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AAAI 2025official proceedings

Text2midi: Generating Symbolic Music from Captions

Keshav Bhandari, Abhinaba Roy, Kyra Wang, Geeta Puri, Simon Colton, Dorien Herremans

PDF 由论文原始站点提供,PaperCompass 不保存论文文件。DOI 10.1609/aaai.v39i22.34516 ↗

摘要

This paper introduces text2midi, an end-to-end model to generate MIDI files from textual descriptions. Leveraging the growing popularity of multimodal generative approaches, text2midi capitalizes on the extensive availability of textual data and the success of large language models (LLMs). Our end-to-end system harnesses the power of LLMs to generate symbolic music in the form of MIDI files. Specifically, we utilize a pretrained LLM encoder to process captions, which then condition an autoregressive transformer decoder to produce MIDI sequences that accurately reflect the provided descriptions. This intuitive and user-friendly method significantly streamlines the music creation process by allowing users to generate music pieces using text prompts. We conduct comprehensive empirical evaluations, incorporating both automated and human studies, that show our model generates MIDI files of high quality that are indeed controllable by text captions that may include music theory terms such as chords, keys, and tempo.