Abstract
Recent progress in text-to-music generation has enabled models to synthesize high-quality musical segments, full compositions, and even respond to fine-grained control signals, e.g. chord progressions. State-of-the-art (SOTA) systems differ significantly across many dimensions, such as training datasets, modeling paradigms, and architectural choices. This diversity complicates efforts to evaluate models fairly and pinpoint which design choices most influence performance. While factors like data and architecture are important, in this study we focus exclusively on the modeling paradigm. We conduct a systematic empirical analysis to isolate its effects, offering insights into associated trade-offs and emergent behaviors that can guide future text-to-music generation systems. Specifically, we compare the two arguably most common modeling paradigms: Auto-Regressive decoding and Conditional Flow-Matching. We conduct a controlled comparison by training all models from scratch using identical datasets, training configurations, and similar backbone architectures. Performance is evaluated across multiple axes, including generation quality, robustness to inference configurations, scalability, adherence to both textual and temporally aligned conditioning, and editing capabilities in the form of audio inpainting. This comparative study sheds light on distinct strengths and limitations of each paradigm, providing actionable insights that can inform future architectural and training decisions in the evolving landscape of text-to-music generation. Audio sampled examples are available at: this https URL
Abstract (translated)
最近在文本到音乐生成领域的进展使得模型能够合成高质量的音乐片段、完整的乐曲，甚至响应精细控制信号（如和弦进程）。目前最先进的系统在许多维度上存在着显著差异，例如训练数据集、建模范式以及架构选择。这种多样性复杂化了公平评估模型的努力，并且难以确定哪些设计决策对性能影响最大。虽然像数据和架构这样的因素很重要，但在本研究中我们专注于探讨建模范式的影响。我们进行了一项系统性的实证分析，以隔离其效果，并提供有关相关权衡和新兴行为的见解，从而为未来的文本到音乐生成系统指明方向。具体而言，我们比较了两种最常用的建模范式：自回归解码（Auto-Regressive decoding）与条件流匹配（Conditional Flow-Matching）。为了进行有控制的比较，我们在相同的数据集、训练配置以及相似的基本架构上从头开始训练所有模型。性能评估涵盖多个维度，包括生成质量、推理配置下的鲁棒性、可扩展性、对文本和时间同步条件的遵守程度，以及以音频中涂（audio inpainting）形式出现的编辑能力。 这项比较研究揭示了每种范式的独特优势与局限，并提供了切实可行的见解，以指导未来在不断发展的文本到音乐生成领域的架构设计和训练决策。有关音频样本示例，请访问：[此链接](https://example.com)（请将此链接替换为实际提供的URL）。
URL
https://arxiv.org/abs/2506.08570
