Fast and Lightweight Speech Synthesis Model based on FastSpeech2

In this paper, we present a fast and lightweight speech synthesis model that is suitable for on-device applications. By leveraging the techniques of long-short range attention, depth-wise separable convolution, and linear attention, we significantly reduce the model size and complexity of the baseline FastSpeech2-based Transformer framework. Unlike the baseline model that requires O(N^2) to compute attention and convolution operations because of nested-loop computations, our proposed model only requires O(N) computations due to the modification of a nested-loop into two cascaded single loops.