  We present SingSong, a system that generates instrumental music to accompany
input vocals, potentially offering musicians and non-musicians alike an
intuitive new way to create music featuring their own voice. To accomplish
this, we build on recent developments in musical source separation and audio
generation. Specifically, we apply a state-of-the-art source separation
algorithm to a large corpus of music audio to produce aligned pairs of vocals
and instrumental sources. Then, we adapt AudioLM (Borsos et al., 2022) -- a
state-of-the-art approach for unconditional audio generation -- to be suitable
for conditional "audio-to-audio" generation tasks, and train it on the
source-separated (vocal, instrumental) pairs. In a pairwise comparison with the
same vocal inputs, listeners expressed a significant preference for
instrumentals generated by SingSong compared to those from a strong retrieval
baseline.
  Sound examples at https://g.co/magenta/singsong
