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Generate complete songs with vocals and accompaniment from lyrics and a style prompt, using an editable symbolic score as the intermediate step.
YuE2 generates an editable melody-and-chord plan alongside audio, so musicians can review the symbolic score, modify it by hand or through an agent, and render a revised version. The agentic editing demo shows iterative changes to harmony, lyrics, tempo, and arrangement through a conversation.
Skip if you need a browser-based point-and-click editor. YuE2 is a Python library and command-line tool with no graphical interface.
YuE2 provides public model weights, a staged Python API, and the WildSongBench evaluation dataset for reproducing published benchmark scores. The pipeline supports swapping decoder variants (YuE2-Vae vs YuE2-Vae-legacy) and calling generation stages independently.
Skip if your research requires commercial licensing for model weights. CC BY-NC 4.0 prohibits commercial use of the YuE2 weights.
SheetSage2 transcribes any audio recording into ABC notation with beat, chord, melody, and structure. Paired with YuE2's cover generation mode, developers can build pipelines that accept a source recording and produce a stylized version in a new genre without cover-specific fine-tuning.
Skip if your environment lacks an NVIDIA GPU with at least 24 GB VRAM. The pipeline does not support CPU-only or low-VRAM setups.
The yue2-music skill packages the full pipeline for AI agents: generating songs, transcribing and covering recordings, editing ABC scores, and comparing versions. The skill supports multi-step editing sessions where each version is retained for review and comparison.
Skip if you need a stable production API. YuE2 is a research release and interfaces may change across versions.
Music generation tools from commercial platforms produce output you cannot inspect. You submit a prompt, receive audio, and have no access to the underlying composition. If the melody is wrong, the only recourse is to try again with a different prompt. This trial-and-error loop is slow, and the gap between your intent and the output widens when the composition is complex.
Running a music generation model locally has historically required stitching together separate audio AI components with no unified pipeline, and the results lagged behind commercial quality. Most available open models either lacked vocal generation, produced artifacts that required post-processing, or offered no structured intermediate that a musician or agent could inspect and modify.
YuE2 generates a melody-and-chord plan in ABC notation before rendering audio. You can read the plan, modify it by hand or with an agent, and supply the edited score back to produce a different recording. The `cot='full'` setting enables this by default for new songs; `cot='off'` skips the plan and generates audio directly.
Give YuE2 lyrics and a style prompt and it outputs 48 kHz stereo audio with a lead vocal track and instrumental accompaniment. The AR-NAR Mixture-of-Transformers backbone generates semantic tokens autoregressively, then uses flow matching to produce acoustic latents that a VAE decodes into stereo audio.
Transcribe any recording with SheetSage2 to get its melody as ABC notation, then supply new lyrics or a style prompt to generate a cover in a different arrangement. On a 948-work evaluation, full-score YuE2 reaches 0.647 CLEWS mAP for source-identity preservation, compared to 0.006 without a score.
An agent can revise the composition by modifying the ABC score, changing harmony, tempo, melody, or arrangement, then re-render the audio. The project demo follows one song through 9 editing steps and 14 versions, moving from Mandarin pop to English jazz with new harmony and a saxophone solo.
On WildSongBench (192 prompts, September 2026), YuE2 best-of-8 scores 6.9632 SongBench Avg, the highest among all 17 evaluated settings. This exceeds Suno v5 (6.8721), Suno v5.5 (6.7150), Suno v6 (6.5562), and Mureka 9 (6.9377). Standard single-pass YuE2 scores 6.7316, also above Suno v5.5 and v6.
Both generate songs from a text prompt, but they differ in transparency, self-hosting, and commercial licensing.
| Feature | YuE2 | Suno |
|---|---|---|
| Code license | Apache 2.0 | Proprietary |
| Model weights | CC BY-NC 4.0 (non-commercial) | Proprietary |
| Self-hosting | Yes | No |
| Editable score | Yes (ABC notation) | No |
| WildSongBench SongBench Avg | 6.9632 (best-of-8) | 6.8721 (v5) |
YuE2's editable symbolic score is the key differentiator versus Suno. When you generate a song with YuE2, you get ABC notation showing the melody and chords before the model renders audio. You can modify that score and produce a different recording from the revised plan. Suno generates audio directly with no intermediate you can read or edit. On WildSongBench, YuE2 best-of-8 outscores all four tested Suno versions on SongBench Avg.
Suno is the better choice when you need a web-based interface with no hardware requirements, when your use case requires commercial licensing, or when you want a fast hosted API with no server to manage. YuE2 requires a Linux machine with a 24 GB VRAM GPU, and its model weights prohibit commercial use.
Udio is a commercial AI music generation service with a managed web interface and API. Like Suno, it generates audio directly from prompts without exposing an editable intermediate composition. YuE2 differs on the same axes: open weights with a CC BY-NC 4.0 model license, self-hosting on Linux with an NVIDIA GPU, and an editable ABC score that can be reviewed and revised before rendering.
Udio is the better choice when you need a no-setup hosted service with commercial licensing and a browser-based editor. YuE2 is the better choice when you need open weights, self-hosted infrastructure, or direct control over the composition before the model renders audio.
Install YuE2 from source on Linux with Python 3.12 and an NVIDIA GPU with at least 24 GB VRAM.
```bash
git clone https://github.com/multimodal-art-projection/YuE.git
cd YuE
python3.12 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install .
python examples/generate.py --output outputs/first-song
```YuE2's source code and agent skill are Apache 2.0 licensed and free for any use, including commercial. The model weights are separately licensed under CC BY-NC 4.0, which permits personal and research use but prohibits commercial applications. You need both code and weights to run the model, so organizations planning commercial products should review the weight license before proceeding.
YuE2 requires Linux, Python 3.12, and an NVIDIA GPU with BF16 support and at least 24 GB of VRAM. It outputs 48 kHz stereo audio without quantization by default. Model files download from Hugging Face on first run. Mac, Windows, and CPU-only configurations are not covered in the official quickstart.
On WildSongBench with 192 prompts evaluated in September 2026, YuE2 best-of-8 scored 6.9632 SongBench Avg, above Suno v5 (6.8721), Suno v5.5 (6.7150), and Suno v6 (6.5562). Standard single-pass YuE2 scored 6.7316. The benchmark documentation notes that the small gap between the top means does not establish statistical significance.
Yes. SheetSage2, included in the YuE2 repository, transcribes a source recording into ABC notation covering beat, chord, melody, and structure. You supply that score to YuE2 with new lyrics or a target style and it generates a cover. On a 948-work evaluation, full-score YuE2 reached 0.647 CLEWS mAP for source-identity preservation, compared to 0.006 without a score.
With cot='full' (the default for new songs), YuE2 generates an editable melody-and-chord plan in ABC notation before producing audio. You can review or modify the plan and supply it back to render a different recording. With cot='off', YuE2 generates audio directly from lyrics and style, skipping the intermediate score. The plan mode enables inspection and editing; direct generation does not.
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