Introduction

YuE (pronounced "yeah") is an open-source foundation model for full-song music generation — an open answer to closed systems like Suno.ai. From a set of lyrics and a genre or style prompt, YuE generates a complete, several-minute song with coordinated vocal and instrumental tracks. Developed by a research collaboration anchored at HKUST and the M-A-P community, the project has crossed 6,200 GitHub stars and become a reference point for anyone studying or building open lyrics-to-music systems, precisely because it ships the architecture, weights, and recipes that commercial alternatives keep closed.

What It Is

YuE is an LLM-based, two-stage music generation system:

• Stage 1 (YuE-s1): A 7B-parameter language model that generates semantic music tokens from the lyrics and conditioning prompt, establishing melody, structure, and vocal content.
• Stage 2 (YuE-s2): A 1B-parameter model that refines and upsamples the Stage 1 output into higher-fidelity acoustic tokens.

This decomposition mirrors how large language models separate planning from realization: the big model decides what the song should be, and the smaller model renders it cleanly. An additional YuE-upsampler handles further audio refinement.

Key Capabilities

Lyrics-to-Song Generation

YuE takes written lyrics and produces a full song in which the generated vocals actually sing those lyrics over a matching instrumental backing — not a hummed melody or an instrumental loop, but a coordinated vocal-plus-accompaniment mix.

Two Inference Modes

The model supports Chain-of-Thought (CoT) reasoning for generation from scratch and In-Context Learning (ICL) for conditioning on a reference. ICL mode enables voice cloning and music style transfer, letting a user steer the output toward the timbre or genre of a provided audio sample.

Single- and Dual-Track Control

Generation supports single-track output (full mix, vocal-only, or instrumental-only) as well as dual-track ICL modes, giving producers separable stems to work with rather than a single baked render.

Multilingual, Multi-Genre Coverage

YuE generates across English, Mandarin, Cantonese, Japanese, and Korean, spanning a range of musical genres rather than a single style, which makes it useful well beyond English-language pop.

Model Variants

The project ships six primary checkpoints covering its supported languages (English, Japanese/Korean, and Mandarin) in both CoT and ICL modes, plus the dedicated upsampler model. All weights are distributed through Hugging Face.

Hardware Requirements

Full-song generation is computationally heavy. A 24GB GPU can handle up to roughly two sessions — for example, one verse and one chorus — while generating complete multi-section songs (four or more sessions) calls for 80GB-class hardware such as an H800 or A100, or a multi-GPU RTX 4090 setup. For throughput context, an H800 generates about 30 seconds of music in 150 seconds, while an RTX 4090 takes around 360 seconds for the same output.

License and Ecosystem

YuE's models and weights were released under the Apache-2.0 license in January 2025, permitting commercial use. The maintainers explicitly encourage artists and creators to sample, incorporate, and even monetize the outputs, recommending attribution to "YuE by HKUST/M-A-P." The work is backed by a research coalition including HKUST, M-A-P, Moonshot AI, ByteDance, 01.ai, and Geely, with an accompanying technical report published on arXiv in March 2025.

Why It Matters

Full-song generation with intelligible, on-lyric vocals has been one of the most visible capability gaps between open-source and commercial music AI. Tools like Suno demonstrated the demand, but their closed nature left researchers and builders unable to study, fine-tune, or self-host the technology. YuE narrows that gap with a transparent, permissively licensed architecture that produces vocals-plus-instrumentals end to end — making it a foundation others can extend rather than a black-box API.

Limitations

The headline constraint is compute: producing a complete song requires high-end accelerators and substantial generation time, which puts real-time or consumer-laptop use out of reach for now. Quality varies across the supported languages and genres, with less-represented styles receiving weaker coverage than the flagship English output. As with any expressive vocal-synthesis system, the voice-cloning capability in ICL mode carries clear potential for misuse, and outputs still benefit from human curation and post-production before release.