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YuE2 · Frontier Music with Symbolic Planning

Source: map-yue2.github.io
95 points by sexy_seedbox 9 hours ago on hackernews | 79 comments

From score to song

Listen to a song, then explore the melody, rhythm, and chords in its symbolic plan.

Selected score

Loading selected song…

The symbolic score

Original score recording

Interactive ABC score Red notes follow the score recording.

View original score pages
All selected scores

Cover & Editing

A familiar song can take a different shape. Listen to changes in melody, lyrics, tempo, and arrangement.

Agentic music editing

A song takes shape through a conversation. Loading the editing story…

Genre Explorer

The listening selection, gathered across genres and languages.

Model & Results

YuE2 (best-of-8) reaches 6.9632 on SongBench, the highest observed mean among 15 evaluated settings on WildSongBench (192 prompts). Suno v5 scores 6.8721 in the same comparison.

Paper Figure 1: a WildSongBench comparison of song quality and text alignment. YuE2 and YuE2 best-of-8 are competitive with the evaluated proprietary systems. Bubble area represents AudioBox production quality.
Song quality and text alignment on WildSongBench. Bubble area shows AudioBox PQ; black outlines mark Pareto optima on the two plotted axes. Bo8 = best-of-8. How to read the indices.

Model architecture

Composing in symbols, performing in audio.

Vector PDF

YuE2 architecture: an AR–NAR Mixture-of-Transformers turns an editable symbolic score into semantic tokens, acoustic latents, and full-song audio. The same generator supports creation, covering, and editing.
An editable score becomes semantic music tokens, acoustic latents, and full-song audio. The model has approximately 3.59B parameters and 28 layers, and supports creation, covering, and editing. The AR and NAR experts share an attention computation while using separate normalization, projections, and MLPs.
Explore benchmark scores WildSongBench · 15 settings · 7 metrics

WildSongBench192 prompts

How to read these results

WildSongBench. 192 prompts and 15 system settings. The table reports automatic evaluation scores. Best-of-8 selects one of eight generations by musicality, prompt control, and lyric accuracy.

Figure 1. Song quality combines SongBench and SongEval; text alignment combines MuLan, AllMusicCaps, and prompt control. Both axes show normalized comparison indices. Bubble area represents AudioBox production quality.

MERT2 · Music representations

Learning the structure behind the sound.

Vector PDF

MERT2 architecture: offline target synthesis combines MuQ and Qwen2-Audio features into four code streams. A ConvNeXt frontend and 24-layer Conformer learn by masked prediction, then branch into full-song representations for SheetSage2 and a causal tokenizer curriculum for YuE2.
MERT2 provides the music representations behind both analysis and generation. A ConvNeXt frontend and 24-layer Conformer learn to predict masked codes built from complementary MuQ and Qwen2-Audio features. Full-song adaptation supplies SheetSage2 with musical context; a separate causal branch becomes YuE2’s 25-Hz semantic tokenizer.

State of the art on MARBLE

MERT2-30s and MERT2-FS (full-song) achieve SOTA on 14 of 15 MARBLE metrics, leading across tagging, key, genre, and emotion recognition.

SOTA metricsMERT2-30s & MERT2-FS
14 / 15

Genre accuracy · GTZANMERT2-30s · score × 100
91.72

Key refined accuracy · GiantStepsMERT2-FS · score × 100
67.05

Explore MERT2 benchmark scores MARBLE · 15 metrics · 2 encoders

SOTA counts use the best score across the two MERT2 encoders against the nine published baselines in this comparison. Both encoders have 632M parameters. MERT2-30s uses a 30-second training context; MERT2-FS uses 300 seconds. These are full-context representation benchmarks. MERT2 reports the best observed results across representations selected using test scores; each ROC-AUC / AP pair uses the same representation.

SheetSage2 · Audio to score

Hear a song. Read its composition.

Vector PDF

SheetSage2 architecture: a full-song MERT2-FS encoder with trainable adapters feeds a six-layer autoregressive decoder. Task prompts select beat, section, key, chord, and melody events, which share a timeline and become ABC notation and a lead sheet.
SheetSage2 turns a recording into an editable lead sheet. A full-song MERT2-FS encoder, adapted with LoRA, feeds a six-layer autoregressive decoder. Task prompts select beats, sections, keys, chords, and melodies; a shared event timeline becomes ABC notation with vocal and instrumental melody voices. These scores supply symbolic training targets for YuE2.

Six transcription tasks, one model

SheetSage2 achieves SOTA on 10 of 13 benchmark metrics with one model for beat, downbeat, key, chord, structure, and melody transcription.

SOTA metricsOne model · six transcription tasks
10 / 13

Vocal melody · RWC-PopPitch-class note F1 · score × 100
82.51

Chord recognition · osu2017Maj/min · score × 100
90.08

Explore SheetSage2 benchmark scores 6 tasks · 13 metrics

SOTA counts refer to the leading scores against SheetSage1, Madmom, and the task-specific systems in this comparison. Results use one model selected by validation loss. Melody F1 measures pitch-class notes; structure F1 measures section boundaries at the stated tolerance. On Chords1217, ChordFormer uses five-fold cross-validation, while SheetSage2 evaluates one fixed model on all 1,217 tracks.

Training data

Our models are trained primarily on CC0 music and synthetic data. Tokenwave.AI provides most of our synthetic training data under license. We are committed to the ethical and responsible use of data.

MERT2
700K hours

SheetSage2
28.4K hours

YuE2
346K hours