WaveFlow: A Compact Flow-based Model for Raw Audio
This addresses the challenge of slow audio synthesis for applications like speech generation, though it is incremental as it builds on existing flow-based and autoregressive models.
The authors tackled the problem of generating high-fidelity raw audio efficiently by proposing WaveFlow, a compact flow-based model that synthesizes speech 42.6 times faster than real-time with only 5.91 million parameters.
In this work, we propose WaveFlow, a small-footprint generative flow for raw audio, which is directly trained with maximum likelihood. It handles the long-range structure of 1-D waveform with a dilated 2-D convolutional architecture, while modeling the local variations using expressive autoregressive functions. WaveFlow provides a unified view of likelihood-based models for 1-D data, including WaveNet and WaveGlow as special cases. It generates high-fidelity speech as WaveNet, while synthesizing several orders of magnitude faster as it only requires a few sequential steps to generate very long waveforms with hundreds of thousands of time-steps. Furthermore, it can significantly reduce the likelihood gap that has existed between autoregressive models and flow-based models for efficient synthesis. Finally, our small-footprint WaveFlow has only 5.91M parameters, which is 15$\times$ smaller than WaveGlow. It can generate 22.05 kHz high-fidelity audio 42.6$\times$ faster than real-time (at a rate of 939.3 kHz) on a V100 GPU without engineered inference kernels.