Updated README for codecov

Author: Spijkervet

README.md

@@ -1,11 +1,19 @@
 # PyTorch Audio Augmentations
 ![CI status](https://github.com/spijkervet/torchaudio-augmentations/actions/workflows/ci.yml/badge.svg)
+[![codecov](https://codecov.io/gh/Spijkervet/torchaudio-augmentations/branch/master/graph/badge.svg?token=0DEFJYJH5K)](https://codecov.io/gh/Spijkervet/torchaudio-augmentations)
 [![Downloads](https://pepy.tech/badge/torchaudio-augmentations)](https://pepy.tech/project/torchaudio-augmentations)
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4748582.svg)](https://zenodo.org/record/4748582#)
 
-Audio augmentations library for PyTorch for audio in the time-domain, with support for stochastic data augmentations as used often in self-supervised / contrastive learning. One can apply a single stochastic augmentation or create as many stochastically augmented examples from a single interface.
+Audio data augmentations library for PyTorch for audio in the time-domain. The focus of this repository is to:
+- Provide many audio transformations in an easy Python interface.
+- Have a high test coverage.
+- Easily control stochastic (sequential) audio transformations.
+- Make every audio transformation differentiable with PyTorch's `nn.Module`.
+- Optimise audio transformations for CPU and GPU.
 
-This package follows the conventions set out by `torchaudio`, with audio defined as a vector of `[channel, time]`. Each individual augmentation can be initialized on its own, or be wrapped around a `RandomApply` interface which will apply the augmentation with probability `p`.
+It supports stochastic transformations as used often in self-supervised, semi-supervised learning methods. One can apply a single stochastic augmentation or create as many stochastically transformed audio examples from a single interface.
+
+This package follows the conventions set out by `torchvision` and `torchaudio`, with audio defined as a tensor of `[channel, time]`, or a batched representation `[batch, channel, time]`. Each individual augmentation can be initialized on its own, or be wrapped around a `RandomApply` interface which will apply the augmentation with probability `p`.
 
 
 ## Usage
@@ -19,7 +27,7 @@ num_samples = sr * 5
 transforms = [
     RandomResizedCrop(n_samples=num_samples),
     RandomApply([PolarityInversion()], p=0.8),
-    RandomApply([Noise(min_snr=0.3, max_snr=0.5)], p=0.3),
+    RandomApply([Noise(min_snr=0.001, max_snr=0.005)], p=0.3),
     RandomApply([Gain()], p=0.2),
     HighLowPass(sample_rate=sr), # this augmentation will always be applied in this aumgentation chain!
     RandomApply([Delay(sample_rate=sr)], p=0.5),
@@ -35,7 +43,7 @@ We can also define a stochastic augmentation on multiple transformations. The fo
 ```python
 transforms = [
     RandomResizedCrop(n_samples=num_samples),
-    RandomApply([PolarityInversion(), Noise(min_snr=0.3, max_snr=0.5)], p=0.8),
+    RandomApply([PolarityInversion(), Noise(min_snr=0.001, max_snr=0.005)], p=0.8),
     RandomApply([Gain()], p=0.2),
     RandomApply([Delay(sample_rate=sr), Reverb(sample_rate=sr)], p=0.5)
 ]