MSR-NV: Neural vocoder using multiple sampling rates

preprint: arXiv:2109.13714
Kentaro Mitsui, Kei Sawada
rinna Co., Ltd.

The development of neural vocoders (NVs) has resulted in the high-quality and fast generation of waveforms. However, conventional NVs target a single sampling rate and require re-training when applied to different sampling rates. A suitable sampling rate varies from application to application due to the trade-off between speech quality and generation speed. In this study, we propose a method to handle multiple sampling rates in a single NV, called the MSR-NV. By generating waveforms step-by-step starting from a low sampling rate, MSR-NV can efficiently learn the characteristics of each frequency band and synthesize high-quality speech at multiple sampling rates. It can be regarded as an extension of the previously proposed NVs, and in this study, we extend the structure of Parallel WaveGAN (PWG). Experimental evaluation results demonstrate that the proposed method achieves remarkably higher subjective quality than the original PWG trained separately at 16, 24, and 48 kHz, without increasing the inference time. We also show that MSR-NV can leverage speech with lower sampling rates to further improve the quality of the synthetic speech.

0. Speech waveforms at different sampling rates

The waveforms generated using the proposed method (left) and target waveforms (right) at multiple sampling rates are listed below.
*Some browsers are not able to play samples of 1 or 2 kHz, so those samples are upsampled to 4 kHz using sox.

Sampling rateGenerated waveformTarget waveform
1 kHz
2 kHz
4 kHz
8 kHz
16 kHz
24 kHz
48 kHz

1. Comparison to baseline in terms of quality

Method used for comparison:
Method16 kHz24 kHz48 kHz

2. Training data amount and synthesis quality

We varied the amount of training data from 1 minute to 8 hours and compared the synthesis quality.

Training data amountAudio sample
1 min
3 min
5 min
10 min
30 min
8 h (full data)

3. Use of speech data with low sampling rates

Method used for comparison:
Training setAudio sample