  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.
