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README-LINUX-CN.md
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## 实时语音克隆 - 中文/普通话
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[](http://choosealicense.com/licenses/mit/)
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### [English](README.md) | 中文
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### [DEMO VIDEO](https://www.bilibili.com/video/BV17Q4y1B7mY/) | [Wiki教程](https://github.com/babysor/MockingBird/wiki/Quick-Start-(Newbie)) ｜ [训练教程](https://vaj2fgg8yn.feishu.cn/docs/doccn7kAbr3SJz0KM0SIDJ0Xnhd)
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## 特性
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🌍 **中文** 支持普通话并使用多种中文数据集进行测试：aidatatang_200zh, magicdata, aishell3, biaobei, MozillaCommonVoice, data_aishell 等
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🤩 **Easy & Awesome** 仅需下载或新训练合成器（synthesizer）就有良好效果，复用预训练的编码器/声码器，或实时的HiFi-GAN作为vocoder
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🌍 **Webserver Ready** 可伺服你的训练结果，供远程调用。
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🤩 **感谢各位小伙伴的支持，本项目将开启新一轮的更新**
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## 1.快速开始
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### 1.1 建议环境
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- Ubuntu 18.04
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- Cuda 11.7 && CuDNN 8.5.0
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- Python 3.8 以上
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- Pytorch 2.0.1 <post cuda-11.7>
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### 1.2 环境配置
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```shell
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# 下载前建议更换国内镜像源
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conda create -n sound python=3.9
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conda activate sound
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git clone https://github.com/babysor/MockingBird.git
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cd MockingBird
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pip install -r requirements.txt
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pip install webrtcvad-wheels
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pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117
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```
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### 1.3 项目文件结构信息
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MockingBird
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├── academicodec
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├── utils.py # common parts of various models
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├── modules # common parts of various models
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├── ...
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├── quantization # common parts of various models
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└── models # parts that are not shared by various models
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..
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├── evaluation_metric
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├── examples
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│ ├── SoundStream*
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│ ├── EnCodec*
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│ └── HiFi-Codec*
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│ ├── start.sh
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│ ├── ...
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│ └── test.sh
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└── README.md
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### 1.4 模型准备
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> 当实在没有设备或者不想慢慢调试，可以使用社区贡献的模型(欢迎持续分享):
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| 作者 | 下载链接 | 效果预览 | 信息 |
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| --- | ----------- | ----- | ----- |
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| 作者 | https://pan.baidu.com/s/1iONvRxmkI-t1nHqxKytY3g [百度盘链接](https://pan.baidu.com/s/1iONvRxmkI-t1nHqxKytY3g) 4j5d | | 75k steps 用3个开源数据集混合训练
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| 作者 | https://pan.baidu.com/s/1fMh9IlgKJlL2PIiRTYDUvw [百度盘链接](https://pan.baidu.com/s/1fMh9IlgKJlL2PIiRTYDUvw) 提取码：om7f | | 25k steps 用3个开源数据集混合训练, 切换到tag v0.0.1使用
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|@FawenYo | https://drive.google.com/file/d/1H-YGOUHpmqKxJ9FRc6vAjPuqQki24UbC/view?usp=sharing [百度盘链接](https://pan.baidu.com/s/1vSYXO4wsLyjnF3Unl-Xoxg) 提取码：1024 | [input](https://github.com/babysor/MockingBird/wiki/audio/self_test.mp3) [output](https://github.com/babysor/MockingBird/wiki/audio/export.wav) | 200k steps 台湾口音需切换到tag v0.0.1使用
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|@miven| https://pan.baidu.com/s/1PI-hM3sn5wbeChRryX-RCQ 提取码：2021 | https://www.bilibili.com/video/BV1uh411B7AD/ | 150k steps 注意：根据[issue](https://github.com/babysor/MockingBird/issues/37)修复 并切换到tag v0.0.1使用
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### 1.5 文件结构准备
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文件结构准备如下所示，算法将自动遍历synthesizer下的.pt模型文件。
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```
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# 以第一个 pretrained-11-7-21_75k.pt 为例
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└── data
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└── ckpt
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└── synthesizer
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└── pretrained-11-7-21_75k.pt
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```
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### 1.6 运行
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```
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python web.py
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```
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## 2.模型训练
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### 2.1 数据准备
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#### 2.1.1 数据下载
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``` shell
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# aidatatang_200zh
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wget https://openslr.elda.org/resources/62/aidatatang_200zh.tgz
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```
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``` shell
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# MAGICDATA
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wget https://openslr.magicdatatech.com/resources/68/train_set.tar.gz
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wget https://openslr.magicdatatech.com/resources/68/dev_set.tar.gz
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wget https://openslr.magicdatatech.com/resources/68/test_set.tar.gz
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```
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``` shell
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# AISHELL-3
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wget https://openslr.elda.org/resources/93/data_aishell3.tgz
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```
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```shell
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# Aishell
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wget https://openslr.elda.org/resources/33/data_aishell.tgz
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```
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#### 2.1.2 数据批量解压
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```shell
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# 该指令为解压当前目录下的所有压缩文件
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for gz in *.gz; do tar -zxvf $gz; done
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```
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### 2.2 encoder模型训练
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#### 2.2.1 数据预处理：
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需要先在`pre.py `头部加入：
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```python
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import torch
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torch.multiprocessing.set_start_method('spawn', force=True)
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```
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使用以下指令对数据预处理：
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```shell
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python pre.py <datasets_root> \
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-d <datasets_name>
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```
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其中`<datasets_root>`为原数据集路径，`<datasets_name>` 为数据集名称。
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支持 `librispeech_other`，`voxceleb1`，`aidatatang_200zh`，使用逗号分割处理多数据集。
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### 2.2.2 encoder模型训练：
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超参数文件路径：`models/encoder/hparams.py`
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```shell
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python encoder_train.py <name> \
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<datasets_root>/SV2TTS/encoder
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```
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其中 `<name>` 是训练产生文件的名称，可自行修改。
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其中 `<datasets_root>` 是经过 `Step 2.1.1` 处理过后的数据集路径。
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#### 2.2.3 开启encoder模型训练数据可视化（可选）
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```shell
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visdom
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```
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### 2.3 synthesizer模型训练
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#### 2.3.1 数据预处理：
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```shell
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python pre.py <datasets_root> \
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-d <datasets_name> \
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-o <datasets_path> \
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-n <number>
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```
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`<datasets_root>` 为原数据集路径，当你的`aidatatang_200zh`路径为`/data/aidatatang_200zh/corpus/train`时，`<datasets_root>` 为 `/data/`。
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`<datasets_name>` 为数据集名称。
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`<datasets_path>` 为数据集处理后的保存路径。
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`<number>` 为数据集处理时进程数，根据CPU情况调整大小。
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#### 2.3.2 新增数据预处理：
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```shell
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python pre.py <datasets_root> \
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-d <datasets_name> \
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-o <datasets_path> \
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-n <number> \
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-s
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```
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当新增数据集时，应加 `-s` 选择数据拼接，不加则为覆盖。
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#### 2.3.3 synthesizer模型训练：
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超参数文件路径：`models/synthesizer/hparams.py`，需将`MockingBird/control/cli/synthesizer_train.py`移成`MockingBird/synthesizer_train.py`结构。
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```shell
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python synthesizer_train.py <name> <datasets_path>
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-m <out_dir>
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```
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其中 `<name>` 是训练产生文件的名称，可自行修改。
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其中 `<datasets_path>` 是经过 `Step 2.2.1` 处理过后的数据集路径。
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其中 `<out_dir> `为训练时所有数据的保存路径。
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### 2.4 vocoder模型训练
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vocoder模型对生成效果影响不大，已预置3款。
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#### 2.4.1 数据预处理
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```shell
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python vocoder_preprocess.py <datasets_root>
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-m <synthesizer_model_path>
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```
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其中`<datasets_root>`为你数据集路径。
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其中 `<synthesizer_model_path>`为synthesizer模型地址。
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#### 2.4.2 wavernn声码器训练:
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```
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python vocoder_train.py <name> <datasets_root>
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```
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#### 2.4.3 hifigan声码器训练:
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```
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python vocoder_train.py <trainid> <datasets_root> hifigan
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```
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#### 2.4.4 fregan声码器训练:
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```
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python vocoder_train.py <trainid> <datasets_root> --config config.json fregan
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```
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将GAN声码器的训练切换为多GPU模式：修改`GAN`文件夹下`.json`文件中的`num_gpus`参数。
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## 3.致谢
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### 3.1 项目致谢
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该库一开始从仅支持英语的[Real-Time-Voice-Cloning](https://github.com/CorentinJ/Real-Time-Voice-Cloning) 分叉出来的，鸣谢作者。
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### 3.2 论文致谢
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| URL | Designation | 标题 | 实现源码 |
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| --- | ----------- | ----- | --------------------- |
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| [1803.09017](https://arxiv.org/abs/1803.09017) | GlobalStyleToken (synthesizer)| Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis | 本代码库 |
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| [2010.05646](https://arxiv.org/abs/2010.05646) | HiFi-GAN (vocoder)| Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis | 本代码库 |
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| [2106.02297](https://arxiv.org/abs/2106.02297) | Fre-GAN (vocoder)| Fre-GAN: Adversarial Frequency-consistent Audio Synthesis | 本代码库 |
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|[**1806.04558**](https://arxiv.org/pdf/1806.04558.pdf) | SV2TTS | Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis | 本代码库 |
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|[1802.08435](https://arxiv.org/pdf/1802.08435.pdf) | WaveRNN (vocoder) | Efficient Neural Audio Synthesis | [fatchord/WaveRNN](https://github.com/fatchord/WaveRNN) |
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|[1703.10135](https://arxiv.org/pdf/1703.10135.pdf) | Tacotron (synthesizer) | Tacotron: Towards End-to-End Speech Synthesis | [fatchord/WaveRNN](https://github.com/fatchord/WaveRNN)
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|[1710.10467](https://arxiv.org/pdf/1710.10467.pdf) | GE2E (encoder)| Generalized End-To-End Loss for Speaker Verification | 本代码库 |
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### 3.3 开发者致谢
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作为一名AI领域的从业者，我们不仅乐于开发一些具有里程碑意义的算法项目，同时也乐于分享项目的成果以及开发当中的喜悦。
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因此，你们的使用是对我们项目的最大认可，同时我们欢迎你们的指正，当你们有想法时可以欢迎issues上留言，这对于项目的后续优化具有十分重大的的意义。
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为了表示感谢，我们将在本项目中留下你们的信息以及相对应的贡献。
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