Overview
Rip YouTube transcripts to text files — built for RAG and LLM workflows.
rippr extracts transcripts from any YouTube video and saves them as clean, structured files — ready to drop into ChatGPT, Claude, RAG pipelines, or any LLM workflow. YouTube's built-in transcript is messy; Timestamps everywhere, broken formatting, fragmented text. rippr fixes that with one click. THREE OUTPUT FORMATS • RAG (.txt) — All text joined into a single continuous block, optimized for chunking and embedding. No timestamps, no clutter — just clean text your AI can process. • Structured (.json) — Every segment with precise start time, duration, and text. Full metadata included: title, channel, URL, language. Perfect for programmatic processing. • Markdown (.md) — Clean, readable format with headers and formatting. Great for documentation, notes, and sharing. HOW IT WORKS 1. Go to any YouTube video 2. Click the rippr button (appears automatically next to Share/Save) 3. Pick your format, toggle timestamps, select language 4. Download — done You can also use the popup: paste any YouTube URL without leaving your current tab. KEY FEATURES • Works on any YouTube video with captions • 80+ languages supported (every language YouTube offers) • Auto-generated caption detection (warns you about quality) • Optional timestamps in any format • Multi-language selector for multilingual videos • Multiple fallback strategies for reliable extraction PRIVACY rippr runs entirely in your browser. No data is collected, stored, or sent to any server. No accounts, no tracking, no analytics. Your transcripts are downloaded directly to your machine. Free and open source: github.com/mrslbt/rippr
Details
- Version2.0.0
- UpdatedApril 1, 2026
- Size38.11KiB
- LanguagesEnglish
- Developer
Email
marsellbait@gmail.com - Non-traderThis developer has not identified itself as a trader. For consumers in the European Union, please note that consumer rights do not apply to contracts between you and this developer.
Privacy
This developer declares that your data is
- Not being sold to third parties, outside of the approved use cases
- Not being used or transferred for purposes that are unrelated to the item's core functionality
- Not being used or transferred to determine creditworthiness or for lending purposes
