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 neural vocoding, bandwidth extension, and speech enhancement. We show that with the improved generator architecture and simplified multi-discriminator training, HiFi++ performs on par with the state-of-the-art in these tasks while spending significantly less memory and computational resources. The effectiveness of our approach is validated through a series of extensive experiments.