5 Commits

Author	SHA1	Message	Date
Claude	02c70f90c8	Extract audio from video files before loading ... Video files (MP4, MKV, etc.) are now processed with ffmpeg to extract audio to a temp WAV file before loading into wavesurfer. This prevents the WebView crash caused by trying to fetch multi-GB files into memory. - New extract_audio Tauri command uses ffmpeg (sidecar-bundled or system) - Frontend detects video extensions and extracts audio automatically - User-friendly error if ffmpeg is not installed with install instructions - Reverted wavesurfer MediaElement approach in favor of clean extraction - Added FFmpeg install guide to USER_GUIDE.md Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-22 20:04:10 -07:00
Claude	35173c54ce	Update README, add User Guide and Contributing docs ... - README: Updated to reflect current architecture (decoupled app/sidecar), Ollama as local AI, CUDA support, split CI workflows - USER_GUIDE.md: Complete how-to including first-time setup, transcription workflow, speaker detection setup, Ollama configuration, export formats, keyboard shortcuts, and troubleshooting - CONTRIBUTING.md: Dev setup, project structure, conventions, CI/CD overview Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-22 12:06:13 -07:00
Josh Knapp	503cc6c0cf	Phase 1 foundation: Tauri shell, Python sidecar, SQLite database ... Tauri v2 + Svelte + TypeScript frontend: - App shell with workspace layout (waveform, transcript, speakers, AI chat) - Placeholder components for all major UI areas - Typed stores (project, transcript, playback, AI) - TypeScript interfaces matching the database schema - Tauri bridge service with typed invoke wrappers - svelte-check passes with 0 errors Rust backend: - Tauri v2 app entry point with command registration - SQLite database layer (rusqlite with bundled SQLite) - Full schema: projects, media_files, speakers, segments, words, ai_outputs, annotations (with indexes) - Model structs with serde serialization - CRUD queries for projects, speakers, segments, words - Segment text editing preserves original text - Schema versioning for future migrations - 6 tests passing - Command stubs for project, transcribe, export, AI, settings, system - App state management Python sidecar: - JSON-line IPC protocol (stdin/stdout) - Message types: IPCMessage, progress, error, ready - Handler registry with routing and error handling - Ping/pong handler for connectivity testing - Service stubs: transcribe, diarize, pipeline, AI, export - Provider stubs: local (llama-server), OpenAI, Anthropic, LiteLLM - Hardware detection stubs - 14 tests passing, ruff clean Also adds: - Testing strategy document (docs/TESTING.md) - Validation script (scripts/validate.sh) - Updated .gitignore for Svelte, Rust, Python artifacts Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-02-26 15:16:06 -08:00
Josh Knapp	c450ef3c0c	Switch local AI from Ollama to bundled llama-server, add MIT license ... - Replace Ollama dependency with bundled llama-server (llama.cpp) so users need no separate install for local AI inference - Rust backend manages llama-server lifecycle (spawn, port, shutdown) - Add MIT license for open source release - Update architecture doc, CLAUDE.md, and README accordingly Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-02-26 09:00:47 -08:00
Josh Knapp	0edb06a913	Add architecture document and project guidelines ... Detailed architecture covering Tauri + Svelte frontend, Rust backend, Python sidecar for ML (faster-whisper, pyannote.audio), IPC protocol, SQLite schema, AI provider system, export formats, and phased implementation plan with agent work breakdown. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-02-26 08:37:45 -08:00