AI写歌终于能先打草稿了
写歌的AI以前是即兴发挥：你给一句提示，它从头到尾一口气生成，中间没法改。这篇把流程拆开了：先用一种乐谱记号(ABC)让模型先写一份「编曲草稿」——和声、节奏、旋律结构都定好，再根据草稿生成最终音频。相当于让AI先打草稿再成稿，而不是闭眼一口气写完。结果是在「人声质量」的公开榜单上，它排第三，仅次于Suno V5.5和Mureka，超过Suno V5。它不是你明天就能上手用的产品，但「先规划再生成」这个思路，是音乐AI从「碰运气」走向「可控制」的一步。
📄 原文摘要(英文)
We introduce StepAudio 3 Music, a large-scale, long-form music generation model that supports explicit musical planning and open-domain text-controlled generation. The StepAudio Music Tokenizer represents audio as a 50-Hz stream from a 65536-entry single codebook, using semantically informed self-supervised and multi-task training to preserve musical structure and reconstruction-relevant information. A flow-matching diffusion Transformer (DiT) predicts continuous StepAudio VAE latents, which our VAE decoder converts into 48-kHz audio. This discrete-continuous design is guided by comparisons of single-codebook VQ, Semantic and Acoustic RVQ, and different DiT configurations. For explicit planning, a Mixture-of-Experts autoregressive model uses ABC notation to produce an intermediate arrangement plan (ABC-CoT) before predicting music tokens, making harmony, rhythm, and melodic structure part of the generation context. A progressive training curriculum and supervised fine-tuning support song and instrumental generation, accompaniment generation from dry vocals, and cover-song synthesis for up to 5 minutes and 30 seconds. With reinforcement learning via direct preference optimization (DPO), the final model achieves the highest AudioBox Content Enjoyment, Content Usefulness, and Production Quality scores and the highest MuQ-MuLan similarity among the evaluated systems, with competitive SongBench results. On the preliminary Artificial Analysis Music Arena Vocals leaderboard, it obtains a Quality Elo of 1105, behind only Suno V5.5 and Mureka and ahead of Suno V5, MiniMax models, and other systems. Audio demonstrations are available at https://stepaudiollm.github.io/step-audio-3-music.