# Music Generation

YuE2 uses a single roughly 3.58B-parameter AR–NAR Mixture-of-Transformers to first write an ABC score specifying melody, chords, key, meter, tempo, and form, then expand it into semantic music tokens and render full-song audio, with companion models MERT2 and SheetSage2 constructing semantic and symbolic supervision from recordings; with the same checkpoint, experts prefer symbolic planning 49.3% to 34.6% for overall quality, YuE2 scores 6.7316 on SongBench Global Avg on WildSongBench (6.9632 with best-of-8), MERT2 surpasses previous best results on 14 of 15 MARBLE metrics, and SheetSage2-AR leads 12 of 15 benchmark–metric pairs.

YuE2 uses a single roughly 3.58B-parameter AR–NAR Mixture-of-Transformers to first write an ABC score specifying melody, chords, key, meter, tempo, and form, then expand it into semantic music tokens and render full-song audio, with companion models MERT2 and SheetSage2 constructing semantic and symbolic supervision from recordings; with the same checkpoint, experts prefer symbolic planning 49.3% to 34.6% for overall quality, YuE2 scores 6.7316 on SongBench Global Avg on WildSongBench (6.9632 with best-of-8), MERT2 surpasses previous best results on 14 of 15 MARBLE metrics, and SheetSage2-AR leads 12 of 15 benchmark–metric pairs.