MidiCaps: A large-scale MIDI dataset with text captions
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866918116221517824 |
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| author | Melechovsky, Jan Roy, Abhinaba Herremans, Dorien |
| author_facet | Melechovsky, Jan Roy, Abhinaba Herremans, Dorien |
| contents | Generative models guided by text prompts are increasingly becoming more popular. However, no text-to-MIDI models currently exist due to the lack of a captioned MIDI dataset. This work aims to enable research that combines LLMs with symbolic music by presenting, the first openly available large-scale MIDI dataset with text captions. MIDI (Musical Instrument Digital Interface) files are widely used for encoding musical information and can capture the nuances of musical composition. They are widely used by music producers, composers, musicologists, and performers alike. Inspired by recent advancements in captioning techniques, we present a curated dataset of over 168k MIDI files with textual descriptions. Each MIDI caption describes the musical content, including tempo, chord progression, time signature, instruments, genre, and mood, thus facilitating multi-modal exploration and analysis. The dataset encompasses various genres, styles, and complexities, offering a rich data source for training and evaluating models for tasks such as music information retrieval, music understanding, and cross-modal translation. We provide detailed statistics about the dataset and have assessed the quality of the captions in an extensive listening study. We anticipate that this resource will stimulate further research at the intersection of music and natural language processing, fostering advancements in both fields. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_02255 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MidiCaps: A large-scale MIDI dataset with text captions Melechovsky, Jan Roy, Abhinaba Herremans, Dorien Audio and Speech Processing Machine Learning Multimedia Sound Generative models guided by text prompts are increasingly becoming more popular. However, no text-to-MIDI models currently exist due to the lack of a captioned MIDI dataset. This work aims to enable research that combines LLMs with symbolic music by presenting, the first openly available large-scale MIDI dataset with text captions. MIDI (Musical Instrument Digital Interface) files are widely used for encoding musical information and can capture the nuances of musical composition. They are widely used by music producers, composers, musicologists, and performers alike. Inspired by recent advancements in captioning techniques, we present a curated dataset of over 168k MIDI files with textual descriptions. Each MIDI caption describes the musical content, including tempo, chord progression, time signature, instruments, genre, and mood, thus facilitating multi-modal exploration and analysis. The dataset encompasses various genres, styles, and complexities, offering a rich data source for training and evaluating models for tasks such as music information retrieval, music understanding, and cross-modal translation. We provide detailed statistics about the dataset and have assessed the quality of the captions in an extensive listening study. We anticipate that this resource will stimulate further research at the intersection of music and natural language processing, fostering advancements in both fields. |
| title | MidiCaps: A large-scale MIDI dataset with text captions |
| topic | Audio and Speech Processing Machine Learning Multimedia Sound |
| url | https://arxiv.org/abs/2406.02255 |