Offline & Secure
Fully local, audit-ready processing with complete data protection.
Structured Output
Chapter-based HTML with clickable timestamps and speaker attribution.
Contextual Sidenotes
LLM-generated observations for in-depth analysis and research.
R2 Mechanics is a modular, fully offline transcription system that transforms audio interviews or hearings into structured, annotated HTML documents – with speaker attribution, time markers, chapter navigation, and contextual notes.
❌ Cloud-based, no local control
❌ Limited transparency / auditability
❌ Generic text output without structure
❌ Privacy concerns for sensitive content
❌ No speaker attribution
❌ Difficult to integrate into archival workflows
R2 Mechanics
✅ Fully local, offline processing
✅ GPU-accelerated transcription with speaker attribution
✅ Clickable, chapter-based HTML output
✅ Customizable annotations and sidenotes
✅ Designed for archives, research and media
✅ Fast processing & delivery
✅ Optional integration of Stable Diffusion images and visuals
Unlike traditional archival tools like OHMS or ELAN (widely used in archives for manual transcript alignment and metadata tagging), which require extensive manual tagging, R2 Mechanics automates speaker separation, chapter structuring, and HTML export. Fully offline, fully auditable, and vendor-independent – ensuring your data remains accessible for decades without lock-in or cloud dependencies.