8 papers
Beyond Reconstruction: Full-Context Generative DiT for Music Generation
Yunjia Li, Menglin Wu, Junyu Dai +13
Hybrid music generators combine the long-range planning of an autoregressive language model with the fidelity of a diffusion- or flow-based acoustic renderer. Yet renderers are tra…
Qwen-Audio-3.0-Gen-Preview Technical Report
Junyu Dai, Xiaoyue Duan, Xinyue Fan +14
Existing single-domain and multi-task audio systems remain limited in directly organizing heterogeneous audio components, ambience, and multiple roles into long-form temporal scene…
Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering
Junyu Dai, Xinyue Fan, Weiqin Li +14
In this report, we present a unified song generation framework capable of producing high-quality full-length music from lyrics, text descriptions, and musical attributes. The propo…
LeVo 2: Stable and Melodious Song Generation via Hierarchical Representation Modeling and Progressive Post-Training
Shun Lei, Huaicheng Zhang, Dapeng Wu +8
Full-length song generation must preserve coherence and musicality, render detailed vocal and accompaniment acoustics, and follow lyrics and prompts. Existing language model-based…
SongBench: A Fine-Grained Multi-Aspect Benchmark for Song Quality Assessment
Dapeng Wu, Shun Lei, Wei Tan +5
Recent advancements in Text-to-Song generation have enabled realistic musical content production, yet existing evaluation benchmarks lack the professional granularity to capture mu…
SongPrep: A Preprocessing Framework and End-to-end Model for Full-song Structure Parsing and Lyrics Transcription
Wei Tan, Shun Lei, Huaicheng Zhang +6
Artificial Intelligence Generated Content (AIGC) is currently a popular research area. Among its various branches, song generation has attracted growing interest. Despite the abund…