  Generative adversarial networks have recently demonstrated outstanding
performance in neural vocoding outperforming best autoregressive and flow-based
models. In this paper, we show that this success can be extended to other tasks
of conditional audio generation. In particular, building upon HiFi vocoders, we
propose a novel HiFi++ general framework for bandwidth extension and speech
enhancement. We show that with the improved generator architecture, HiFi++
performs better or comparably with the state-of-the-art in these tasks while
spending significantly less computational resources. The effectiveness of our
approach is validated through a series of extensive experiments.
