Introducing ViT Captioner: Transform Your Videos with AI-Powered Captions

Struggling with Video Captioning? Meet Your New Solution

Have you ever needed to extract meaningful captions from video content but found the process tedious and time-consuming? Whether you are a content creator, researcher, developer, or accessibility advocate, ViT Captioner is an open-source tool for turning video frames into useful text descriptions.

What Is ViT Captioner?

ViT Captioner is a Python package that uses the ViT-GPT2 image-captioning model to extract keyframes from videos and generate natural-language captions. It bridges computer vision and natural language processing by producing descriptions of visually important moments in a video.

The result can be used as subtitles, structured metadata, or captioned keyframe images for review and indexing.

Key Features

  • Intelligent keyframe extraction: Uses Katna to identify meaningful frames, with a fallback to uniform sampling.
  • Image caption generation: Produces descriptive captions using the ViT-GPT2 model.
  • Flexible output formats: Creates SRT subtitle files, JSON data, and captioned images.
  • Timeline visualization: Shows keyframes and timestamps on an interactive timeline.
  • Batch-friendly workflow: Includes progress indicators and resource-aware processing.
  • Developer-friendly API: Provides a Python interface for integration into other applications.
  • Command-line interface: Supports quick video captioning from the terminal.

Real-World Applications

  • Content creators can generate subtitle drafts and improve video discoverability.
  • Researchers can summarize and inspect video datasets with frame-level descriptions.
  • Developers can add lightweight video-understanding features to applications.
  • Educators can make instructional videos easier to review and search.
  • Media archivists can index video collections based on visual content.

See It in Action

ViT Captioner can produce SRT subtitle files like this:

1
00:00:00,000 --> 00:00:00,922
a piece of meat on a plate on a counter

2
00:00:00,922 --> 00:00:01,844
a piece of meat is being cooked in a pan

It can also create structured JSON data and captioned keyframe images, making it easier to inspect what the model saw at each selected timestamp.

Getting Started

Install the package with pip:

pip install vit-captioner

Generate captions for a video from the command line:

vit-captioner caption-video -V /path/to/video.mp4 -N 10 -v

Use the Python API for integration into your own workflow:

from vit_captioner.captioning.video import VideoToCaption

converter = VideoToCaption("/path/to/video.mp4", num_frames=10, verbose=True)
converter.convert()

For reproducible results, check the installed package version and available command options locally:

vit-captioner --help
python -c "import vit_captioner; print(vit_captioner.__version__)"

Built on Open-Source Foundations

ViT Captioner builds on several open-source projects:

Try ViT Captioner

ViT Captioner is available on GitHub and PyPI:

Give it a star if you find it useful, and contributions are welcome.

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