Lip-Sync AI Model with Wav2Lip

This repository contains an AI model that utilizes Wav2Lip for lip-syncing, which is the process of synchronizing an audio file with a video file. Wav2Lip is a deep learning-based approach that generates realistic lip movements from input audio. This model can be used for various applications, such as dubbing, video editing, or creating lip-sync animations.

Note: The updated version of this model can process video files that may not have faces in all frames, but have faces in some frames.

🚀 How It Works

The lip-sync AI model is built using the following components and steps:

1. Wav2Lip: Wav2Lip is a lip-sync model that takes an input audio waveform and a video file containing the target speaker's face as input. It generates lip movements that are synchronized with the provided audio.

2. Preprocessing: The input video file is preprocessed to extract the frames containing the target speaker's face. These frames are used as the visual input for Wav2Lip.

3. Audio Processing: The input audio file is processed to extract the audio waveform, which serves as the audio input for Wav2Lip.

4. Lip-Sync Generation: Wav2Lip takes the extracted audio waveform and the corresponding video frames as input and generates lip movements that match the provided audio.

5. Output Video: The lip-synced video is created by combining the original video frames with the generated lip movements. The resulting video file has synchronized audio and lip movements.

📝 Getting Started

To use the lip-sync AI model, follow these steps:

1. Requirements: Make sure you have the necessary dependencies installed. This may include Python, TensorFlow, OpenCV, and other libraries. You can find the specific requirements in the requirements.txt file.

2. Data Preparation: Prepare the input video file and audio file that you want to synchronize. The video file should contain the target speaker's face, and the audio file should correspond to the lip movements you want to generate.

3. Preprocessing: Use a video processing tool or library to extract the frames containing the target speaker's face from the video file. Save these frames in a directory.

4. Audio Processing: If necessary, preprocess the audio file to ensure it is in a suitable format for Wav2Lip. This may involve converting the audio to a specific sample rate or format.

5. Run the Model: Run the lip-sync AI model by providing the preprocessed video frames and audio file as input to Wav2Lip. The model will generate lip movements synchronized with the provided audio.

6. Output Video: Combine the generated lip movements with the original video frames to create the final lip-synced video. You can use a video editing tool or library for this step.

💻 Example Usage

Here's an example of how to use the lip-sync AI model:

python lip_sync_model.py --video video_frames/ --audio audio.wav --output lip_synced_video.mp4

🙏 Acknowledgements

This lip-sync AI model is based on the Wav2Lip project developed by Aruni RoyChowdhury and reference from Wav2Lip by Markfryazino.

"The future is now!" - Lip-Sync AI Team 💫