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arXiv · 2510.09016

DiTSinger: Scaling Singing Voice Synthesis with Diffusion Transformer and Implicit Alignment

Zongcai Du·Guilin Deng·Xiaofeng Guo·Xin Gao·Linke Li·Kaichang Cheng·Fubo Han·Siyu Yang·Peng Liu·Pan Zhong·Qiang Fu
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Abstract

Recent progress in diffusion-based Singing Voice Synthesis (SVS) demonstrates strong expressiveness but remains limited by data scarcity and model scalability. We introduce a two-stage pipeline: a compact seed set of human-sung recordings is constructed by pairing fixed melodies with diverse LLM-generated lyrics, and melody-specific models are trained to synthesize over 500 hours of high-quality Chinese singing data. Building on this corpus, we propose DiTSinger, a Diffusion Transformer with RoPE and qk-norm, systematically scaled in depth, width, and resolution for enhanced fidelity. Furthermore, we design an implicit alignment mechanism that obviates phoneme-level duration labels by constraining phoneme-to-acoustic attention within character-level spans, thereby improving robustness under noisy or uncertain alignments. Extensive experiments validate that our approach enables scalable, alignment-free, and high-fidelity SVS.

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Zongcai Du, Guilin Deng, Xiaofeng Guo, Xin Gao, Linke Li, Kaichang Cheng, Fubo Han, Siyu Yang, Peng Liu, Pan Zhong, Qiang Fu. 2025-10-10. DiTSinger: Scaling Singing Voice Synthesis with Diffusion Transformer and Implicit Alignment. https://arxiv.org/abs/2510.09016

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