title	emoji	colorFrom	colorTo	sdk	sdk_version	app_file	pinned	license
Compressed Wav2Lip	🌟	indigo	pink	gradio	4.13.0	app.py	true	apache-2.0

28× Compressed Wav2Lip by Nota AI

Official codebase for Accelerating Speech-Driven Talking Face Generation with 28× Compressed Wav2Lip.

• Presented at ICCV'23 Demo Track; On-Device Intelligence Workshop @ MLSys'23; NVIDIA GTC 2023 Poster.

Installation

Docker (recommended)

git clone https://github.com/Nota-NetsPresso/nota-wav2lip.git
cd nota-wav2lip
docker compose run --service-ports --name nota-compressed-wav2lip compressed-wav2lip bash

Conda

Click

git clone https://github.com/Nota-NetsPresso/nota-wav2lip.git
cd nota-wav2lip
apt-get update
apt-get install ffmpeg libsm6 libxext6 tmux git -y
conda create -n nota-wav2lip python=3.9
conda activate nota-wav2lip
pip install -r requirements.txt

Gradio Demo

Use the below script to run the nota-ai/compressed-wav2lip demo. The models and sample data will be downloaded automatically.

bash app.sh

Inference

(1) Download YouTube videos in the LRS3-TED label text file and preprocess them properly.

• Download lrs3_v0.4_txt.zip from this link.
• Unzip the file and make a folder structure: ./data/lrs3_v0.4_txt/lrs3_v0.4/test
• Run bash download.sh
• Run bash preprocess.sh

(2) Run the script to compare the original Wav2Lip with Nota's compressed version.

bash inference.sh

License

• All rights related to this repository and the compressed models are reserved by Nota Inc.
• The intended use is strictly limited to research and non-commercial projects.

Contact

• To obtain compression code and assistance, kindly contact Nota AI (contact@nota.ai). These are provided as part of our business solutions.
• For Q&A about this repo, use this board: Nota-NetsPresso/discussions

Acknowledgment

• NVIDIA Applied Research Accelerator Program for supporting this research.
• Wav2Lip and LRS3-TED for facilitating the development of the original Wav2Lip.

Citation

@article{kim2023unified,
      title={A Unified Compression Framework for Efficient Speech-Driven Talking-Face Generation}, 
      author={Kim, Bo-Kyeong and Kang, Jaemin and Seo, Daeun and Park, Hancheol and Choi, Shinkook and Song, Hyoung-Kyu and Kim, Hyungshin and Lim, Sungsu},
      journal={MLSys Workshop on On-Device Intelligence (ODIW)},
      year={2023},
      url={https://arxiv.org/abs/2304.00471}
}