Hacker News · AIpublished Sep 11, 00:33 UTC (4d ago)ingested Sep 11, 02:16 UTCsexy_seedbox

Part of a story covered by 3 sources: “YuE2 open music model tops Suno v5 on SongBench; DeepSeek ships 552B DeepSeek-V4.1-Flash with 1M-token context” — merged summary and timeline →

YuE2 · Frontier Music with Symbolic Planning

infoModel releaseimportance 45

AI summary · glm-5.3-flash

YuE2, a 3.59B-parameter music generation model, scores 6.9632 on SongBench, beating Suno v5 via symbolic planning.

YuE2 is a music generation model of roughly 3.59B parameters and 28 layers supporting song creation, covering, and agentic editing through editable ABC symbolic scores. Its best-of-8 setting reaches 6.9632 on SongBench, the highest mean among 15 evaluated settings on WildSongBench (192 prompts), ahead of Suno v5 at 6.8721. The project also introduces MERT2, whose 632M-parameter encoders achieve state of the art on 14 of 15 MARBLE metrics, and SheetSage2, which transcribes beats, downbeats, key, chords, structure, and melody with SOTA on 10 of 13 benchmark metrics.

YuE2 (3.59B parameters, 28 layers) generates, covers, and edits songs via semantic music tokens and ABC scores
Best-of-8 YuE2 hits 6.9632 on SongBench, topping Suno v5 (6.8721) across 15 WildSongBench settings
MERT2 encoders (632M parameters) achieve SOTA on 14 of 15 MARBLE music-representation metrics
SheetSage2 reaches SOTA on 10 of 13 audio-to-score transcription benchmarks across six tasks

Read the original Hacker News · 43 points · 35 comments

ProductsSuno v5

AI modelsYuE2 MERT2 SheetSage2

Tagsyue2 mert2 sheetsage2 music-generation symbolic-music songbench wildsongbench

Full article975 words · extracted from map-yue2.github.io · click to collapse

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

Selected score

Loading selected song…

The generated song

The symbolic score

Original score recording

Interactive ABC score Red notes follow the score recording.

View original score pages

All selected scores

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…

The listening selection, gathered across genres and languages.

Search Language Genre Generation

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.

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

Compare by

WildSongBench · SongBench

Rank	System / setting	Prompts	SongBench ↑

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 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

Scores × 100 · higher is better · bold marks the highest displayed score.

Benchmark / metric	Best published baseline	MERT2-30s	MERT2-FS
MTT · TaggingROC-AUC	91.70PupuJEPA-Large	91.91	91.74
MTT · TaggingAverage precision	40.80PupuJEPA-Large	41.29	41.20
GiantSteps · KeyRefined accuracy	66.10PupuJEPA-Large	66.97	67.05
GTZAN · GenreAccuracy	86.90PupuJEPA-Large	91.72	90.69
GTZAN · BeatF1	91.00PupuJEPA-Large	90.59	90.57
EmoMusic · ValenceR²	62.50PupuJEPA-Large	63.23	63.52
EmoMusic · ArousalR²	78.50PupuJEPA-Huge	80.01	78.14
MTG-Jamendo · InstrumentROC-AUC	78.40PupuJEPA-Large	80.27	80.27
MTG-Jamendo · InstrumentAverage precision	21.20PupuJEPA-Large	22.89	23.51
MTG-Jamendo · Mood / themeROC-AUC	76.20PupuJEPA-Large	79.44	78.74
MTG-Jamendo · Mood / themeAverage precision	15.50Dasheng-1.2B	16.68	15.74
MTG-Jamendo · GenreROC-AUC	86.30AudioMAE++	88.01	87.98
MTG-Jamendo · GenreAverage precision	20.10PupuJEPA-Large / PupuJEPA-Huge	21.22	20.66
MTG-Jamendo · Top 50ROC-AUC	83.10AudioMAE++ / PupuJEPA-Huge	84.18	84.13
MTG-Jamendo · Top 50Average precision	31.10AudioMAE++	32.17	31.62

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 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

Scores × 100 · higher is better · bold marks the highest displayed score.

Benchmark / task	Metric	Best comparison	SheetSage2
GTZANBeat	F1	88.75Beat This!	85.65
osu2017Beat	F1	91.55Madmom	92.29
GTZANDownbeat	F1	78.28Beat This!	79.51
osu2017Downbeat	F1	84.99Beat This!	91.97
GiantStepsKey	Weighted score	74.62Madmom	77.73
GTZANKey	Weighted score	74.43S-KEY	75.77
osu2017Chord	Maj/min	84.59Jiang et al. 2019	90.08
Chords1217Chord	Maj/min	84.09ChordFormer	83.81
HarmonixSetStructure	Accuracy	80.03SongFormer	80.51
HarmonixSetStructure	Boundary F1 · 0.5 s	70.63SongFormer	67.96
HarmonixSetStructure	Boundary F1 · 3 s	79.50SongFormer	82.86
RWC-PopMelody	Vocal pitch-class F1	62.71SheetSage1	82.51
RWC-PopMelody	Full pitch-class F1	64.02SheetSage1	75.29

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

Text extracted automatically; images, tables and formatting may be missing. Original: https://map-yue2.github.io/