| # Wav2Lip-HD: Improving Wav2Lip to achieve High-Fidelity Videos | |
| This repository contains code for achieving high-fidelity lip-syncing in videos, using the [Wav2Lip algorithm](https://github.com/Rudrabha/Wav2Lip) for lip-syncing and the [Real-ESRGAN algorithm](https://github.com/xinntao/Real-ESRGAN) for super-resolution. The combination of these two algorithms allows for the creation of lip-synced videos that are both highly accurate and visually stunning. | |
| ## Algorithm | |
| The algorithm for achieving high-fidelity lip-syncing with Wav2Lip and Real-ESRGAN can be summarized as follows: | |
| 1. The input video and audio are given to `Wav2Lip` algorithm. | |
| 2. Python script is written to extract frames from the video generated by wav2lip. | |
| 3. Frames are provided to Real-ESRGAN algorithm to improve quality. | |
| 4. Then, the high-quality frames are converted to video using ffmpeg, along with the original audio. | |
| 5. The result is a high-quality lip-syncing video. | |
| 6. The specific steps for running this algorithm are described in the [Testing Model](https://github.com/saifhassan/Wav2Lip-HD#testing-model) section of this README. | |
| ## Testing Model | |
| To test the "Wav2Lip-HD" model, follow these steps: | |
| 1. Clone this repository and install requirements using following command (Make sure, Python and CUDA are already installed): | |
| ``` | |
| git clone https://github.com/saifhassan/Wav2Lip-HD.git | |
| cd Wav2Lip-HD | |
| pip install -r requirements.txt | |
| ``` | |
| 2. Downloading weights | |
| | Model | Directory | Download Link | | |
| | :------------- |:-------------| :-----:| | |
| | Wav2Lip | [checkpoints/](https://github.com/saifhassan/Wav2Lip-HD/tree/main/checkpoints) | [Link](https://drive.google.com/drive/folders/1tB_uz-TYMePRMZzrDMdShWUZZ0JK3SIZ?usp=sharing) | | |
| | ESRGAN | [experiments/001_ESRGAN_x4_f64b23_custom16k_500k_B16G1_wandb/models/](https://github.com/saifhassan/Wav2Lip-HD/tree/main/experiments/001_ESRGAN_x4_f64b23_custom16k_500k_B16G1_wandb/models) | [Link](https://drive.google.com/file/d/1Al8lEpnx2K-kDX7zL2DBcAuDnSKXACPb/view?usp=sharing) | | |
| | Face_Detection | [face_detection/detection/sfd/](https://github.com/saifhassan/Wav2Lip-HD/tree/main/face_detection/detection/sfd) | [Link](https://drive.google.com/file/d/1uNLYCPFFmO-og3WSHyFytJQLLYOwH5uY/view?usp=sharing) | | |
| | Real-ESRGAN | Real-ESRGAN/gfpgan/weights/ | [Link](https://drive.google.com/drive/folders/1BLx6aMpHgFt41fJ27_cRmT8bt53kVAYG?usp=sharing) | | |
| | Real-ESRGAN | Real-ESRGAN/weights/ | [Link](https://drive.google.com/file/d/1qNIf8cJl_dQo3ivelPJVWFkApyEAGnLi/view?usp=sharing) | | |
| 3. Put input video to `input_videos` directory and input audio to `input_audios` directory. | |
| 4. Open `run_final.sh` file and modify following parameters: | |
| `filename=kennedy` (just video file name without extension) | |
| `input_audio=input_audios/ai.wav` (audio filename with extension) | |
| 5. Execute `run_final.sh` using following command: | |
| ``` | |
| bash run_final.sh | |
| ``` | |
| 6. Outputs | |
| - `output_videos_wav2lip` directory contains video output generated by wav2lip algorithm. | |
| - `frames_wav2lip` directory contains frames extracted from video (generated by wav2lip algorithm). | |
| - `frames_hd` directory contains frames after performing super-resolution using Real-ESRGAN algorithm. | |
| - `output_videos_hd` directory contains final high quality video output generated by Wav2Lip-HD. | |
| ## Results | |
| The results produced by Wav2Lip-HD are in two forms, one is frames and other is videos. Both are shared below: | |
| ### Example output frames </summary> | |
| <table> | |
| <tr> | |
| <td>Frame by Wav2Lip</td> | |
| <td>Optimized Frame</td> | |
| </tr> | |
| <tr> | |
| <td><img src="examples/1_low.jpg" width=500></td> | |
| <td><img src="examples/1_hd.jpg" width=500></td> | |
| </tr> | |
| <tr> | |
| <td><img src="examples/kennedy_low.jpg" width=500></td> | |
| <td><img src="examples/kennedy_hd.jpg" width=500></td> | |
| </tr> | |
| </tr> | |
| <tr> | |
| <td><img src="examples/mona_low.jpg" width=500></td> | |
| <td><img src="examples/mona_hd.jpg" width=500></td> | |
| </tr> | |
| </table> | |
| </Details> | |
| ### Example output videos | |
| | Video by Wav2Lip | Optimized Video | | |
| | ------------- | ------------- | | |
| | <video src="https://user-images.githubusercontent.com/11873763/229389410-56d96244-8c67-4add-a43e-a4900aa9db88.mp4" width="500"> | <video src="https://user-images.githubusercontent.com/11873763/229389414-d5cb6d33-7772-47a7-b829-9e3d5c3945a1.mp4" width="500">| | |
| | <video src="https://user-images.githubusercontent.com/11873763/229389751-507669f1-7772-4863-ab23-8df7f206a065.mp4" width="500"> | <video src="https://user-images.githubusercontent.com/11873763/229389962-5373b765-ce3a-4af2-bd6a-8be8543ee933.mp4" width="500">| | |
| ## Acknowledgements | |
| We would like to thank the following repositories and libraries for their contributions to our work: | |
| 1. The [Wav2Lip](https://github.com/Rudrabha/Wav2Lip) repository, which is the core model of our algorithm that performs lip-sync. | |
| 2. The [face-parsing.PyTorch](https://github.com/zllrunning/face-parsing.PyTorch) repository, which provides us with a model for face segmentation. | |
| 3. The [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) repository, which provides the super resolution component for our algorithm. | |
| 4. [ffmpeg](https://ffmpeg.org), which we use for converting frames to video. | |