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

README-CN.md

@@ -6,6 +6,8 @@
 
 ### [English](README.md)  | 中文
 
+### [DEMO VIDEO](https://www.bilibili.com/video/BV1sA411P7wM/)
+
 ## 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
 
@@ -22,6 +24,7 @@
 **Python 3.7 或更高版本** 需要运行工具箱。
 
 * 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)。
 * 运行`pip install -r requirements.txt` 来安装剩余的必要包。
 * 安装 webrtcvad 用 `pip install webrtcvad-wheels`。
@@ -33,6 +36,8 @@
 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：
 `python synthesizer_preprocess_embeds.py <datasets_root>/SV2TTS/synthesizer`
 
@@ -47,9 +52,9 @@
 ### 2.2 使用预先训练好的合成器
 > 实在没有设备或者不想慢慢调试，可以使用网友贡献的模型(欢迎持续分享):
 
-| 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
 然后您可以尝试使用工具箱：

README.md

@@ -3,14 +3,14 @@
 [![MIT License](https://img.shields.io/badge/license-MIT-blue.svg?style=flat)](http://choosealicense.com/licenses/mit/)
 > This repository is forked from [Real-Time-Voice-Cloning](https://github.com/CorentinJ/Real-Time-Voice-Cloning) which only support English.
 
-> English | [中文](README-CN.md) 
+> English | [中文](README-CN.md)
 
 ## Features
 🌍 **Chinese** supported mandarin and tested with multiple datasets: aidatatang_200zh, magicdata
 
 🤩 **PyTorch** worked for pytorch, tested in version of 1.9.0(latest in August 2021), with GPU Tesla T4 and GTX 2060
 
-🌍 **Windows + Linux** tested in both Windows OS and linux OS after fixing nits 
+🌍 **Windows + Linux** tested in both Windows OS and linux OS after fixing nits
 
 🤩 **Easy & Awesome** effect with only newly-trained synthesizer, by reusing the pretrained encoder/vocoder
 
@@ -24,21 +24,26 @@
 **Python 3.7 or higher ** is needed to run the toolbox.
 
 * 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).
 * 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.
 ### 2. Train synthesizer with your dataset
 * 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:
 `python synthesizer_preprocess_audio.py <datasets_root>`
 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:
 `python synthesizer_preprocess_embeds.py <datasets_root>/SV2TTS/synthesizer`
 
 * Train the synthesizer:
 `python synthesizer_train.py mandarin <datasets_root>/SV2TTS/synthesizer`
 
-* Go to next step when you see attention line show and loss meet your need in training folder *synthesizer/saved_models/*. 
+* Go to next step when you see attention line show and loss meet your need in training folder *synthesizer/saved_models/*.
 > FYI, my attention came after 18k steps and loss became lower than 0.4 after 50k steps.
 ![attention_step_20500_sample_1](https://user-images.githubusercontent.com/7423248/128587252-f669f05a-f411-4811-8784-222156ea5e9d.png)
 ![step-135500-mel-spectrogram_sample_1](https://user-images.githubusercontent.com/7423248/128587255-4945faa0-5517-46ea-b173-928eff999330.png)
@@ -46,8 +51,8 @@ Allow parameter `--dataset {dataset}` to support adatatang_200zh, magicdata
 ### 2.2 Use pretrained model of synthesizer
 > Thanks to the community, some models will be shared:
 
-| author | Download link | Previow Video | 
+| author | Download link | Previow Video |
-| --- | ----------- | ----- | 
+| --- | ----------- | ----- |
 |@miven| https://pan.baidu.com/s/1PI-hM3sn5wbeChRryX-RCQ code：2021 | https://www.bilibili.com/video/BV1uh411B7AD/
 
 > A link to my early trained model: [Baidu Yun](https://pan.baidu.com/s/10t3XycWiNIg5dN5E_bMORQ)
@@ -55,9 +60,9 @@ Code：aid4
 ### 3. Launch the Toolbox
 You can then try the toolbox:
 
-`python demo_toolbox.py -d <datasets_root>`  
+`python demo_toolbox.py -d <datasets_root>`
-or  
+or
-`python demo_toolbox.py`  
+`python demo_toolbox.py`
 
 > Good news🤩: Chinese Characters are supported