  Diffusion-based audio and music generation models commonly perform generation
by constructing an image representation of audio (e.g., a mel-spectrogram) and
then convert it to audio using a phase reconstruction model or vocoder. Typical
vocoders, however, produce monophonic audio at lower resolutions (e.g., 16-24
kHz), which limits their usefulness. We propose MusicHiFi -- an efficient
high-fidelity stereophonic vocoder. Our method employs a cascade of three
generative adversarial networks (GANs) that convert low-resolution
mel-spectrograms to audio, upsamples to high-resolution audio via bandwidth
extension, and upmixes to stereophonic audio. Compared to past work, we propose
1) a unified GAN-based generator and discriminator architecture and training
procedure for each stage of our cascade, 2) a new fast, near
downsampling-compatible bandwidth extension module, and 3) a new fast
downmix-compatible mono-to-stereo upmixer that ensures the preservation of
monophonic content in the output. We evaluate our approach using objective and
subjective listening tests and find our approach yields comparable or better
audio quality, better spatialization control, and significantly faster
inference speed compared to past work. Sound examples are at
\url{https://MusicHiFi.github.io/web/}.
