TVLT-BASE Model

Introduction

The Textless Vision-Language Transformer (TVLT) model is a pre-trained-only model developed for audio-visual pre-training. It was introduced by Tang et al. in the paper titled "TVLT: Textless Vision-Language Transformer" and is based on the Masked Autoencoders (MAE) model, extending it to handle both audio and video data.

Architecture

TVLT builds upon the MAE model architecture, integrating capabilities for processing both visual and auditory data. This makes it well-suited for tasks that require understanding and interpreting multi-modal inputs without relying on text.

Training

The model is pre-trained on a combination of audio and visual inputs, allowing it to learn a rich representation of multi-modal data. However, for specific tasks, it is recommended to fine-tune the model to adapt it to particular requirements involving audio and/or video.

Guide: Running Locally

1. Setup Environment:

   • Install Python and necessary packages, including PyTorch and Transformers.

2. Download TVLT-BASE:

   • Obtain the model from the Hugging Face repository.

3. Fine-tune the Model:

   • Use task-specific datasets to fine-tune TVLT for your application.

4. Inference:

   • Run inference using the fine-tuned model on your input data.

For efficient training and inference, it is advisable to use cloud GPUs, such as those provided by AWS, Google Cloud, or Azure, to handle the computational demands.

License

The TVLT model is released under the MIT License, allowing for broad use and modification with minimal restrictions.