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End-to-end generation of musical audio using deep learning techniques hasseen an explosion of activity recently. However, most models concentrate ongenerating fully mixed music in response to abstract conditioning information.In this work, we present an alternative paradigm for producing music generationmodels that can listen and respond to musical context. We describe how such amodel can be constructed using a non-autoregressive, transformer-based modelarchitecture and present a number of novel architectural and samplingimprovements. We train the described architecture on both an open-source and aproprietary dataset. We evaluate the produced models using standard qualitymetrics and a new approach based on music information retrieval descriptors.The resulting model reaches the audio quality of state-of-the-arttext-conditioned models, as well as exhibiting strong musical coherence withits context.

StemGen: A music generation model that listens