

Wav2lip
Overview :
Wav2Lip is an open-source project aimed at achieving high synchronization between characters' lips and arbitrary target speech in videos using deep learning technology. The project provides complete training codes, inference codes, and pre-trained models, supporting any identity, voice, and language, including CGI faces and synthetic voices. The technology behind Wav2Lip is based on the paper 'A Lip Sync Expert Is All You Need for Speech to Lip Generation In the Wild,' which was published at ACM Multimedia 2020. The project also features an interactive demo and a Google Colab notebook for users to quickly get started. Furthermore, the project offers new, reliable evaluation benchmarks and metrics, along with explanations on how to calculate these metrics from the paper.
Target Users :
Wav2Lip is designed for video editors, game developers, animators, and any professionals needing lip synchronization between characters and speech in videos. It enables these users to quickly achieve high-quality lip sync effects without complex manual adjustments, thereby saving time and enhancing productivity.
Use Cases
Video producers use Wav2Lip to add or modify character dialogues in films or videos.
Game developers leverage Wav2Lip to generate natural lip movements for game characters, enhancing the realism of the game.
Educators employ Wav2Lip to add or modify narration in instructional videos, making them more engaging and lively.
Features
High-precision lip synchronization: Accurately syncs any video with target speech.
Supports multiple identities, voices, and languages: Including CGI faces and synthetic voices.
Provides complete training and inference codes: Facilitates customization and optimization according to user needs.
Pre-trained models: Users can directly utilize pre-trained models for lip synchronization.
Interactive demo and Google Colab notebook: Quick start to using Wav2Lip.
New evaluation benchmarks and metrics: Provides assessment methods and metrics used in the project.
Commercial use support: Although the open-source code is limited to research/academic/personal use, the project offers API services for commercial purposes.
How to Use
1. Install the necessary software environment, such as Python 3.6 and ffmpeg.
2. Download and install the required pre-trained models.
3. Use the provided inference code to specify the video file and audio source, and perform lip synchronization.
4. Adjust parameters in the inference code, such as the bounding box for face detection, to achieve better synchronization results.
5. Optionally, train your own models to fit specific datasets or requirements.
6. Use the project's evaluation tools and metrics to assess the effectiveness of the lip synchronization.
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