README.md

metadata

title: Starry
emoji: ✨
colorFrom: blue
colorTo: yellow
sdk: docker
pinned: false
license: bsd
short_description: End-to-end Optical Music Recognition (OMR) demo
app_port: 7860

STARRY — Optical Music Recognition

Starry is a demo application for an end-to-end Optical Music Recognition (OMR) system. It transforms scanned sheet music images into structured digital music notation through a multi-stage ML pipeline, and provides an interactive platform for reviewing, editing, and managing the recognition results.

What Makes Starry Different

• Multi-stage ML pipeline — Seven specialized models work in sequence: layout detection, staff gauge prediction, foreground/background mask separation, symbol semantic recognition, text location detection, OCR, and bracket recognition. Each model focuses on a specific subtask, enabling high overall accuracy.
• Automatic regulation — An ONNX-based solver analyzes recognized measures for timing consistency and automatically corrects quantization errors, reducing the need for manual intervention.
• Measure-level quality tracking — The system identifies and flags problematic measures for human review, with an annotation workflow that tracks corrections per-measure.
• Real-time progress — WebSocket streaming keeps the UI updated during long-running recognition and regulation tasks.
• Score collection management — Organize recognized scores into music sets with tagging and categorization, useful for building OMR datasets or curating repertoire.

Architecture

Frontend (React)
  ↓ nginx (port 7860)
  ├─ /api/score → cluster-server (NestJS)
  └─ /api/*     → omr-service (Fastify)
                    ├─ async task worker
                    ├─ PostgreSQL
                    └─ ZMQ → Python ML services (×7)

Note

This is a lightweight demo deployment on HuggingFace Spaces. The ML prediction services (layout, gauge, mask, semantic, text, OCR, brackets) are not included in this Space — it is intended for showcasing the review and editing workflow with pre-computed results.