[![arXiv](https://img.shields.io/badge/arXiv_paper-2412.09262-b31b1b)](https://arxiv.org/abs/2412.09262) [![arXiv](https://img.shields.io/badge/%F0%9F%A4%97%20space-HuggingFace-yellow)](https://huggingface.co/spaces/fffiloni/LatentSync)

LatentSync uses the [Whisper](https://github.com/openai/whisper) to convert melspectrogram into audio embeddings, which are then integrated into the U-Net via cross-attention layers. The reference and masked frames are channel-wise concatenated with noised latents as the input of U-Net. In the training process, we use a one-step method to get estimated clean latents from predicted noises, which are then decoded to obtain the estimated clean frames. The TREPA, [LPIPS](https://arxiv.org/abs/1801.03924) and [SyncNet](https://www.robots.ox.ac.uk/~vgg/publications/2016/Chung16a/chung16a.pdf) losses are added in the pixel space. ## 🎬 Demo

Original video	Lip-synced video

(Photorealistic videos are filmed by contracted models, and anime videos are from [VASA-1](https://www.microsoft.com/en-us/research/project/vasa-1/) and [EMO](https://humanaigc.github.io/emote-portrait-alive/)) ## 📑 Open-source Plan - [x] Inference code and checkpoints - [x] Data processing pipeline - [x] Training code ## 🔧 Setting up the Environment Install the required packages and download the checkpoints via: ```bash source setup_env.sh ``` If the download is successful, the checkpoints should appear as follows: ``` ./checkpoints/ |-- latentsync_unet.pt |-- latentsync_syncnet.pt |-- whisper | `-- tiny.pt |-- auxiliary | |-- 2DFAN4-cd938726ad.zip | |-- i3d_torchscript.pt | |-- koniq_pretrained.pkl | |-- s3fd-619a316812.pth | |-- sfd_face.pth | |-- syncnet_v2.model | |-- vgg16-397923af.pth | `-- vit_g_hybrid_pt_1200e_ssv2_ft.pth ``` These already include all the checkpoints required for latentsync training and inference. If you just want to try inference, you only need to download `latentsync_unet.pt` and `tiny.pt` from our [HuggingFace repo](https://huggingface.co/ByteDance/LatentSync) ## 🚀 Inference There are two ways to perform inference, and both require 6.5 GB of VRAM. ### 1. Gradio App Run the Gradio app for inference: ```bash python gradio_app.py ``` ### 2. Command Line Interface Run the script for inference: ```bash ./inference.sh ``` You can change the parameters `inference_steps` and `guidance_scale` to see more results. ## 🔄 Data Processing Pipeline The complete data processing pipeline includes the following steps: 1. Remove the broken video files. 2. Resample the video FPS to 25, and resample the audio to 16000 Hz. 3. Scene detect via [PySceneDetect](https://github.com/Breakthrough/PySceneDetect). 4. Split each video into 5-10 second segments. 5. Remove videos where the face is smaller than 256 $\times$ 256, as well as videos with more than one face. 6. Affine transform the faces according to the landmarks detected by [face-alignment](https://github.com/1adrianb/face-alignment), then resize to 256 $\times$ 256. 7. Remove videos with [sync confidence score](https://www.robots.ox.ac.uk/~vgg/publications/2016/Chung16a/chung16a.pdf) lower than 3, and adjust the audio-visual offset to 0. 8. Calculate [hyperIQA](https://openaccess.thecvf.com/content_CVPR_2020/papers/Su_Blindly_Assess_Image_Quality_in_the_Wild_Guided_by_a_CVPR_2020_paper.pdf) score, and remove videos with scores lower than 40. Run the script to execute the data processing pipeline: ```bash ./data_processing_pipeline.sh ``` You can change the parameter `input_dir` in the script to specify the data directory to be processed. The processed data will be saved in the `high_visual_quality` directory. Each step will generate a new directory to prevent the need to redo the entire pipeline in case the process is interrupted by an unexpected error. ## 🏋️‍♂️ Training U-Net Before training, you must process the data as described above and download all the checkpoints. We released a pretrained SyncNet with 94% accuracy on the VoxCeleb2 dataset for the supervision of U-Net training. Note that this SyncNet is trained on affine transformed videos, so when using or evaluating this SyncNet, you need to perform affine transformation on the video first (the code of affine transformation is included in the data processing pipeline). If all the preparations are complete, you can train the U-Net with the following script: ```bash ./train_unet.sh ``` You should change the parameters in U-Net config file to specify the data directory, checkpoint save path, and other training hyperparameters. ## 🏋️‍♂️ Training SyncNet In case you want to train SyncNet on your own datasets, you can run the following script. The data processing pipeline for SyncNet is the same as U-Net. ```bash ./train_syncnet.sh ``` After `validations_steps` training, the loss charts will be saved in `train_output_dir`. They contain both the training and validation loss. ## 📊 Evaluation You can evaluate the [sync confidence score](https://www.robots.ox.ac.uk/~vgg/publications/2016/Chung16a/chung16a.pdf) of a generated video by running the following script: ```bash ./eval/eval_sync_conf.sh ``` You can evaluate the accuracy of SyncNet on a dataset by running the following script: ```bash ./eval/eval_syncnet_acc.sh ``` ## 🙏 Acknowledgement - Our code is built on [AnimateDiff](https://github.com/guoyww/AnimateDiff). - Some code are borrowed from [MuseTalk](https://github.com/TMElyralab/MuseTalk), [StyleSync](https://github.com/guanjz20/StyleSync), [SyncNet](https://github.com/joonson/syncnet_python), [Wav2Lip](https://github.com/Rudrabha/Wav2Lip). Thanks for their generous contributions to the open-source community.