rin.kagamine
3500c73167
- Convert generate_waveform_svg() from sync to async (subprocess.run -> asyncio.create_subprocess_exec) - Add early downsampling to ~2000 samples before numpy processing (~95% memory reduction for 120s tracks) - Pre-generate waveform on clip completion in _generate_and_save() - Add Cache-Control headers (public, max-age=86400) to waveform endpoint - Preserve on-demand generation as lazy fallback
Lyria Studio
A webapp for generating, playing, and storing music clips using Google's Lyria music generation models.
Built by Kagamine Rin & Ryan.
Quick Start
# Backend
cd backend
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
cd src
uvicorn main:app --reload
# Frontend (new terminal)
cd frontend
npm install
npm run dev
Architecture
- Backend: FastAPI (Python) — async Lyria API client, SQLite metadata, file storage
- Frontend: React + Vite + TypeScript + Tailwind — dark theme, audio player
- Storage: SQLite for clip metadata, local filesystem for MP3 files
API
| Endpoint | Method | Description |
|---|---|---|
/api/health |
GET | Health check |
/api/generate |
POST | Start clip generation |
/api/clips |
GET | List all clips |
/api/clips/{id} |
GET | Get clip metadata |
/api/clips/{id}/audio |
GET | Stream MP3 |
/api/clips/{id} |
DELETE | Delete clip |
Models
- v0.1: Lyria 3 Clip Preview (30s, REST)
- v0.2: Lyria 3 Pro (structure-aware, ~3min)
- v0.3: Lyria RealTime (WebSocket streaming)
License
MIT — Hardcore will never die.