58faa83cb3
- PyInstaller frozen sidecar: spec file, build script, and ffmpeg path resolver for self-contained distribution without Python prerequisites - Dual-mode sidecar launcher: frozen binary (production) with dev mode fallback - Parallel transcription + diarization pipeline (~30-40% faster) - GPU auto-detection for diarization (CUDA when available) - Async run_pipeline command for real-time progress event delivery - Web Audio API backend for instant playback and seeking - OpenAI-compatible provider replacing LiteLLM client-side routing - Cross-platform RAM detection (Linux/macOS/Windows) - Settings: speaker count hint, token reveal toggles, dark dropdown styling - Loading splash screen, flexbox layout fix for viewport overflow - Gitea Actions CI/CD pipeline (Linux, Windows, macOS ARM) - Updated README and CLAUDE.md documentation Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
53 lines
2.4 KiB
Markdown
53 lines
2.4 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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- **Local AI:** Bundled llama-server (llama.cpp) — default, no install needed
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- **Cloud AI providers:** OpenAI, Anthropic, OpenAI-compatible endpoints (optional, user-configured)
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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. Bundled llama-server (llama.cpp) is the default local AI — no separate install needed.
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- Rust backend manages llama-server lifecycle (start/stop/port allocation).
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- Project is open source (MIT license).
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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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## Distribution
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- Python sidecar is frozen via PyInstaller into a standalone binary for distribution
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- Tauri bundles the sidecar via `externalBin` — no Python required for end users
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- CI/CD builds on Gitea Actions (Linux, Windows, macOS ARM)
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- Dev mode uses system Python (`VOICE_TO_NOTES_DEV=1` or debug builds)
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## Platform Targets
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- Linux x86_64 (primary development target)
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- Windows x86_64
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- macOS aarch64 (Apple Silicon)
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