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fix: small error #17

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fix: small error #17

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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -37,7 +37,7 @@ Implementation (PyTorch) of Google Brain's high-fidelity WaveGrad vocoder ([pape
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## About

WaveGrad is a conditional model for waveform generation through estimating gradients of the data density with WaveNet-similar sampling quality. **This vocoder is neither GAN, nor Normalizing Flow, nor classical autoregressive model**. The main concept of vocoder is based on *Denoising Diffusion Probabilistic Models* (DDPM), which utilize *Langevin dynamics* and *score matching* frameworks. Furthemore, comparing to classic DDPM, WaveGrad achieves super-fast convergence (6 iterations and probably lower) w.r.t. Langevin dynamics iterative sampling scheme.
WaveGrad is a conditional model for waveform generation through estimating gradients of the data density with WaveNet-similar sampling quality. **This vocoder is neither GAN, nor Normalizing Flow, nor classical autoregressive model**. The main concept of vocoder is based on *Denoising Diffusion Probabilistic Models* (DDPM), which utilize *Langevin dynamics* and *score matching* frameworks. Furthermore, comparing to classic DDPM, WaveGrad achieves super-fast convergence (6 iterations and probably lower) w.r.t. Langevin dynamics iterative sampling scheme.

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## Installation
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