Wav2lip dataset. Training on datasets other than LRS2 Training on other datasets might require modifications to . Please read the following before you raise an issue: Apr 27, 2023 ยท They show that Wav2Lip beats LipGAN across three datasets (LRS 1, 2 and 3) for visual quality (as measured by FID and user studies), and for lip sync (as measured by their novel metrics LSE-C and The arguments for both files are similar. See here for a few suggestions regarding training on other datasets. 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. LRS2 dataset folder structure Look at python wav2lip_train. You can also set additional less commonly-used hyper-parameters at the bottom of the hparams. py file. It works for any identity, voice, and language, and can even handle CGI faces and synthetic voices. Training on datasets other than LRS2 Training on other datasets might require modifications to the code. Please read the following before you raise an issue: How would you describe this dataset? Well-documented 0 Well-maintained 0 Clean data 0 Original 0 High-quality notebooks 0 Other text_snippet Wav2Lip is an AI model that can accurately lip-sync videos to any target speech with high accuracy. py --help for more details. Look at python wav2lip_train. In both cases, you can resume training as well. For instance, you can try adjusting the detected face bounding box or Look at python wav2lip_train. Preparing LRS2 for training Our models are trained on LRS2. But what makes it unique? It's incredibly efficient and can produce high-quality results with minimal experimentation. gdxibm yrmj ksgxa yhvjxluv qabufgf lifnzzj ecn hncs aubqng bbbou