[](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)
## Features
🌍 **Chinese** supported mandarin and tested with multiple datasets: aidatatang_200zh, magicdata, aishell3
🤩 **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
🤩 **Easy & Awesome** effect with only newly-trained synthesizer, by reusing the pretrained encoder/vocoder
### [DEMO VIDEO](https://www.bilibili.com/video/BV1sA411P7wM/)
## Quick Start
### 1. Install Requirements
> Follow the original repo to test if you got all environment ready.
**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 other 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, aishell3
>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/* .
> FYI, my attention came after 18k steps and loss became lower than 0.4 after 50k steps.
### 2.2 Use pretrained model of synthesizer
> Thanks to the community, some models will be shared:
| author | Download link | Previow Video |
| --- | ----------- | ----- |
|@miven| https://pan.baidu.com/s/1PI-hM3sn5wbeChRryX-RCQ code：
> A link to my early trained model: [Baidu Yun](https://pan.baidu.com/s/10t3XycWiNIg5dN5E_bMORQ)
Code：
### 3. Launch the Toolbox
You can then try the toolbox:
`python demo_toolbox.py -d <datasets_root>`
or
`python demo_toolbox.py`
> Good news🤩: Chinese Characters are supported
## TODO
- [x] Add demo video
- [X] Add support for more dataset
- [X] Upload pretrained model
- [ ] Support parallel tacotron
- [ ] Service orianted and docterize
- 🙏 Welcome to add more
