SIMD-size aware weight regularization for fast neural vocoding on CPU
This is an incremental improvement for enabling efficient neural vocoding on CPUs, relevant to speech synthesis applications.
The paper tackles the problem of maintaining speech quality while pruning neural vocoders for CPU-based real-time synthesis by proposing SIMD-size aware weight regularization, which achieves comparable naturalness to unpruned models and meaningfully faster performance than conventional methods.
This paper proposes weight regularization for a faster neural vocoder. Pruning time-consuming DNN modules is a promising way to realize a real-time vocoder on a CPU (e.g. WaveRNN, LPCNet). Regularization that encourages sparsity is also effective in avoiding the quality degradation created by pruning. However, the orders of weight matrices must be contiguous in SIMD size for fast vocoding. To ensure this order, we propose explicit SIMD size aware regularization. Our proposed method reshapes a weight matrix into a tensor so that the weights are aligned by group size in advance, and then computes the group Lasso-like regularization loss. Experiments on 70% sparse subband WaveRNN show that pruning in conventional Lasso and column-wise group Lasso degrades the synthetic speech's naturalness. The vocoder with proposed regularization 1) achieves comparable naturalness to that without pruning and 2) performs meaningfully faster than other conventional vocoders using regularization.