WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis

• URL: http://arxiv.org/abs/2106.09660v2
• Date: Sat, 19 Jun 2021 01:47:11 GMT
• Title: WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis
• Authors: Nanxin Chen, Yu Zhang, Heiga Zen, Ron J. Weiss, Mohammad Norouzi, Najim Dehak, William Chan
• Abstract summary: WaveGrad 2 is a non-autoregressive generative model for text-to-speech synthesis. It can generate high fidelity audio, approaching the performance of a state-of-the-art neural TTS system.
• Score: 80.60577805727624
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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