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Merge branch 'main' of https://github.com/babysor/Realtime-Voice-Clone-Chinese into main

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README-CN.md
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### [English](README.md) | 中文 ### [English](README.md) | 中文
### [DEMO VIDEO](https://www.bilibili.com/video/BV1sA411P7wM/)
## 特性 ## 特性
🌍 **中文** 支持普通话并使用多种中文数据集进行测试：adatatang_200zh, magicdata 🌍 **中文** 支持普通话并使用多种中文数据集进行测试：adatatang_200zh, magicdata
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**Python 3.7 或更高版本** 需要运行工具箱。 **Python 3.7 或更高版本** 需要运行工具箱。
* 安装 [PyTorch](https://pytorch.org/get-started/locally/)。 * 安装 [PyTorch](https://pytorch.org/get-started/locally/)。
> 如果在用 pip 方式安装的时候出现 `ERROR: Could not find a version that satisfies the requirement torch==1.9.0+cu102 (from versions: 0.1.2, 0.1.2.post1, 0.1.2.post2)` 这个错误可能是 python 版本过低，3.9 可以安装成功
* 安装 [ffmpeg](https://ffmpeg.org/download.html#get-packages)。 * 安装 [ffmpeg](https://ffmpeg.org/download.html#get-packages)。
* 运行`pip install -r requirements.txt` 来安装剩余的必要包。 * 运行`pip install -r requirements.txt` 来安装剩余的必要包。
* 安装 webrtcvad 用 `pip install webrtcvad-wheels`。 * 安装 webrtcvad 用 `pip install webrtcvad-wheels`。
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可以传入参数 --dataset `{dataset}` 支持 adatatang_200zh, magicdata 可以传入参数 --dataset `{dataset}` 支持 adatatang_200zh, magicdata
> 假如你下载的 `aidatatang_200zh`文件放在D盘，`train`文件路径为 `D:\data\aidatatang_200zh\corpus\train` , 你的`datasets_root`就是 `D:\data\` > 假如你下载的 `aidatatang_200zh`文件放在D盘，`train`文件路径为 `D:\data\aidatatang_200zh\corpus\train` , 你的`datasets_root`就是 `D:\data\`
>假如發生 `頁面文件太小，無法完成操作`，請參考這篇[文章](https://blog.csdn.net/qq_17755303/article/details/112564030)，將虛擬內存更改為100G(102400)，例如:档案放置D槽就更改D槽的虚拟内存
* 预处理嵌入： * 预处理嵌入：
`python synthesizer_preprocess_embeds.py <datasets_root>/SV2TTS/synthesizer` `python synthesizer_preprocess_embeds.py <datasets_root>/SV2TTS/synthesizer`
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| 作者 | 下载链接 | 效果预览 | | 作者 | 下载链接 | 效果预览 |
| --- | ----------- | ----- | | --- | ----------- | ----- |
|@miven| https://pan.baidu.com/s/1PI-hM3sn5wbeChRryX-RCQ 提取码：2021 | https://www.bilibili.com/video/BV1uh411B7AD/ |@miven| https://pan.baidu.com/s/1PI-hM3sn5wbeChRryX-RCQ 提取码：2021 | https://www.bilibili.com/video/BV1uh411B7AD/(暂时不可访问)
### 3. 启动工具箱 ### 3. 启动工具箱
然后您可以尝试使用工具箱： 然后您可以尝试使用工具箱：

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**Python 3.7 or higher ** is needed to run the toolbox. **Python 3.7 or higher ** is needed to run the toolbox.
* Install [PyTorch](https://pytorch.org/get-started/locally/). * Install [PyTorch](https://pytorch.org/get-started/locally/).
> If you get an `ERROR: Could not find a version that satisfies the requirement torch==1.9.0+cu102 (from versions: 0.1.2, 0.1.2.post1, 0.1.2.post2 )` This error is probably due to a low version of python, try using 3.9 and it will install successfully
* Install [ffmpeg](https://ffmpeg.org/download.html#get-packages). * Install [ffmpeg](https://ffmpeg.org/download.html#get-packages).
* Run `pip install -r requirements.txt` to install the remaining necessary packages. * Run `pip install -r requirements.txt` to install the remaining necessary packages.
* Install webrtcvad `pip install webrtcvad-wheels`(If you need)
> Note that we are using the pretrained encoder/vocoder but synthesizer, since the original model is incompatible with the Chinese sympols. It means the demo_cli is not working at this moment. > Note that we are using the pretrained encoder/vocoder but synthesizer, since the original model is incompatible with the Chinese sympols. It means the demo_cli is not working at this moment.
### 2. Train synthesizer with your dataset ### 2. Train synthesizer with your dataset
* Download aidatatang_200zh or SLR68 dataset and unzip: make sure you can access all .wav in *train* folder * Download aidatatang_200zh or SLR68 dataset and unzip: make sure you can access all .wav in *train* folder
* Preprocess with the audios and the mel spectrograms: * Preprocess with the audios and the mel spectrograms:
`python synthesizer_preprocess_audio.py <datasets_root>` `python synthesizer_preprocess_audio.py <datasets_root>`
Allow parameter `--dataset {dataset}` to support adatatang_200zh, magicdata Allow parameter `--dataset {dataset}` to support adatatang_200zh, magicdata
>If it happens `the page file is too small to complete the operation`, please refer to this [video](https://www.youtube.com/watch?v=Oh6dga-Oy10&ab_channel=CodeProf) and change the virtual memory to 100G (102400), for example : When the file is placed in the D disk, the virtual memory of the D disk is changed.
* Preprocess the embeddings: * Preprocess the embeddings:
`python synthesizer_preprocess_embeds.py <datasets_root>/SV2TTS/synthesizer` `python synthesizer_preprocess_embeds.py <datasets_root>/SV2TTS/synthesizer`