• Python 3.6
• ffmpeg: sudo apt-get install ffmpeg
• Install necessary packages using pip install -r requirements.txt. Alternatively, instructions for using a docker image is provided here. Have a look at this comment and comment on the gist if you encounter any issues.
• Face detection pre-trained model should be downloaded to face_detection/detection/sfd/s3fd.pth. Alternative link if the above does not work.

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.

• Experiment with the --pads argument to adjust the detected face bounding box. Often leads to improved results. You might need to increase the bottom padding to include the chin region. E.g. --pads 0 20 0 0.
• If you see the mouth position dislocated or some weird artifacts such as two mouths, then it can be because of over-smoothing the face detections. Use the --nosmooth argument and give another try.
• Experiment with the --resize_factor argument, to get a lower resolution video. Why? The models are trained on faces which were at a lower resolution. You might get better, visually pleasing results for 720p videos than for 1080p videos (in many cases, the latter works well too).
• The Wav2Lip model without GAN usually needs more experimenting with the above two to get the most ideal results, and sometimes, can give you a better result as well.