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arXiv · 2206.15276

R-MelNet: Reduced Mel-Spectral Modeling for Neural TTS

Kyle Kastner·Aaron Courville
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Abstract

This paper introduces R-MelNet, a two-part autoregressive architecture with a frontend based on the first tier of MelNet and a backend WaveRNN-style audio decoder for neural text-to-speech synthesis. Taking as input a mixed sequence of characters and phonemes, with an optional audio priming sequence, this model produces low-resolution mel-spectral features which are interpolated and used by a WaveRNN decoder to produce an audio waveform. Coupled with half precision training, R-MelNet uses under 11 gigabytes of GPU memory on a single commodity GPU (NVIDIA 2080Ti). We detail a number of critical implementation details for stable half precision training, including an approximate, numerically stable mixture of logistics attention. Using a stochastic, multi-sample per step inference scheme, the resulting model generates highly varied audio, while enabling text and audio based controls to modify output waveforms. Qualitative and quantitative evaluations of an R-MelNet system trained on a single speaker TTS dataset demonstrate the effectiveness of our approach.

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BibTeXRIS

Kyle Kastner, Aaron Courville. 2022-06-30. R-MelNet: Reduced Mel-Spectral Modeling for Neural TTS. https://arxiv.org/abs/2206.15276

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