For the problem of waveform synthesis from spectrograms, presented herein are embodiments of an efficient neural network architecture, based on transposed convolutions to achieve a high compute intensity and fast inference. In one or more embodiments, for training of the convolutional vocoder architecture, losses are used that are related to perceptual audio quality, as well as a GAN framework toguide with a critic that discerns unrealistic waveforms. While yielding a high-quality audio, embodiments of the model can achieve more than 500 times faster than real-time audio synthesis. Multi-headconvolutional neural network (MCNN) embodiments for waveform synthesis from spectrograms are also disclosed. MCNN embodiments enable significantly better utilization of modern multi-core processors than commonly-used iterative algorithms like Griffin-Lim and yield very fast (more than 300 times real-time) waveform synthesis. Embodiments herein yield high-quality speech synthesis, without any iterative algorithms or autoregression in computations.
SPECTROGRAM TO WAVEFORM SYNTHESIS USING CONVOLUTIONAL NETWORKS