论文标题
SIMD大小的意识重量正规化用于CPU快速神经辅助
SIMD-size aware weight regularization for fast neural vocoding on CPU
论文作者
论文摘要
本文提出了更快的神经声码器的重量正则化。修剪时间耗时的DNN模块是在CPU上实现实时声码器（例如Wavernn，lpcnet）的一种有希望的方法。鼓励稀疏性的正规化也有效地避免了修剪产生的质量降解。但是，重量矩阵的顺序必须在SIMD大小中连续，以进行快速录音。为了确保此订单，我们提出明确的SIMD大小意识到正规化。我们提出的方法将重量矩阵重新调整为张量，以使权重提前按组大小对齐，然后计算组套管样的正则化损失。 70％稀疏子带Wavernn的实验表明，传统的套索和柱状组的修剪会使合成语音的自然性降解。具有拟议正则化的声码器1）在没有修剪的情况下实现可比的自然性，而2）比使用正则化的其他常规声码器的表现更快。
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.
