  This paper introduces WaveGrad 2, a non-autoregressive generative model for
text-to-speech synthesis. WaveGrad 2 is trained to estimate the gradient of the
log conditional density of the waveform given a phoneme sequence. The model
takes an input phoneme sequence, and through an iterative refinement process,
generates an audio waveform. This contrasts to the original WaveGrad vocoder
which conditions on mel-spectrogram features, generated by a separate model.
The iterative refinement process starts from Gaussian noise, and through a
series of refinement steps (e.g., 50 steps), progressively recovers the audio
sequence. WaveGrad 2 offers a natural way to trade-off between inference speed
and sample quality, through adjusting the number of refinement steps.
Experiments show that the model can generate high fidelity audio, approaching
the performance of a state-of-the-art neural TTS system. We also report various
ablation studies over different model configurations. Audio samples are
available at https://wavegrad.github.io/v2.
