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README.md

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+# 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.
+