surroundings
- windows 10 64bit
- wav2lip -hq
- pytorch 1.12.1+cu113
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foreword
The previous blog post, the mouth synchronization model Wav2Lip , introduced the mouth synchronization model. This article introduces the high-definition version of wav2lip
. On the basis of the original, super-resolution images and face segmentation technology are used to improve the overall effect.
practice
First, pull the source code
git clone https://github.com/Markfryazino/ wav2lip -hq.git cd wav2lip -hq # 创建个新的虚拟环境conda create -n wav2liphq python=3.8 conda activate wav2liphq # 安装torch pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113 # 安装其它依赖库,将其中的torch、torchvision注释掉,前面已经安装了gpu版本pip install -r requirements.txt
Then go to download the model, here you need 3 models, the first download address: https://drive.google.com/file/d/1aB-jqBikcZPJnFrJXWUEpvF2RFCuerSe/view?usp=sharing , copy it to the directory checkpoints
after downloading; The two models are face models. The download address is: https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth . After downloading, copy it to the face_detection/detection/sfd
directory and rename it to s3fd.pth
; the third is the segmentation
model of the face, download address: https://drive.google.com/open?id=154JgKpzCPW82qINcVieuPH3fZ2e0P812 , copy it to the checkpoints
directory, and rename it to face_segmentation.pth
Finally, we prepare an audio file and a video file for testing, execute the command
python.exe inference.py --checkpoint_path checkpoints\wav2lip_gan.pth --segmentation_path checkpoints\face_segmentation.pth --sr_path checkpoints\esrgan_yunying.pth --face test.mp4 --audio test.mp3 --outfile output.mp4
References
- https://github.com/Markfryazino/wav2lip-hq
- https://github.com/zllrunning/face-parsing.PyTorch.git
- https://github.com/xinntao/BasicSR.git
- https://github.com/1adrianb/face-alignment
- https://xugaoxiang.com/2021/03/05/wav2lip/
This article is reprinted from https://xugaoxiang.com/2022/09/15/wav2lip-hq/
This site is for inclusion only, and the copyright belongs to the original author.