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The authors introduce OMAR-RQ, an open-source music audio representation model trained on a large dataset (330k+ hours) using self-supervision via masked token prediction. The model explores different input features and quantization strategies to learn general-purpose music representations. OMAR-RQ achieves state-of-the-art performance among open self-supervised models on a suite of music understanding tasks, including tagging, pitch estimation, and beat tracking.
OMAR-RQ sets a new open-source benchmark for music audio understanding, outperforming existing self-supervised models across a diverse set of music information retrieval tasks.
Open-source foundation models are essential for advancing music audio understanding and ensuring access to general-purpose representations for music information retrieval. To this end, we present OMAR-RQ, a model trained with self-supervision via masked token prediction using a large-scale dataset with over 330,000 hours of music audio. We experiment with various input features and quantization options, outperforming existing open self-supervised models in music tagging, pitch estimation, chord recognition, beat tracking, segmentation, and difficulty estimation. Finally, we release our training and evaluation pipelines and model weights at https://github.com/mtg/omar-rq.
