If you’ve ever wished that searching for specific moments in YouTube videos could be as easy as typing a phrase, you’re in the right place! With the power of OpenAI’s CLIP neural network, you can now perform natural language searches within YouTube videos. This guide will walk you through how to set it all up and troubleshoot any potential issues along the way.
How It Works
The magic of this tool lies in a series of systematic steps to decode and align video frames with your search queries:
- Download the YouTube video.
- Extract every N-th frame from the video.
- Encode all frames using the CLIP model.
- Encode your natural language search query with CLIP.
- Identify the images that best match your search query.
For more details, you can check the notebook.
Running the Notebook
You can easily try out this functionality by running the notebook on Google Colab. Simply click the link below to get started:
Understanding the Process Through Analogy
Imagine you are a librarian in a huge library filled with thousands of books (in our case, YouTube videos). When a visitor comes in and mentions something they would like to find (like “a fire truck”), there are several steps that you undertake:
- You go through shelves of books and pull out every 10th book to save time.
- Next, you skim through each chosen book and take a snapshot of important pages (the video frames).
- Then, using a special tool, you label each snapshot based on the content (encoding with CLIP).
- When the visitor asks for their request, you also keep a mental note of what they said (encoding the search query).
- Finally, you find the snapshots that best represent the visitor’s request and present them!
In this analogy, you are the sophisticated AI, effectively managing and searching through a massive database of visual information to provide users with the specific moments they seek in videos!
Examples
To show how effective this search process is, here are some example searches that can be conducted:
- A fire truck!
- Road works!
- People crossing the street!
- The Embarcadero!
- Waiting at the red light!
- Green bike lane!
- A street with tram tracks!
- The Transamerica Pyramid!
Troubleshooting Ideas
If you encounter issues while setting up or running the search process, here are a few troubleshooting tips:
- Ensure that the YouTube video is downloadable without restrictions.
- Double-check that you are extracting frames at a consistent interval.
- Make sure you have the required libraries installed for running CLIP.
- Look at the console for any error messages; they will guide you on what went wrong.
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Other Related Projects
Besides searching on YouTube, you can also explore another project that allows you to search through 2M images on Unsplash using natural language queries: Natural Language Image Search.
Conclusion
Using natural language processing to search through YouTube videos opens up a world of possibilities for content discovery. With the step-by-step guide above, you can easily set up and run your own searches to find exactly what you’re looking for in vast video libraries.
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.