This repository is forked from Real-Time-Voice-Cloning which only support English.

English | 中文

Features

🌍 Chinese supported mandarin and tested with multiple datasets: aidatatang_200zh, SLR68

🤩 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

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.
• Install ffmpeg.
• Run pip install -r requirements.txt to install the remaining necessary packages.

2. Reuse the pretrained encoder/vocoder

• Download the following models and extract the encoder and vocoder models to the according directory of this project. Don't use the synthesizer https://github.com/CorentinJ/Real-Time-Voice-Cloning/wiki/Pretrained-models

Note that we need to specify the newly trained synthesizer model, since the original model is incompatible with the Chinese sympols. It means the demo_cli is not working at this moment.

3. 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, SLR68

• 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. A link to my early trained model: Baidu Yun Code：aid4

4. 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