Automatic melody-to-lyric generation is a task in which song lyrics are generated to go with a given melody. It is of significant practical interest and more challenging than unconstrained lyric generation as the music imposes additional constraints onto the lyrics. The training data is limited as most songs are copyrighted, resulting in models that underfit the complicated cross-modal relationship between melody and lyrics. In this work, we propose a method for generating high-quality lyrics without training on any aligned melody-lyric data. Specifically, we design a hierarchical lyric generation framework that first generates a song outline and second the complete lyrics. The framework enables disentanglement of training (based purely on text) from inference (melody-guided text generation) to circumvent the shortage of parallel data. We leverage the segmentation and rhythm alignment between melody and lyrics to compile the given melody into decoding constraints as guidance during inference. The two-step hierarchical design also enables content control via the lyric outline, a much-desired feature for democratizing collaborative song creation. Experimental results show that our model can generate high-quality lyrics that are more on-topic, singable, intelligible, and coherent than strong baselines, for example SongMASS, a SOTA model trained on a parallel dataset, with a 24% relative overall quality improvement based on human ratings.

翻译：自动旋律到歌词生成是一项任务，旨在根据给定旋律生成对应的歌曲歌词。该任务具有重要的实际应用价值，且比无约束歌词生成更具挑战性，因为音乐对歌词施加了额外的约束条件。由于大多数歌曲受版权保护，训练数据十分有限，这导致模型难以充分拟合旋律与歌词之间复杂的跨模态关系。本研究提出了一种无需在成对旋律-歌词数据上训练即可生成高质量歌词的方法。具体而言，我们设计了一个分层歌词生成框架，首先生成歌曲大纲，再生成完整歌词。该框架通过解耦训练（纯文本基础）与推理（旋律引导文本生成）过程，规避了并行数据不足的问题。我们利用旋律与歌词之间的分段与节奏对齐特性，将给定旋律编译为推理时的解码约束指导。这种两步分层设计还通过歌词大纲实现了内容控制，这正是促进协作歌曲创作大众化所需的理想特性。实验结果表明，与强基线模型（例如基于并行数据集训练的SOTA模型SongMASS）相比，我们的模型能够生成主题更贴合、更易演唱、更清晰连贯的高质量歌词，基于人工评分的整体质量相对提升了24%。