Audio

WaveNet

WaveNet: A Generative Model for Raw Audio (2016) by DeepMind
WaveNet: A generative model for raw audio | DeepMind

• We show that WaveNets can generate raw speech signals with subjective naturalness never before reported in the field of text-to-speech (TTS), as assessed by human raters.
• In order to deal with long-range temporal dependencies needed for raw audio generation, we develop new architectures based on dilated causal convolutions, which exhibit very large receptive fields.
• We show that when conditioned on a speaker identity, a single model can be used to generate different voices.
• The same architecture shows strong results when tested on a small speech recognition dataset, and is promising when used to generate other audio modalities such as music

P.S.

We observed that adding speakers resulted in better validation set performance compared to training solely on a single speaker. This suggests that WaveNet’s internal representation was shared among multiple speakers