我来实际训一个版本.

0.py 输入数据在data_train里面. 一堆.mp4

1.py syncnet

2.py 训练wav2lip网络

跑通代码.png 是我跑通的图.跑的wav2lip, 1.py也跑通了.

训完还是inference.py代码 使用方法跟wav2lip一样.

目前计算资源有限还没训好. 有训完的可以issue一下交流.

Wav2Lip - a modified wav2lip 384 version

Lip-syncing videos using the pre-trained models (Inference)

You can lip-sync any video to any audio:

python inference.py --checkpoint_path <ckpt> --face <video.mp4> --audio <an-audio-source> 

The result is saved (by default) in results/result_voice.mp4. You can specify it as an argument, similar to several other available options. The audio source can be any file supported by FFMPEG containing audio data: *.wav, *.mp3 or even a video file, from which the code will automatically extract the audio.

Train!

There are two major steps: (i) Train the expert lip-sync discriminator, (ii) Train the Wav2Lip model(s).

Training the expert discriminator

You can use your own data (with resolution 384x384)

python parallel_syncnet_tanh.py

Training the Wav2Lip models

You can either train the model without the additional visual quality disriminator (< 1 day of training) or use the discriminator (~2 days). For the former, run:

python parallel_wav2lip_margin.py

wav2lip384_my

wav2lip384_my

wav2lip384_my2

"# wav2lip384_my2" "# wav2lip384_my2"