FeatherWave: An efficient high-fidelity neural vocoder with multi-band linear prediction

Abstract

In this paper, we propose the FeatherWave, yet another variant of WaveRNN vocoder combining the multi-band signal processing and the linear predictive coding. The LPCNet, a recently proposed neural vocoder which utilized the linear predictive characteristic of speech signal in the WaveRNN architecture, can generate high quality speech with a speed faster than real-time on a single CPU core. However, LPCNet is still not efficient enough for online speech generation tasks. To address this issue, we adopt the multi-band linear predictive coding for WaveRNN vocoder. The multi-band method enables the model to generate several speech samples in parallel at one step. Therefore, it can significantly improve the efficiency of speech synthesis. The proposed model with 4 sub-bands needs less than 1.6 GFLOPS for speech generation. In our experiments, it can generate 24 kHz high-fidelity audio 9x faster than real-time on a single CPU, which is much faster than the LPCNet vocoder. Furthermore, our subjective listening test shows that the FeatherWave can generate speech with better quality than LPCNet.

arXiv commentAccepted by INTERSPEECH 2020

References in corpus (8)

• Adam: A Method for Stochastic Optimization
• WaveNet: A Generative Model for Raw Audio
• Block-Sparse Recurrent Neural Networks
• DurIAN: Duration Informed Attention Network For Multimodal Synthesis
• Balanced Sparsity for Efficient DNN Inference on GPU
• Efficient Neural Audio Synthesis
• ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech
• Probability density distillation with generative adversarial networks for high-quality parallel waveform generation

Cited by in corpus (6)

• TFGAN: Time and Frequency Domain Based Generative Adversarial Network for High-fidelity Speech Synthesis
• High-Fidelity and Low-Latency Universal Neural Vocoder based on Multiband WaveRNN with Data-Driven Linear Prediction for Discrete Waveform Modeling
• Improve GAN-based Neural Vocoder using Pointwise Relativistic LeastSquare GAN
• FeatherTTS: Robust and Efficient attention based Neural TTS
• Basis-MelGAN: Efficient Neural Vocoder Based on Audio Decomposition
• Empirical Evaluation of Deep Learning Model Compression Techniques on the WaveNet Vocoder