            2 * filter_channels, filter_channels, batch_first=True, bidirectional=True
+        )
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+        self.output_layer = nn.Sequential(
+            nn.Linear(2 * filter_channels, 1), nn.Sigmoid()
+        )
+
+    def forward_probability(self, x, dur):
+        dur = self.dur_proj(dur)
+        x = torch.cat([x, dur], dim=1)
+        x = x.transpose(1, 2)
+        x, _ = self.LSTM(x)
+        output_prob = self.output_layer(x)
+        return output_prob
+
+    def forward(self, x, x_mask, dur_r, dur_hat, g=None):
+        x = torch.detach(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_2(x)
+        x = self.drop(x)
+
+        output_probs = []
+        for dur in [dur_r, dur_hat]:
+            output_prob = self.forward_probability(x, dur)
+            output_probs.append(output_prob)
+
+        return output_probs
+
+
+class TransformerCouplingBlock(nn.Module):
+    def __init__(
+            self,
+            channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            n_flows=4,
+            gin_channels=0,
+            share_parameter=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+
+        self.wn = (
+            attentions.FFT(
+                hidden_channels,
+                filter_channels,
+                n_heads,
+                n_layers,
+                kernel_size,
+                p_dropout,
+                isflow=True,
+                gin_channels=self.gin_channels,
+            )
+            if share_parameter
+            else None
+        )
+
+        for i in range(n_flows):
+            self.flows.append(
+                modules.TransformerCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    n_layers,
+                    n_heads,
+                    p_dropout,
+                    filter_channels,
+                    mean_only=True,
+                    wn_sharing_parameter=self.wn,
+                    gin_channels=self.gin_channels,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        if not reverse:
+            for flow in self.flows:
+                x, _ = flow(x, x_mask, g=g, reverse=reverse)
+        else:
+            for flow in reversed(self.flows):
+                x = flow(x, x_mask, g=g, reverse=reverse)
+        return x
+
+
+class StochasticDurationPredictor(nn.Module):
+    def __init__(
+            self,
+            in_channels,
+            filter_channels,
+            kernel_size,
+            p_dropout,
+            n_flows=4,
+            gin_channels=0,
+    ):
+        super().__init__()
+        filter_channels = in_channels  # it needs to be removed from future version.
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.log_flow = modules.Log()
+        self.flows = nn.ModuleList()
+        self.flows.append(modules.ElementwiseAffine(2))
+        for i in range(n_flows):
+            self.flows.append(
+                modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
+            )
+            self.flows.append(modules.Flip())
+
+        self.post_pre = nn.Conv1d(1, filter_channels, 1)
+        self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.post_convs = modules.DDSConv(
+            filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
+        )
+        self.post_flows = nn.ModuleList()
+        self.post_flows.append(modules.ElementwiseAffine(2))
+        for i in range(4):
+            self.post_flows.append(
+                modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
+            )
+            self.post_flows.append(modules.Flip())
+
+        self.pre = nn.Conv1d(in_channels, filter_channels, 1)
+        self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.convs = modules.DDSConv(
+            filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
+        )
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
+
+    def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
+        x = torch.detach(x)
+        x = self.pre(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.convs(x, x_mask)
+        x = self.proj(x) * x_mask
+
+        if not reverse:
+            flows = self.flows
+            assert w is not None
+
+            logdet_tot_q = 0
+            h_w = self.post_pre(w)
+            h_w = self.post_convs(h_w, x_mask)
+            h_w = self.post_proj(h_w) * x_mask
+            e_q = (
+                    torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype)
+                    * x_mask
+            )
+            z_q = e_q
+            for flow in self.post_flows:
+                z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
+                logdet_tot_q += logdet_q
+            z_u, z1 = torch.split(z_q, [1, 1], 1)
+            u = torch.sigmoid(z_u) * x_mask
+            z0 = (w - u) * x_mask
+            logdet_tot_q += torch.sum(
+                (F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2]
+            )
+            logq = (
+                    torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q ** 2)) * x_mask, [1, 2])
+                    - logdet_tot_q
+            )
+
+            logdet_tot = 0
+            z0, logdet = self.log_flow(z0, x_mask)
+            logdet_tot += logdet
+            z = torch.cat([z0, z1], 1)
+            for flow in flows:
+                z, logdet = flow(z, x_mask, g=x, reverse=reverse)
+                logdet_tot = logdet_tot + logdet
+            nll = (
+                    torch.sum(0.5 * (math.log(2 * math.pi) + (z ** 2)) * x_mask, [1, 2])
+                    - logdet_tot
+            )
+            return nll + logq  # [b]
+        else:
+            flows = list(reversed(self.flows))
+            flows = flows[:-2] + [flows[-1]]  # remove a useless vflow
+            z = (
+                    torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype)
+                    * noise_scale
+            )
+            for flow in flows:
+                z = flow(z, x_mask, g=x, reverse=reverse)
+            z0, z1 = torch.split(z, [1, 1], 1)
+            logw = z0
+            return logw
+
+
+class DurationPredictor(nn.Module):
+    def __init__(
+            self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+
+        self.drop = nn.Dropout(p_dropout)
+        self.conv_1 = nn.Conv1d(
+            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_1 = modules.LayerNorm(filter_channels)
+        self.conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_2 = modules.LayerNorm(filter_channels)
+        self.proj = nn.Conv1d(filter_channels, 1, 1)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+    def forward(self, x, x_mask, g=None):
+        x = torch.detach(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_2(x)
+        x = self.drop(x)
+        x = self.proj(x * x_mask)
+        return x * x_mask
+
+
+class Bottleneck(nn.Sequential):
+    def __init__(self, in_dim, hidden_dim):
+        c_fc1 = nn.Linear(in_dim, hidden_dim, bias=False)
+        c_fc2 = nn.Linear(in_dim, hidden_dim, bias=False)
+        super().__init__(*[c_fc1, c_fc2])
+
+
+class Block(nn.Module):
+    def __init__(self, in_dim, hidden_dim) -> None:
+        super().__init__()
+        self.norm = nn.LayerNorm(in_dim)
+        self.mlp = MLP(in_dim, hidden_dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x + self.mlp(self.norm(x))
+        return x
+
+
+class MLP(nn.Module):
+    def __init__(self, in_dim, hidden_dim):
+        super().__init__()
+        self.c_fc1 = nn.Linear(in_dim, hidden_dim, bias=False)
+        self.c_fc2 = nn.Linear(in_dim, hidden_dim, bias=False)
+        self.c_proj = nn.Linear(hidden_dim, in_dim, bias=False)
+
+    def forward(self, x: torch.Tensor):
+        x = F.silu(self.c_fc1(x)) * self.c_fc2(x)
+        x = self.c_proj(x)
+        return x
+
+
+class TextEncoder(nn.Module):
+    def __init__(
+            self,
+            n_vocab,
+            out_channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            gin_channels=0,
+    ):
+        super().__init__()
+        self.n_vocab = n_vocab
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+        self.emb = nn.Embedding(len(symbols), hidden_channels)
+        nn.init.normal_(self.emb.weight, 0.0, hidden_channels ** -0.5)
+        self.tone_emb = nn.Embedding(num_tones, hidden_channels)
+        nn.init.normal_(self.tone_emb.weight, 0.0, hidden_channels ** -0.5)
+        self.language_emb = nn.Embedding(num_languages, hidden_channels)
+        nn.init.normal_(self.language_emb.weight, 0.0, hidden_channels ** -0.5)
+        self.bert_proj = nn.Conv1d(1024, hidden_channels, 1)
+        # self.bert_pre_proj = nn.Conv1d(2048, 1024, 1)
+        # self.en_bert_proj = nn.Conv1d(1024, hidden_channels, 1)
+        self.in_feature_net = nn.Sequential(
+            # input is assumed to an already normalized embedding
+            nn.Linear(512, 1028, bias=False),
+            nn.GELU(),
+            nn.LayerNorm(1028),
+            *[Block(1028, 512) for _ in range(1)],
+            nn.Linear(1028, 512, bias=False),
+            # normalize before passing to VQ?
+            # nn.GELU(),
+            # nn.LayerNorm(512),
+        )
+        self.emo_vq = VectorQuantize(
+            dim=512,
+            # codebook_size=128,
+            codebook_size=256,
+            codebook_dim=16,
+            # codebook_dim=32,
+            commitment_weight=0.1,
+            decay=0.99,
+            heads=32,
+            kmeans_iters=20,
+            separate_codebook_per_head=True,
+            stochastic_sample_codes=True,
+            threshold_ema_dead_code=2,
+            use_cosine_sim=True,
+        )
+        self.out_feature_net = nn.Linear(512, hidden_channels)
+
+        self.encoder = attentions.Encoder(
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            gin_channels=self.gin_channels,
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, x, x_lengths, tone, language, bert, emo, g=None):
+        bert_emb = self.bert_proj(bert).transpose(1, 2)
+        # en_bert_emb = self.en_bert_proj(en_bert).transpose(1, 2)
+        emo_emb = self.in_feature_net(emo)
+        emo_emb, _, loss_commit = self.emo_vq(emo_emb.unsqueeze(1))
+        loss_commit = loss_commit.mean()
+        emo_emb = self.out_feature_net(emo_emb)
+        x = (
+                    self.emb(x)
+                    + self.tone_emb(tone)
+                    + self.language_emb(language)
+                    + bert_emb
+                    # + en_bert_emb
+                    + emo_emb
+            ) * math.sqrt(
+            self.hidden_channels
+        )  # [b, t, h]
+        x = torch.transpose(x, 1, -1)  # [b, h, t]
+        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
+            x.dtype
+        )
+
+        x = self.encoder(x * x_mask, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        return x, m, logs, x_mask, loss_commit
+
+
+class ResidualCouplingBlock(nn.Module):
+    def __init__(
+            self,
+            channels,
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            n_flows=4,
+            gin_channels=0,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+        for i in range(n_flows):
+            self.flows.append(
+                modules.ResidualCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    dilation_rate,
+                    n_layers,
+                    gin_channels=gin_channels,
+                    mean_only=True,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        if not reverse:
+            for flow in self.flows:
+                x, _ = flow(x, x_mask, g=g, reverse=reverse)
+        else:
+            for flow in reversed(self.flows):
+                x = flow(x, x_mask, g=g, reverse=reverse)
+        return x
+
+
+class PosteriorEncoder(nn.Module):
+    def __init__(
+            self,
+            in_channels,
+            out_channels,
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            gin_channels=0,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.gin_channels = gin_channels
+
+        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+        self.enc = modules.WN(
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            gin_channels=gin_channels,
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, x, x_lengths, g=None):
+        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
+            x.dtype
+        )
+        x = self.pre(x) * x_mask
+        x = self.enc(x, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
+        return z, m, logs, x_mask
+
+
+class Generator(torch.nn.Module):
+    def __init__(
+            self,
+            initial_channel,
+            resblock,
+            resblock_kernel_sizes,
+            resblock_dilation_sizes,
+            upsample_rates,
+            upsample_initial_channel,
+            upsample_kernel_sizes,
+            gin_channels=0,
+    ):
+        super(Generator, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = Conv1d(
+            initial_channel, upsample_initial_channel, 7, 1, padding=3
+        )
+        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(
+                weight_norm(
+                    ConvTranspose1d(
+                        upsample_initial_channel // (2 ** i),
+                        upsample_initial_channel // (2 ** (i + 1)),
+                        k,
+                        u,
+                        padding=(k - u) // 2,
+                    )
+                )
+            )
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel // (2 ** (i + 1))
+            for j, (k, d) in enumerate(
+                    zip(resblock_kernel_sizes, resblock_dilation_sizes)
+            ):
+                self.resblocks.append(resblock(ch, k, d))
+
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+    def forward(self, x, g=None):
+        x = self.conv_pre(x)
+        if g is not None:
+            x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        print("Removing weight norm...")
+        for layer in self.ups:
+            remove_weight_norm(layer)
+        for layer in self.resblocks:
+            layer.remove_weight_norm()
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        self.use_spectral_norm = use_spectral_norm
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(
+                    Conv2d(
+                        1,
+                        32,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        32,
+                        128,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        128,
+                        512,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        512,
+                        1024,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        1024,
+                        1024,
+                        (kernel_size, 1),
+                        1,
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+            ]
+        )
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x):
+        fmap = []
+
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0:  # pad first
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for layer in self.convs:
+            x = layer(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
+                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
+                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
+                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
+                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
+                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
+            ]
+        )
+        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        fmap = []
+
+        for layer in self.convs:
+            x = layer(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(MultiPeriodDiscriminator, self).__init__()
+        periods = [2, 3, 5, 7, 11]
+
+        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+        discs = discs + [
+            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
+        ]
+        self.discriminators = nn.ModuleList(discs)
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            y_d_gs.append(y_d_g)
+            fmap_rs.append(fmap_r)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+class WavLMDiscriminator(nn.Module):
+    """docstring for Discriminator."""
+
+    def __init__(
+            self, slm_hidden=768, slm_layers=13, initial_channel=64, use_spectral_norm=False
+    ):
+        super(WavLMDiscriminator, self).__init__()
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.pre = norm_f(
+            Conv1d(slm_hidden * slm_layers, initial_channel, 1, 1, padding=0)
+        )
+
+        self.convs = nn.ModuleList(
+            [
+                norm_f(
+                    nn.Conv1d(
+                        initial_channel, initial_channel * 2, kernel_size=5, padding=2
+                    )
+                ),
+                norm_f(
+                    nn.Conv1d(
+                        initial_channel * 2,
+                        initial_channel * 4,
+                        kernel_size=5,
+                        padding=2,
+                    )
+                ),
+                norm_f(
+                    nn.Conv1d(initial_channel * 4, initial_channel * 4, 5, 1, padding=2)
+                ),
+            ]
+        )
+
+        self.conv_post = norm_f(Conv1d(initial_channel * 4, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        x = self.pre(x)
+
+        fmap = []
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x
+
+
+class ReferenceEncoder(nn.Module):
+    """
+    inputs --- [N, Ty/r, n_mels*r]  mels
+    outputs --- [N, ref_enc_gru_size]
+    """
+
+    def __init__(self, spec_channels, gin_channels=0):
+        super().__init__()
+        self.spec_channels = spec_channels
+        ref_enc_filters = [32, 32, 64, 64, 128, 128]
+        K = len(ref_enc_filters)
+        filters = [1] + ref_enc_filters
+        convs = [
+            weight_norm(
+                nn.Conv2d(
+                    in_channels=filters[i],
+                    out_channels=filters[i + 1],
+                    kernel_size=(3, 3),
+                    stride=(2, 2),
+                    padding=(1, 1),
+                )
+            )
+            for i in range(K)
+        ]
+        self.convs = nn.ModuleList(convs)
+        # self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)]) # noqa: E501
+
+        out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K)
+        self.gru = nn.GRU(
+            input_size=ref_enc_filters[-1] * out_channels,
+            hidden_size=256 // 2,
+            batch_first=True,
+        )
+        self.proj = nn.Linear(128, gin_channels)
+
+    def forward(self, inputs, mask=None):
+        N = inputs.size(0)
+        out = inputs.view(N, 1, -1, self.spec_channels)  # [N, 1, Ty, n_freqs]
+        for conv in self.convs:
+            out = conv(out)
+            # out = wn(out)
+            out = F.relu(out)  # [N, 128, Ty//2^K, n_mels//2^K]
+
+        out = out.transpose(1, 2)  # [N, Ty//2^K, 128, n_mels//2^K]
+        T = out.size(1)
+        N = out.size(0)
+        out = out.contiguous().view(N, T, -1)  # [N, Ty//2^K, 128*n_mels//2^K]
+
+        self.gru.flatten_parameters()
+        memory, out = self.gru(out)  # out --- [1, N, 128]
+
+        return self.proj(out.squeeze(0))
+
+    def calculate_channels(self, L, kernel_size, stride, pad, n_convs):
+        for i in range(n_convs):
+            L = (L - kernel_size + 2 * pad) // stride + 1
+        return L
+
+
+class SynthesizerTrn(nn.Module):
+    """
+    Synthesizer for Training
+    """
+
+    def __init__(
+            self,
+            n_vocab,
+            spec_channels,
+            segment_size,
+            inter_channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            resblock,
+            resblock_kernel_sizes,
+            resblock_dilation_sizes,
+            upsample_rates,
+            upsample_initial_channel,
+            upsample_kernel_sizes,
+            n_speakers=256,
+            gin_channels=256,
+            use_sdp=True,
+            n_flow_layer=4,
+            n_layers_trans_flow=6,
+            flow_share_parameter=False,
+            use_transformer_flow=True,
+            **kwargs
+    ):
+        super().__init__()
+        self.n_vocab = n_vocab
+        self.spec_channels = spec_channels
+        self.inter_channels = inter_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.resblock = resblock
+        self.resblock_kernel_sizes = resblock_kernel_sizes
+        self.resblock_dilation_sizes = resblock_dilation_sizes
+        self.upsample_rates = upsample_rates
+        self.upsample_initial_channel = upsample_initial_channel
+        self.upsample_kernel_sizes = upsample_kernel_sizes
+        self.segment_size = segment_size
+        self.n_speakers = n_speakers
+        self.gin_channels = gin_channels
+        self.n_layers_trans_flow = n_layers_trans_flow
+        self.use_spk_conditioned_encoder = kwargs.get(
+            "use_spk_conditioned_encoder", True
+        )
+        self.use_sdp = use_sdp
+        self.use_noise_scaled_mas = kwargs.get("use_noise_scaled_mas", False)
+        self.mas_noise_scale_initial = kwargs.get("mas_noise_scale_initial", 0.01)
+        self.noise_scale_delta = kwargs.get("noise_scale_delta", 2e-6)
+        self.current_mas_noise_scale = self.mas_noise_scale_initial
+        if self.use_spk_conditioned_encoder and gin_channels > 0:
+            self.enc_gin_channels = gin_channels
+        self.enc_p = TextEncoder(
+            n_vocab,
+            inter_channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            gin_channels=self.enc_gin_channels,
+        )
+        self.dec = Generator(
+            inter_channels,
+            resblock,
+            resblock_kernel_sizes,
+            resblock_dilation_sizes,
+            upsample_rates,
+            upsample_initial_channel,
+            upsample_kernel_sizes,
+            gin_channels=gin_channels,
+        )
+        self.enc_q = PosteriorEncoder(
+            spec_channels,
+            inter_channels,
+            hidden_channels,
+            5,
+            1,
+            16,
+            gin_channels=gin_channels,
+        )
+        if use_transformer_flow:
+            self.flow = TransformerCouplingBlock(
+                inter_channels,
+                hidden_channels,
+                filter_channels,
+                n_heads,
+                n_layers_trans_flow,
+                5,
+                p_dropout,
+                n_flow_layer,
+                gin_channels=gin_channels,
+                share_parameter=flow_share_parameter,
+            )
+        else:
+            self.flow = ResidualCouplingBlock(
+                inter_channels,
+                hidden_channels,
+                5,
+                1,
+                n_flow_layer,
+                gin_channels=gin_channels,
+            )
+        self.sdp = StochasticDurationPredictor(
+            hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels
+        )
+        self.dp = DurationPredictor(
+            hidden_channels, 256, 3, 0.5, gin_channels=gin_channels
+        )
+
+        if n_speakers >= 1:
+            self.emb_g = nn.Embedding(n_speakers, gin_channels)
+        else:
+            self.ref_enc = ReferenceEncoder(spec_channels, gin_channels)
+
+    def infer(
+            self,
+            x,
+            x_lengths,
+            sid,
+            tone,
+            language,
+            ja_bert,
+            emo,
+            noise_scale=0.667,
+            length_scale=1,
+            noise_scale_w=0.8,
+            max_len=None,
+            sdp_ratio=0,
+            y=None,
+            **kwargs
+    ):
+        # x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, ja_bert)
+        # g = self.gst(y)
+        if self.n_speakers > 0:
+            g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
+        else:
+            g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1)
+        x, m_p, logs_p, x_mask, _ = self.enc_p(
+            x, x_lengths, tone, language, ja_bert, emo, g=g
+        )
+        logw = self.sdp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) * (
+            sdp_ratio
+        ) + self.dp(x, x_mask, g=g) * (1 - sdp_ratio)
+        w = torch.exp(logw) * x_mask * length_scale
+        w_ceil = torch.ceil(w)
+        y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
+        y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(
+            x_mask.dtype
+        )
+        attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
+        attn = commons.generate_path(w_ceil, attn_mask)
+
+        m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(
+            1, 2
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+        logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(
+            1, 2
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+
+        z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
+        z = self.flow(z_p, y_mask, g=g, reverse=True)
+        o = self.dec((z * y_mask)[:, :, :max_len], g=g)
+        return o, attn, y_mask, (z, z_p, m_p, logs_p)

bert_vits2/models_v230.py

@@ -0,0 +1,1019 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+from vector_quantize_pytorch import VectorQuantize
+
+from bert_vits2 import commons
+from bert_vits2 import modules
+from bert_vits2 import attentions
+
+from torch.nn import Conv1d, ConvTranspose1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+
+from bert_vits2.commons import init_weights, get_padding
+from bert_vits2.text import symbols, num_tones, num_languages
+
+
+class DurationDiscriminator(nn.Module):  # vits2
+    def __init__(
+            self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+
+        self.drop = nn.Dropout(p_dropout)
+        self.conv_1 = nn.Conv1d(
+            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_1 = modules.LayerNorm(filter_channels)
+        self.conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_2 = modules.LayerNorm(filter_channels)
+        self.dur_proj = nn.Conv1d(1, filter_channels, 1)
+
+        self.LSTM = nn.LSTM(
+            2 * filter_channels, filter_channels, batch_first=True, bidirectional=True
+        )
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+        self.output_layer = nn.Sequential(
+            nn.Linear(2 * filter_channels, 1), nn.Sigmoid()
+        )
+
+    def forward_probability(self, x, dur):
+        dur = self.dur_proj(dur)
+        x = torch.cat([x, dur], dim=1)
+        x = x.transpose(1, 2)
+        x, _ = self.LSTM(x)
+        output_prob = self.output_layer(x)
+        return output_prob
+
+    def forward(self, x, x_mask, dur_r, dur_hat, g=None):
+        x = torch.detach(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_2(x)
+        x = self.drop(x)
+
+        output_probs = []
+        for dur in [dur_r, dur_hat]:
+            output_prob = self.forward_probability(x, dur)
+            output_probs.append(output_prob)
+
+        return output_probs
+
+
+class TransformerCouplingBlock(nn.Module):
+    def __init__(
+            self,
+            channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            n_flows=4,
+            gin_channels=0,
+            share_parameter=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+
+        self.wn = (
+            attentions.FFT(
+                hidden_channels,
+                filter_channels,
+                n_heads,
+                n_layers,
+                kernel_size,
+                p_dropout,
+                isflow=True,
+                gin_channels=self.gin_channels,
+            )
+            if share_parameter
+            else None
+        )
+
+        for i in range(n_flows):
+            self.flows.append(
+                modules.TransformerCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    n_layers,
+                    n_heads,
+                    p_dropout,
+                    filter_channels,
+                    mean_only=True,
+                    wn_sharing_parameter=self.wn,
+                    gin_channels=self.gin_channels,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        if not reverse:
+            for flow in self.flows:
+                x, _ = flow(x, x_mask, g=g, reverse=reverse)
+        else:
+            for flow in reversed(self.flows):
+                x = flow(x, x_mask, g=g, reverse=reverse)
+        return x
+
+
+class StochasticDurationPredictor(nn.Module):
+    def __init__(
+            self,
+            in_channels,
+            filter_channels,
+            kernel_size,
+            p_dropout,
+            n_flows=4,
+            gin_channels=0,
+    ):
+        super().__init__()
+        filter_channels = in_channels  # it needs to be removed from future version.
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.log_flow = modules.Log()
+        self.flows = nn.ModuleList()
+        self.flows.append(modules.ElementwiseAffine(2))
+        for i in range(n_flows):
+            self.flows.append(
+                modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
+            )
+            self.flows.append(modules.Flip())
+
+        self.post_pre = nn.Conv1d(1, filter_channels, 1)
+        self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.post_convs = modules.DDSConv(
+            filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
+        )
+        self.post_flows = nn.ModuleList()
+        self.post_flows.append(modules.ElementwiseAffine(2))
+        for i in range(4):
+            self.post_flows.append(
+                modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
+            )
+            self.post_flows.append(modules.Flip())
+
+        self.pre = nn.Conv1d(in_channels, filter_channels, 1)
+        self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.convs = modules.DDSConv(
+            filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
+        )
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
+
+    def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
+        x = torch.detach(x)
+        x = self.pre(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.convs(x, x_mask)
+        x = self.proj(x) * x_mask
+
+        if not reverse:
+            flows = self.flows
+            assert w is not None
+
+            logdet_tot_q = 0
+            h_w = self.post_pre(w)
+            h_w = self.post_convs(h_w, x_mask)
+            h_w = self.post_proj(h_w) * x_mask
+            e_q = (
+                    torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype)
+                    * x_mask
+            )
+            z_q = e_q
+            for flow in self.post_flows:
+                z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
+                logdet_tot_q += logdet_q
+            z_u, z1 = torch.split(z_q, [1, 1], 1)
+            u = torch.sigmoid(z_u) * x_mask
+            z0 = (w - u) * x_mask
+            logdet_tot_q += torch.sum(
+                (F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2]
+            )
+            logq = (
+                    torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q ** 2)) * x_mask, [1, 2])
+                    - logdet_tot_q
+            )
+
+            logdet_tot = 0
+            z0, logdet = self.log_flow(z0, x_mask)
+            logdet_tot += logdet
+            z = torch.cat([z0, z1], 1)
+            for flow in flows:
+                z, logdet = flow(z, x_mask, g=x, reverse=reverse)
+                logdet_tot = logdet_tot + logdet
+            nll = (
+                    torch.sum(0.5 * (math.log(2 * math.pi) + (z ** 2)) * x_mask, [1, 2])
+                    - logdet_tot
+            )
+            return nll + logq  # [b]
+        else:
+            flows = list(reversed(self.flows))
+            flows = flows[:-2] + [flows[-1]]  # remove a useless vflow
+            z = (
+                    torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype)
+                    * noise_scale
+            )
+            for flow in flows:
+                z = flow(z, x_mask, g=x, reverse=reverse)
+            z0, z1 = torch.split(z, [1, 1], 1)
+            logw = z0
+            return logw
+
+
+class DurationPredictor(nn.Module):
+    def __init__(
+            self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+
+        self.drop = nn.Dropout(p_dropout)
+        self.conv_1 = nn.Conv1d(
+            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_1 = modules.LayerNorm(filter_channels)
+        self.conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_2 = modules.LayerNorm(filter_channels)
+        self.proj = nn.Conv1d(filter_channels, 1, 1)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+    def forward(self, x, x_mask, g=None):
+        x = torch.detach(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_2(x)
+        x = self.drop(x)
+        x = self.proj(x * x_mask)
+        return x * x_mask
+
+
+class Bottleneck(nn.Sequential):
+    def __init__(self, in_dim, hidden_dim):
+        c_fc1 = nn.Linear(in_dim, hidden_dim, bias=False)
+        c_fc2 = nn.Linear(in_dim, hidden_dim, bias=False)
+        super().__init__(*[c_fc1, c_fc2])
+
+
+class Block(nn.Module):
+    def __init__(self, in_dim, hidden_dim) -> None:
+        super().__init__()
+        self.norm = nn.LayerNorm(in_dim)
+        self.mlp = MLP(in_dim, hidden_dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x + self.mlp(self.norm(x))
+        return x
+
+
+class MLP(nn.Module):
+    def __init__(self, in_dim, hidden_dim):
+        super().__init__()
+        self.c_fc1 = nn.Linear(in_dim, hidden_dim, bias=False)
+        self.c_fc2 = nn.Linear(in_dim, hidden_dim, bias=False)
+        self.c_proj = nn.Linear(hidden_dim, in_dim, bias=False)
+
+    def forward(self, x: torch.Tensor):
+        x = F.silu(self.c_fc1(x)) * self.c_fc2(x)
+        x = self.c_proj(x)
+        return x
+
+
+class TextEncoder(nn.Module):
+    def __init__(
+            self,
+            n_vocab,
+            out_channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            gin_channels=0,
+            zh_bert_extra=False,
+    ):
+        super().__init__()
+        self.n_vocab = n_vocab
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+        self.emb = nn.Embedding(len(symbols), hidden_channels)
+        nn.init.normal_(self.emb.weight, 0.0, hidden_channels ** -0.5)
+        self.tone_emb = nn.Embedding(num_tones, hidden_channels)
+        nn.init.normal_(self.tone_emb.weight, 0.0, hidden_channels ** -0.5)
+        self.language_emb = nn.Embedding(num_languages, hidden_channels)
+        nn.init.normal_(self.language_emb.weight, 0.0, hidden_channels ** -0.5)
+        self.bert_proj = nn.Conv1d(1024, hidden_channels, 1)
+        self.zh_bert_extra = zh_bert_extra
+        if self.zh_bert_extra:
+            self.bert_pre_proj = nn.Conv1d(2048, 1024, 1)
+            self.in_feature_net = nn.Sequential(
+                # input is assumed to an already normalized embedding
+                nn.Linear(512, 1028, bias=False),
+                nn.GELU(),
+                nn.LayerNorm(1028),
+                *[Block(1028, 512) for _ in range(1)],
+                nn.Linear(1028, 512, bias=False),
+                # normalize before passing to VQ?
+                # nn.GELU(),
+                # nn.LayerNorm(512),
+            )
+            self.emo_vq = VectorQuantize(
+                dim=512,
+                codebook_size=64,
+                codebook_dim=32,
+                commitment_weight=0.1,
+                decay=0.85,
+                heads=32,
+                kmeans_iters=20,
+                separate_codebook_per_head=True,
+                stochastic_sample_codes=True,
+                threshold_ema_dead_code=2,
+            )
+            self.out_feature_net = nn.Linear(512, hidden_channels)
+        else:
+            self.ja_bert_proj = nn.Conv1d(1024, hidden_channels, 1)
+            self.en_bert_proj = nn.Conv1d(1024, hidden_channels, 1)
+
+        self.encoder = attentions.Encoder(
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            gin_channels=self.gin_channels,
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, x, x_lengths, tone, language, zh_bert, ja_bert, en_bert, emo=None, g=None):
+        x = self.emb(x) + self.tone_emb(tone) + self.language_emb(language)
+
+        if self.zh_bert_extra:
+            zh_bert = self.bert_pre_proj(zh_bert)
+            emo_emb = self.in_feature_net(emo)
+            emo_emb, _, _ = self.emo_vq(emo_emb.unsqueeze(1))
+            emo_emb = self.out_feature_net(emo_emb)
+            x += emo_emb
+        x += self.bert_proj(zh_bert).transpose(1, 2)
+        if not self.zh_bert_extra:
+            x += self.ja_bert_proj(ja_bert).transpose(1, 2)
+            x += self.en_bert_proj(en_bert).transpose(1, 2)
+
+        x *= math.sqrt(self.hidden_channels)  # [b, t, h]
+        x = torch.transpose(x, 1, -1)  # [b, h, t]
+        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
+            x.dtype
+        )
+
+        x = self.encoder(x * x_mask, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        return x, m, logs, x_mask
+
+
+class ResidualCouplingBlock(nn.Module):
+    def __init__(
+            self,
+            channels,
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            n_flows=4,
+            gin_channels=0,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+        for i in range(n_flows):
+            self.flows.append(
+                modules.ResidualCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    dilation_rate,
+                    n_layers,
+                    gin_channels=gin_channels,
+                    mean_only=True,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        if not reverse:
+            for flow in self.flows:
+                x, _ = flow(x, x_mask, g=g, reverse=reverse)
+        else:
+            for flow in reversed(self.flows):
+                x = flow(x, x_mask, g=g, reverse=reverse)
+        return x
+
+
+class PosteriorEncoder(nn.Module):
+    def __init__(
+            self,
+            in_channels,
+            out_channels,
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            gin_channels=0,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.gin_channels = gin_channels
+
+        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+        self.enc = modules.WN(
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            gin_channels=gin_channels,
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, x, x_lengths, g=None):
+        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
+            x.dtype
+        )
+        x = self.pre(x) * x_mask
+        x = self.enc(x, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
+        return z, m, logs, x_mask
+
+
+class Generator(torch.nn.Module):
+    def __init__(
+            self,
+            initial_channel,
+            resblock,
+            resblock_kernel_sizes,
+            resblock_dilation_sizes,
+            upsample_rates,
+            upsample_initial_channel,
+            upsample_kernel_sizes,
+            gin_channels=0,
+    ):
+        super(Generator, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = Conv1d(
+            initial_channel, upsample_initial_channel, 7, 1, padding=3
+        )
+        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(
+                weight_norm(
+                    ConvTranspose1d(
+                        upsample_initial_channel // (2 ** i),
+                        upsample_initial_channel // (2 ** (i + 1)),
+                        k,
+                        u,
+                        padding=(k - u) // 2,
+                    )
+                )
+            )
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel // (2 ** (i + 1))
+            for j, (k, d) in enumerate(
+                    zip(resblock_kernel_sizes, resblock_dilation_sizes)
+            ):
+                self.resblocks.append(resblock(ch, k, d))
+
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+    def forward(self, x, g=None):
+        x = self.conv_pre(x)
+        if g is not None:
+            x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        print("Removing weight norm...")
+        for layer in self.ups:
+            remove_weight_norm(layer)
+        for layer in self.resblocks:
+            layer.remove_weight_norm()
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        self.use_spectral_norm = use_spectral_norm
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(
+                    Conv2d(
+                        1,
+                        32,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        32,
+                        128,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        128,
+                        512,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        512,
+                        1024,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        1024,
+                        1024,
+                        (kernel_size, 1),
+                        1,
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+            ]
+        )
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x):
+        fmap = []
+
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0:  # pad first
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for layer in self.convs:
+            x = layer(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
+                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
+                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
+                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
+                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
+                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
+            ]
+        )
+        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        fmap = []
+
+        for layer in self.convs:
+            x = layer(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(MultiPeriodDiscriminator, self).__init__()
+        periods = [2, 3, 5, 7, 11]
+
+        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+        discs = discs + [
+            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
+        ]
+        self.discriminators = nn.ModuleList(discs)
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            y_d_gs.append(y_d_g)
+            fmap_rs.append(fmap_r)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+class WavLMDiscriminator(nn.Module):
+    """docstring for Discriminator."""
+
+    def __init__(
+            self, slm_hidden=768, slm_layers=13, initial_channel=64, use_spectral_norm=False
+    ):
+        super(WavLMDiscriminator, self).__init__()
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.pre = norm_f(
+            Conv1d(slm_hidden * slm_layers, initial_channel, 1, 1, padding=0)
+        )
+
+        self.convs = nn.ModuleList(
+            [
+                norm_f(
+                    nn.Conv1d(
+                        initial_channel, initial_channel * 2, kernel_size=5, padding=2
+                    )
+                ),
+                norm_f(
+                    nn.Conv1d(
+                        initial_channel * 2,
+                        initial_channel * 4,
+                        kernel_size=5,
+                        padding=2,
+                    )
+                ),
+                norm_f(
+                    nn.Conv1d(initial_channel * 4, initial_channel * 4, 5, 1, padding=2)
+                ),
+            ]
+        )
+
+        self.conv_post = norm_f(Conv1d(initial_channel * 4, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        x = self.pre(x)
+
+        fmap = []
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x
+
+
+class ReferenceEncoder(nn.Module):
+    """
+    inputs --- [N, Ty/r, n_mels*r]  mels
+    outputs --- [N, ref_enc_gru_size]
+    """
+
+    def __init__(self, spec_channels, gin_channels=0):
+        super().__init__()
+        self.spec_channels = spec_channels
+        ref_enc_filters = [32, 32, 64, 64, 128, 128]
+        K = len(ref_enc_filters)
+        filters = [1] + ref_enc_filters
+        convs = [
+            weight_norm(
+                nn.Conv2d(
+                    in_channels=filters[i],
+                    out_channels=filters[i + 1],
+                    kernel_size=(3, 3),
+                    stride=(2, 2),
+                    padding=(1, 1),
+                )
+            )
+            for i in range(K)
+        ]
+        self.convs = nn.ModuleList(convs)
+        # self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)]) # noqa: E501
+
+        out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K)
+        self.gru = nn.GRU(
+            input_size=ref_enc_filters[-1] * out_channels,
+            hidden_size=256 // 2,
+            batch_first=True,
+        )
+        self.proj = nn.Linear(128, gin_channels)
+
+    def forward(self, inputs, mask=None):
+        N = inputs.size(0)
+        out = inputs.view(N, 1, -1, self.spec_channels)  # [N, 1, Ty, n_freqs]
+        for conv in self.convs:
+            out = conv(out)
+            # out = wn(out)
+            out = F.relu(out)  # [N, 128, Ty//2^K, n_mels//2^K]
+
+        out = out.transpose(1, 2)  # [N, Ty//2^K, 128, n_mels//2^K]
+        T = out.size(1)
+        N = out.size(0)
+        out = out.contiguous().view(N, T, -1)  # [N, Ty//2^K, 128*n_mels//2^K]
+
+        self.gru.flatten_parameters()
+        memory, out = self.gru(out)  # out --- [1, N, 128]
+
+        return self.proj(out.squeeze(0))
+
+    def calculate_channels(self, L, kernel_size, stride, pad, n_convs):
+        for i in range(n_convs):
+            L = (L - kernel_size + 2 * pad) // stride + 1
+        return L
+
+
+class SynthesizerTrn(nn.Module):
+    """
+    Synthesizer for Training
+    """
+
+    def __init__(
+            self,
+            n_vocab,
+            spec_channels,
+            segment_size,
+            inter_channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            resblock,
+            resblock_kernel_sizes,
+            resblock_dilation_sizes,
+            upsample_rates,
+            upsample_initial_channel,
+            upsample_kernel_sizes,
+            n_speakers=256,
+            gin_channels=256,
+            use_sdp=True,
+            n_flow_layer=4,
+            n_layers_trans_flow=4,
+            flow_share_parameter=False,
+            use_transformer_flow=True,
+            zh_bert_extra=False,
+            **kwargs
+    ):
+        super().__init__()
+        self.n_vocab = n_vocab
+        self.spec_channels = spec_channels
+        self.inter_channels = inter_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.resblock = resblock
+        self.resblock_kernel_sizes = resblock_kernel_sizes
+        self.resblock_dilation_sizes = resblock_dilation_sizes
+        self.upsample_rates = upsample_rates
+        self.upsample_initial_channel = upsample_initial_channel
+        self.upsample_kernel_sizes = upsample_kernel_sizes
+        self.segment_size = segment_size
+        self.n_speakers = n_speakers
+        self.gin_channels = gin_channels
+        self.n_layers_trans_flow = n_layers_trans_flow
+        self.use_spk_conditioned_encoder = kwargs.get(
+            "use_spk_conditioned_encoder", True
+        )
+        self.use_sdp = use_sdp
+        self.use_noise_scaled_mas = kwargs.get("use_noise_scaled_mas", False)
+        self.mas_noise_scale_initial = kwargs.get("mas_noise_scale_initial", 0.01)
+        self.noise_scale_delta = kwargs.get("noise_scale_delta", 2e-6)
+        self.current_mas_noise_scale = self.mas_noise_scale_initial
+        if self.use_spk_conditioned_encoder and gin_channels > 0:
+            self.enc_gin_channels = gin_channels
+        self.enc_p = TextEncoder(
+            n_vocab,
+            inter_channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            gin_channels=self.enc_gin_channels,
+            zh_bert_extra=zh_bert_extra,
+        )
+        self.dec = Generator(
+            inter_channels,
+            resblock,
+            resblock_kernel_sizes,
+            resblock_dilation_sizes,
+            upsample_rates,
+            upsample_initial_channel,
+            upsample_kernel_sizes,
+            gin_channels=gin_channels,
+        )
+        self.enc_q = PosteriorEncoder(
+            spec_channels,
+            inter_channels,
+            hidden_channels,
+            5,
+            1,
+            16,
+            gin_channels=gin_channels,
+        )
+        if use_transformer_flow:
+            self.flow = TransformerCouplingBlock(
+                inter_channels,
+                hidden_channels,
+                filter_channels,
+                n_heads,
+                n_layers_trans_flow,
+                5,
+                p_dropout,
+                n_flow_layer,
+                gin_channels=gin_channels,
+                share_parameter=flow_share_parameter,
+            )
+        else:
+            self.flow = ResidualCouplingBlock(
+                inter_channels,
+                hidden_channels,
+                5,
+                1,
+                n_flow_layer,
+                gin_channels=gin_channels,
+            )
+        self.sdp = StochasticDurationPredictor(
+            hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels
+        )
+        self.dp = DurationPredictor(
+            hidden_channels, 256, 3, 0.5, gin_channels=gin_channels
+        )
+
+        if n_speakers >= 1:
+            self.emb_g = nn.Embedding(n_speakers, gin_channels)
+        else:
+            self.ref_enc = ReferenceEncoder(spec_channels, gin_channels)
+
+    def infer(
+            self,
+            x,
+            x_lengths,
+            sid,
+            tone,
+            language,
+            zh_bert,
+            ja_bert,
+            en_bert,
+            emo=None,
+            noise_scale=0.667,
+            length_scale=1,
+            noise_scale_w=0.8,
+            max_len=None,
+            sdp_ratio=0,
+            y=None,
+            **kwargs,
+    ):
+        # x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert)
+        # g = self.gst(y)
+        if self.n_speakers > 0:
+            g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
+        else:
+            g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1)
+        x, m_p, logs_p, x_mask = self.enc_p(
+            x, x_lengths, tone, language, zh_bert, ja_bert, en_bert, emo=emo, g=g
+        )
+        logw = self.sdp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) * (
+            sdp_ratio
+        ) + self.dp(x, x_mask, g=g) * (1 - sdp_ratio)
+        w = torch.exp(logw) * x_mask * length_scale
+        w_ceil = torch.ceil(w)
+        y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
+        y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(
+            x_mask.dtype
+        )
+        attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
+        attn = commons.generate_path(w_ceil, attn_mask)
+
+        m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(
+            1, 2
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+        logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(
+            1, 2
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+
+        z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
+        z = self.flow(z_p, y_mask, g=g, reverse=True)
+        o = self.dec((z * y_mask)[:, :, :max_len], g=g)
+        return o, attn, y_mask, (z, z_p, m_p, logs_p)

bert_vits2/modules.py

@@ -0,0 +1,459 @@
+import copy
+import math
+import numpy as np
+import scipy
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm
+
+from bert_vits2 import commons
+from bert_vits2.commons import init_weights, get_padding
+from bert_vits2.transforms import piecewise_rational_quadratic_transform
+from bert_vits2.attentions import Encoder
+
+LRELU_SLOPE = 0.1
+
+
+class LayerNorm(nn.Module):
+    def __init__(self, channels, eps=1e-5):
+        super().__init__()
+        self.channels = channels
+        self.eps = eps
+
+        self.gamma = nn.Parameter(torch.ones(channels))
+        self.beta = nn.Parameter(torch.zeros(channels))
+
+    def forward(self, x):
+        x = x.transpose(1, -1)
+        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+        return x.transpose(1, -1)
+
+
+class ConvReluNorm(nn.Module):
+    def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
+        super().__init__()
+        self.in_channels = in_channels
+        self.hidden_channels = hidden_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.p_dropout = p_dropout
+        assert n_layers > 1, "Number of layers should be larger than 0."
+
+        self.conv_layers = nn.ModuleList()
+        self.norm_layers = nn.ModuleList()
+        self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2))
+        self.norm_layers.append(LayerNorm(hidden_channels))
+        self.relu_drop = nn.Sequential(
+            nn.ReLU(),
+            nn.Dropout(p_dropout))
+        for _ in range(n_layers - 1):
+            self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2))
+            self.norm_layers.append(LayerNorm(hidden_channels))
+        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
+        self.proj.weight.data.zero_()
+        self.proj.bias.data.zero_()
+
+    def forward(self, x, x_mask):
+        x_org = x
+        for i in range(self.n_layers):
+            x = self.conv_layers[i](x * x_mask)
+            x = self.norm_layers[i](x)
+            x = self.relu_drop(x)
+        x = x_org + self.proj(x)
+        return x * x_mask
+
+
+class DDSConv(nn.Module):
+    """
+    Dialted and Depth-Separable Convolution
+    """
+
+    def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
+        super().__init__()
+        self.channels = channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.p_dropout = p_dropout
+
+        self.drop = nn.Dropout(p_dropout)
+        self.convs_sep = nn.ModuleList()
+        self.convs_1x1 = nn.ModuleList()
+        self.norms_1 = nn.ModuleList()
+        self.norms_2 = nn.ModuleList()
+        for i in range(n_layers):
+            dilation = kernel_size ** i
+            padding = (kernel_size * dilation - dilation) // 2
+            self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
+                                            groups=channels, dilation=dilation, padding=padding
+                                            ))
+            self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
+            self.norms_1.append(LayerNorm(channels))
+            self.norms_2.append(LayerNorm(channels))
+
+    def forward(self, x, x_mask, g=None):
+        if g is not None:
+            x = x + g
+        for i in range(self.n_layers):
+            y = self.convs_sep[i](x * x_mask)
+            y = self.norms_1[i](y)
+            y = F.gelu(y)
+            y = self.convs_1x1[i](y)
+            y = self.norms_2[i](y)
+            y = F.gelu(y)
+            y = self.drop(y)
+            x = x + y
+        return x * x_mask
+
+
+class WN(torch.nn.Module):
+    def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
+        super(WN, self).__init__()
+        assert (kernel_size % 2 == 1)
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size,
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.gin_channels = gin_channels
+        self.p_dropout = p_dropout
+
+        self.in_layers = torch.nn.ModuleList()
+        self.res_skip_layers = torch.nn.ModuleList()
+        self.drop = nn.Dropout(p_dropout)
+
+        if gin_channels != 0:
+            cond_layer = torch.nn.Conv1d(gin_channels, 2 * hidden_channels * n_layers, 1)
+            self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
+
+        for i in range(n_layers):
+            dilation = dilation_rate ** i
+            padding = int((kernel_size * dilation - dilation) / 2)
+            in_layer = torch.nn.Conv1d(hidden_channels, 2 * hidden_channels, kernel_size,
+                                       dilation=dilation, padding=padding)
+            in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
+            self.in_layers.append(in_layer)
+
+            # last one is not necessary
+            if i < n_layers - 1:
+                res_skip_channels = 2 * hidden_channels
+            else:
+                res_skip_channels = hidden_channels
+
+            res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
+            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
+            self.res_skip_layers.append(res_skip_layer)
+
+    def forward(self, x, x_mask, g=None, **kwargs):
+        output = torch.zeros_like(x)
+        n_channels_tensor = torch.IntTensor([self.hidden_channels])
+
+        if g is not None:
+            g = self.cond_layer(g)
+
+        for i in range(self.n_layers):
+            x_in = self.in_layers[i](x)
+            if g is not None:
+                cond_offset = i * 2 * self.hidden_channels
+                g_l = g[:, cond_offset:cond_offset + 2 * self.hidden_channels, :]
+            else:
+                g_l = torch.zeros_like(x_in)
+
+            acts = commons.fused_add_tanh_sigmoid_multiply(
+                x_in,
+                g_l,
+                n_channels_tensor)
+            acts = self.drop(acts)
+
+            res_skip_acts = self.res_skip_layers[i](acts)
+            if i < self.n_layers - 1:
+                res_acts = res_skip_acts[:, :self.hidden_channels, :]
+                x = (x + res_acts) * x_mask
+                output = output + res_skip_acts[:, self.hidden_channels:, :]
+            else:
+                output = output + res_skip_acts
+        return output * x_mask
+
+    def remove_weight_norm(self):
+        if self.gin_channels != 0:
+            torch.nn.utils.remove_weight_norm(self.cond_layer)
+        for l in self.in_layers:
+            torch.nn.utils.remove_weight_norm(l)
+        for l in self.res_skip_layers:
+            torch.nn.utils.remove_weight_norm(l)
+
+
+class ResBlock1(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
+        super(ResBlock1, self).__init__()
+        self.convs1 = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
+                               padding=get_padding(kernel_size, dilation[0]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
+                               padding=get_padding(kernel_size, dilation[1]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
+                               padding=get_padding(kernel_size, dilation[2])))
+        ])
+        self.convs1.apply(init_weights)
+
+        self.convs2 = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1)))
+        ])
+        self.convs2.apply(init_weights)
+
+    def forward(self, x, x_mask=None):
+        for c1, c2 in zip(self.convs1, self.convs2):
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c1(xt)
+            xt = F.leaky_relu(xt, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c2(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs1:
+            remove_weight_norm(l)
+        for l in self.convs2:
+            remove_weight_norm(l)
+
+
+class ResBlock2(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
+        super(ResBlock2, self).__init__()
+        self.convs = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
+                               padding=get_padding(kernel_size, dilation[0]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
+                               padding=get_padding(kernel_size, dilation[1])))
+        ])
+        self.convs.apply(init_weights)
+
+    def forward(self, x, x_mask=None):
+        for c in self.convs:
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs:
+            remove_weight_norm(l)
+
+
+class Log(nn.Module):
+    def forward(self, x, x_mask, reverse=False, **kwargs):
+        if not reverse:
+            y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
+            logdet = torch.sum(-y, [1, 2])
+            return y, logdet
+        else:
+            x = torch.exp(x) * x_mask
+            return x
+
+
+class Flip(nn.Module):
+    def forward(self, x, *args, reverse=False, **kwargs):
+        x = torch.flip(x, [1])
+        if not reverse:
+            logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
+            return x, logdet
+        else:
+            return x
+
+
+class ElementwiseAffine(nn.Module):
+    def __init__(self, channels):
+        super().__init__()
+        self.channels = channels
+        self.m = nn.Parameter(torch.zeros(channels, 1))
+        self.logs = nn.Parameter(torch.zeros(channels, 1))
+
+    def forward(self, x, x_mask, reverse=False, **kwargs):
+        if not reverse:
+            y = self.m + torch.exp(self.logs) * x
+            y = y * x_mask
+            logdet = torch.sum(self.logs * x_mask, [1, 2])
+            return y, logdet
+        else:
+            x = (x - self.m) * torch.exp(-self.logs) * x_mask
+            return x
+
+
+class ResidualCouplingLayer(nn.Module):
+    def __init__(self,
+                 channels,
+                 hidden_channels,
+                 kernel_size,
+                 dilation_rate,
+                 n_layers,
+                 p_dropout=0,
+                 gin_channels=0,
+                 mean_only=False):
+        assert channels % 2 == 0, "channels should be divisible by 2"
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.half_channels = channels // 2
+        self.mean_only = mean_only
+
+        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
+        self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout,
+                      gin_channels=gin_channels)
+        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
+        self.post.weight.data.zero_()
+        self.post.bias.data.zero_()
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
+        h = self.pre(x0) * x_mask
+        h = self.enc(h, x_mask, g=g)
+        stats = self.post(h) * x_mask
+        if not self.mean_only:
+            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
+        else:
+            m = stats
+            logs = torch.zeros_like(m)
+
+        if not reverse:
+            x1 = m + x1 * torch.exp(logs) * x_mask
+            x = torch.cat([x0, x1], 1)
+            logdet = torch.sum(logs, [1, 2])
+            return x, logdet
+        else:
+            x1 = (x1 - m) * torch.exp(-logs) * x_mask
+            x = torch.cat([x0, x1], 1)
+            return x
+
+
+class ConvFlow(nn.Module):
+    def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
+        super().__init__()
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.num_bins = num_bins
+        self.tail_bound = tail_bound
+        self.half_channels = in_channels // 2
+
+        self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
+        self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
+        self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
+        self.proj.weight.data.zero_()
+        self.proj.bias.data.zero_()
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
+        h = self.pre(x0)
+        h = self.convs(h, x_mask, g=g)
+        h = self.proj(h) * x_mask
+
+        b, c, t = x0.shape
+        h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2)  # [b, cx?, t] -> [b, c, t, ?]
+
+        unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
+        unnormalized_heights = h[..., self.num_bins:2 * self.num_bins] / math.sqrt(self.filter_channels)
+        unnormalized_derivatives = h[..., 2 * self.num_bins:]
+
+        x1, logabsdet = piecewise_rational_quadratic_transform(x1,
+                                                               unnormalized_widths,
+                                                               unnormalized_heights,
+                                                               unnormalized_derivatives,
+                                                               inverse=reverse,
+                                                               tails='linear',
+                                                               tail_bound=self.tail_bound
+                                                               )
+
+        x = torch.cat([x0, x1], 1) * x_mask
+        logdet = torch.sum(logabsdet * x_mask, [1, 2])
+        if not reverse:
+            return x, logdet
+        else:
+            return x
+
+
+class TransformerCouplingLayer(nn.Module):
+    def __init__(self,
+                 channels,
+                 hidden_channels,
+                 kernel_size,
+                 n_layers,
+                 n_heads,
+                 p_dropout=0,
+                 filter_channels=0,
+                 mean_only=False,
+                 wn_sharing_parameter=None,
+                 gin_channels=0
+                 ):
+        assert channels % 2 == 0, "channels should be divisible by 2"
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.half_channels = channels // 2
+        self.mean_only = mean_only
+
+        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
+        self.enc = Encoder(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, isflow=True,
+                           gin_channels=gin_channels) if wn_sharing_parameter is None else wn_sharing_parameter
+        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
+        self.post.weight.data.zero_()
+        self.post.bias.data.zero_()
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
+        h = self.pre(x0) * x_mask
+        h = self.enc(h, x_mask, g=g)
+        stats = self.post(h) * x_mask
+        if not self.mean_only:
+            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
+        else:
+            m = stats
+            logs = torch.zeros_like(m)
+
+        if not reverse:
+            x1 = m + x1 * torch.exp(logs) * x_mask
+            x = torch.cat([x0, x1], 1)
+            logdet = torch.sum(logs, [1, 2])
+            return x, logdet
+        else:
+            x1 = (x1 - m) * torch.exp(-logs) * x_mask
+            x = torch.cat([x0, x1], 1)
+            return x
+
+        x1, logabsdet = piecewise_rational_quadratic_transform(x1,
+                                                               unnormalized_widths,
+                                                               unnormalized_heights,
+                                                               unnormalized_derivatives,
+                                                               inverse=reverse,
+                                                               tails='linear',
+                                                               tail_bound=self.tail_bound
+                                                               )
+
+        x = torch.cat([x0, x1], 1) * x_mask
+        logdet = torch.sum(logabsdet * x_mask, [1, 2])
+        if not reverse:
+            return x, logdet
+        else:
+            return x

bert_vits2/requirements.txt

@@ -0,0 +1,15 @@
+Cython
+librosa==0.9.1
+matplotlib==3.3.1
+numpy
+phonemizer
+scipy
+tensorboard
+torch
+torchvision
+Unidecode
+amfm_decompy
+jieba
+transformers
+pypinyin
+cn2an

bert_vits2/text/__init__.py

@@ -0,0 +1,25 @@
+from bert_vits2.text.symbols import *
+
+
+def cleaned_text_to_sequence_v111(cleaned_text, tones, language, _symbol_to_id):
+    """version <= 1.1.1"""
+    phones = [_symbol_to_id[symbol] for symbol in cleaned_text]
+    tone_start = language_tone_start_map_v111[language]
+    tones = [i + tone_start for i in tones]
+    lang_id = language_id_map[language]
+    lang_ids = [lang_id for i in phones]
+    return phones, tones, lang_ids
+
+def cleaned_text_to_sequence(cleaned_text, tones, language, _symbol_to_id):
+    """Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
+    Args:
+      text: string to convert to a sequence
+    Returns:
+      List of integers corresponding to the symbols in the text
+    """
+    phones = [_symbol_to_id[symbol] for symbol in cleaned_text]
+    tone_start = language_tone_start_map[language]
+    tones = [i + tone_start for i in tones]
+    lang_id = language_id_map[language]
+    lang_ids = [lang_id for i in phones]
+    return phones, tones, lang_ids

bert_vits2/text/chinese.py

@@ -0,0 +1,198 @@
+import os
+import re
+
+import cn2an
+from pypinyin import lazy_pinyin, Style
+
+from bert_vits2.text.symbols import punctuation
+from bert_vits2.text.tone_sandhi import ToneSandhi
+
+current_file_path = os.path.dirname(__file__)
+pinyin_to_symbol_map = {line.split("\t")[0]: line.strip().split("\t")[1] for line in
+                        open(os.path.join(current_file_path, 'opencpop-strict.txt')).readlines()}
+
+import jieba.posseg as psg
+from jieba import lcut
+
+lcut("预加载")
+
+rep_map = {
+    "：": ",",
+    "；": ",",
+    "，": ",",
+    "。": ".",
+    "！": "!",
+    "？": "?",
+    "\n": ".",
+    "·": ",",
+    "、": ",",
+    "...": "…",
+    "$": ".",
+    "“": "'",
+    "”": "'",
+    '"': "'",
+    "‘": "'",
+    "’": "'",
+    "（": "'",
+    "）": "'",
+    "(": "'",
+    ")": "'",
+    "《": "'",
+    "》": "'",
+    "【": "'",
+    "】": "'",
+    "[": "'",
+    "]": "'",
+    "—": "-",
+    "～": "-",
+    "~": "-",
+    "「": "'",
+    "」": "'",
+}
+
+tone_modifier = ToneSandhi()
+
+
+def replace_punctuation(text):
+    text = text.replace("嗯", "恩").replace("呣", "母")
+    pattern = re.compile('|'.join(re.escape(p) for p in rep_map.keys()))
+
+    replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
+
+    replaced_text = re.sub(r'[^\u4e00-\u9fa5' + "".join(punctuation) + r']+', '', replaced_text)
+
+    return replaced_text
+
+
+def g2p(text, **kwargs):
+    pattern = r'(?<=[{0}])\s*'.format(''.join(punctuation))
+    sentences = [i for i in re.split(pattern, text) if i.strip() != '']
+    phones, tones, word2ph = _g2p(sentences)
+    assert sum(word2ph) == len(phones)
+    assert len(word2ph) == len(text)  # Sometimes it will crash,you can add a try-catch.
+    phones = ['_'] + phones + ["_"]
+    tones = [0] + tones + [0]
+    word2ph = [1] + word2ph + [1]
+    return phones, tones, word2ph
+
+
+def _get_initials_finals(word):
+    initials = []
+    finals = []
+    orig_initials = lazy_pinyin(
+        word, neutral_tone_with_five=True, style=Style.INITIALS)
+    orig_finals = lazy_pinyin(
+        word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
+    for c, v in zip(orig_initials, orig_finals):
+        initials.append(c)
+        finals.append(v)
+    return initials, finals
+
+
+def _g2p(segments, **kwargs):
+    phones_list = []
+    tones_list = []
+    word2ph = []
+    for seg in segments:
+        pinyins = []
+        # Replace all English words in the sentence
+        seg = re.sub('[a-zA-Z]+', '', seg)
+        seg_cut = psg.lcut(seg)
+        initials = []
+        finals = []
+        seg_cut = tone_modifier.pre_merge_for_modify(seg_cut)
+        for word, pos in seg_cut:
+            if pos == 'eng':
+                continue
+            sub_initials, sub_finals = _get_initials_finals(word)
+            sub_finals = tone_modifier.modified_tone(word, pos,
+                                                     sub_finals)
+            initials.append(sub_initials)
+            finals.append(sub_finals)
+
+            # assert len(sub_initials) == len(sub_finals) == len(word)
+        initials = sum(initials, [])
+        finals = sum(finals, [])
+        #
+        for c, v in zip(initials, finals):
+            raw_pinyin = c + v
+            # NOTE: post process for pypinyin outputs
+            # we discriminate i, ii and iii
+            if c == v:
+                assert c in punctuation
+                phone = [c]
+                tone = '0'
+                word2ph.append(1)
+            else:
+                v_without_tone = v[:-1]
+                tone = v[-1]
+
+                pinyin = c + v_without_tone
+                assert tone in '12345'
+
+                if c:
+                    # 多音节
+                    v_rep_map = {
+                        "uei": 'ui',
+                        'iou': 'iu',
+                        'uen': 'un',
+                    }
+                    if v_without_tone in v_rep_map.keys():
+                        pinyin = c + v_rep_map[v_without_tone]
+                else:
+                    # 单音节
+                    pinyin_rep_map = {
+                        'ing': 'ying',
+                        'i': 'yi',
+                        'in': 'yin',
+                        'u': 'wu',
+                    }
+                    if pinyin in pinyin_rep_map.keys():
+                        pinyin = pinyin_rep_map[pinyin]
+                    else:
+                        single_rep_map = {
+                            'v': 'yu',
+                            'e': 'e',
+                            'i': 'y',
+                            'u': 'w',
+                        }
+                        if pinyin[0] in single_rep_map.keys():
+                            pinyin = single_rep_map[pinyin[0]] + pinyin[1:]
+
+                assert pinyin in pinyin_to_symbol_map.keys(), (pinyin, seg, raw_pinyin)
+                phone = pinyin_to_symbol_map[pinyin].split(' ')
+                word2ph.append(len(phone))
+
+            phones_list += phone
+            tones_list += [int(tone)] * len(phone)
+    return phones_list, tones_list, word2ph
+
+
+def text_normalize(text):
+    # numbers = re.findall(r'\d+(?:\.?\d+)?', text)
+    # for number in numbers:
+    #     text = text.replace(number, cn2an.an2cn(number), 1)
+    text = cn2an.transform(text, "an2cn")
+    text = replace_punctuation(text)
+    return text
+
+
+def get_bert_feature(text, word2ph):
+    from bert_vits2.text import chinese_bert
+    return chinese_bert.get_bert_feature(text, word2ph)
+
+
+if __name__ == '__main__':
+    text = "啊！但是《原神》是由,米哈\游自主，  [研发]的一款全.新开放世界.冒险游戏。"
+    text = text_normalize(text)
+    print(text)
+    phones, tones, word2ph = g2p(text)
+
+    print(phones, tones, word2ph)
+    bert = get_bert_feature(text, word2ph)
+
+    print(bert.shape)
+
+# # 示例用法
+# text = "这是一个示例文本：,你好！这是一个测试...."
+# print(g2p_paddle(text))  # 输出: 这是一个示例文本你好这是一个测试

bert_vits2/text/chinese_bert.py

@@ -0,0 +1,59 @@
+import torch
+
+from contants import config
+
+
+def get_bert_feature(text, word2ph, tokenizer, model, device=config.system.device, style_text=None, style_weight=0.7,
+                     **kwargs):
+    with torch.no_grad():
+        inputs = tokenizer(text, return_tensors='pt')
+        for i in inputs:
+            inputs[i] = inputs[i].to(device)
+        res = model(**inputs, output_hidden_states=True)
+        res = torch.cat(res['hidden_states'][-3:-2], -1)[0].float().cpu()
+        if style_text:
+            style_inputs = tokenizer(style_text, return_tensors="pt")
+            for i in style_inputs:
+                style_inputs[i] = style_inputs[i].to(device)
+            style_res = model(**style_inputs, output_hidden_states=True)
+            style_res = torch.cat(style_res["hidden_states"][-3:-2], -1)[0].float().cpu()
+            style_res_mean = style_res.mean(0)
+
+    assert len(word2ph) == len(text) + 2
+    word2phone = word2ph
+    phone_level_feature = []
+    for i in range(len(word2phone)):
+        if style_text:
+            repeat_feature = (
+                    res[i].repeat(word2phone[i], 1) * (1 - style_weight)
+                    + style_res_mean.repeat(word2phone[i], 1) * style_weight
+            )
+        else:
+            repeat_feature = res[i].repeat(word2phone[i], 1)
+        phone_level_feature.append(repeat_feature)
+
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+
+    return phone_level_feature.T
+
+
+if __name__ == '__main__':
+
+    word_level_feature = torch.rand(38, 1024)  # 12个词,每个词1024维特征
+    word2phone = [1, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2,
+                  2, 2, 2, 1]
+
+    # 计算总帧数
+    total_frames = sum(word2phone)
+    print(word_level_feature.shape)
+    print(word2phone)
+    phone_level_feature = []
+    for i in range(len(word2phone)):
+        print(word_level_feature[i].shape)
+
+        # 对每个词重复word2phone[i]次
+        repeat_feature = word_level_feature[i].repeat(word2phone[i], 1)
+        phone_level_feature.append(repeat_feature)
+
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+    print(phone_level_feature.shape)  # torch.Size([36, 1024])

bert_vits2/text/chinese_bert_extra.py

@@ -0,0 +1,60 @@
+import torch
+
+from contants import config
+
+
+def get_bert_feature(text, word2ph, tokenizer, model, device=config.system.device, style_text=None, style_weight=0.7,
+                     **kwargs):
+    with torch.no_grad():
+        inputs = tokenizer(text, return_tensors='pt')
+        for i in inputs:
+            inputs[i] = inputs[i].to(device)
+        res = model(**inputs, output_hidden_states=True)
+        res = torch.nn.functional.normalize(torch.cat(res["hidden_states"][-3:-2], -1)[0], dim=0).float().cpu()
+        if style_text:
+            style_inputs = tokenizer(style_text, return_tensors="pt")
+            for i in style_inputs:
+                style_inputs[i] = style_inputs[i].to(device)
+            style_res = model(**style_inputs, output_hidden_states=True)
+            style_res = torch.nn.functional.normalize(
+                torch.cat(style_res["hidden_states"][-3:-2], -1)[0], dim=0
+            ).float().cpu()
+            style_res_mean = style_res.mean(0)
+    assert len(word2ph) == len(text) + 2
+    word2phone = word2ph
+    phone_level_feature = []
+    for i in range(len(word2phone)):
+        if style_text:
+            repeat_feature = (
+                    res[i].repeat(word2phone[i], 1) * (1 - style_weight)
+                    + style_res_mean.repeat(word2phone[i], 1) * style_weight
+            )
+        else:
+            repeat_feature = res[i].repeat(word2phone[i], 1)
+        phone_level_feature.append(repeat_feature)
+
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+
+    return phone_level_feature.T
+
+
+if __name__ == '__main__':
+
+    word_level_feature = torch.rand(38, 2048)  # 12个词,每个词2048维特征
+    word2phone = [1, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2,
+                  2, 2, 2, 1]
+
+    # 计算总帧数
+    total_frames = sum(word2phone)
+    print(word_level_feature.shape)
+    print(word2phone)
+    phone_level_feature = []
+    for i in range(len(word2phone)):
+        print(word_level_feature[i].shape)
+
+        # 对每个词重复word2phone[i]次
+        repeat_feature = word_level_feature[i].repeat(word2phone[i], 1)
+        phone_level_feature.append(repeat_feature)
+
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+    print(phone_level_feature.shape)  # torch.Size([36, 2048])

bert_vits2/text/chinese_v100.py

@@ -0,0 +1,197 @@
+import os
+import re
+
+import cn2an
+from pypinyin import lazy_pinyin, Style
+
+from bert_vits2.text.symbols import punctuation
+from bert_vits2.text.tone_sandhi import ToneSandhi
+
+current_file_path = os.path.dirname(__file__)
+pinyin_to_symbol_map = {line.split("\t")[0]: line.strip().split("\t")[1] for line in
+                        open(os.path.join(current_file_path, 'opencpop-strict.txt')).readlines()}
+
+import jieba.posseg as psg
+from jieba import lcut
+
+lcut("预加载")
+
+rep_map = {
+    '：': ',',
+    '；': ',',
+    '，': ',',
+    '。': '.',
+    '！': '!',
+    '？': '?',
+    '\n': '.',
+    "·": ",",
+    '、': ",",
+    '...': '…',
+    '$': '.',
+    '“': "'",
+    '”': "'",
+    '‘': "'",
+    '’': "'",
+    '（': "'",
+    '）': "'",
+    '(': "'",
+    ')': "'",
+    '《': "'",
+    '》': "'",
+    '【': "'",
+    '】': "'",
+    '[': "'",
+    ']': "'",
+    '—': "-",
+    '～': "-",
+    '~': "-",
+    '「': "'",
+    '」': "'",
+}
+
+tone_modifier = ToneSandhi()
+
+
+def replace_punctuation(text):
+    text = text.replace("嗯", "恩").replace("呣", "母")
+    pattern = re.compile('|'.join(re.escape(p) for p in rep_map.keys()))
+
+    replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
+
+    replaced_text = re.sub(r'[^\u4e00-\u9fa5' + "".join(punctuation) + r']+', '', replaced_text)
+
+    return replaced_text
+
+
+def g2p(text, **kwargs):
+    pattern = r'(?<=[{0}])\s*'.format(''.join(punctuation))
+    sentences = [i for i in re.split(pattern, text) if i.strip() != '']
+    phones, tones, word2ph = _g2p(sentences)
+    assert sum(word2ph) == len(phones)
+    assert len(word2ph) == len(text)  # Sometimes it will crash,you can add a try-catch.
+    phones = ['_'] + phones + ["_"]
+    tones = [0] + tones + [0]
+    word2ph = [1] + word2ph + [1]
+    return phones, tones, word2ph
+
+
+def _get_initials_finals(word):
+    initials = []
+    finals = []
+    orig_initials = lazy_pinyin(
+        word, neutral_tone_with_five=True, style=Style.INITIALS)
+    orig_finals = lazy_pinyin(
+        word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
+    for c, v in zip(orig_initials, orig_finals):
+        initials.append(c)
+        finals.append(v)
+    return initials, finals
+
+
+def _g2p(segments):
+    phones_list = []
+    tones_list = []
+    word2ph = []
+    for seg in segments:
+        pinyins = []
+        # Replace all English words in the sentence
+        seg = re.sub('[a-zA-Z]+', '', seg)
+        seg_cut = psg.lcut(seg)
+        initials = []
+        finals = []
+        seg_cut = tone_modifier.pre_merge_for_modify(seg_cut)
+        for word, pos in seg_cut:
+            if pos == 'eng':
+                continue
+            sub_initials, sub_finals = _get_initials_finals(word)
+            sub_finals = tone_modifier.modified_tone(word, pos,
+                                                     sub_finals)
+            initials.append(sub_initials)
+            finals.append(sub_finals)
+
+            # assert len(sub_initials) == len(sub_finals) == len(word)
+        initials = sum(initials, [])
+        finals = sum(finals, [])
+        #
+        for c, v in zip(initials, finals):
+            raw_pinyin = c + v
+            # NOTE: post process for pypinyin outputs
+            # we discriminate i, ii and iii
+            if c == v:
+                assert c in punctuation
+                phone = [c]
+                tone = '0'
+                word2ph.append(1)
+            else:
+                v_without_tone = v[:-1]
+                tone = v[-1]
+
+                pinyin = c + v_without_tone
+                assert tone in '12345'
+
+                if c:
+                    # 多音节
+                    v_rep_map = {
+                        "uei": 'ui',
+                        'iou': 'iu',
+                        'uen': 'un',
+                    }
+                    if v_without_tone in v_rep_map.keys():
+                        pinyin = c + v_rep_map[v_without_tone]
+                else:
+                    # 单音节
+                    pinyin_rep_map = {
+                        'ing': 'ying',
+                        'i': 'yi',
+                        'in': 'yin',
+                        'u': 'wu',
+                    }
+                    if pinyin in pinyin_rep_map.keys():
+                        pinyin = pinyin_rep_map[pinyin]
+                    else:
+                        single_rep_map = {
+                            'v': 'yu',
+                            'e': 'e',
+                            'i': 'y',
+                            'u': 'w',
+                        }
+                        if pinyin[0] in single_rep_map.keys():
+                            pinyin = single_rep_map[pinyin[0]] + pinyin[1:]
+
+                assert pinyin in pinyin_to_symbol_map.keys(), (pinyin, seg, raw_pinyin)
+                phone = pinyin_to_symbol_map[pinyin].split(' ')
+                word2ph.append(len(phone))
+
+            phones_list += phone
+            tones_list += [int(tone)] * len(phone)
+    return phones_list, tones_list, word2ph
+
+
+def text_normalize(text):
+    # numbers = re.findall(r'\d+(?:\.?\d+)?', text)
+    # for number in numbers:
+    #     text = text.replace(number, cn2an.an2cn(number), 1)
+    text = cn2an.transform(text, "an2cn")
+    text = replace_punctuation(text)
+    return text
+
+
+def get_bert_feature(text, word2ph):
+    from bert_vits2.text import chinese_bert
+    return chinese_bert.get_bert_feature(text, word2ph)
+
+
+if __name__ == '__main__':
+    text = "啊！但是《原神》是由,米哈\游自主，  [研发]的一款全.新开放世界.冒险游戏。"
+    text = text_normalize(text)
+    print(text)
+    phones, tones, word2ph = g2p(text)
+
+    print(phones, tones, word2ph)
+    bert = get_bert_feature(text, word2ph)
+
+    print(bert.shape)
+
+# # 示例用法
+# text = "这是一个示例文本：,你好！这是一个测试...."
+# print(g2p_paddle(text))  # 输出: 这是一个示例文本你好这是一个测试

bert_vits2/text/chinese_v240.py

@@ -0,0 +1,211 @@
+import os
+import re
+
+from pypinyin import Style
+from bert_vits2.text.symbols import punctuation
+from bert_vits2.text.tone_sandhi import ToneSandhi
+
+import cn2an
+
+normalizer = lambda x: cn2an.transform(x, "an2cn")
+
+current_file_path = os.path.dirname(__file__)
+pinyin_to_symbol_map = {
+    line.split("\t")[0]: line.strip().split("\t")[1]
+    for line in open(os.path.join(current_file_path, "opencpop-strict.txt")).readlines()
+}
+
+import jieba.posseg as psg
+
+rep_map = {
+    "：": ",",
+    "；": ",",
+    "，": ",",
+    "。": ".",
+    "！": "!",
+    "？": "?",
+    "\n": ".",
+    "·": ",",
+    "、": ",",
+    "...": "…",
+    "$": ".",
+    "“": "'",
+    "”": "'",
+    '"': "'",
+    "‘": "'",
+    "’": "'",
+    "（": "'",
+    "）": "'",
+    "(": "'",
+    ")": "'",
+    "《": "'",
+    "》": "'",
+    "【": "'",
+    "】": "'",
+    "[": "'",
+    "]": "'",
+    "—": "-",
+    "～": "-",
+    "~": "-",
+    "「": "'",
+    "」": "'",
+}
+
+tone_modifier = ToneSandhi()
+
+
+def replace_punctuation(text):
+    text = text.replace("嗯", "恩").replace("呣", "母")
+    pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
+
+    replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
+
+    replaced_text = re.sub(
+        r"[^\u4e00-\u9fa5" + "".join(punctuation) + r"]+", "", replaced_text
+    )
+
+    return replaced_text
+
+
+def g2p(text, pinyinPlus=None, **kwargs):
+    pattern = r"(?<=[{0}])\s*".format("".join(punctuation))
+    sentences = [i for i in re.split(pattern, text) if i.strip() != ""]
+    phones, tones, word2ph = _g2p(sentences, pinyinPlus)
+    assert sum(word2ph) == len(phones)
+    assert len(word2ph) == len(text)  # Sometimes it will crash,you can add a try-catch.
+    phones = ["_"] + phones + ["_"]
+    tones = [0] + tones + [0]
+    word2ph = [1] + word2ph + [1]
+    return phones, tones, word2ph
+
+
+def _get_initials_finalsV2(word, orig_initials, orig_finals):
+    initials = []
+    finals = []
+    for c, v in zip(orig_initials, orig_finals):
+        initials.append(c)
+        finals.append(v)
+    return initials, finals
+
+
+def _g2p(segments, pinyinPlus, **kwargs):
+    phones_list = []
+    tones_list = []
+    word2ph = []
+    for seg in segments:
+        # Replace all English words in the sentence
+
+        seg = re.sub("[a-zA-Z]+", "", seg)
+
+        seg_cut = psg.lcut(seg)
+        initials = []
+        finals = []
+        seg_cut = tone_modifier.pre_merge_for_modify(seg_cut)
+        allWords = ""
+        for word, pos in seg_cut:
+            allWords = allWords + word
+
+        orig_initials = pinyinPlus.lazy_pinyin(
+            allWords, neutral_tone_with_five=True, style=Style.INITIALS
+        )
+        orig_finals = pinyinPlus.lazy_pinyin(
+            allWords, neutral_tone_with_five=True, style=Style.FINALS_TONE3
+        )
+        currentIndex = 0
+        for word, pos in seg_cut:
+            curr_orig_initials = orig_initials[currentIndex: currentIndex + len(word)]
+            curr_orig_finalss = orig_finals[currentIndex: currentIndex + len(word)]
+            currentIndex = currentIndex + len(word)
+            if pos == "eng":
+                continue
+            sub_initials, sub_finals = _get_initials_finalsV2(
+                word, curr_orig_initials, curr_orig_finalss
+            )
+            sub_finals = tone_modifier.modified_tone(word, pos, sub_finals)
+            initials.append(sub_initials)
+            finals.append(sub_finals)
+
+            # assert len(sub_initials) == len(sub_finals) == len(word)
+        initials = sum(initials, [])
+        finals = sum(finals, [])
+        #
+        for c, v in zip(initials, finals):
+            raw_pinyin = c + v
+            # NOTE: post process for pypinyin outputs
+            # we discriminate i, ii and iii
+            if c == v:
+                assert c in punctuation
+                phone = [c]
+                tone = "0"
+                word2ph.append(1)
+            else:
+                v_without_tone = v[:-1]
+                tone = v[-1]
+
+                pinyin = c + v_without_tone
+                assert tone in "12345"
+
+                if c:
+                    # 多音节
+                    v_rep_map = {
+                        "uei": "ui",
+                        "iou": "iu",
+                        "uen": "un",
+                    }
+                    if v_without_tone in v_rep_map.keys():
+                        pinyin = c + v_rep_map[v_without_tone]
+                else:
+                    # 单音节
+                    pinyin_rep_map = {
+                        "ing": "ying",
+                        "i": "yi",
+                        "in": "yin",
+                        "u": "wu",
+                    }
+                    if pinyin in pinyin_rep_map.keys():
+                        pinyin = pinyin_rep_map[pinyin]
+                    else:
+                        single_rep_map = {
+                            "v": "yu",
+                            "e": "e",
+                            "i": "y",
+                            "u": "w",
+                        }
+                        if pinyin[0] in single_rep_map.keys():
+                            pinyin = single_rep_map[pinyin[0]] + pinyin[1:]
+
+                assert pinyin in pinyin_to_symbol_map.keys(), (pinyin, seg, raw_pinyin)
+                phone = pinyin_to_symbol_map[pinyin].split(" ")
+                word2ph.append(len(phone))
+
+            phones_list += phone
+            tones_list += [int(tone)] * len(phone)
+    return phones_list, tones_list, word2ph
+
+
+def text_normalize(text):
+    text = normalizer(text)
+    text = replace_punctuation(text)
+    return text
+
+
+def get_bert_feature(text, word2ph):
+    from bert_vits2.text import chinese_bert_extra as chinese_bert
+
+    return chinese_bert.get_bert_feature(text, word2ph)
+
+
+if __name__ == "__main__":
+    from bert_vits2.text.chinese_bert import get_bert_feature
+
+    text = "欸，这个「勾玉」的形状，是不是和那边门上的凹槽很像？"
+    text = text_normalize(text)
+    print(text)
+    phones, tones, word2ph = g2p(text)
+    bert = get_bert_feature(text, word2ph)
+
+    print(phones, tones, word2ph, bert.shape)
+
+# # 示例用法
+# text = "这是一个示例文本：,你好！这是一个测试...."
+# print(g2p_paddle(text))  # 输出: 这是一个示例文本你好这是一个测试

bert_vits2/text/cleaner.py

@@ -0,0 +1,53 @@
+from bert_vits2.text import chinese, japanese, english, cleaned_text_to_sequence, japanese_v111, chinese_v100, \
+    japanese_v200, english_v200, english_v230, chinese_v240, japanese_extra
+
+language_module_map = {
+    'zh': chinese,
+    'ja': japanese,
+    'en': english,
+    'ja_v111': japanese_v111,
+    'zh_v100': chinese_v100,
+    'ja_v200': japanese_v200,
+    'en_v200': english_v200,
+    'en_v230': english_v230,
+    'zh_v240': chinese_v240,
+    'ja_extra': japanese_extra,
+}
+
+
+# _loaded_modules = {}
+# 
+# 
+# def get_language_module(language):
+#     if language not in _loaded_modules:
+#         module_path = language_module_map.get(language)
+#         if not module_path:
+#             raise ValueError(f"Unsupported language: {language}")
+# 
+#         _loaded_modules[language] = importlib.import_module(module_path)
+# 
+#     return _loaded_modules[language]
+
+
+def clean_text(text, language, tokenizer, pinyinPlus=None):
+    language_module = language_module_map[language]
+    norm_text = language_module.text_normalize(text)
+    phones, tones, word2ph = language_module.g2p(norm_text, tokenizer=tokenizer, pinyinPlus=pinyinPlus)
+    return norm_text, phones, tones, word2ph
+
+
+# def clean_text_bert(text, language, tokenizer):
+#     language_module = language_module_map[language]
+#     norm_text = language_module.text_normalize(text)
+#     phones, tones, word2ph = language_module.g2p(norm_text, tokenizer)
+#     bert = language_module.get_bert_feature(norm_text, word2ph)
+#     return phones, tones, bert
+
+
+def text_to_sequence(text, language, tokenizer):
+    norm_text, phones, tones, word2ph = clean_text(text, language, tokenizer)
+    return cleaned_text_to_sequence(phones, tones, language)
+
+
+if __name__ == '__main__':
+    pass

bert_vits2/text/cmudict_cache.pickle

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b9b21b20325471934ba92f2e4a5976989e7d920caa32e7a286eacb027d197949
+size 6212655

bert_vits2/text/english.py

@@ -0,0 +1,449 @@
+import pickle
+import os
+import re
+from g2p_en import G2p
+
+from bert_vits2.text import symbols
+
+current_file_path = os.path.dirname(__file__)
+CMU_DICT_PATH = os.path.join(current_file_path, "cmudict.rep")
+CACHE_PATH = os.path.join(current_file_path, "cmudict_cache.pickle")
+_g2p = G2p()
+
+arpa = {
+    "AH0",
+    "S",
+    "AH1",
+    "EY2",
+    "AE2",
+    "EH0",
+    "OW2",
+    "UH0",
+    "NG",
+    "B",
+    "G",
+    "AY0",
+    "M",
+    "AA0",
+    "F",
+    "AO0",
+    "ER2",
+    "UH1",
+    "IY1",
+    "AH2",
+    "DH",
+    "IY0",
+    "EY1",
+    "IH0",
+    "K",
+    "N",
+    "W",
+    "IY2",
+    "T",
+    "AA1",
+    "ER1",
+    "EH2",
+    "OY0",
+    "UH2",
+    "UW1",
+    "Z",
+    "AW2",
+    "AW1",
+    "V",
+    "UW2",
+    "AA2",
+    "ER",
+    "AW0",
+    "UW0",
+    "R",
+    "OW1",
+    "EH1",
+    "ZH",
+    "AE0",
+    "IH2",
+    "IH",
+    "Y",
+    "JH",
+    "P",
+    "AY1",
+    "EY0",
+    "OY2",
+    "TH",
+    "HH",
+    "D",
+    "ER0",
+    "CH",
+    "AO1",
+    "AE1",
+    "AO2",
+    "OY1",
+    "AY2",
+    "IH1",
+    "OW0",
+    "L",
+    "SH",
+}
+
+
+def post_replace_ph(ph):
+    rep_map = {
+        "：": ",",
+        "；": ",",
+        "，": ",",
+        "。": ".",
+        "！": "!",
+        "？": "?",
+        "\n": ".",
+        "·": ",",
+        "、": ",",
+        "…": "...",
+        "···": "...",
+        "・・・": "...",
+        "v": "V",
+    }
+    if ph in rep_map.keys():
+        ph = rep_map[ph]
+    if ph in symbols:
+        return ph
+    if ph not in symbols:
+        ph = "UNK"
+    return ph
+
+
+rep_map = {
+    "：": ",",
+    "；": ",",
+    "，": ",",
+    "。": ".",
+    "！": "!",
+    "？": "?",
+    "\n": ".",
+    "．": ".",
+    "…": "...",
+    "···": "...",
+    "・・・": "...",
+    "·": ",",
+    "・": ",",
+    "、": ",",
+    "$": ".",
+    "“": "'",
+    "”": "'",
+    '"': "'",
+    "‘": "'",
+    "’": "'",
+    "（": "'",
+    "）": "'",
+    "(": "'",
+    ")": "'",
+    "《": "'",
+    "》": "'",
+    "【": "'",
+    "】": "'",
+    "[": "'",
+    "]": "'",
+    "—": "-",
+    "−": "-",
+    "～": "-",
+    "~": "-",
+    "「": "'",
+    "」": "'",
+}
+
+
+def replace_punctuation(text):
+    pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
+
+    replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
+
+    # replaced_text = re.sub(
+    #     r"[^\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\u3005"
+    #     + "".join(punctuation)
+    #     + r"]+",
+    #     "",
+    #     replaced_text,
+    # )
+
+    return replaced_text
+
+
+def read_dict():
+    g2p_dict = {}
+    start_line = 49
+    with open(CMU_DICT_PATH) as f:
+        line = f.readline()
+        line_index = 1
+        while line:
+            if line_index >= start_line:
+                line = line.strip()
+                word_split = line.split("  ")
+                word = word_split[0]
+
+                syllable_split = word_split[1].split(" - ")
+                g2p_dict[word] = []
+                for syllable in syllable_split:
+                    phone_split = syllable.split(" ")
+                    g2p_dict[word].append(phone_split)
+
+            line_index = line_index + 1
+            line = f.readline()
+
+    return g2p_dict
+
+
+def cache_dict(g2p_dict, file_path):
+    with open(file_path, "wb") as pickle_file:
+        pickle.dump(g2p_dict, pickle_file)
+
+
+def get_dict():
+    if os.path.exists(CACHE_PATH):
+        with open(CACHE_PATH, "rb") as pickle_file:
+            g2p_dict = pickle.load(pickle_file)
+    else:
+        g2p_dict = read_dict()
+        cache_dict(g2p_dict, CACHE_PATH)
+
+    return g2p_dict
+
+
+eng_dict = get_dict()
+
+
+def refine_ph(phn):
+    tone = 0
+    if re.search(r"\d$", phn):
+        tone = int(phn[-1]) + 1
+        phn = phn[:-1]
+    return phn.lower(), tone
+
+
+def refine_syllables(syllables):
+    tones = []
+    phonemes = []
+    for phn_list in syllables:
+        for i in range(len(phn_list)):
+            phn = phn_list[i]
+            phn, tone = refine_ph(phn)
+            phonemes.append(phn)
+            tones.append(tone)
+    return phonemes, tones
+
+
+import re
+import inflect
+
+_inflect = inflect.engine()
+_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])")
+_decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)")
+_pounds_re = re.compile(r"£([0-9\,]*[0-9]+)")
+_dollars_re = re.compile(r"\$([0-9\.\,]*[0-9]+)")
+_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)")
+_number_re = re.compile(r"[0-9]+")
+
+# List of (regular expression, replacement) pairs for abbreviations:
+_abbreviations = [
+    (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
+    for x in [
+        ("mrs", "misess"),
+        ("mr", "mister"),
+        ("dr", "doctor"),
+        ("st", "saint"),
+        ("co", "company"),
+        ("jr", "junior"),
+        ("maj", "major"),
+        ("gen", "general"),
+        ("drs", "doctors"),
+        ("rev", "reverend"),
+        ("lt", "lieutenant"),
+        ("hon", "honorable"),
+        ("sgt", "sergeant"),
+        ("capt", "captain"),
+        ("esq", "esquire"),
+        ("ltd", "limited"),
+        ("col", "colonel"),
+        ("ft", "fort"),
+    ]
+]
+
+# List of (ipa, lazy ipa) pairs:
+_lazy_ipa = [
+    (re.compile("%s" % x[0]), x[1])
+    for x in [
+        ("r", "ɹ"),
+        ("æ", "e"),
+        ("ɑ", "a"),
+        ("ɔ", "o"),
+        ("ð", "z"),
+        ("θ", "s"),
+        ("ɛ", "e"),
+        ("ɪ", "i"),
+        ("ʊ", "u"),
+        ("ʒ", "ʥ"),
+        ("ʤ", "ʥ"),
+        ("ˈ", "↓"),
+    ]
+]
+
+# List of (ipa, lazy ipa2) pairs:
+_lazy_ipa2 = [
+    (re.compile("%s" % x[0]), x[1])
+    for x in [
+        ("r", "ɹ"),
+        ("ð", "z"),
+        ("θ", "s"),
+        ("ʒ", "ʑ"),
+        ("ʤ", "dʑ"),
+        ("ˈ", "↓"),
+    ]
+]
+
+# List of (ipa, ipa2) pairs
+_ipa_to_ipa2 = [
+    (re.compile("%s" % x[0]), x[1]) for x in [("r", "ɹ"), ("ʤ", "dʒ"), ("ʧ", "tʃ")]
+]
+
+
+def _expand_dollars(m):
+    match = m.group(1)
+    parts = match.split(".")
+    if len(parts) > 2:
+        return match + " dollars"  # Unexpected format
+    dollars = int(parts[0]) if parts[0] else 0
+    cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
+    if dollars and cents:
+        dollar_unit = "dollar" if dollars == 1 else "dollars"
+        cent_unit = "cent" if cents == 1 else "cents"
+        return "%s %s, %s %s" % (dollars, dollar_unit, cents, cent_unit)
+    elif dollars:
+        dollar_unit = "dollar" if dollars == 1 else "dollars"
+        return "%s %s" % (dollars, dollar_unit)
+    elif cents:
+        cent_unit = "cent" if cents == 1 else "cents"
+        return "%s %s" % (cents, cent_unit)
+    else:
+        return "zero dollars"
+
+
+def _remove_commas(m):
+    return m.group(1).replace(",", "")
+
+
+def _expand_ordinal(m):
+    return _inflect.number_to_words(m.group(0))
+
+
+def _expand_number(m):
+    num = int(m.group(0))
+    if num > 1000 and num < 3000:
+        if num == 2000:
+            return "two thousand"
+        elif num > 2000 and num < 2010:
+            return "two thousand " + _inflect.number_to_words(num % 100)
+        elif num % 100 == 0:
+            return _inflect.number_to_words(num // 100) + " hundred"
+        else:
+            return _inflect.number_to_words(
+                num, andword="", zero="oh", group=2
+            ).replace(", ", " ")
+    else:
+        return _inflect.number_to_words(num, andword="")
+
+
+def _expand_decimal_point(m):
+    return m.group(1).replace(".", " point ")
+
+
+def normalize_numbers(text):
+    text = re.sub(_comma_number_re, _remove_commas, text)
+    text = re.sub(_pounds_re, r"\1 pounds", text)
+    text = re.sub(_dollars_re, _expand_dollars, text)
+    text = re.sub(_decimal_number_re, _expand_decimal_point, text)
+    text = re.sub(_ordinal_re, _expand_ordinal, text)
+    text = re.sub(_number_re, _expand_number, text)
+    return text
+
+
+def text_normalize(text):
+    text = normalize_numbers(text)
+    text = replace_punctuation(text)
+    text = re.sub(r"([,;.\?\!])([\w])", r"\1 \2", text)
+    return text
+
+
+def distribute_phone(n_phone, n_word):
+    phones_per_word = [0] * n_word
+    for task in range(n_phone):
+        min_tasks = min(phones_per_word)
+        min_index = phones_per_word.index(min_tasks)
+        phones_per_word[min_index] += 1
+    return phones_per_word
+
+
+def sep_text(text):
+    words = re.split(r"([,;.\?\!\s+])", text)
+    words = [word for word in words if word.strip() != ""]
+    return words
+
+
+def g2p(text, tokenizer, **kwargs):
+    phones = []
+    tones = []
+    # word2ph = []
+    words = sep_text(text)
+    tokens = [tokenizer.tokenize(i) for i in words]
+    for word in words:
+        if word.upper() in eng_dict:
+            phns, tns = refine_syllables(eng_dict[word.upper()])
+            phones.append([post_replace_ph(i) for i in phns])
+            tones.append(tns)
+            # word2ph.append(len(phns))
+        else:
+            phone_list = list(filter(lambda p: p != " ", _g2p(word)))
+            phns = []
+            tns = []
+            for ph in phone_list:
+                if ph in arpa:
+                    ph, tn = refine_ph(ph)
+                    phns.append(ph)
+                    tns.append(tn)
+                else:
+                    phns.append(ph)
+                    tns.append(0)
+            phones.append([post_replace_ph(i) for i in phns])
+            tones.append(tns)
+            # word2ph.append(len(phns))
+    # phones = [post_replace_ph(i) for i in phones]
+
+    word2ph = []
+    for token, phoneme in zip(tokens, phones):
+        phone_len = len(phoneme)
+        word_len = len(token)
+
+        aaa = distribute_phone(phone_len, word_len)
+        word2ph += aaa
+
+    phones = ["_"] + [j for i in phones for j in i] + ["_"]
+    tones = [0] + [j for i in tones for j in i] + [0]
+    word2ph = [1] + word2ph + [1]
+    assert len(phones) == len(tones), text
+    assert len(phones) == sum(word2ph), text
+
+    return phones, tones, word2ph
+
+
+def get_bert_feature(text, word2ph):
+    from bert_vits2.text import english_bert_mock
+
+    return english_bert_mock.get_bert_feature(text, word2ph)
+
+
+if __name__ == "__main__":
+    # print(get_dict())
+    # print(eng_word_to_phoneme("hello"))
+    print(g2p("In this paper, we propose 1 DSPGAN, a GAN-based universal vocoder."))
+    # all_phones = set()
+    # for k, syllables in eng_dict.items():
+    #     for group in syllables:
+    #         for ph in group:
+    #             all_phones.add(ph)
+    # print(all_phones)

bert_vits2/text/english_bert_mock.py

@@ -0,0 +1,36 @@
+import torch
+
+from contants import config
+
+
+def get_bert_feature(text, word2ph, tokenizer, model, device=config.system.device, style_text=None, style_weight=0.7,
+                     **kwargs):
+    with torch.no_grad():
+        inputs = tokenizer(text, return_tensors="pt")
+        for i in inputs:
+            inputs[i] = inputs[i].to(device)
+        res = model(**inputs, output_hidden_states=True)
+        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].float().cpu()
+        if style_text:
+            style_inputs = tokenizer(style_text, return_tensors="pt")
+            for i in style_inputs:
+                style_inputs[i] = style_inputs[i].to(device)
+            style_res = model(**style_inputs, output_hidden_states=True)
+            style_res = torch.cat(style_res["hidden_states"][-3:-2], -1)[0].float().cpu()
+            style_res_mean = style_res.mean(0)
+    assert len(word2ph) == res.shape[0], (text, res.shape[0], len(word2ph))
+    word2phone = word2ph
+    phone_level_feature = []
+    for i in range(len(word2phone)):
+        if style_text:
+            repeat_feature = (
+                    res[i].repeat(word2phone[i], 1) * (1 - style_weight)
+                    + style_res_mean.repeat(word2phone[i], 1) * style_weight
+            )
+        else:
+            repeat_feature = res[i].repeat(word2phone[i], 1)
+        phone_level_feature.append(repeat_feature)
+
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+
+    return phone_level_feature.T

bert_vits2/text/english_bert_mock_v200.py

@@ -0,0 +1,22 @@
+import torch
+
+from contants import config
+
+
+def get_bert_feature(text, word2ph, tokenizer, model, device=config.system.device, **kwargs):
+    with torch.no_grad():
+        inputs = tokenizer(text, return_tensors="pt")
+        for i in inputs:
+            inputs[i] = inputs[i].to(device)
+        res = model(**inputs, output_hidden_states=True)
+        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].float().cpu()
+    # assert len(word2ph) == len(text)+2
+    word2phone = word2ph
+    phone_level_feature = []
+    for i in range(len(word2phone)):
+        repeat_feature = res[i].repeat(word2phone[i], 1)
+        phone_level_feature.append(repeat_feature)
+
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+
+    return phone_level_feature.T

bert_vits2/text/english_v200.py

@@ -0,0 +1,360 @@
+import pickle
+import os
+from g2p_en import G2p
+
+from bert_vits2.text import symbols
+
+current_file_path = os.path.dirname(__file__)
+CMU_DICT_PATH = os.path.join(current_file_path, "cmudict.rep")
+CACHE_PATH = os.path.join(current_file_path, "cmudict_cache.pickle")
+_g2p = G2p()
+
+arpa = {
+    "AH0",
+    "S",
+    "AH1",
+    "EY2",
+    "AE2",
+    "EH0",
+    "OW2",
+    "UH0",
+    "NG",
+    "B",
+    "G",
+    "AY0",
+    "M",
+    "AA0",
+    "F",
+    "AO0",
+    "ER2",
+    "UH1",
+    "IY1",
+    "AH2",
+    "DH",
+    "IY0",
+    "EY1",
+    "IH0",
+    "K",
+    "N",
+    "W",
+    "IY2",
+    "T",
+    "AA1",
+    "ER1",
+    "EH2",
+    "OY0",
+    "UH2",
+    "UW1",
+    "Z",
+    "AW2",
+    "AW1",
+    "V",
+    "UW2",
+    "AA2",
+    "ER",
+    "AW0",
+    "UW0",
+    "R",
+    "OW1",
+    "EH1",
+    "ZH",
+    "AE0",
+    "IH2",
+    "IH",
+    "Y",
+    "JH",
+    "P",
+    "AY1",
+    "EY0",
+    "OY2",
+    "TH",
+    "HH",
+    "D",
+    "ER0",
+    "CH",
+    "AO1",
+    "AE1",
+    "AO2",
+    "OY1",
+    "AY2",
+    "IH1",
+    "OW0",
+    "L",
+    "SH",
+}
+
+
+def post_replace_ph(ph):
+    rep_map = {
+        "：": ",",
+        "；": ",",
+        "，": ",",
+        "。": ".",
+        "！": "!",
+        "？": "?",
+        "\n": ".",
+        "·": ",",
+        "、": ",",
+        "...": "…",
+        "v": "V",
+    }
+    if ph in rep_map.keys():
+        ph = rep_map[ph]
+    if ph in symbols:
+        return ph
+    if ph not in symbols:
+        ph = "UNK"
+    return ph
+
+
+def read_dict():
+    g2p_dict = {}
+    start_line = 49
+    with open(CMU_DICT_PATH) as f:
+        line = f.readline()
+        line_index = 1
+        while line:
+            if line_index >= start_line:
+                line = line.strip()
+                word_split = line.split("  ")
+                word = word_split[0]
+
+                syllable_split = word_split[1].split(" - ")
+                g2p_dict[word] = []
+                for syllable in syllable_split:
+                    phone_split = syllable.split(" ")
+                    g2p_dict[word].append(phone_split)
+
+            line_index = line_index + 1
+            line = f.readline()
+
+    return g2p_dict
+
+
+def cache_dict(g2p_dict, file_path):
+    with open(file_path, "wb") as pickle_file:
+        pickle.dump(g2p_dict, pickle_file)
+
+
+def get_dict():
+    if os.path.exists(CACHE_PATH):
+        with open(CACHE_PATH, "rb") as pickle_file:
+            g2p_dict = pickle.load(pickle_file)
+    else:
+        g2p_dict = read_dict()
+        cache_dict(g2p_dict, CACHE_PATH)
+
+    return g2p_dict
+
+
+eng_dict = get_dict()
+
+
+def refine_ph(phn):
+    tone = 0
+    if re.search(r"\d$", phn):
+        tone = int(phn[-1]) + 1
+        phn = phn[:-1]
+    return phn.lower(), tone
+
+
+def refine_syllables(syllables):
+    tones = []
+    phonemes = []
+    for phn_list in syllables:
+        for i in range(len(phn_list)):
+            phn = phn_list[i]
+            phn, tone = refine_ph(phn)
+            phonemes.append(phn)
+            tones.append(tone)
+    return phonemes, tones
+
+
+import re
+import inflect
+
+_inflect = inflect.engine()
+_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])")
+_decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)")
+_pounds_re = re.compile(r"£([0-9\,]*[0-9]+)")
+_dollars_re = re.compile(r"\$([0-9\.\,]*[0-9]+)")
+_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)")
+_number_re = re.compile(r"[0-9]+")
+
+# List of (regular expression, replacement) pairs for abbreviations:
+_abbreviations = [
+    (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
+    for x in [
+        ("mrs", "misess"),
+        ("mr", "mister"),
+        ("dr", "doctor"),
+        ("st", "saint"),
+        ("co", "company"),
+        ("jr", "junior"),
+        ("maj", "major"),
+        ("gen", "general"),
+        ("drs", "doctors"),
+        ("rev", "reverend"),
+        ("lt", "lieutenant"),
+        ("hon", "honorable"),
+        ("sgt", "sergeant"),
+        ("capt", "captain"),
+        ("esq", "esquire"),
+        ("ltd", "limited"),
+        ("col", "colonel"),
+        ("ft", "fort"),
+    ]
+]
+
+# List of (ipa, lazy ipa) pairs:
+_lazy_ipa = [
+    (re.compile("%s" % x[0]), x[1])
+    for x in [
+        ("r", "ɹ"),
+        ("æ", "e"),
+        ("ɑ", "a"),
+        ("ɔ", "o"),
+        ("ð", "z"),
+        ("θ", "s"),
+        ("ɛ", "e"),
+        ("ɪ", "i"),
+        ("ʊ", "u"),
+        ("ʒ", "ʥ"),
+        ("ʤ", "ʥ"),
+        ("ˈ", "↓"),
+    ]
+]
+
+# List of (ipa, lazy ipa2) pairs:
+_lazy_ipa2 = [
+    (re.compile("%s" % x[0]), x[1])
+    for x in [
+        ("r", "ɹ"),
+        ("ð", "z"),
+        ("θ", "s"),
+        ("ʒ", "ʑ"),
+        ("ʤ", "dʑ"),
+        ("ˈ", "↓"),
+    ]
+]
+
+# List of (ipa, ipa2) pairs
+_ipa_to_ipa2 = [
+    (re.compile("%s" % x[0]), x[1]) for x in [("r", "ɹ"), ("ʤ", "dʒ"), ("ʧ", "tʃ")]
+]
+
+
+def _expand_dollars(m):
+    match = m.group(1)
+    parts = match.split(".")
+    if len(parts) > 2:
+        return match + " dollars"  # Unexpected format
+    dollars = int(parts[0]) if parts[0] else 0
+    cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
+    if dollars and cents:
+        dollar_unit = "dollar" if dollars == 1 else "dollars"
+        cent_unit = "cent" if cents == 1 else "cents"
+        return "%s %s, %s %s" % (dollars, dollar_unit, cents, cent_unit)
+    elif dollars:
+        dollar_unit = "dollar" if dollars == 1 else "dollars"
+        return "%s %s" % (dollars, dollar_unit)
+    elif cents:
+        cent_unit = "cent" if cents == 1 else "cents"
+        return "%s %s" % (cents, cent_unit)
+    else:
+        return "zero dollars"
+
+
+def _remove_commas(m):
+    return m.group(1).replace(",", "")
+
+
+def _expand_ordinal(m):
+    return _inflect.number_to_words(m.group(0))
+
+
+def _expand_number(m):
+    num = int(m.group(0))
+    if num > 1000 and num < 3000:
+        if num == 2000:
+            return "two thousand"
+        elif num > 2000 and num < 2010:
+            return "two thousand " + _inflect.number_to_words(num % 100)
+        elif num % 100 == 0:
+            return _inflect.number_to_words(num // 100) + " hundred"
+        else:
+            return _inflect.number_to_words(
+                num, andword="", zero="oh", group=2
+            ).replace(", ", " ")
+    else:
+        return _inflect.number_to_words(num, andword="")
+
+
+def _expand_decimal_point(m):
+    return m.group(1).replace(".", " point ")
+
+
+def normalize_numbers(text):
+    text = re.sub(_comma_number_re, _remove_commas, text)
+    text = re.sub(_pounds_re, r"\1 pounds", text)
+    text = re.sub(_dollars_re, _expand_dollars, text)
+    text = re.sub(_decimal_number_re, _expand_decimal_point, text)
+    text = re.sub(_ordinal_re, _expand_ordinal, text)
+    text = re.sub(_number_re, _expand_number, text)
+    return text
+
+
+def text_normalize(text):
+    text = normalize_numbers(text)
+    return text
+
+
+def g2p(text, **kwargs):
+    phones = []
+    tones = []
+    word2ph = []
+    words = re.split(r"([,;.\-\?\!\s+])", text)
+    words = [word for word in words if word.strip() != ""]
+    for word in words:
+        if word.upper() in eng_dict:
+            phns, tns = refine_syllables(eng_dict[word.upper()])
+            phones += phns
+            tones += tns
+            word2ph.append(len(phns))
+        else:
+            phone_list = list(filter(lambda p: p != " ", _g2p(word)))
+            for ph in phone_list:
+                if ph in arpa:
+                    ph, tn = refine_ph(ph)
+                    phones.append(ph)
+                    tones.append(tn)
+                else:
+                    phones.append(ph)
+                    tones.append(0)
+            word2ph.append(len(phone_list))
+
+    phones = [post_replace_ph(i) for i in phones]
+
+    phones = ["_"] + phones + ["_"]
+    tones = [0] + tones + [0]
+    word2ph = [1] + word2ph + [1]
+
+    return phones, tones, word2ph
+
+
+def get_bert_feature(text, word2ph):
+    from bert_vits2.text import english_bert_mock
+
+    return english_bert_mock.get_bert_feature(text, word2ph)
+
+
+if __name__ == "__main__":
+    # print(get_dict())
+    # print(eng_word_to_phoneme("hello"))
+    print(g2p("In this paper, we propose 1 DSPGAN, a GAN-based universal vocoder."))
+    # all_phones = set()
+    # for k, syllables in eng_dict.items():
+    #     for group in syllables:
+    #         for ph in group:
+    #             all_phones.add(ph)
+    # print(all_phones)

bert_vits2/text/english_v230.py

@@ -0,0 +1,493 @@
+import pickle
+import os
+from g2p_en import G2p
+from transformers import DebertaV2Tokenizer
+
+from bert_vits2.text import symbols
+from bert_vits2.text.symbols import punctuation
+
+current_file_path = os.path.dirname(__file__)
+CMU_DICT_PATH = os.path.join(current_file_path, "cmudict.rep")
+CACHE_PATH = os.path.join(current_file_path, "cmudict_cache.pickle")
+_g2p = G2p()
+LOCAL_PATH = "./bert/deberta-v3-large"
+# tokenizer = DebertaV2Tokenizer.from_pretrained(LOCAL_PATH)
+
+arpa = {
+    "AH0",
+    "S",
+    "AH1",
+    "EY2",
+    "AE2",
+    "EH0",
+    "OW2",
+    "UH0",
+    "NG",
+    "B",
+    "G",
+    "AY0",
+    "M",
+    "AA0",
+    "F",
+    "AO0",
+    "ER2",
+    "UH1",
+    "IY1",
+    "AH2",
+    "DH",
+    "IY0",
+    "EY1",
+    "IH0",
+    "K",
+    "N",
+    "W",
+    "IY2",
+    "T",
+    "AA1",
+    "ER1",
+    "EH2",
+    "OY0",
+    "UH2",
+    "UW1",
+    "Z",
+    "AW2",
+    "AW1",
+    "V",
+    "UW2",
+    "AA2",
+    "ER",
+    "AW0",
+    "UW0",
+    "R",
+    "OW1",
+    "EH1",
+    "ZH",
+    "AE0",
+    "IH2",
+    "IH",
+    "Y",
+    "JH",
+    "P",
+    "AY1",
+    "EY0",
+    "OY2",
+    "TH",
+    "HH",
+    "D",
+    "ER0",
+    "CH",
+    "AO1",
+    "AE1",
+    "AO2",
+    "OY1",
+    "AY2",
+    "IH1",
+    "OW0",
+    "L",
+    "SH",
+}
+
+
+def post_replace_ph(ph):
+    rep_map = {
+        "：": ",",
+        "；": ",",
+        "，": ",",
+        "。": ".",
+        "！": "!",
+        "？": "?",
+        "\n": ".",
+        "·": ",",
+        "、": ",",
+        "…": "...",
+        "···": "...",
+        "・・・": "...",
+        "v": "V",
+    }
+    if ph in rep_map.keys():
+        ph = rep_map[ph]
+    if ph in symbols:
+        return ph
+    if ph not in symbols:
+        ph = "UNK"
+    return ph
+
+
+rep_map = {
+    "：": ",",
+    "；": ",",
+    "，": ",",
+    "。": ".",
+    "！": "!",
+    "？": "?",
+    "\n": ".",
+    "．": ".",
+    "…": "...",
+    "···": "...",
+    "・・・": "...",
+    "·": ",",
+    "・": ",",
+    "、": ",",
+    "$": ".",
+    "“": "'",
+    "”": "'",
+    '"': "'",
+    "‘": "'",
+    "’": "'",
+    "（": "'",
+    "）": "'",
+    "(": "'",
+    ")": "'",
+    "《": "'",
+    "》": "'",
+    "【": "'",
+    "】": "'",
+    "[": "'",
+    "]": "'",
+    "—": "-",
+    "−": "-",
+    "～": "-",
+    "~": "-",
+    "「": "'",
+    "」": "'",
+}
+
+
+def replace_punctuation(text):
+    pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
+
+    replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
+
+    # replaced_text = re.sub(
+    #     r"[^\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\u3005"
+    #     + "".join(punctuation)
+    #     + r"]+",
+    #     "",
+    #     replaced_text,
+    # )
+
+    return replaced_text
+
+
+def read_dict():
+    g2p_dict = {}
+    start_line = 49
+    with open(CMU_DICT_PATH) as f:
+        line = f.readline()
+        line_index = 1
+        while line:
+            if line_index >= start_line:
+                line = line.strip()
+                word_split = line.split("  ")
+                word = word_split[0]
+
+                syllable_split = word_split[1].split(" - ")
+                g2p_dict[word] = []
+                for syllable in syllable_split:
+                    phone_split = syllable.split(" ")
+                    g2p_dict[word].append(phone_split)
+
+            line_index = line_index + 1
+            line = f.readline()
+
+    return g2p_dict
+
+
+def cache_dict(g2p_dict, file_path):
+    with open(file_path, "wb") as pickle_file:
+        pickle.dump(g2p_dict, pickle_file)
+
+
+def get_dict():
+    if os.path.exists(CACHE_PATH):
+        with open(CACHE_PATH, "rb") as pickle_file:
+            g2p_dict = pickle.load(pickle_file)
+    else:
+        g2p_dict = read_dict()
+        cache_dict(g2p_dict, CACHE_PATH)
+
+    return g2p_dict
+
+
+eng_dict = get_dict()
+
+
+def refine_ph(phn):
+    tone = 0
+    if re.search(r"\d$", phn):
+        tone = int(phn[-1]) + 1
+        phn = phn[:-1]
+    else:
+        tone = 3
+    return phn.lower(), tone
+
+
+def refine_syllables(syllables):
+    tones = []
+    phonemes = []
+    for phn_list in syllables:
+        for i in range(len(phn_list)):
+            phn = phn_list[i]
+            phn, tone = refine_ph(phn)
+            phonemes.append(phn)
+            tones.append(tone)
+    return phonemes, tones
+
+
+import re
+import inflect
+
+_inflect = inflect.engine()
+_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])")
+_decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)")
+_pounds_re = re.compile(r"£([0-9\,]*[0-9]+)")
+_dollars_re = re.compile(r"\$([0-9\.\,]*[0-9]+)")
+_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)")
+_number_re = re.compile(r"[0-9]+")
+
+# List of (regular expression, replacement) pairs for abbreviations:
+_abbreviations = [
+    (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
+    for x in [
+        ("mrs", "misess"),
+        ("mr", "mister"),
+        ("dr", "doctor"),
+        ("st", "saint"),
+        ("co", "company"),
+        ("jr", "junior"),
+        ("maj", "major"),
+        ("gen", "general"),
+        ("drs", "doctors"),
+        ("rev", "reverend"),
+        ("lt", "lieutenant"),
+        ("hon", "honorable"),
+        ("sgt", "sergeant"),
+        ("capt", "captain"),
+        ("esq", "esquire"),
+        ("ltd", "limited"),
+        ("col", "colonel"),
+        ("ft", "fort"),
+    ]
+]
+
+# List of (ipa, lazy ipa) pairs:
+_lazy_ipa = [
+    (re.compile("%s" % x[0]), x[1])
+    for x in [
+        ("r", "ɹ"),
+        ("æ", "e"),
+        ("ɑ", "a"),
+        ("ɔ", "o"),
+        ("ð", "z"),
+        ("θ", "s"),
+        ("ɛ", "e"),
+        ("ɪ", "i"),
+        ("ʊ", "u"),
+        ("ʒ", "ʥ"),
+        ("ʤ", "ʥ"),
+        ("ˈ", "↓"),
+    ]
+]
+
+# List of (ipa, lazy ipa2) pairs:
+_lazy_ipa2 = [
+    (re.compile("%s" % x[0]), x[1])
+    for x in [
+        ("r", "ɹ"),
+        ("ð", "z"),
+        ("θ", "s"),
+        ("ʒ", "ʑ"),
+        ("ʤ", "dʑ"),
+        ("ˈ", "↓"),
+    ]
+]
+
+# List of (ipa, ipa2) pairs
+_ipa_to_ipa2 = [
+    (re.compile("%s" % x[0]), x[1]) for x in [("r", "ɹ"), ("ʤ", "dʒ"), ("ʧ", "tʃ")]
+]
+
+
+def _expand_dollars(m):
+    match = m.group(1)
+    parts = match.split(".")
+    if len(parts) > 2:
+        return match + " dollars"  # Unexpected format
+    dollars = int(parts[0]) if parts[0] else 0
+    cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
+    if dollars and cents:
+        dollar_unit = "dollar" if dollars == 1 else "dollars"
+        cent_unit = "cent" if cents == 1 else "cents"
+        return "%s %s, %s %s" % (dollars, dollar_unit, cents, cent_unit)
+    elif dollars:
+        dollar_unit = "dollar" if dollars == 1 else "dollars"
+        return "%s %s" % (dollars, dollar_unit)
+    elif cents:
+        cent_unit = "cent" if cents == 1 else "cents"
+        return "%s %s" % (cents, cent_unit)
+    else:
+        return "zero dollars"
+
+
+def _remove_commas(m):
+    return m.group(1).replace(",", "")
+
+
+def _expand_ordinal(m):
+    return _inflect.number_to_words(m.group(0))
+
+
+def _expand_number(m):
+    num = int(m.group(0))
+    if num > 1000 and num < 3000:
+        if num == 2000:
+            return "two thousand"
+        elif num > 2000 and num < 2010:
+            return "two thousand " + _inflect.number_to_words(num % 100)
+        elif num % 100 == 0:
+            return _inflect.number_to_words(num // 100) + " hundred"
+        else:
+            return _inflect.number_to_words(
+                num, andword="", zero="oh", group=2
+            ).replace(", ", " ")
+    else:
+        return _inflect.number_to_words(num, andword="")
+
+
+def _expand_decimal_point(m):
+    return m.group(1).replace(".", " point ")
+
+
+def normalize_numbers(text):
+    text = re.sub(_comma_number_re, _remove_commas, text)
+    text = re.sub(_pounds_re, r"\1 pounds", text)
+    text = re.sub(_dollars_re, _expand_dollars, text)
+    text = re.sub(_decimal_number_re, _expand_decimal_point, text)
+    text = re.sub(_ordinal_re, _expand_ordinal, text)
+    text = re.sub(_number_re, _expand_number, text)
+    return text
+
+
+def text_normalize(text):
+    text = normalize_numbers(text)
+    text = replace_punctuation(text)
+    text = re.sub(r"([,;.\?\!])([\w])", r"\1 \2", text)
+    return text
+
+
+def distribute_phone(n_phone, n_word):
+    phones_per_word = [0] * n_word
+    for task in range(n_phone):
+        min_tasks = min(phones_per_word)
+        min_index = phones_per_word.index(min_tasks)
+        phones_per_word[min_index] += 1
+    return phones_per_word
+
+
+def sep_text(text):
+    words = re.split(r"([,;.\?\!\s+])", text)
+    words = [word for word in words if word.strip() != ""]
+    return words
+
+
+def text_to_words(text, tokenizer):
+    tokens = tokenizer.tokenize(text)
+    words = []
+    for idx, t in enumerate(tokens):
+        if t.startswith("▁"):
+            words.append([t[1:]])
+        else:
+            if t in punctuation:
+                if idx == len(tokens) - 1:
+                    words.append([f"{t}"])
+                else:
+                    if (
+                            not tokens[idx + 1].startswith("▁")
+                            and tokens[idx + 1] not in punctuation
+                    ):
+                        if idx == 0:
+                            words.append([])
+                        words[-1].append(f"{t}")
+                    else:
+                        words.append([f"{t}"])
+            else:
+                if idx == 0:
+                    words.append([])
+                words[-1].append(f"{t}")
+    return words
+
+
+def g2p(text, tokenizer, **kwargs):
+    phones = []
+    tones = []
+    phone_len = []
+    # words = sep_text(text)
+    # tokens = [tokenizer.tokenize(i) for i in words]
+    words = text_to_words(text, tokenizer)
+
+    for word in words:
+        temp_phones, temp_tones = [], []
+        if len(word) > 1:
+            if "'" in word:
+                word = ["".join(word)]
+        for w in word:
+            if w in punctuation:
+                temp_phones.append(w)
+                temp_tones.append(0)
+                continue
+            if w.upper() in eng_dict:
+                phns, tns = refine_syllables(eng_dict[w.upper()])
+                temp_phones += [post_replace_ph(i) for i in phns]
+                temp_tones += tns
+                # w2ph.append(len(phns))
+            else:
+                phone_list = list(filter(lambda p: p != " ", _g2p(w)))
+                phns = []
+                tns = []
+                for ph in phone_list:
+                    if ph in arpa:
+                        ph, tn = refine_ph(ph)
+                        phns.append(ph)
+                        tns.append(tn)
+                    else:
+                        phns.append(ph)
+                        tns.append(0)
+                temp_phones += [post_replace_ph(i) for i in phns]
+                temp_tones += tns
+        phones += temp_phones
+        tones += temp_tones
+        phone_len.append(len(temp_phones))
+        # phones = [post_replace_ph(i) for i in phones]
+
+    word2ph = []
+    for token, pl in zip(words, phone_len):
+        word_len = len(token)
+
+        aaa = distribute_phone(pl, word_len)
+        word2ph += aaa
+
+    phones = ["_"] + phones + ["_"]
+    tones = [0] + tones + [0]
+    word2ph = [1] + word2ph + [1]
+    assert len(phones) == len(tones), text
+    assert len(phones) == sum(word2ph), text
+
+    return phones, tones, word2ph
+
+
+def get_bert_feature(text, word2ph):
+    from bert_vits2.text import english_bert_mock
+
+    return english_bert_mock.get_bert_feature(text, word2ph)
+
+
+if __name__ == "__main__":
+    # print(get_dict())
+    # print(eng_word_to_phoneme("hello"))
+    print(g2p("In this paper, we propose 1 DSPGAN, a GAN-based universal vocoder."))
+    # all_phones = set()
+    # for k, syllables in eng_dict.items():
+    #     for group in syllables:
+    #         for ph in group:
+    #             all_phones.add(ph)
+    # print(all_phones)

bert_vits2/text/japanese.py

@@ -0,0 +1,428 @@
+# Convert Japanese text to phonemes which is
+# compatible with Julius https://github.com/julius-speech/segmentation-kit
+import re
+import unicodedata
+
+from bert_vits2.text import punctuation, symbols
+
+from num2words import num2words
+
+import pyopenjtalk
+import jaconv
+
+
+def kata2phoneme(text: str) -> str:
+    """Convert katakana text to phonemes."""
+    text = text.strip()
+    if text == "ー":
+        return ["ー"]
+    elif text.startswith("ー"):
+        return ["ー"] + kata2phoneme(text[1:])
+    res = []
+    prev = None
+    while text:
+        if re.match(_MARKS, text):
+            res.append(text)
+            text = text[1:]
+            continue
+        if text.startswith("ー"):
+            if prev:
+                res.append(prev[-1])
+            text = text[1:]
+            continue
+        res += pyopenjtalk.g2p(text).lower().replace("cl", "q").split(" ")
+        break
+    # res = _COLON_RX.sub(":", res)
+    return res
+
+
+def hira2kata(text: str) -> str:
+    return jaconv.hira2kata(text)
+
+
+_SYMBOL_TOKENS = set(list("・、。？！"))
+_NO_YOMI_TOKENS = set(list("「」『』―（）［］[]"))
+_MARKS = re.compile(
+    r"[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]"
+)
+
+
+def text2kata(text: str) -> str:
+    parsed = pyopenjtalk.run_frontend(text)
+
+    res = []
+    for parts in parsed:
+        word, yomi = replace_punctuation(parts["string"]), parts["pron"].replace(
+            "’", ""
+        )
+        if yomi:
+            if re.match(_MARKS, yomi):
+                if len(word) > 1:
+                    word = [replace_punctuation(i) for i in list(word)]
+                    yomi = word
+                    res += yomi
+                    sep += word
+                    continue
+                elif word not in rep_map.keys() and word not in rep_map.values():
+                    word = ","
+                yomi = word
+            res.append(yomi)
+        else:
+            if word in _SYMBOL_TOKENS:
+                res.append(word)
+            elif word in ("っ", "ッ"):
+                res.append("ッ")
+            elif word in _NO_YOMI_TOKENS:
+                pass
+            else:
+                res.append(word)
+    return hira2kata("".join(res))
+
+
+def text2sep_kata(text: str) -> (list, list):
+    parsed = pyopenjtalk.run_frontend(text)
+
+    res = []
+    sep = []
+    for parts in parsed:
+        word, yomi = replace_punctuation(parts["string"]), parts["pron"].replace(
+            "’", ""
+        )
+        if yomi:
+            if re.match(_MARKS, yomi):
+                if len(word) > 1:
+                    word = [replace_punctuation(i) for i in list(word)]
+                    yomi = word
+                    res += yomi
+                    sep += word
+                    continue
+                elif word not in rep_map.keys() and word not in rep_map.values():
+                    word = ","
+                yomi = word
+            res.append(yomi)
+        else:
+            if word in _SYMBOL_TOKENS:
+                res.append(word)
+            elif word in ("っ", "ッ"):
+                res.append("ッ")
+            elif word in _NO_YOMI_TOKENS:
+                pass
+            else:
+                res.append(word)
+        sep.append(word)
+    return sep, [hira2kata(i) for i in res], get_accent(parsed)
+
+
+def get_accent(parsed):
+    labels = pyopenjtalk.make_label(parsed)
+
+    phonemes = []
+    accents = []
+    for n, label in enumerate(labels):
+        phoneme = re.search(r"\-([^\+]*)\+", label).group(1)
+        if phoneme not in ["sil", "pau"]:
+            phonemes.append(phoneme.replace("cl", "q").lower())
+        else:
+            continue
+        a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
+        a2 = int(re.search(r"\+(\d+)\+", label).group(1))
+        if re.search(r"\-([^\+]*)\+", labels[n + 1]).group(1) in ["sil", "pau"]:
+            a2_next = -1
+        else:
+            a2_next = int(re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
+        # Falling
+        if a1 == 0 and a2_next == a2 + 1:
+            accents.append(-1)
+        # Rising
+        elif a2 == 1 and a2_next == 2:
+            accents.append(1)
+        else:
+            accents.append(0)
+    return list(zip(phonemes, accents))
+
+
+_ALPHASYMBOL_YOMI = {
+    "#": "シャープ",
+    "%": "パーセント",
+    "&": "アンド",
+    "+": "プラス",
+    "-": "マイナス",
+    ":": "コロン",
+    ";": "セミコロン",
+    "<": "小なり",
+    "=": "イコール",
+    ">": "大なり",
+    "@": "アット",
+    "a": "エー",
+    "b": "ビー",
+    "c": "シー",
+    "d": "ディー",
+    "e": "イー",
+    "f": "エフ",
+    "g": "ジー",
+    "h": "エイチ",
+    "i": "アイ",
+    "j": "ジェー",
+    "k": "ケー",
+    "l": "エル",
+    "m": "エム",
+    "n": "エヌ",
+    "o": "オー",
+    "p": "ピー",
+    "q": "キュー",
+    "r": "アール",
+    "s": "エス",
+    "t": "ティ���",
+    "u": "ユー",
+    "v": "ブイ",
+    "w": "ダブリュー",
+    "x": "エックス",
+    "y": "ワイ",
+    "z": "ゼット",
+    "α": "アルファ",
+    "β": "ベータ",
+    "γ": "ガンマ",
+    "δ": "デルタ",
+    "ε": "イプシロン",
+    "ζ": "ゼータ",
+    "η": "イータ",
+    "θ": "シータ",
+    "ι": "イオタ",
+    "κ": "カッパ",
+    "λ": "ラムダ",
+    "μ": "ミュー",
+    "ν": "ニュー",
+    "ξ": "クサイ",
+    "ο": "オミクロン",
+    "π": "パイ",
+    "ρ": "ロー",
+    "σ": "シグマ",
+    "τ": "タウ",
+    "υ": "ウプシロン",
+    "φ": "ファイ",
+    "χ": "カイ",
+    "ψ": "プサイ",
+    "ω": "オメガ",
+}
+
+_NUMBER_WITH_SEPARATOR_RX = re.compile("[0-9]{1,3}(,[0-9]{3})+")
+_CURRENCY_MAP = {"$": "ドル", "¥": "円", "£": "ポンド", "€": "ユーロ"}
+_CURRENCY_RX = re.compile(r"([$¥£€])([0-9.]*[0-9])")
+_NUMBER_RX = re.compile(r"[0-9]+(\.[0-9]+)?")
+
+
+def japanese_convert_numbers_to_words(text: str) -> str:
+    res = _NUMBER_WITH_SEPARATOR_RX.sub(lambda m: m[0].replace(",", ""), text)
+    res = _CURRENCY_RX.sub(lambda m: m[2] + _CURRENCY_MAP.get(m[1], m[1]), res)
+    res = _NUMBER_RX.sub(lambda m: num2words(m[0], lang="ja"), res)
+    return res
+
+
+def japanese_convert_alpha_symbols_to_words(text: str) -> str:
+    return "".join([_ALPHASYMBOL_YOMI.get(ch, ch) for ch in text.lower()])
+
+
+def japanese_text_to_phonemes(text: str) -> str:
+    """Convert Japanese text to phonemes."""
+    res = unicodedata.normalize("NFKC", text)
+    res = japanese_convert_numbers_to_words(res)
+    # res = japanese_convert_alpha_symbols_to_words(res)
+    res = text2kata(res)
+    res = kata2phoneme(res)
+    return res
+
+
+def is_japanese_character(char):
+    # 定义日语文字系统的 Unicode 范围
+    japanese_ranges = [
+        (0x3040, 0x309F),  # 平假名
+        (0x30A0, 0x30FF),  # 片假名
+        (0x4E00, 0x9FFF),  # 汉字 (CJK Unified Ideographs)
+        (0x3400, 0x4DBF),  # 汉字扩展 A
+        (0x20000, 0x2A6DF),  # 汉字扩展 B
+        # 可以根据需要添加其他汉字扩展范围
+    ]
+
+    # 将字符的 Unicode 编码转换为整数
+    char_code = ord(char)
+
+    # 检查字符是否在任何一个日语范围内
+    for start, end in japanese_ranges:
+        if start <= char_code <= end:
+            return True
+
+    return False
+
+
+rep_map = {
+    "：": ",",
+    "；": ",",
+    "，": ",",
+    "。": ".",
+    "！": "!",
+    "？": "?",
+    "\n": ".",
+    "．": ".",
+    "…": "...",
+    "···": "...",
+    "・・・": "...",
+    "·": ",",
+    "・": ",",
+    "、": ",",
+    "$": ".",
+    "“": "'",
+    "”": "'",
+    '"': "'",
+    "‘": "'",
+    "’": "'",
+    "（": "'",
+    "）": "'",
+    "(": "'",
+    ")": "'",
+    "《": "'",
+    "》": "'",
+    "【": "'",
+    "】": "'",
+    "[": "'",
+    "]": "'",
+    "—": "-",
+    "−": "-",
+    "～": "-",
+    "~": "-",
+    "「": "'",
+    "」": "'",
+}
+
+
+def replace_punctuation(text):
+    pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
+
+    replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
+
+    replaced_text = re.sub(
+        r"[^\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\u3005"
+        + "".join(punctuation)
+        + r"]+",
+        "",
+        replaced_text,
+    )
+
+    return replaced_text
+
+
+def text_normalize(text):
+    res = unicodedata.normalize("NFKC", text)
+    res = japanese_convert_numbers_to_words(res)
+    # res = "".join([i for i in res if is_japanese_character(i)])
+    res = replace_punctuation(res)
+    res = res.replace("゙", "")
+    return res
+
+
+def distribute_phone(n_phone, n_word):
+    phones_per_word = [0] * n_word
+    for task in range(n_phone):
+        min_tasks = min(phones_per_word)
+        min_index = phones_per_word.index(min_tasks)
+        phones_per_word[min_index] += 1
+    return phones_per_word
+
+
+def handle_long(sep_phonemes):
+    for i in range(len(sep_phonemes)):
+        if sep_phonemes[i][0] == "ー":
+            sep_phonemes[i][0] = sep_phonemes[i - 1][-1]
+        if "ー" in sep_phonemes[i]:
+            for j in range(len(sep_phonemes[i])):
+                if sep_phonemes[i][j] == "ー":
+                    sep_phonemes[i][j] = sep_phonemes[i][j - 1][-1]
+    return sep_phonemes
+
+
+def align_tones(phones, tones):
+    res = []
+    for pho in phones:
+        temp = [0] * len(pho)
+        for idx, p in enumerate(pho):
+            if len(tones) == 0:
+                break
+            if p == tones[0][0]:
+                temp[idx] = tones[0][1]
+                if idx > 0:
+                    temp[idx] += temp[idx - 1]
+                tones.pop(0)
+        temp = [0] + temp
+        temp = temp[:-1]
+        if -1 in temp:
+            temp = [i + 1 for i in temp]
+        res.append(temp)
+    res = [i for j in res for i in j]
+    assert not any([i < 0 for i in res]) and not any([i > 1 for i in res])
+    return res
+
+
+def rearrange_tones(tones, phones):
+    res = [0] * len(tones)
+    for i in range(len(tones)):
+        if i == 0:
+            if tones[i] not in punctuation:
+                res[i] = 1
+        elif tones[i] == prev:
+            if phones[i] in punctuation:
+                res[i] = 0
+            else:
+                res[i] = 1
+        elif tones[i] > prev:
+            res[i] = 2
+        elif tones[i] < prev:
+            res[i - 1] = 3
+            res[i] = 1
+        prev = tones[i]
+    return res
+
+
+def g2p(norm_text, tokenizer, **kwargs):
+    sep_text, sep_kata, acc = text2sep_kata(norm_text)
+    sep_tokenized = []
+    for i in sep_text:
+        if i not in punctuation:
+            sep_tokenized.append(tokenizer.tokenize(i))
+        else:
+            sep_tokenized.append([i])
+
+    sep_phonemes = handle_long([kata2phoneme(i) for i in sep_kata])
+    # 异常处理，MeCab不认识的词的话会一路传到这里来，然后炸掉。目前来看只有那些超级稀有的生僻词会出现这种情况
+    for i in sep_phonemes:
+        for j in i:
+            assert j in symbols, (sep_text, sep_kata, sep_phonemes)
+    tones = align_tones(sep_phonemes, acc)
+
+    word2ph = []
+    for token, phoneme in zip(sep_tokenized, sep_phonemes):
+        phone_len = len(phoneme)
+        word_len = len(token)
+
+        aaa = distribute_phone(phone_len, word_len)
+        word2ph += aaa
+    phones = ["_"] + [j for i in sep_phonemes for j in i] + ["_"]
+    # tones = [0] + rearrange_tones(tones, phones[1:-1]) + [0]
+    tones = [0] + tones + [0]
+    word2ph = [1] + word2ph + [1]
+    assert len(phones) == len(tones)
+    return phones, tones, word2ph
+
+
+if __name__ == "__main__":
+    from manager import model_handler
+
+    tokenizer, _ = model_handler.get_bert_model("DEBERTA_V2_LARGE_JAPANESE_CHAR_WWM")
+    text = "hello,こんにちは、世界ー！……"
+    from bert_vits2.text.japanese_bert import get_bert_feature
+
+    text = text_normalize(text)
+    print(text)
+
+    phones, tones, word2ph = g2p(text, tokenizer)
+    bert = get_bert_feature(text, word2ph)
+
+    print(phones, tones, word2ph, bert.shape)

bert_vits2/text/japanese_bert.py

@@ -0,0 +1,43 @@
+import torch
+
+from contants import config
+from bert_vits2.text.japanese import text2sep_kata
+
+LOCAL_PATH = "./bert/deberta-v2-large-japanese-char-wwm"
+
+
+def get_bert_feature(text, word2ph, tokenizer, model, device=config.system.device, style_text=None, style_weight=0.7,
+                     **kwargs):
+    text = "".join(text2sep_kata(text)[0])
+    if style_text:
+        style_text = "".join(text2sep_kata(style_text)[0])
+    with torch.no_grad():
+        inputs = tokenizer(text, return_tensors="pt")
+        for i in inputs:
+            inputs[i] = inputs[i].to(device)
+        res = model(**inputs, output_hidden_states=True)
+        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].float().cpu()
+        if style_text:
+            style_inputs = tokenizer(style_text, return_tensors="pt")
+            for i in style_inputs:
+                style_inputs[i] = style_inputs[i].to(device)
+            style_res = model(**style_inputs, output_hidden_states=True)
+            style_res = torch.cat(style_res["hidden_states"][-3:-2], -1)[0].float().cpu()
+            style_res_mean = style_res.mean(0)
+
+    assert len(word2ph) == len(text) + 2
+    word2phone = word2ph
+    phone_level_feature = []
+    for i in range(len(word2phone)):
+        if style_text:
+            repeat_feature = (
+                    res[i].repeat(word2phone[i], 1) * (1 - style_weight)
+                    + style_res_mean.repeat(word2phone[i], 1) * style_weight
+            )
+        else:
+            repeat_feature = res[i].repeat(word2phone[i], 1)
+        phone_level_feature.append(repeat_feature)
+
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+
+    return phone_level_feature.T

bert_vits2/text/japanese_bert_extra.py

@@ -0,0 +1,42 @@
+import torch
+
+from contants import config
+from bert_vits2.text.japanese import text2sep_kata
+
+
+def get_bert_feature(text, word2ph, tokenizer, model, device=config.system.device, style_text=None, style_weight=0.7,
+                     **kwargs):
+    text = "".join(text2sep_kata(text)[0])
+    if style_text:
+        style_text = "".join(text2sep_kata(style_text)[0])
+
+    with torch.no_grad():
+        inputs = tokenizer(text, return_tensors="pt")
+        for i in inputs:
+            inputs[i] = inputs[i].to(device)
+        res = model(**inputs, output_hidden_states=True)
+        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].float().cpu()
+        if style_text:
+            style_inputs = tokenizer(style_text, return_tensors="pt")
+            for i in style_inputs:
+                style_inputs[i] = style_inputs[i].to(device)
+            style_res = model(**style_inputs, output_hidden_states=True)
+            style_res = torch.cat(style_res["hidden_states"][-3:-2], -1)[0].float().cpu()
+            style_res_mean = style_res.mean(0)
+
+    assert len(word2ph) == len(text) + 2
+    word2phone = word2ph
+    phone_level_feature = []
+    for i in range(len(word2phone)):
+        if style_text:
+            repeat_feature = (
+                    res[i].repeat(word2phone[i], 1) * (1 - style_weight)
+                    + style_res_mean.repeat(word2phone[i], 1) * style_weight
+            )
+        else:
+            repeat_feature = res[i].repeat(word2phone[i], 1)
+        phone_level_feature.append(repeat_feature)
+
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+
+    return phone_level_feature.T

bert_vits2/text/japanese_bert_v111.py

@@ -0,0 +1,22 @@
+import torch
+
+from contants import config
+
+
+def get_bert_feature(text, word2ph, tokenizer, model, device=config.system.device, **kwargs):
+    with torch.no_grad():
+        inputs = tokenizer(text, return_tensors="pt")
+        for i in inputs:
+            inputs[i] = inputs[i].to(device)
+        res = model(**inputs, output_hidden_states=True)
+        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].float().cpu()
+    assert inputs["input_ids"].shape[-1] == len(word2ph)
+    word2phone = word2ph
+    phone_level_feature = []
+    for i in range(len(word2phone)):
+        repeat_feature = res[i].repeat(word2phone[i], 1)
+        phone_level_feature.append(repeat_feature)
+
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+
+    return phone_level_feature.T

bert_vits2/text/japanese_bert_v200.py

@@ -0,0 +1,39 @@
+import torch
+
+from contants import config
+from bert_vits2.text.japanese_v200 import text2sep_kata
+
+
+def get_bert_feature(text, word2ph, tokenizer, model, device=config.system.device, **kwargs):
+    sep_text, _, _ = text2sep_kata(text)
+    sep_tokens = [tokenizer.tokenize(t) for t in sep_text]
+    sep_ids = [tokenizer.convert_tokens_to_ids(t) for t in sep_tokens]
+    sep_ids = [2] + [item for sublist in sep_ids for item in sublist] + [3]
+    return get_bert_feature_with_token(sep_ids, word2ph, tokenizer, model, device)
+
+
+def get_bert_feature_with_token(tokens, word2ph, tokenizer, model, device=config.system.device):
+    with torch.no_grad():
+        inputs = torch.tensor(tokens).to(device).unsqueeze(0)
+        token_type_ids = torch.zeros_like(inputs).to(device)
+        attention_mask = torch.ones_like(inputs).to(device)
+        inputs = {
+            "input_ids": inputs,
+            "token_type_ids": token_type_ids,
+            "attention_mask": attention_mask,
+        }
+
+        # for i in inputs:
+        #     inputs[i] = inputs[i].to(device)
+        res = model(**inputs, output_hidden_states=True)
+        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].float().cpu()
+    assert inputs["input_ids"].shape[-1] == len(word2ph)
+    word2phone = word2ph
+    phone_level_feature = []
+    for i in range(len(word2phone)):
+        repeat_feature = res[i].repeat(word2phone[i], 1)
+        phone_level_feature.append(repeat_feature)
+
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+
+    return phone_level_feature.T

bert_vits2/text/japanese_extra.py

@@ -0,0 +1,524 @@
+# Convert Japanese text to phonemes which is
+# compatible with Julius https://github.com/julius-speech/segmentation-kit
+import re
+import unicodedata
+
+import pyopenjtalk
+from num2words import num2words
+
+from bert_vits2.text import punctuation
+from bert_vits2.text.japanese_mora_list import (
+    mora_kata_to_mora_phonemes,
+)
+
+# 子音の集合
+COSONANTS = set(
+    [
+        cosonant
+        for cosonant, _ in mora_kata_to_mora_phonemes.values()
+        if cosonant is not None
+    ]
+)
+
+# 母音の集合
+VOWELS = {"a", "i", "u", "e", "o"}
+
+# 正規化で記号を変換するための辞書
+rep_map = {
+    "：": ",",
+    "；": ",",
+    "，": ",",
+    "。": ".",
+    "！": "!",
+    "？": "?",
+    "\n": ".",
+    "．": ".",
+    "…": "...",
+    "···": "...",
+    "・・・": "...",
+    "·": ",",
+    "・": ",",
+    "、": ",",
+    "$": ".",
+    "“": "'",
+    "”": "'",
+    '"': "'",
+    "‘": "'",
+    "’": "'",
+    "（": "'",
+    "）": "'",
+    "(": "'",
+    ")": "'",
+    "《": "'",
+    "》": "'",
+    "【": "'",
+    "】": "'",
+    "[": "'",
+    "]": "'",
+    "—": "-",
+    "−": "-",
+    # "～": "-",  # これは長音記号「ー」として扱うよう変更
+    # "~": "-",  # これは長音記号「ー」として扱うよう変更
+    "「": "'",
+    "」": "'",
+}
+
+
+def text_normalize(text):
+    """
+    日本語のテキストを正規化する。
+    結果は、ちょうど次の文字のみからなる：
+    - ひらがな
+    - カタカナ（全角長音記号「ー」が入る！）
+    - 漢字
+    - 半角アルファベット（大文字と小文字）
+    - ギリシャ文字
+    - `.` （句点`。`や`…`の一部や改行等）
+    - `,` （読点`、`や`:`等）
+    - `?` （疑問符`？`）
+    - `!` （感嘆符`！`）
+    - `'` （`「`や`」`等）
+    - `-` （`―`（ダッシュ、長音記号ではない）や`-`等）
+
+    注意点:
+    - 三点リーダー`…`は`...`に変換される（`なるほど…。` → `なるほど....`）
+    - 数字は漢字に変換される（`1,100円` → `千百円`、`52.34` → `五十二点三四`）
+    - 読点や疑問符等の位置・個数等は保持される（`??あ、、！！！` → `??あ,,!!!`）
+    """
+    # print(f"Before normalization: {text}")
+    # ここでアルファベットは半角になり、三点リーダは`...`になる
+    res = unicodedata.normalize("NFKC", text)
+
+    res = japanese_convert_numbers_to_words(res)  # 「100円」→「百円」等
+
+    # 「～」と「~」も長音記号として扱う
+    res = res.replace("~", "ー")
+    res = res.replace("～", "ー")
+
+    res = replace_punctuation(res)  # 句読点等正規化、読めない文字を削除
+
+    # 結合文字の濁点・半濁点を削除
+    # 通常の「ば」等はそのままのこされる、「あ゛」は上で「あ゙」になりここで「あ」になる
+    res = res.replace("\u3099", "")  # 結合文字の濁点を削除、る゙ → る
+    res = res.replace("\u309A", "")  # 結合文字の半濁点を削除、な゚ → な
+    return res
+
+
+def replace_punctuation(text: str) -> str:
+    """句読点等を「.」「,」「!」「?」「'」「-」に正規化し、OpenJTalkで読みが取得できるもののみ残す：
+    漢字・平仮名・カタカナ、アルファベット、ギリシャ文字
+    """
+    pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
+
+    # 句読点を辞書で置換
+    replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
+
+    replaced_text = re.sub(
+        # ↓ ひらがな、カタカナ、漢字
+        r"[^\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\u3005"
+        # ↓ 半角アルファベット（大文字と小文字）
+        + r"\u0041-\u005A\u0061-\u007A"
+        # ↓ 全角アルファベット（大文字と小文字）
+        + r"\uFF21-\uFF3A\uFF41-\uFF5A"
+        # ↓ ギリシャ文字
+        + r"\u0370-\u03FF\u1F00-\u1FFF"
+        # ↓ "!", "?", "…", ",", ".", "'", "-", 但し`…`はすでに`...`に変換されている
+        + "".join(punctuation) + r"]+",
+        # 上述以外の文字を削除
+        "",
+        replaced_text,
+    )
+
+    return replaced_text
+
+
+_NUMBER_WITH_SEPARATOR_RX = re.compile("[0-9]{1,3}(,[0-9]{3})+")
+_CURRENCY_MAP = {"$": "ドル", "¥": "円", "£": "ポンド", "€": "ユーロ"}
+_CURRENCY_RX = re.compile(r"([$¥£€])([0-9.]*[0-9])")
+_NUMBER_RX = re.compile(r"[0-9]+(\.[0-9]+)?")
+
+
+def japanese_convert_numbers_to_words(text: str) -> str:
+    res = _NUMBER_WITH_SEPARATOR_RX.sub(lambda m: m[0].replace(",", ""), text)
+    res = _CURRENCY_RX.sub(lambda m: m[2] + _CURRENCY_MAP.get(m[1], m[1]), res)
+    res = _NUMBER_RX.sub(lambda m: num2words(m[0], lang="ja"), res)
+    return res
+
+
+def g2p(norm_text: str, tokenizer, **kwargs) -> tuple[list[str], list[int], list[int]]:
+    """
+    他で使われるメインの関数。`text_normalize()`で正規化された`norm_text`を受け取り、
+    - phones: 音素のリスト（ただし`!`や`,`や`.`等punctuationが含まれうる）
+    - tones: アクセントのリスト、0（低）と1（高）からなり、phonesと同じ長さ
+    - word2ph: 元のテキストの各文字に音素が何個割り当てられるかを表すリスト
+    のタプルを返す。
+    ただし`phones`と`tones`の最初と終わりに`_`が入り、応じて`word2ph`の最初と最後に1が追加される。
+    """
+    # pyopenjtalkのフルコンテキストラベルを使ってアクセントを取り出すと、punctuationの位置が消えてしまい情報が失われてしまう：
+    # 「こんにちは、世界。」と「こんにちは！世界。」と「こんにちは！！！？？？世界……。」は全て同じになる。
+    # よって、まずpunctuation無しの音素とアクセントのリストを作り、
+    # それとは別にpyopenjtalk.run_frontend()で得られる音素リスト（こちらはpunctuationが保持される）を使い、
+    # アクセント割当をしなおすことによってpunctuationを含めた音素とアクセントのリストを作る。
+
+    # punctuationがすべて消えた、音素とアクセントのタプルのリスト
+    phone_tone_list_wo_punct = g2phone_tone_wo_punct(norm_text)
+
+    # sep_text: 単語単位の単語のリスト
+    # sep_kata: 単語単位の単語のカタカナ読みのリスト
+    sep_text, sep_kata = text2sep_kata(norm_text)
+
+    # sep_phonemes: 各単語ごとの音素のリストのリスト
+    sep_phonemes = handle_long([kata2phoneme_list(i) for i in sep_kata])
+
+    # phone_w_punct: sep_phonemesを結合した、punctuationを元のまま保持した音素列
+    phone_w_punct: list[str] = []
+    for i in sep_phonemes:
+        phone_w_punct += i
+
+    # punctuation無しのアクセント情報を使って、punctuationを含めたアクセント情報を作る
+    phone_tone_list = align_tones(phone_w_punct, phone_tone_list_wo_punct)
+    # word2phは厳密な解答は不可能なので（「今日」「眼鏡」等の熟字訓が存在）、
+    # Bert-VITS2では、単語単位の分割を使って、単語の文字ごとにだいたい均等に音素を分配する
+
+    # sep_textから、各単語を1文字1文字分割して、文字のリスト（のリスト）を作る
+    sep_tokenized: list[list[str]] = []
+    for i in sep_text:
+        if i not in punctuation:
+            sep_tokenized.append(tokenizer.tokenize(i))  # ここでおそらく`i`が文字単位に分割される
+        else:
+            sep_tokenized.append([i])
+
+    # 各単語について、音素の数と文字の数を比較して、均等っぽく分配する
+    word2ph = []
+    for token, phoneme in zip(sep_tokenized, sep_phonemes):
+        phone_len = len(phoneme)
+        word_len = len(token)
+        word2ph += distribute_phone(phone_len, word_len)
+
+    # 最初と最後に`_`記号を追加、アクセントは0（低）、word2phもそれに合わせて追加
+    phone_tone_list = [("_", 0)] + phone_tone_list + [("_", 0)]
+    word2ph = [1] + word2ph + [1]
+
+    phones = [phone for phone, _ in phone_tone_list]
+    tones = [tone for _, tone in phone_tone_list]
+
+    assert len(phones) == sum(word2ph), f"{len(phones)} != {sum(word2ph)}"
+
+    return phones, tones, word2ph
+
+
+def g2phone_tone_wo_punct(text: str) -> list[tuple[str, int]]:
+    """
+    テキストに対して、音素とアクセント（0か1）のペアのリストを返す。
+    ただし「!」「.」「?」等の非音素記号(punctuation)は全て消える（ポーズ記号も残さない）。
+    非音素記号を含める処理は`align_tones()`で行われる。
+    また「っ」は「cl」でなく「q」に変換される（「ん」は「N」のまま）。
+    例: "こんにちは、世界ー。。元気？！" →
+    [('k', 0), ('o', 0), ('N', 1), ('n', 1), ('i', 1), ('ch', 1), ('i', 1), ('w', 1), ('a', 1), ('s', 1), ('e', 1), ('k', 0), ('a', 0), ('i', 0), ('i', 0), ('g', 1), ('e', 1), ('N', 0), ('k', 0), ('i', 0)]
+    """
+    prosodies = pyopenjtalk_g2p_prosody(text, drop_unvoiced_vowels=True)
+    result: list[tuple[str, int]] = []
+    current_phrase: list[tuple[str, int]] = []
+    current_tone = 0
+    for i, letter in enumerate(prosodies):
+        # 特殊記号の処理
+
+        # 文頭記号、無視する
+        if letter == "^":
+            assert i == 0, "Unexpected ^"
+        # アクセント句の終わりに来る記号
+        elif letter in ("$", "?", "_", "#"):
+            # 保持しているフレーズを、アクセント数値を0-1に修正し結果に追加
+            result.extend(fix_phone_tone(current_phrase))
+            # 末尾に来る終了記号、無視（文中の疑問文は`_`になる）
+            if letter in ("$", "?"):
+                assert i == len(prosodies) - 1, f"Unexpected {letter}"
+            # あとは"_"���ポーズ）と"#"（アクセント句の境界）のみ
+            # これらは残さず、次のアクセント句に備える。
+            current_phrase = []
+            # 0を基準点にしてそこから上昇・下降する（負の場合は上の`fix_phone_tone`で直る）
+            current_tone = 0
+        # アクセント上昇記号
+        elif letter == "[":
+            current_tone = current_tone + 1
+        # アクセント下降記号
+        elif letter == "]":
+            current_tone = current_tone - 1
+        # それ以外は通常の音素
+        else:
+            if letter == "cl":  # 「っ」の処理
+                letter = "q"
+            current_phrase.append((letter, current_tone))
+    return result
+
+
+def text2sep_kata(norm_text: str) -> tuple[list[str], list[str]]:
+    """
+    `text_normalize`で正規化済みの`norm_text`を受け取り、それを単語分割し、
+    分割された単語リストとその読み（カタカナor記号1文字）のリストのタプルを返す。
+    単語分割結果は、`g2p()`の`word2ph`で1文字あたりに割り振る音素記号の数を決めるために使う。
+    例:
+    `私はそう思う!って感じ?` →
+    ["私", "は", "そう", "思う", "!", "って", "感じ", "?"], ["ワタシ", "ワ", "ソー", "オモウ", "!", "ッテ", "カンジ", "?"]
+    """
+    # parsed: OpenJTalkの解析結果
+    parsed = pyopenjtalk.run_frontend(norm_text)
+    sep_text: list[str] = []
+    sep_kata: list[str] = []
+    for parts in parsed:
+        # word: 実際の単語の文字列
+        # yomi: その読み、但し無声化サインの`’`は除去
+        word, yomi = replace_punctuation(parts["string"]), parts["pron"].replace(
+            "’", ""
+        )
+        """
+        ここで`yomi`の取りうる値は以下の通りのはず。
+        - `word`が通常単語 → 通常の読み（カタカナ）
+            （カタカナからなり、長音記号も含みうる、`アー` 等）
+        - `word`が`ー` から始まる → `ーラー` や `ーーー` など
+        - `word`が句読点や空白等 → `、`
+        - `word`が`?` → `？`（全角になる）
+        他にも`word`が読めないキリル文字アラビア文字等が来ると`、`になるが、正規化でこの場合は起きないはず。
+        また元のコードでは`yomi`が空白の場合の処理があったが、これは起きないはず。
+        処理すべきは`yomi`が`、`の場合のみのはず。
+        """
+        assert yomi != "", f"Empty yomi: {word}"
+        if yomi == "、":
+            # wordは正規化されているので、`.`, `,`, `!`, `'`, `-`のいずれか
+            if word not in (
+                    ".",
+                    ",",
+                    "!",
+                    "'",
+                    "-",
+            ):
+                # ここはpyopenjtalkが読めない文字等のときに起こる
+                raise ValueError(f"Cannot read: {word} in:\n{norm_text}")
+            # yomiは元の記号のままに変更
+            yomi = word
+        elif yomi == "？":
+            assert word == "?", f"yomi `？` comes from: {word}"
+            yomi = "?"
+        sep_text.append(word)
+        sep_kata.append(yomi)
+    return sep_text, sep_kata
+
+
+# ESPnetの実装から引用、変更点無し
+# https://github.com/espnet/espnet/blob/master/espnet2/text/phoneme_tokenizer.py
+def pyopenjtalk_g2p_prosody(text: str, drop_unvoiced_vowels: bool = True) -> list[str]:
+    """Extract phoneme + prosoody symbol sequence from input full-context labels.
+
+    The algorithm is based on `Prosodic features control by symbols as input of
+    sequence-to-sequence acoustic modeling for neural TTS`_ with some r9y9's tweaks.
+
+    Args:
+        text (str): Input text.
+        drop_unvoiced_vowels (bool): whether to drop unvoiced vowels.
+
+    Returns:
+        List[str]: List of phoneme + prosody symbols.
+
+    Examples:
+        #>>> from espnet2.text.phoneme_tokenizer import pyopenjtalk_g2p_prosody
+        #>>> pyopenjtalk_g2p_prosody("こんにちは。")
+        ['^', 'k', 'o', '[', 'N', 'n', 'i', 'ch', 'i', 'w', 'a', '$']
+
+    .. _`Prosodic features control by symbols as input of sequence-to-sequence acoustic
+        modeling for neural TTS`: https://doi.org/10.1587/transinf.2020EDP7104
+
+    """
+    labels = pyopenjtalk.make_label(pyopenjtalk.run_frontend(text))
+    N = len(labels)
+
+    phones = []
+    for n in range(N):
+        lab_curr = labels[n]
+
+        # current phoneme
+        p3 = re.search(r"\-(.*?)\+", lab_curr).group(1)
+        # deal unvoiced vowels as normal vowels
+        if drop_unvoiced_vowels and p3 in "AEIOU":
+            p3 = p3.lower()
+
+        # deal with sil at the beginning and the end of text
+        if p3 == "sil":
+            assert n == 0 or n == N - 1
+            if n == 0:
+                phones.append("^")
+            elif n == N - 1:
+                # check question form or not
+                e3 = _numeric_feature_by_regex(r"!(\d+)_", lab_curr)
+                if e3 == 0:
+                    phones.append("$")
+                elif e3 == 1:
+                    phones.append("?")
+            continue
+        elif p3 == "pau":
+            phones.append("_")
+            continue
+        else:
+            phones.append(p3)
+
+        # accent type and position info (forward or backward)
+        a1 = _numeric_feature_by_regex(r"/A:([0-9\-]+)\+", lab_curr)
+        a2 = _numeric_feature_by_regex(r"\+(\d+)\+", lab_curr)
+        a3 = _numeric_feature_by_regex(r"\+(\d+)/", lab_curr)
+
+        # number of mora in accent phrase
+        f1 = _numeric_feature_by_regex(r"/F:(\d+)_", lab_curr)
+
+        a2_next = _numeric_feature_by_regex(r"\+(\d+)\+", labels[n + 1])
+        # accent phrase border
+        if a3 == 1 and a2_next == 1 and p3 in "aeiouAEIOUNcl":
+            phones.append("#")
+        # pitch falling
+        elif a1 == 0 and a2_next == a2 + 1 and a2 != f1:
+            phones.append("]")
+        # pitch rising
+        elif a2 == 1 and a2_next == 2:
+            phones.append("[")
+
+    return phones
+
+
+def _numeric_feature_by_regex(regex, s):
+    match = re.search(regex, s)
+    if match is None:
+        return -50
+    return int(match.group(1))
+
+
+def fix_phone_tone(phone_tone_list: list[tuple[str, int]]) -> list[tuple[str, int]]:
+    """
+    `phone_tone_list`のtone（アクセントの値）を0か1の範囲に修正する。
+    例: [(a, 0), (i, -1), (u, -1)] → [(a, 1), (i, 0), (u, 0)]
+    """
+    tone_values = set(tone for _, tone in phone_tone_list)
+    if len(tone_values) == 1:
+        assert tone_values == {0}, tone_values
+        return phone_tone_list
+    elif len(tone_values) == 2:
+        if tone_values == {0, 1}:
+            return phone_tone_list
+        elif tone_values == {-1, 0}:
+            return [
+                (letter, 0 if tone == -1 else 1) for letter, tone in phone_tone_list
+            ]
+        else:
+            raise ValueError(f"Unexpected tone values: {tone_values}")
+    else:
+        raise ValueError(f"Unexpected tone values: {tone_values}")
+
+
+def distribute_phone(n_phone: int, n_word: int) -> list[int]:
+    """
+    左から右に1ずつ振り分け、次にまた左から右に1ずつ増やし、というふうに、
+    音素の数`n_phone`を単語の数`n_word`に分配する。
+    """
+    phones_per_word = [0] * n_word
+    for _ in range(n_phone):
+        min_tasks = min(phones_per_word)
+        min_index = phones_per_word.index(min_tasks)
+        phones_per_word[min_index] += 1
+    return phones_per_word
+
+
+def handle_long(sep_phonemes: list[list[str]]) -> list[list[str]]:
+    for i in range(len(sep_phonemes)):
+        if sep_phonemes[i][0] == "ー":
+            sep_phonemes[i][0] = sep_phonemes[i - 1][-1]
+        if "ー" in sep_phonemes[i]:
+            for j in range(len(sep_phonemes[i])):
+                if sep_phonemes[i][j] == "ー":
+                    sep_phonemes[i][j] = sep_phonemes[i][j - 1][-1]
+    return sep_phonemes
+
+
+def align_tones(
+        phones_with_punct: list[str], phone_tone_list: list[tuple[str, int]]
+) -> list[tuple[str, int]]:
+    """
+    例:
+    …私は、、そう思う。
+    phones_with_punct:
+    [".", ".", ".", "w", "a", "t", "a", "sh", "i", "w", "a", ",", ",", "s", "o", "o", "o", "m", "o", "u", "."]
+    phone_tone_list:
+    [("w", 0), ("a", 0), ("t", 1), ("a", 1), ("sh", 1), ("i", 1), ("w", 1), ("a", 1), ("s", 0), ("o", 0), ("o", 1), ("o", 1), ("m", 1), ("o", 1), ("u", 0))]
+    Return:
+    [(".", 0), (".", 0), (".", 0), ("w", 0), ("a", 0), ("t", 1), ("a", 1), ("sh", 1), ("i", 1), ("w", 1), ("a", 1), (",", 0), (",", 0), ("s", 0), ("o", 0), ("o", 1), ("o", 1), ("m", 1), ("o", 1), ("u", 0), (".", 0)]
+    """
+    result: list[tuple[str, int]] = []
+    tone_index = 0
+    for phone in phones_with_punct:
+        if tone_index >= len(phone_tone_list):
+            # 余ったpunctuationがある場合 → (punctuation, 0)を追加
+            result.append((phone, 0))
+        elif phone == phone_tone_list[tone_index][0]:
+            # phone_tone_listの現在の音素と一致する場合 → toneをそこから取得、(phone, tone)を追加
+            result.append((phone, phone_tone_list[tone_index][1]))
+            # 探すindexを1つ進める
+            tone_index += 1
+        elif phone in punctuation:
+            # phoneがpunctuationの場合 → (phone, 0)を追加
+            result.append((phone, 0))
+        else:
+            print(f"phones: {phones_with_punct}")
+            print(f"phone_tone_list: {phone_tone_list}")
+            print(f"result: {result}")
+            print(f"tone_index: {tone_index}")
+            print(f"phone: {phone}")
+            raise ValueError(f"Unexpected phone: {phone}")
+    return result
+
+
+def kata2phoneme_list(text: str) -> list[str]:
+    """
+    原則カタカナの`text`を受け取り、それをそのままいじらずに音素記号のリストに変換。
+    注意点：
+    - punctuationが来た場合（punctuationが1文字の場合がありうる）、処理せず1文字のリストを返す
+    - 冒頭に続く「ー」はそのまま「ー」のままにする（`handle_long()`で処理される）
+    - 文中の「ー」は前の音素記号の最後の音素記号に変換される。
+    例：
+    `ーーソーナノカーー` → ["ー", "ー", "s", "o", "o", "n", "a", "n", "o", "k", "a", "a", "a"]
+    `?` → ["?"]
+    """
+    if text in punctuation:
+        return [text]
+    # `text`がカタカナ（`ー`含む）のみからなるかどうかをチェック
+    if re.fullmatch(r"[\u30A0-\u30FF]+", text) is None:
+        raise ValueError(f"Input must be katakana only: {text}")
+    sorted_keys = sorted(mora_kata_to_mora_phonemes.keys(), key=len, reverse=True)
+    pattern = "|".join(map(re.escape, sorted_keys))
+
+    def mora2phonemes(mora: str) -> str:
+        cosonant, vowel = mora_kata_to_mora_phonemes[mora]
+        if cosonant is None:
+            return f" {vowel}"
+        return f" {cosonant} {vowel}"
+
+    spaced_phonemes = re.sub(pattern, lambda m: mora2phonemes(m.group()), text)
+
+    # 長音記号「ー」の処理
+    long_pattern = r"(\w)(ー*)"
+    long_replacement = lambda m: m.group(1) + (" " + m.group(1)) * len(m.group(2))
+    spaced_phonemes = re.sub(long_pattern, long_replacement, spaced_phonemes)
+    return spaced_phonemes.strip().split(" ")
+
+
+if __name__ == "__main__":
+    from manager import model_handler
+
+    tokenizer, _ = model_handler.get_bert_model("DEBERTA_V2_LARGE_JAPANESE_CHAR_WWM")
+    text = "hello,こんにちは、世界ー~！……"
+
+    from bert_vits2.text.japanese_bert import get_bert_feature
+
+    text = text_normalize(text)
+    print(text)
+
+    phones, tones, word2ph = g2p(text)
+    print(phones, tones, word2ph)
+    bert = get_bert_feature(text, word2ph)
+
+    print(phones, tones, word2ph, bert.shape)