Hugging Face's logo

Raizudeen
/
LipSyncModel

TensorBoard
Model card Files Metrics Community
1
LipSyncModel
File size: 5,468 Bytes 
6be1ab7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
 
# 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.