FloWaveNet A Generative Flow for Raw Audio
Most modern text to speech architectures use a WaveNet vocoder for synthesizing high fidelity waveform audio, but there have been limitations, such as high inference time, in its practical application due to its ancestral sampling scheme. The recently suggested Parallel WaveNet and ClariNet have achieved real time audio synthesis capability by incorporating inverse autoregressive flow for parallel sampling. However, these approaches require a two stage training pipeline with a well trained teacher network and can only produce natural sound by using probability distillation along with auxiliary loss terms. We propose FloWaveNet, a flow based generative model for raw audio synthesis. FloWaveNet requires only a single stage training procedure and a single maximum likelihood loss, without any additional auxiliary terms, and it is inherently parallel due to the characteristics of generative flow. The model can efficiently sample raw audio in real time, with clarity comparable to previous two stage parallel models. The code and samples for all models, including our FloWaveNet, are publicly available.
ICML