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arXiv · 2607.27768

VocalRender: Score-Native Singing Voice Synthesis for Real-World Composition

Yukun Chen·Tianrui Wang·Zhaoxi Mu·Xinyu Yang·EngSiong Chng
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Abstract

Existing singing voice synthesis systems often require predefined durations, explicit duration prediction, or time-aligned acoustic guidance, which limits their compatibility with practical composition workflows. We propose VocalRender, a score-native system that directly synthesizes singing from lyrics, pitches, symbolic note values, and tempo. It uses an interleaved lyric--note representation and an autoregressive diffusion model to generate continuous acoustic latents while predicting the output length, eliminating the need for explicit duration prediction. Trained on a 2,300-hour singing dataset, VocalRender achieves strong intelligibility, strong melody control, and high speaker similarity across both in-domain and out-of-domain benchmarks. Notably, it outperforms the strongest baseline by $0.42$ points in naturalness CMOS, demonstrating the effectiveness of our proposed score-native architecture.

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Yukun Chen, Tianrui Wang, Zhaoxi Mu, Xinyu Yang, EngSiong Chng. 2026-07-30. VocalRender: Score-Native Singing Voice Synthesis for Real-World Composition. https://arxiv.org/abs/2607.27768

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