This paper introduces HybridNet, a hybrid neural network to speed-up autoregressive
 models for raw audio waveform generation. As an example, we propose
 a hybrid model that combines an autoregressive network named WaveNet and a
 conventional LSTM model to address speech synthesis. Instead of generating
 one sample per time-step, the proposed HybridNet generates multiple samples per
 time-step by exploiting the long-term memory utilization property of LSTMs. In
 the evaluation, when applied to text-to-speech, HybridNet yields state-of-art performance.
 HybridNet achieves a 3.83 subjective 5-scale mean opinion score on
 US English, largely outperforming the same size WaveNet in terms of naturalness
 and provide 2x speed up at inference.
It is a hybrid neural architecture to speed-up autoregressive model.  Concludes that in order to scale up the model size without increasing inference time for sequential prediction, use a model that predicts multiple timesteps at once. This paper presents HybridNet, a neural speech and other audio synthesis system that combines the WaveNet model with an LSTM with the goal of offering a model with faster inference-time audio generation.