Josh Knapp
0edb06a913
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>
44 lines
1.9 KiB
Markdown
44 lines
1.9 KiB
Markdown
# Voice to Notes — Project Guidelines
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## Project Overview
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Desktop app for transcribing audio/video with speaker identification. Runs locally on user's computer. See `docs/ARCHITECTURE.md` for full architecture.
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## Tech Stack
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- **Desktop shell:** Tauri v2 (Rust backend + Svelte/TypeScript frontend)
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- **ML pipeline:** Python sidecar process (faster-whisper, pyannote.audio, wav2vec2)
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- **Database:** SQLite (via rusqlite in Rust)
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- **AI providers:** LiteLLM, OpenAI, Anthropic, Ollama (local)
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- **Caption export:** pysubs2 (Python)
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- **Audio UI:** wavesurfer.js
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- **Transcript editor:** TipTap (ProseMirror)
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## Key Architecture Decisions
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- Python sidecar communicates with Rust via JSON-line IPC (stdin/stdout)
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- All ML models must work on CPU. GPU (CUDA) is optional acceleration.
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- AI cloud providers are optional. Local models (Ollama) are a first-class option.
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- SQLite database is per-project, stored alongside media files.
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- Word-level timestamps are required for click-to-seek playback sync.
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## Directory Structure
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```
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src/ # Svelte frontend source
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src-tauri/ # Rust backend source
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python/ # Python sidecar source
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voice_to_notes/ # Python package
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tests/ # Python tests
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docs/ # Architecture and design documents
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```
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## Conventions
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- Rust: follow standard Rust conventions, use `cargo fmt` and `cargo clippy`
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- Python: Python 3.11+, use type hints, follow PEP 8, use `ruff` for linting
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- TypeScript: strict mode, prefer Svelte stores for state management
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- IPC messages: JSON-line format, each message has `id`, `type`, `payload` fields
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- Database: UUIDs as primary keys (TEXT type in SQLite)
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- All timestamps in milliseconds (integer) relative to media file start
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## Platform Targets
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- Linux (primary development target)
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- Windows (must work, tested before release)
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- macOS (future, not yet targeted)
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