@inproceedings{jiang-etal-2026-muse,
title = "Muse: Towards Reproducible Long-Form Song Generation with Fine-Grained Style Control",
author = "Jiang, Changhao and
Chen, Jiahao and
Xiang, Zhenghao and
Yang, Zhixiong and
Wang, Hanchen and
Zhuang, Jiabao and
Che, Xinmeng and
Sun, Jiajun and
Li, Hui and
Cao, Yifei and
Dou, Shihan and
Zhang, Ming and
Ye, Junjie and
Ji, Tao and
Gui, Tao and
Zhang, Qi and
Huang, Xuanjing",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1129/",
pages = "22492--22512",
ISBN = "979-8-89176-395-1",
abstract = "Recent commercial systems such as Suno demonstrate strong capabilities in long-form song generation, while academic research remains largely non-reproducible due to the lack of publicly available training data, hindering fair comparison and progress. To this end, we release a fully open-source system for long-form song generation with fine-grained style conditioning, including a licensed synthetic dataset, training and evaluation pipelines, and Muse, an easy-to-deploy song generation model. The dataset consists of 116k fully licensed synthetic songs with automatically generated lyrics and style descriptions paired with audio synthesized by SunoV5. We train Muse via single-stage supervised finetuning of a Qwen-based language model extended with discrete audio tokens using MuCodec, without task-specific losses, auxiliary objectives, or additional architectural components. Our evaluations find that although Muse is trained with a modest data scale and model size, it achieves competitive performance on phoneme error rate, text{--}music style similarity, and audio aesthetic quality, while enabling controllable segment-level generation across different musical structures. All data, model weights, and training and evaluation pipelines will be publicly released, paving the way for continued progress in controllable long-form song generation research."
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<abstract>Recent commercial systems such as Suno demonstrate strong capabilities in long-form song generation, while academic research remains largely non-reproducible due to the lack of publicly available training data, hindering fair comparison and progress. To this end, we release a fully open-source system for long-form song generation with fine-grained style conditioning, including a licensed synthetic dataset, training and evaluation pipelines, and Muse, an easy-to-deploy song generation model. The dataset consists of 116k fully licensed synthetic songs with automatically generated lyrics and style descriptions paired with audio synthesized by SunoV5. We train Muse via single-stage supervised finetuning of a Qwen-based language model extended with discrete audio tokens using MuCodec, without task-specific losses, auxiliary objectives, or additional architectural components. Our evaluations find that although Muse is trained with a modest data scale and model size, it achieves competitive performance on phoneme error rate, text–music style similarity, and audio aesthetic quality, while enabling controllable segment-level generation across different musical structures. All data, model weights, and training and evaluation pipelines will be publicly released, paving the way for continued progress in controllable long-form song generation research.</abstract>
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%0 Conference Proceedings
%T Muse: Towards Reproducible Long-Form Song Generation with Fine-Grained Style Control
%A Jiang, Changhao
%A Chen, Jiahao
%A Xiang, Zhenghao
%A Yang, Zhixiong
%A Wang, Hanchen
%A Zhuang, Jiabao
%A Che, Xinmeng
%A Sun, Jiajun
%A Li, Hui
%A Cao, Yifei
%A Dou, Shihan
%A Zhang, Ming
%A Ye, Junjie
%A Ji, Tao
%A Gui, Tao
%A Zhang, Qi
%A Huang, Xuanjing
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F jiang-etal-2026-muse
%X Recent commercial systems such as Suno demonstrate strong capabilities in long-form song generation, while academic research remains largely non-reproducible due to the lack of publicly available training data, hindering fair comparison and progress. To this end, we release a fully open-source system for long-form song generation with fine-grained style conditioning, including a licensed synthetic dataset, training and evaluation pipelines, and Muse, an easy-to-deploy song generation model. The dataset consists of 116k fully licensed synthetic songs with automatically generated lyrics and style descriptions paired with audio synthesized by SunoV5. We train Muse via single-stage supervised finetuning of a Qwen-based language model extended with discrete audio tokens using MuCodec, without task-specific losses, auxiliary objectives, or additional architectural components. Our evaluations find that although Muse is trained with a modest data scale and model size, it achieves competitive performance on phoneme error rate, text–music style similarity, and audio aesthetic quality, while enabling controllable segment-level generation across different musical structures. All data, model weights, and training and evaluation pipelines will be publicly released, paving the way for continued progress in controllable long-form song generation research.
%U https://aclanthology.org/2026.findings-acl.1129/
%P 22492-22512
Markdown (Informal)
[Muse: Towards Reproducible Long-Form Song Generation with Fine-Grained Style Control](https://aclanthology.org/2026.findings-acl.1129/) (Jiang et al., Findings 2026)
ACL
- Changhao Jiang, Jiahao Chen, Zhenghao Xiang, Zhixiong Yang, Hanchen Wang, Jiabao Zhuang, Xinmeng Che, Jiajun Sun, Hui Li, Yifei Cao, Shihan Dou, Ming Zhang, Junjie Ye, Tao Ji, Tao Gui, Qi Zhang, and Xuanjing Huang. 2026. Muse: Towards Reproducible Long-Form Song Generation with Fine-Grained Style Control. In Findings of the Association for Computational Linguistics: ACL 2026, pages 22492–22512, San Diego, California, United States. Association for Computational Linguistics.