Generating music from text descriptions is a user-friendly mode since the text is a relatively easy interface for user engagement. While some approaches utilize texts to control music audio generation, editing musical elements in generated audio is challenging for users. In contrast, symbolic music offers ease of editing, making it more accessible for users to manipulate specific musical elements. In this paper, we propose MuseCoco, which generates symbolic music from text descriptions with musical attributes as the bridge to break down the task into text-to-attribute understanding and attribute-to-music generation stages. MuseCoCo stands for Music Composition Copilot that empowers musicians to generate music directly from given text descriptions, offering a significant improvement in efficiency compared to creating music entirely from scratch. The system has two main advantages: Firstly, it is data efficient. In the attribute-to-music generation stage, the attributes can be directly extracted from music sequences, making the model training self-supervised. In the text-to-attribute understanding stage, the text is synthesized and refined by ChatGPT based on the defined attribute templates. Secondly, the system can achieve precise control with specific attributes in text descriptions and offers multiple control options through attribute-conditioned or text-conditioned approaches. MuseCoco outperforms baseline systems in terms of musicality, controllability, and overall score by at least 1.27, 1.08, and 1.32 respectively. Besides, there is a notable enhancement of about 20% in objective control accuracy. In addition, we have developed a robust large-scale model with 1.2 billion parameters, showcasing exceptional controllability and musicality.

翻译：从文本描述生成音乐是一种用户友好的模式，因为文本是相对易于用户参与的接口。尽管已有方法利用文本控制音乐音频的生成，但编辑生成音频中的音乐元素对用户而言仍具挑战性。相比之下，符号音乐易于编辑，使用户能更便捷地操纵特定音乐元素。本文提出MuseCoco，通过音乐属性作为桥梁，将任务分解为文本到属性理解与属性到音乐生成两个阶段，实现从文本描述生成符号音乐。MuseCoco（Music Composition Copilot）使音乐人能够直接从给定文本描述生成音乐，与完全从零开始创作相比，显著提升效率。该系统具有两大优势：其一，数据高效。在属性到音乐生成阶段，属性可直接从音乐序列中提取，使模型训练实现自监督；在文本到属性理解阶段，文本由ChatGPT基于定义的属性模板合成与精炼。其二，该系统能通过文本描述中的特定属性实现精准控制，并提供基于属性条件或文本条件的多种控制选项。MuseCoco在音乐性、可控性和总体评分上分别以至少1.27、1.08和1.32的优势超越基线系统，同时客观控制准确率提升约20%。此外，我们开发了包含12亿参数的强大大规模模型，展现出卓越的可控性与音乐性。