MuseCoco: Generating Symbolic Music from Text
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.
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Peiling Lu
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Xin Xu
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2023-06-02T12:46:40.922Z
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Xin Xu
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Chenfei Kang
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Botao Yu
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Chengyi Xing
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Chengyi Xing
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Xu Tan
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2023-06-02T03:26:21.827Z
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Xu Tan
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Jiang Bian
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2025-06-19T09:12:39.727Z
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Jiang Bian
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jiangbian22
MuseCoco: Generating Symbolic Music from Text
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.
MuseCoco generates symbolic music from text descriptions using a two-stage process that enhances control and efficiency compared to existing systems.
symbolic music text-to-attribute understanding attribute-to-music generation self-supervised attribute-conditioned text-conditioned