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            Efficient WaveRNN: Optimizing Nonlinearities
            Implementing efficient nonlinearities for WaveRNN CPU inference is tricky but critical.
            
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            Efficient WaveRNN: Block Sparsity
            WaveRNN inference can be accelerated by using block-sparse weight matrices combined with specialized block-sparse matrix-vector multiplication kernels.
            
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            Efficient WaveRNN: Optimizing Arithmetic
            WaveRNN inference can be made dramatically faster with SIMD inference kernels and int16 quantization.
            
                Full Story
            
        
        
            Efficient WaveRNN: Autoregressive Inference
            If implemented in Python and Pytorch, WaveRNN inference is too slow, but we can make it faster with several simple optimizations.
            
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            Efficient WaveRNN: Reducing Quantization Noise
            Discretizing audio samples to 8 bits each during WaveRNN inferences introduces significant quantization noise, which we can reduce using µ-law companding and pre-emphasis.
            
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            Efficient WaveRNN: Intro
            WaveRNN is an autoregressive neural vocoder, a neural network based process for converting low-dimensional acoustic features into an audio waveform by predicting the next sample in a stream of samples. Specialized compute kernels are necessary to make WaveRNN inference fast.
            
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                Recent Posts
            
            
                    
                        Efficient WaveRNN: Optimizing Nonlinearities
                        Implementing efficient nonlinearities for WaveRNN CPU inference is tricky but critical.
                    
                    
                        Efficient WaveRNN: Block Sparsity
                        WaveRNN inference can be accelerated by using block-sparse weight matrices combined with specialized block-sparse matrix-vector multiplication kernels.
                    
                    
                        Efficient WaveRNN: Optimizing Arithmetic
                        WaveRNN inference can be made dramatically faster with SIMD inference kernels and int16 quantization.
                    
            
        


        

    

    