Existing methods for expressive music performance rendering rely on supervised learning over small labeled datasets, which limits scaling of both data volume and model size, despite the availability of vast unlabeled music, as in vision and language. To address this gap, we introduce Pianist Transformer, with four key contributions: 1) a unified Musical Instrument Digital Interface (MIDI) data representation for learning the shared principles of musical structure and expression without explicit annotation; 2) an efficient asymmetric architecture, enabling longer contexts and faster inference without sacrificing rendering quality; 3) a self-supervised pre-training pipeline with 10B tokens and 135M-parameter model, unlocking data and model scaling advantages for expressive performance rendering; 4) a state-of-the-art performance model, which achieves strong objective metrics and human-level subjective ratings. Overall, Pianist Transformer establishes a scalable path toward human-like performance synthesis in the music domain.

翻译：现有的富有表现力的音乐演奏渲染方法依赖于对小型标注数据集进行监督学习，这限制了数据量和模型规模的扩展，尽管在视觉和语言领域已有大量未标注音乐可用。为填补这一空白，我们提出了Pianist Transformer，其包含四个关键贡献：1）一种统一的乐器数字接口（MIDI）数据表示，用于学习音乐结构和表达的共享原理，无需显式标注；2）一种高效的非对称架构，能够在保持渲染质量的同时支持更长上下文和更快推理；3）一个包含100亿个标记和1.35亿参数模型的自监督预训练流程，为富有表现力的演奏渲染解锁了数据和模型规模优势；4）一个达到先进水平的演奏模型，在客观指标和主观评分方面均表现出色，达到人类水平。总体而言，Pianist Transformer为音乐领域实现类人演奏合成开辟了一条可扩展的路径。