Here’s a README.md file template for your WAV2LIP-GAN Lip Sync App that you can use for GitHub:

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WAV2LIP-GAN Lip Sync App

This repository contains a web-based application that generates lip-synced videos by synchronizing facial movements with an audio file using the WAV2LIP-GAN model. The app allows users to upload an avatar image and an audio file, processes the input through a pre-trained lip-sync model, and outputs a video with synchronized lip movements.

Features

• Upload a custom audio file (WAV format) and an avatar image (JPG, PNG).
• Generates lip-synced videos using the WAV2LIP-GAN model.
• Built with Streamlit for a user-friendly interface.
• Outputs video in MP4 format.
• Error handling for missing files and dependencies.
• Displays debugging information when errors occur.

Demo

Include a demo GIF or image of the app here to showcase the functionality.

Installation

Prerequisites

• Python 3.7+
• Ensure you have the following libraries installed in your environment. You can install the required packages using requirements.txt.

Install Dependencies

git clone https://github.com/yourusername/wav2lip-gan-lipsync-app.git
cd wav2lip-gan-lipsync-app

# Install required dependencies
pip install -r requirements.txt

Pre-trained Model

• Download the pre-trained WAV2LIP-GAN model from the official WAV2LIP repository.
• Save the checkpoint model in the checkpoints/ directory:
  • Example: checkpoints/wav2lip_gan.pth

Usage

Running the App

Once all dependencies are installed and the model is downloaded, you can run the app locally using the following command:

streamlit run app.py

How to Use the App

1. Upload an audio file (WAV format).
2. Upload an avatar image (PNG, JPG, or JPEG format).
3. Click on Generate Lip-Synced Video.
4. The app will process the inputs and generate a lip-synced video, which you can download.

Output

• The lip-synced video is generated in the Outputs directory and displayed in the Streamlit app interface.

File Structure

wav2lip-gan-lipsync-app/
│
├── app.py                  # Streamlit app script
├── requirements.txt         # Python dependencies
├── checkpoints/             # Directory to store the model checkpoint
│   └── wav2lip_gan.pth      # Pre-trained GAN model
├── Outputs/                 # Output folder for generated videos
└── README.md                # This README file

Requirements

The required Python libraries are listed in the requirements.txt file. Install them by running:

pip install -r requirements.txt

Example

Here's an example command for running the WAV2LIP inference from the terminal:

python inference.py --checkpoint_path checkpoints/wav2lip_gan.pth --face input.jpg --audio input.wav --outfile Outputs/output_video.mp4

Troubleshooting

If the app fails to run, check for missing Python modules in the terminal and ensure the paths to the model checkpoint and input files are correct.

Acknowledgments

• This app utilizes the WAV2LIP model from the original WAV2LIP repository.
• Thanks to the authors for their work on the WAV2LIP-GAN model.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

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You can modify this README file as needed to fit your project or add further sections like Contributing or Known Issues if applicable.