From 30056f72d4a2a9bf5314dfd9313903eee49ff176 Mon Sep 17 00:00:00 2001
From: 0xflotus <0xflotus@gmail.com>
Date: Sat, 2 Jan 2021 02:55:22 +0100
Subject: [PATCH] fix: small error

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 9f86585..d0ce519 100644
--- a/README.md
+++ b/README.md
@@ -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