  The deep learning community has witnessed an exponentially growing interest
in self-supervised learning (SSL). However, it still remains unexplored how to
build a framework for learning useful representations of raw music waveforms in
a self-supervised manner. In this work, we design Music2Vec, a framework
exploring different SSL algorithmic components and tricks for music audio
recordings. Our model achieves comparable results to the state-of-the-art
(SOTA) music SSL model Jukebox, despite being significantly smaller with less
than 2% of parameters of the latter. The model will be released on
Huggingface(Please refer to: https://huggingface.co/m-a-p/music2vec-v1)
