Practical overview
When it fits
Use it when you can describe the visual content you want to find but do not know where it appears in a video.
Who it is for
- People looking for a visual event or object in a YouTube video
- Researchers and developers exploring text-based video frame search
Documented uses
- Locate frames showing a described object, scene or event
- Explore a video for visual details such as road works, a landmark or a street crossing
Requirements & limits
The project provides a Google Colab notebook and documents a Gradio demo hosted on Hugging Face Spaces. The public repository is licensed under MIT.
- The search uses every N-th frame, so the sampling interval affects which moments are represented.
- Results are visual frame matches rather than transcript or audio search.
- The documented implementation is a Jupyter notebook using CLIP.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Search a YouTube video with a natural-language query in Colab
Use the project notebook to find sampled video frames that best match a text description.
Before you start
- The project notebook, available through its Google Colab link.
- A YouTube video to search.
Steps
Open the project notebook in Google Colab.
Download the YouTube video and extract every N-th frame.
Encode the sampled frames and your natural-language query with CLIP.
Find the frames that best match the query.
Expected outcome
A set of sampled video frames ranked or selected by their match to the text query.
Guide limitations
- Search is performed on sampled frames, not every moment in the video.
- The README does not specify the frame-sampling interval, query limits, or ranking details.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 17 October 2026
- Original website title
- haltakov/natural-language-youtube-search · Official homepage title · retained in its source language