Composing coherent long-form music remains a significant challenge due to the complexity of modeling long-range dependencies and the prohibitive memory and computational requirements associated with lengthy audio representations. In this work, we propose a simple yet powerful trick: we assume that AI models can understand and generate time-accelerated (speeded-up) audio at rates such as 2x, 4x, or even 8x. By first generating a high-speed version of the music, we greatly reduce the temporal length and resource requirements, making it feasible to handle long-form music that would otherwise exceed memory or computational limits. The generated audio is then restored to its original speed, recovering the full temporal structure. This temporal speed-up and slow-down strategy naturally follows the principle of hierarchical generation from abstract to detailed content, and can be conveniently applied to existing music generation models to enable long-form music generation. We instantiate this idea in SqueezeComposer, a framework that employs diffusion models for generation in the accelerated domain and refinement in the restored domain. We validate the effectiveness of this approach on two tasks: long-form music generation, which evaluates temporal-wise control (including continuation, completion, and generation from scratch), and whole-song singing accompaniment generation, which evaluates track-wise control. Experimental results demonstrate that our simple temporal speed-up trick enables efficient, scalable, and high-quality long-form music generation. Audio samples are available at https://SqueezeComposer.github.io/.

翻译：创作连贯的长篇幅音乐仍是一项重大挑战，原因在于长程依赖关系的建模复杂性，以及冗长音频表示所带来的巨大内存与计算开销。本文提出一个简洁而有效的技巧：假设AI模型能够理解并生成以2倍、4倍甚至8倍速加速的音频。通过先生成音乐的高速版本，大幅缩短时间长度并降低资源需求，从而能够处理原本超出内存或计算极限的长篇幅音乐。随后将生成的音频恢复至原速，重建完整的时间结构。这种时间加速与减速策略自然遵循从抽象到细节的层级生成原则，可便捷地应用于现有音乐生成模型，实现长篇幅音乐生成。我们将这一思想实例化为SqueezeComposer框架，该框架在加速域使用扩散模型进行生成，在恢复域进行细化。我们在两个任务上验证了该方法的有效性：评估时间维度控制（包括续写、补全与从头生成）的长篇幅音乐生成，及评估轨道维度控制的整曲歌唱伴奏生成。实验结果表明，我们简单的时间加速技巧能够实现高效、可扩展且高质量的长篇幅音乐生成。音频样本请访问https://SqueezeComposer.github.io/。