WaveGrad: Estimating Gradients for Waveform Generation
This work addresses audio synthesis for applications like speech generation, offering a method that balances speed and quality, though it builds on existing diffusion models and is incremental in its approach.
The paper tackles waveform generation by introducing WaveGrad, a conditional model that estimates gradients of data density to generate high fidelity audio from mel-spectrograms, achieving results with as few as six iterations and matching strong autoregressive baselines using fewer sequential operations.
This paper introduces WaveGrad, a conditional model for waveform generation which estimates gradients of the data density. The model is built on prior work on score matching and diffusion probabilistic models. It starts from a Gaussian white noise signal and iteratively refines the signal via a gradient-based sampler conditioned on the mel-spectrogram. WaveGrad offers a natural way to trade inference speed for sample quality by adjusting the number of refinement steps, and bridges the gap between non-autoregressive and autoregressive models in terms of audio quality. We find that it can generate high fidelity audio samples using as few as six iterations. Experiments reveal WaveGrad to generate high fidelity audio, outperforming adversarial non-autoregressive baselines and matching a strong likelihood-based autoregressive baseline using fewer sequential operations. Audio samples are available at https://wavegrad.github.io/.