Qwen-Audio-3.0-Gen-Preview Technical Report

Junyu Dai; Xiaoyue Duan; Xinyue Fan; Yihan Feng; Jingbei Li; Xiangang Li; Yunjia Li; Lejun Min; Yufei Shi; Xingchen Song; Yiran Wang; Cheng Wen; Menglin Wu; Bajian Xiang; Huaicheng Zhang; Han Zhao; Ruichen Zheng

arXiv · 2607.27011

Qwen-Audio-3.0-Gen-Preview Technical Report

Junyu Dai·Xiaoyue Duan·Xinyue Fan·Yihan Feng·Jingbei Li·Xiangang Li·Yunjia Li·Lejun Min·Yufei Shi·Xingchen Song·Yiran Wang·Cheng Wen·Menglin Wu·Bajian Xiang·Huaicheng Zhang·Han Zhao·Ruichen Zheng

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Abstract

Existing single-domain and multi-task audio systems remain limited in directly organizing heterogeneous audio components, ambience, and multiple roles into long-form temporal scenes. We present Qwen-Audio-3.0-Gen-Preview, a unified non-autoregressive framework that uses a Diffusion Transformer (DiT) and a shared variational autoencoder (VAE) to generate the complete mixed waveform. Prompt enhancement converts free-form requests into structured temporal records that are rendered as textual conditions, while a two-stage data curriculum and semantic conditional views train the proposed model to use these conditions across standalone and mixed-scene audio. A shared continuous VAE compresses 48kHz stereo waveforms into 25Hz latent sequences and incorporates semantic supervision, providing one representation for heterogeneous audio. On the public reference-conditioned benchmark, speaker similarity is the proposed model's clearest strength across all three subsets. Across the multi-speaker and rich-timeline benchmarks, its clearest comparative strengths are cross-turn consistency in both languages and temporal localization, respectively. On AudioCaps, its advantages are concentrated in evaluations using large audio-language models and AudioBox. These results demonstrate the potential of unified generation for temporally structured audio without task-specific branches.

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BibTeX RIS

Junyu Dai, Xiaoyue Duan, Xinyue Fan, Yihan Feng, Jingbei Li, Xiangang Li, Yunjia Li, Lejun Min, Yufei Shi, Xingchen Song, Yiran Wang, Cheng Wen, Menglin Wu, Bajian Xiang, Huaicheng Zhang, Han Zhao, Ruichen Zheng. 2026-07-30. Qwen-Audio-3.0-Gen-Preview Technical Report. https://arxiv.org/abs/2607.27011

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