# 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: ```bash python inference.py --checkpoint_path --face --audio ``` 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) ```bash 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: ```bash python parallel_wav2lip_margin.py ``` # wav2lip384_my # wav2lip384_my # wav2lip384_my2 "# wav2lip384_my2" "# wav2lip384_my2" ##Lip-syncing videos using the pre-trained models (Inference) You can lip-sync any video to any audio: python inference.py --checkpoint_path --face --audio 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