Ultra-lightweight Neural Differential DSP Vocoder For High Quality Speech Synthesis

• URL: http://arxiv.org/abs/2401.10460v1
• Date: Fri, 19 Jan 2024 02:51:00 GMT
• Title: Ultra-lightweight Neural Differential DSP Vocoder For High Quality Speech Synthesis
• Abstract summary: A DSP vocoder often gets a lower audio quality due to consuming over-smoothed acoustic model predictions. We propose an ultra-lightweight DSP (DDSP) vocoder that uses a jointly optimized acoustic model with a DSP vocoder, and learns without an extracted spectral feature for the vocal tract.
• Score: 9.916195285279931
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural vocoders model the raw audio waveform and synthesize high-quality audio, but even the highly efficient ones, like MB-MelGAN and LPCNet, fail to run real-time on a low-end device like a smartglass. A pure digital signal processing (DSP) based vocoder can be implemented via lightweight fast Fourier transforms (FFT), and therefore, is a magnitude faster than any neural vocoder. A DSP vocoder often gets a lower audio quality due to consuming over-smoothed acoustic model predictions of approximate representations for the vocal tract. In this paper, we propose an ultra-lightweight differential DSP (DDSP) vocoder that uses a jointly optimized acoustic model with a DSP vocoder, and learns without an extracted spectral feature for the vocal tract. The model achieves audio quality comparable to neural vocoders with a high average MOS of 4.36 while being efficient as a DSP vocoder. Our C++ implementation, without any hardware-specific optimization, is at 15 MFLOPS, surpasses MB-MelGAN by 340 times in terms of FLOPS, and achieves a vocoder-only RTF of 0.003 and overall RTF of 0.044 while running single-threaded on a 2GHz Intel Xeon CPU.

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