Hugging Face's logo

duyv
/
API_MC_AI

ONNX
Model card Files
xet
Community
API_MC_AI / Wav2Lip-HD /readme.md
duyv's picture
duyv
Upload 138 files
fcac53f verified
preview code
|
raw
history blame contribute delete
7.7 kB

pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 torchaudio==2.0.2+cu118 --index-url https://download.pytorch.org/whl/cu118 pip install numpy==1.19.5

D:\CODE\Wav2Lip-HD\

Chạy lệnh Python cho Wav2Lip:

python D:\CODE\Wav2Lip-HD\inference.py --checkpoint_path "D:\CODE\Wav2Lip-HD\checkpoints\wav2lip_gan.pth" --segmentation_path "D:\CODE\Wav2Lip-HD\checkpoints\face_segmentation.pth" --sr_path "D:\CODE\Wav2Lip-HD\checkpoints\esrgan_yunying.pth" --face D:\CODE\Wav2Lip-HD\input_videos\kurumi.jpg --audio D:\CODE\Wav2Lip-HD\input_audios\TRA GUNG DEN VO DUNG.mp3 --save_frames --gt_path "D:\CODE\Wav2Lip-HD\data\gt" --pred_path "D:\CODE\Wav2Lip-HD\data\lq" --no_sr --no_segmentation --outfile D:\CODE\Wav2Lip-HD\output_videos_wav2lip\mona.mp4

python D:\CODE\Wav2Lip-HD\inference.py --checkpoint_path "D:\CODE\Wav2Lip-HD\checkpoints\wav2lip_gan.pth" --segmentation_path "D:\CODE\Wav2Lip-HD\checkpoints\face_segmentation.pth" --sr_path "D:\CODE\Wav2Lip-HD\checkpoints\esrgan_yunying.pth" --face D:\CODE\Wav2Lip-HD\input_videos\kurumi.jpg --audio D:\CODE\Wav2Lip-HD\input_audios\TRA_GUNG_DEN_VO_DUNG.mp3 --save_frames --gt_path "D:\CODE\Wav2Lip-HD\data\gt" --pred_path "D:\CODE\Wav2Lip-HD\data\lq" --outfile D:\CODE\Wav2Lip-HD\output_videos_wav2lip\mona.mp4

Chạy lệnh video2frames:

python D:\CODE\Wav2Lip-HD\video2frames.py --input_video D:\CODE\Wav2Lip-HD\output_videos_wav2lip\mona.mp4 --frames_path D:\CODE\Wav2Lip-HD\frames_wav2lip\mona

Chạy Real-ESRGAN: python D:\CODE\Wav2Lip-HD\Real-ESRGAN\inference_realesrgan.py -n RealESRGAN_x4plus -i D:\CODE\Wav2Lip-HD\frames_wav2lip\mona --output D:\CODE\Wav2Lip-HD\frames_hd\mona --outscale 3.5 --face_enhance

Chạy FFmpeg để tạo video từ frames (tùy chọn)

ffmpeg -r 20 -i D:\CODE\Wav2Lip-HD\frames_wav2lip\mona\frame_%%05d.jpg -i D:\CODE\Wav2Lip-HD\input_audios\ai.wav -vcodec libx264 -crf 25 -preset veryslow -acodec copy D:\CODE\Wav2Lip-HD\output_videos_hd\mona.mkv

ffmpeg -r 20 -i D:\CODE\Wav2Lip-HD\frames_wav2lip\mona\frame_%05d.jpg -i D:\CODE\Wav2Lip-HD\input_audios\ai.wav -vcodec libx264 -crf 25 -preset veryslow -acodec copy D:\CODE\Wav2Lip-HD\output_videos_hd\mona.mkv

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 for lip-syncing and the Real-ESRGAN algorithm 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 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/ Link
ESRGAN experiments/001_ESRGAN_x4_f64b23_custom16k_500k_B16G1_wandb/models/ Link
Face_Detection face_detection/detection/sfd/ Link
Real-ESRGAN Real-ESRGAN/gfpgan/weights/ Link
Real-ESRGAN Real-ESRGAN/weights/ Link

  1. Put input video to input_videos directory and input audio to input_audios directory.

  2. 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)

  3. Execute run_final.sh using following command:

    bash run_final.sh

  4. 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

Frame by Wav2Lip Optimized Frame

Example output videos

Video by Wav2Lip Optimized Video

Acknowledgements

We would like to thank the following repositories and libraries for their contributions to our work:

  1. The Wav2Lip repository, which is the core model of our algorithm that performs lip-sync.
  2. The face-parsing.PyTorch repository, which provides us with a model for face segmentation.
  3. The Real-ESRGAN repository, which provides the super resolution component for our algorithm.
  4. ffmpeg, which we use for converting frames to video.