# YuE2 · Frontier Music with Symbolic Planning > Source: > Published: 2026-09-11 00:33:26+00:00 Selected score ## From score to song Listen to a song, then explore the melody, rhythm, and chords in its symbolic plan. ## 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. ## Explore benchmark scores WildSongBench · 15 settings · 7 metrics **WildSongBench** 192 prompts | WildSongBench · SongBench | | | | |---|---|---|---| | Rank | System / setting | Prompts | SongBench ↑ | |---|---|---|---| September 5, 2026 evaluation · rankings vary by metric. [Download results](data/benchmark-results.csv?v=20260908-wsb6) ## 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. #### 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 | **91.00** PupuJEPA-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. #### 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 | **88.75** Beat 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 | **84.09** ChordFormer | 83.81 | | HarmonixSetStructure | Accuracy | 80.03SongFormer | **80.51** | | HarmonixSetStructure | Boundary F1 · 0.5 s | **70.63** SongFormer | 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. Full comparison includes SheetSage1, Madmom, and task-specific systems. [Download all results](data/sheetsage2-benchmark-results.csv) ## Training data Our models are trained primarily on CC0 music and synthetic data. [Tokenwave.AI](https://www.tokenwave.us/) 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