We introduce an extensive new dataset of MIDI files, created by transcribing audio recordings of piano performances into their constituent notes. The data pipeline we use is multi-stage, employing a language model to autonomously crawl and score audio recordings from the internet based on their metadata, followed by a stage of pruning and segmentation using an audio classifier. The resulting dataset contains over one million distinct MIDI files, comprising roughly 100,000 hours of transcribed audio. We provide an in-depth analysis of our techniques, offering statistical insights, and investigate the content by extracting metadata tags, which we also provide. Dataset available atthis https URL.
View on arXiv@article{bradshaw2025_2504.15071,
title={ Aria-MIDI: A Dataset of Piano MIDI Files for Symbolic Music Modeling },
author={ Louis Bradshaw and Simon Colton },
journal={arXiv preprint arXiv:2504.15071},
year={ 2025 }
}