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license: wtfpl |
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# Wav2Lip-HD: Improving Wav2Lip to achieve High-Fidelity Videos |
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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. |
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## Algorithm |
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The algorithm for achieving high-fidelity lip-syncing with Wav2Lip and Real-ESRGAN can be summarized as follows: |
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1. The input video and audio are given to `Wav2Lip` algorithm. |
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2. Python script is written to extract frames from the video generated by wav2lip. |
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3. Frames are provided to Real-ESRGAN algorithm to improve quality. |
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4. Then, the high-quality frames are converted to video using ffmpeg, along with the original audio. |
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5. The result is a high-quality lip-syncing video. |
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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. |
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## Testing Model |
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To test the "Wav2Lip-HD" model, follow these steps: |
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1. Clone this repository and install requirements using following command (Make sure, Python and CUDA are already installed): |
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``` |
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git clone https://github.com/saifhassan/Wav2Lip-HD.git |
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cd Wav2Lip-HD |
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pip install -r requirements.txt |
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``` |
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2. Downloading weights |
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| Model | Directory | Download Link | |
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| :------------- |:-------------| :-----:| |
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| Wav2Lip | [checkpoints/](https://github.com/saifhassan/Wav2Lip-HD/tree/main/checkpoints) | [Link](https://drive.google.com/drive/folders/1tB_uz-TYMePRMZzrDMdShWUZZ0JK3SIZ?usp=sharing) | |
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| 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) | |
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| 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) | |
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| Real-ESRGAN | Real-ESRGAN/gfpgan/weights/ | [Link](https://drive.google.com/drive/folders/1BLx6aMpHgFt41fJ27_cRmT8bt53kVAYG?usp=sharing) | |
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| Real-ESRGAN | Real-ESRGAN/weights/ | [Link](https://drive.google.com/file/d/1qNIf8cJl_dQo3ivelPJVWFkApyEAGnLi/view?usp=sharing) | |
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3. Put input video to `input_videos` directory and input audio to `input_audios` directory. |
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4. Open `run_final.sh` file and modify following parameters: |
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`filename=kennedy` (just video file name without extension) |
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`input_audio=input_audios/ai.wav` (audio filename with extension) |
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5. Execute `run_final.sh` using following command: |
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``` |
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bash run_final.sh |
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``` |
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6. Outputs |
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- `output_videos_wav2lip` directory contains video output generated by wav2lip algorithm. |
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- `frames_wav2lip` directory contains frames extracted from video (generated by wav2lip algorithm). |
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- `frames_hd` directory contains frames after performing super-resolution using Real-ESRGAN algorithm. |
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- `output_videos_hd` directory contains final high quality video output generated by Wav2Lip-HD. |
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## Results |
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The results produced by Wav2Lip-HD are in two forms, one is frames and other is videos. Both are shared below: |
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### Example output frames </summary> |
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<table> |
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<tr> |
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<td>Frame by Wav2Lip</td> |
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<td>Optimized Frame</td> |
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</tr> |
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<tr> |
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<td><img src="examples/1_low.jpg" width=500></td> |
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<td><img src="examples/1_hd.jpg" width=500></td> |
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</tr> |
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<tr> |
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<td><img src="examples/kennedy_low.jpg" width=500></td> |
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<td><img src="examples/kennedy_hd.jpg" width=500></td> |
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</tr> |
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</tr> |
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<tr> |
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<td><img src="examples/mona_low.jpg" width=500></td> |
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<td><img src="examples/mona_hd.jpg" width=500></td> |
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</tr> |
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</table> |
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</Details> |
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### Example output videos |
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| Video by Wav2Lip | Optimized Video | |
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| ------------- | ------------- | |
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| <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">| |
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| <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">| |
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## Acknowledgements |
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We would like to thank the following repositories and libraries for their contributions to our work: |
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1. The [Wav2Lip](https://github.com/Rudrabha/Wav2Lip) repository, which is the core model of our algorithm that performs lip-sync. |
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2. The [face-parsing.PyTorch](https://github.com/zllrunning/face-parsing.PyTorch) repository, which provides us with a model for face segmentation. |
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3. The [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) repository, which provides the super resolution component for our algorithm. |
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4. [ffmpeg](https://ffmpeg.org), which we use for converting frames to video. |
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