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Seed-Music: A Unified Framework for High Quality and Controlled Music Generation

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arxiv 2409.09214 v3 pith:EQN37Z7I submitted 2024-09-13 cs.SD eess.AS

Ye Bai , Haonan Chen , Jitong Chen , Zhuo Chen , Yi Deng , Xiaohong Dong , Lamtharn Hantrakul , Weituo Hao
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Qingqing Huang Zhongyi Huang Dongya Jia Feihu La Duc Le Bochen Li Chumin Li Hui Li Xingxing Li Shouda Liu Wei-Tsung Lu Yiqing Lu Andrew Shaw Janne Spijkervet Yakun Sun Bo Wang Ju-Chiang Wang Yuping Wang Yuxuan Wang Ling Xu Yifeng Yang Chao Yao Shuo Zhang Yang Zhang Yilin Zhang Hang Zhao Ziyi Zhao Dejian Zhong Shicen Zhou Pei Zou
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classification cs.SDeess.AS
keywords musicgenerationseed-musicaudiocontrollededitingdoubaoframework
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We introduce Seed-Music, a suite of music generation systems capable of producing high-quality music with fine-grained style control. Our unified framework leverages both auto-regressive language modeling and diffusion approaches to support two key music creation workflows: controlled music generation and post-production editing. For controlled music generation, our system enables vocal music generation with performance controls from multi-modal inputs, including style descriptions, audio references, musical scores, and voice prompts. For post-production editing, it offers interactive tools for editing lyrics and vocal melodies directly in the generated audio. We encourage readers to listen to demo audio examples at https://team.doubao.com/seed-music "https://team.doubao.com/seed-music".

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Forward citations

Cited by 4 Pith papers

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  1. Stable Autoregressive Speech Generation with Low-Frame-Rate High-Dimensional Continuous Tokens

    eess.AS 2026-07 conditional novelty 6.0 of 10

    Autoregressive TTS from 8-Hz, 768-dimensional continuous tokens works when the tokenizer shapes its latent space with a low-dimensional core and an energy hierarchy, and the generator separates guidance into local, se...

  2. LaDA-Band: Language Diffusion Models for Vocal-to-Accompaniment Generation

    cs.SD 2026-04 unverdicted novelty 6.0 of 10

    LaDA-Band generates complete vocal-aligned instrumental accompaniments with discrete masked diffusion, claiming simultaneous gains in audio fidelity, long-range coherence, and orchestration quality.

  3. Auto-Regressive vs Flow-Matching: a Comparative Study of Modeling Paradigms for Text-to-Music Generation

    cs.SD 2025-06 conditional novelty 6.0 of 10

    Under matched training conditions, auto-regressive models slightly outperform flow-matching on music quality and temporal control, while flow-matching offers faster inference and better inpainting flexibility.

  4. Towards Hallucination-Free Music: A Reinforcement Learning Preference Optimization Framework for Reliable Song Generation

    cs.SD 2025-08 conditional novelty 5.0 of 10

    PER-based preference optimization (DPO, PPO, GRPO) reduces lyric-to-song hallucination in an audio language model, with the largest gains from DPO plus reject sampling.

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