STARS: A Unified Framework for Singing Transcription, Alignment, and Refined Style Annotation

TL;DR

STARS is a comprehensive framework that automates singing transcription, alignment, and style annotation, significantly improving dataset creation and controllable singing voice synthesis.

Contribution

It introduces the first unified system for multi-level singing annotation, combining transcription, alignment, and style characterization in a hierarchical, non-autoregressive architecture.

Findings

Outperforms existing annotation methods in accuracy and robustness.

Enhances singing voice synthesis with better style control and naturalness.

Enables scalable creation of high-quality singing datasets.

Abstract

Recent breakthroughs in singing voice synthesis (SVS) have heightened the demand for high-quality annotated datasets, yet manual annotation remains prohibitively labor-intensive and resource-intensive. Existing automatic singing annotation (ASA) methods, however, primarily tackle isolated aspects of the annotation pipeline. To address this fundamental challenge, we present STARS, which is, to our knowledge, the first unified framework that simultaneously addresses singing transcription, alignment, and refined style annotation. Our framework delivers comprehensive multi-level annotations encompassing: (1) precise phoneme-audio alignment, (2) robust note transcription and temporal localization, (3) expressive vocal technique identification, and (4) global stylistic characterization including emotion and pace. The proposed architecture employs hierarchical acoustic feature processing across frame, word, phoneme, note, and sentence levels. The novel non-autoregressive local acoustic encoders enable structured hierarchical representation learning. Experimental validation confirms the framework's superior performance across multiple evaluation dimensions compared to existing annotation approaches. Furthermore, applications in SVS training demonstrate that models utilizing STARS-annotated data achieve significantly enhanced perceptual naturalness and precise style control. This work not only overcomes critical scalability challenges in the creation of singing datasets but also pioneers new methodologies for controllable singing voice synthesis. Audio samples are available at https://gwx314.github.io/stars-demo/.