Recent advances in neural song generation have enabled high-quality synthesis from lyrics and global textual prompts. However, most systems fail to model temporally varying attributes of songs, severely limiting fine-grained control over musical structure and dynamics. To address this, we propose SegTune, a Diffusion Transformer-based framework enabling structured and fine-grained controllability by allowing users or large language models (LLMs) to specify local musical descriptions aligned to song segments. These segment prompts are temporally broadcast to corresponding time windows, while global prompts ensure stylistic coherence. To support precise lyric-to-music alignment, we introduce an LLM-based duration predictor that autoregressively generates sentence-level timestamps in LyRiCs format. We further construct a large-scale data pipeline for high-quality song collection with aligned lyrics and prompts, and propose new metrics to evaluate segment alignment and vocal consistency. Experiments demonstrate that SegTune outperforms existing baselines in both musicality and controllability. Visit our project page (https://github.com/KlingAIResearch/SegTune) for codes and more generated songs.

翻译：近期神经歌曲生成的进展已能从歌词和全局文本提示中合成高质量音频。然而，现有系统大多无法建模歌曲随时间变化的属性，严重限制了音乐结构与动态的细粒度控制。为此，我们提出SegTune——一种基于扩散Transformer的框架，通过允许用户或大语言模型（LLM）为歌曲段落指定局部音乐描述，实现了结构化且细粒度的可控性。这些段落提示会被时序广播至对应时间窗口，同时全局提示确保风格连贯性。为支持精确的歌词-音乐对齐，我们引入基于LLM的时长预测器，以LyRiCs格式自回归生成句子级时间戳。此外，我们构建了大规模数据流水线以收集带有对齐歌词与提示的高质量歌曲，并提出了评估段落对齐与声部一致性的新指标。实验表明，SegTune在音乐性与可控性上均优于现有基线方法。访问项目页面（https://github.com/KlingAIResearch/SegTune）获取代码及更多生成歌曲。