{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/uniaudio-an-audio-foundation-model-toward-1","title":"UniAudio: An Audio Foundation Model Toward Universal Audio Generation","arxiv_id":"2310.00704","date":"2023-10-01","proceeding":null,"authors":["Dongchao Yang","Jinchuan Tian","Xu Tan","Rongjie Huang","Songxiang Liu","Xuankai Chang","Jiatong Shi","Sheng Zhao","Jiang Bian","Zhou Zhao","Xixin Wu","Helen Meng"],"abstract":"Large Language models (LLM) have demonstrated the capability to handle a variety of generative tasks. This paper presents the UniAudio system, which, unlike prior task-specific approaches, leverages LLM techniques to generate multiple types of audio (including speech, sounds, music, and singing) with given input conditions. UniAudio 1) first tokenizes all types of target audio along with other condition modalities, 2) concatenates source-target pair as a single sequence, and 3) performs next-token prediction using LLM. Also, a multi-scale Transformer model is proposed to handle the overly long sequences caused by the residual vector quantization based neural codec in tokenization. Training of UniAudio is scaled up to 165K hours of audio and 1B parameters, based on all generative tasks, aiming to obtain sufficient prior knowledge not only in the intrinsic properties of audio but also the inter-relationship between audio and other modalities. Therefore, the trained UniAudio model has the potential to become a foundation model for universal audio generation: it shows strong capability in all trained tasks and can seamlessly support new audio generation tasks after simple fine-tuning. Experiments demonstrate that UniAudio achieves state-of-the-art or at least competitive results on most of the 11 tasks. Demo and code are released at https://github.com/yangdongchao/UniAudio","url_abs":"https://arxiv.org/abs/2310.00704v6","url_pdf":"https://arxiv.org/pdf/2310.00704v6.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"uniaudio-an-audio-foundation-model-toward-1","repo_url":"https://github.com/yangdongchao/uniaudio","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"uniaudio-an-audio-foundation-model-toward-1","repo_url":"https://github.com/yangdongchao/UniAudio_demo","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/text-to-music-generation-on-musiccaps","task":"Text-to-Music Generation","dataset":"MusicCaps","model":"UniAudio","rank_in_archive_order":12,"of":21,"metrics":{"FAD":"3.65","KL_passt":"1.87"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2310.00704","atlas_url":"https://app.syntology.ai/?focus=2310.00704","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.00704"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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