BrainWi2ard is a user-friendly GUI software that integrates features from multiple deep learning projects to create a comprehensive tool for real-time voice cloning, deepfake video generation, and lip-syncing. This software is compatible with macOS, Linux, and Windows.

• Real-Time Voice Cloning: Clone a voice in 5 seconds to generate arbitrary speech in real-time.
• Deepfake Video Generation: Create deepfake videos using pre-trained models or train your own.
• Lip-Syncing: Accurately lip-sync videos in any language.

• Python 3.7+
• ffmpeg
• PyTorch

1. Clone the Repositories:

   git clone https://github.com/iperov/DeepFaceLive.git
   git clone https://github.com/CorentinJ/Real-Time-Voice-Cloning.git
   git clone https://github.com/snehitvaddi/Deepfake-using-Wave2Lip.git
   git clone https://github.com/hectorgie/DeepFaceLab.git

2. Install Requirements:

   cd DeepFaceLive
   pip install -r requirements.txt
   cd ../Real-Time-Voice-Cloning
   pip install -r requirements.txt
   cd ../Deepfake-using-Wave2Lip
   pip install -r requirements.txt
   cd ../DeepFaceLab
   pip install -r requirements.txt

3. Download Pre-trained Models (if applicable):

   • Follow the instructions in each repository to download and set up pre-trained models.

4. Run the GUI:

   • Implement a unified GUI using a framework like PyQt or Tkinter, integrating features from all repositories.

1. Navigate to the Real-Time-Voice-Cloning directory:

   cd Real-Time-Voice-Cloning

2. Run the toolbox:

   python demo_toolbox.py -d <datasets_root>

1. Follow the instructions in the DeepFaceLab repository to create and train deepfake models.
2. Use DeepFaceLive to apply real-time face swaps during video calls or streams.

1. Navigate to the Deepfake-using-Wave2Lip directory:

   cd Deepfake-using-Wave2Lip

2. Run the lip-sync script with your base video and audio file:

   python inference.py --checkpoint_path <path_to_checkpoint> --face <path_to_video> --audio <path_to_audio>

Contributions are welcome! Please fork this repository and submit pull requests.

This project is licensed under the MIT License.

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