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[![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) 

## Features
🌍 **Chinese** supported mandarin and tested with dataset: aidatatang_200zh

🤩 **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

## 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/).
* Install [ffmpeg](https://ffmpeg.org/download.html#get-packages).
* Run `pip install -r requirements.txt` to install the remaining necessary packages.


### 2. Train synthesizer with aidatatang_200zh
* Download aidatatang_200zh 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>`

* 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.

### 3. Launch the Toolbox
You can then try the toolbox:

`python demo_toolbox.py -d <datasets_root>`  
or  
`python demo_toolbox.py`  

## TODO
- Add demo video
- Add support for more dataset
- Upload pretrained model
- 🙏 Welcome to add more