Fast and user-controllable music generation could enable novel ways of
composing or performing music. However, state-of-the-art music generation
systems require large amounts of data and computational resources for training,
and are slow at inference. This makes them impractical for real-time
interactive use. In this work, we introduce Musika, a music generation system
that can be trained on hundreds of hours of music using a single consumer GPU,
and that allows for much faster than real-time generation of music of arbitrary
length on a consumer CPU. We achieve this by first learning a compact
invertible representation of spectrogram magnitudes and phases with adversarial
autoencoders, then training a Generative Adversarial Network (GAN) on this
representation for a particular music domain. A latent coordinate system
enables generating arbitrarily long sequences of excerpts in parallel, while a
global context vector allows the music to remain stylistically coherent through
time. We perform quantitative evaluations to assess the quality of the
generated samples and showcase options for user control in piano and techno
music generation. We release the source code and pretrained autoencoder weights
at github.com/marcoppasini/musika, such that a GAN can be trained on a new
music domain with a single GPU in a matter of hours.

Musika 是一個快速的音樂生成系統，透過將 spectrogram 轉換成可逆表示並透過生成對抗網路以一個特定的音樂領域進行訓練，使用單一普通消費 GPU 進行訓練並能在消費 CPU 上以比實時更快的速度生成任意長度的音樂，並提供使用者控制選項。