Learn how to enhance YouTube search functionality using OpenAI's Whisper, a state-of-the-art speech-to-text model. Explore the concept of improved search capabilities and build a solution using Whisper, transformers, and vector search. Discover how to download YouTube videos, transcribe audio, create sentence embeddings, and implement scalable vector search. Gain hands-on experience with tools like pytube, Sentence transformers, Pinecone vector database, Streamlit, and Hugging Face spaces. Follow along to create a more efficient YouTube search experience that allows users to find specific, concise answers within lengthy videos.

Syllabus

OpenAI's Whisper
Idea Behind Better Search
Downloading Audio for Whisper
Download YouTube Videos with Python
Speech-to-Text with OpenAI Whisper
Hugging Face Datasets and Preprocessing
Using a Sentence Transformer
Initializing a Vector Database
Build Embeddings and Vector Index
Asking Questions
Hugging Face Ask YouTube App