  We introduce VampNet, a masked acoustic token modeling approach to music
synthesis, compression, inpainting, and variation. We use a variable masking
schedule during training which allows us to sample coherent music from the
model by applying a variety of masking approaches (called prompts) during
inference. VampNet is non-autoregressive, leveraging a bidirectional
transformer architecture that attends to all tokens in a forward pass. With
just 36 sampling passes, VampNet can generate coherent high-fidelity musical
waveforms. We show that by prompting VampNet in various ways, we can apply it
to tasks like music compression, inpainting, outpainting, continuation, and
looping with variation (vamping). Appropriately prompted, VampNet is capable of
maintaining style, genre, instrumentation, and other high-level aspects of the
music. This flexible prompting capability makes VampNet a powerful music
co-creation tool. Code and audio samples are available online.
