Global: September 17, 2026 · Text-to-Audio: September 20, 2026
YuE2 brings frontier song quality to music generation with an editable composition. Give it lyrics and a style prompt: it writes a melody-and-chord plan, then realizes that plan as a complete song with vocals and accompaniment.
Frontier quality. YuE2 is competitive with Suno v5/v6 on WildSongBench. YuE2 (best-of-8) achieves 6.9632 SongBench Avg, the highest observed mean among all evaluated settings.
White-box music generation through symbolic planning. Read, play, and change the composition before rendering it. Melody and chords become explicit controls that a person or an agent can inspect and edit.
Zero-shot covers and agentic editing. Reimagine a transcribed song in a new style, or refine a song through a conversation about its score, arrangement, and lyrics—all with the same generation checkpoint.
192 WildSongBench prompts. Both YuE2 settings use symbolic planning. Bo8 = best-of-8. The axes are normalized comparison indices; bubble area represents AudioBox production quality. Scores and evaluation protocol. Vector PDF · SVG.
News
🎧 YuE2 needs your ears. We're running a public blind listening study comparing YuE2 with leading proprietary music generation systems. Hear anonymous clips and choose A, B, or a tie. Listen & vote → · No account needed; headphones recommended.
🚀 Try YuE2 online for free. Create a song in your browser with the NOIZ-hosted demo →. No installation required.
The agentic demo follows The Last Train through 9 steps and 14 versions, from Mandarin pop to English jazz with new harmony and a saxophone solo. Listen to each version and inspect its conversation, score, prompt, and lyrics.
How it works
One AR–NAR Mixture-of-Transformers backbone predicts the score and semantic tokens autoregressively, then generates acoustic latents with flow matching. A VAE decodes those latents into stereo audio. Creation, covering, and editing differ in where the score comes from: YuE2, a transcribed recording, or an edited composition.
The staged Python API exposes plan() → generate_semantic() → synthesize() → decode(). See the generation guide for exact-plan reuse and decoder selection.
Quick start
Linux · Python 3.12 · NVIDIA GPU with BF16 support and 24 GB VRAM. YuE2 produces 48 kHz stereo audio without quantization. Model files download from Hugging Face on first use.
Open outputs/first-song/audio.flac. The output directory also retains the score, semantic tokens, acoustic latents, generation settings, and model identities.
The Python interface is equally short:
import json
from pathlib import Path
from yue2 import YuE2Pipeline
request = json.loads(Path("examples/song.json").read_text(encoding="utf-8"))
with YuE2Pipeline.from_pretrained("m-a-p/YuE2-3B", device="cuda") as pipe:
song = pipe(**request)
song.save_artifacts("outputs/my-song")
print(song.truncated)
Setting
Behavior
cot="full"
Generate an editable melody-and-chord plan; the default for new songs
cot="melody"
Use a melody plan with free accompaniment; recommended for covers
Transcribe a source recording with 🤗 SheetSage2, review its melody ABC, and provide new lyrics or a target style. For covers, use cot="melody" and a score without chord symbols so the accompaniment can adapt to the new style.
from pathlib import Path
from yue2 import YuE2Pipeline
with YuE2Pipeline.from_pretrained("m-a-p/YuE2-3B", device="cuda") as pipe:
cover = pipe(
style="English, jazz-funk, warm lead vocal, Rhodes, bass and drums",
lyrics=Path("cover-lyrics.txt").read_text(encoding="utf-8"),
abc=Path("cover-score/score.abc").read_text(encoding="utf-8"),
cot="melody",
seed=42,
)
cover.save_artifacts("outputs/cover")
SheetSage2 runs in a separate environment and loads its MERT2 encoder automatically. The cover guide gives the complete transcription and generation commands. An included original melody example also lets you try score-conditioned generation immediately.
Edit a composition
Export a plan, revise the musical details, and render the edited score:
import json
from pathlib import Path
from yue2 import YuE2Pipeline
request = json.loads(Path("examples/song.json").read_text(encoding="utf-8"))
with YuE2Pipeline.from_pretrained("m-a-p/YuE2-3B", device="cuda") as pipe:
plan = pipe.plan(**request)
plan.save("outputs/plan")
Copy outputs/plan/score.abc to edited.abc, then ask an agent to change its harmony, melody, tempo, or form. Supply the edited file as a new score:
The editable score is the white-box interface: you can inspect the intended composition and intervene on it. Editing generates a new complete recording; it does not preserve the original waveform outside an edit. Editing guide and a reproducible harmony example.
Agent skill
The yue2-music skill teaches an agent how to generate songs, transcribe and cover recordings, edit ABC scores, check musical invariants, and organize listening comparisons. It includes portable helpers and references to the released model interfaces.
Use skills/yue2-music/ from this repository with an agent that supports SKILL.md packages. Install it using your agent's skill-directory or import mechanism; the Python runtime is installed separately with pip install .. The earlier v0.1.6 skill ZIP remains available under its bundled license.
Try a concrete request:
Use the yue2-music skill to create an English piano-pop song. Keep the original audio and score. Make a second version with jazz harmony, preserve the vocal melody and lyric order, and give me both versions to compare.
Benchmarks
WildSongBench: 192 prompts, automatic evaluation, September 12, 2026.
System / setting
SongBench Avg ↑
AudioBox PQ ↑
MuLan ↑
PER ↓
YuE2 (best-of-8) †
6.9632
8.2714
0.5051
9.79%
Mureka 9
6.9377
8.0226
0.4394
11.69%
Suno v5
6.8721
8.1698
0.5428
8.10%
YuE2 †
6.7316
8.2598
0.5068
8.44%
Suno v5.5
6.7150
8.1955
0.5089
5.96%
Suno v4.5
6.6995
8.2541
0.5022
5.80%
Suno v6
6.5562
8.1296
0.4916
7.58%
Suno v6 Wild
6.4195
8.1785
0.4999
7.45%
LeVo 2 †
6.3247
8.3966
0.3542
26.12%
MiniMax Music 2.6
6.3222
8.1711
0.4251
24.55%
MiniMax Music 3 †
6.2830
8.2825
0.3928
6.27%
HeartMuLa †
6.2483
8.2933
0.3823
10.71%
Muse †
6.0349
8.0517
0.3937
33.42%
ACE-Step 1.5 †
6.0118
8.0518
0.4372
7.46%
DiffRhythm 2 †
5.2428
7.9782
0.3782
18.41%
YuE 1 †
4.9165
7.8683
0.2623
36.38%
SongBloom †
4.2350
8.1539
0.2697
19.19%
† Publicly available model weights. All 17 evaluated settings are shown, sorted by SongBench Avg; bold values mark the best result in each column.
Both YuE2 settings use symbolic planning and the benchmark decoder, YuE2-Vae-legacy. Standard YuE2 selects from two candidates; best-of-8 selects from eight. Rankings vary by metric; the small gap between the highest means does not establish statistical significance. Full results and selection protocols.
Zero-shot covers. On 948 works, full-score YuE2 reaches 0.647 CLEWS mAP, compared with 0.006 without a score, while using the general generator without cover-specific fine-tuning. Source-identity preservation and target-style quality are measured separately; melody-only covers offer more freedom to change the arrangement. Cover evaluation.
Reproduce the benchmarks
To reproduce the reported benchmark scores, follow the instructions on 🤗 WildSongBench (WSB).
MERT2
State-of-the-art music understanding: SOTA on 14 of 15 MARBLE metrics, with 91.72% genre accuracy on GTZAN.
Free to use YuE2 and monetize generated outputs, with no fees or royalties payable to us.
Academic research and education
Free for non-commercial use.
Commercial use by companies
Contact us to discuss a commercial license for the model weights.
We strongly encourage crediting YuE2 or using #YuE2 when sharing generated work; attribution is optional.
Responsible use. The additional creator permission prohibits illegal, harmful, deceptive, or unethical use. YuE2 is provided as is, without warranties. Users are responsible for their inputs, outputs, and use; liability limits are set out in the full terms.
Copyright (c) 2026 the YuE2 authors. Third-party components and earlier releases retain their respective licenses.
Citation
The YuE2 technical report is coming soon. For now, please cite MERT and YuE:
@article{li2023mert,
title = {{MERT}: Acoustic Music Understanding Model with Large-Scale Self-supervised Training},
author = {Li, Yizhi and Yuan, Ruibin and Zhang, Ge and Ma, Yinghao and Chen, Xingran and Yin, Hanzhi and Xiao, Chenghao and Lin, Chenghua and Ragni, Anton and Benetos, Emmanouil and Gyenge, Norbert and Dannenberg, Roger and Liu, Ruibo and Chen, Wenhu and Xia, Gus and Shi, Yemin and Huang, Wenhao and Wang, Zili and Guo, Yike and Fu, Jie},
journal = {arXiv preprint arXiv:2306.00107},
year = {2023},
eprint = {2306.00107},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2306.00107}
}
@article{yuan2025yue,
title = {{YuE}: Scaling Open Foundation Models for Long-Form Music Generation},
author = {Yuan, Ruibin and Lin, Hanfeng and Guo, Shuyue and Zhang, Ge and Pan, Jiahao and Zang, Yongyi and Liu, Haohe and Liang, Yiming and Ma, Wenye and Du, Xingjian and Du, Xinrun and Ye, Zhen and Zheng, Tianyu and Jiang, Zhengxuan and Ma, Yinghao and Liu, Minghao and Tian, Zeyue and Zhou, Ziya and Xue, Liumeng and Qu, Xingwei and Li, Yizhi and Wu, Shangda and Shen, Tianhao and Ma, Ziyang and Zhan, Jun and Wang, Chunhui and Wang, Yatian and Chi, Xiaowei and Zhang, Xinyue and Yang, Zhenzhu and Wang, Xiangzhou and Liu, Shansong and Mei, Lingrui and Li, Peng and Wang, Junjie and Yu, Jianwei and Pang, Guojian and Li, Xu and Wang, Zihao and Zhou, Xiaohuan and Yu, Lijun and Benetos, Emmanouil and Chen, Yong and Lin, Chenghua and Chen, Xie and Xia, Gus and Zhang, Zhaoxiang and Zhang, Chao and Chen, Wenhu and Zhou, Xinyu and Qiu, Xipeng and Dannenberg, Roger and Liu, Jiaheng and Yang, Jian and Huang, Wenhao and Xue, Wei and Tan, Xu and Guo, Yike},
journal = {arXiv preprint arXiv:2503.08638},
year = {2025},
eprint = {2503.08638},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2503.08638}
}
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YuE2 是一个开源音乐生成模型：输入歌词和风格提示词，它会先生成一份可编辑的旋律与和弦规划，再据此生成一首带人声和伴奏的完整歌曲。它还能做零样本翻唱，以及通过智能体驱动的编辑。