minor adjustments to readme and license (#16)

Author: gfdb

LICENSE

@@ -175,18 +175,7 @@
 
    END OF TERMS AND CONDITIONS
 
-   APPENDIX: How to apply the Apache License to your work.
-
-      To apply the Apache License to your work, attach the following
-      boilerplate notice, with the fields enclosed by brackets "[]"
-      replaced with your own identifying information. (Don't include
-      the brackets!)  The text should be enclosed in the appropriate
-      comment syntax for the file format. We also recommend that a
-      file or class name and description of purpose be included on the
-      same "printed page" as the copyright notice for easier
-      identification within third-party archives.
-
-   Copyright [yyyy] [name of copyright owner]
+   Copyright 2026 Gianfranco Dumoulin Bertucci
 
    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.

readme.md

@@ -1,14 +1,9 @@
-# 🎛️ Wav2Aug: Toward Universal Time-Domain Speech Augmentation
+# 🎛️ Wav2Aug: Task-Agnostic Waveform Augmentation Beyond ASR
 
-A minimalistic PyTorch-based audio augmentation library for speech and audio augmentation. The goal of this library is to provide a general purpose speech augmentation policy that can be used on any task and perform well without having to tune augmentation hyperparameters. Just install, and start augmenting. Applies two random augmentations per call.
+A minimalistic PyTorch-based audio augmentation library for speech augmentation. The goal of this library is to provide a general purpose speech augmentation policy that can be used on any task and perform reasonably well without having to tune augmentation hyperparameters. Just install, and start augmenting. Applies two random augmentations per call. Just install and start augmenting!
 
-![Diagram](https://raw.githubusercontent.com/gfdb/wav2aug/main/wav2aug.png)
-
-## ⚙️ Features
 
-* **9 core augmentations**: amplitude scaling/clipping, noise addition, frequency dropout, polarity inversion, chunk swapping, speed perturbation, time dropout, and babble noise.
-* **Simplicity**: just install and start augmenting!
-* **Randomness**: all stochastic ops use PyTorch RNGs. Set a single seed and be done, e.g. torch.manual_seed(0); torch.cuda.manual_seed_all(0)
+![Diagram](https://raw.githubusercontent.com/gfdb/wav2aug/main/wav2aug.png)
 
 ## 📦 Installation
 
@@ -38,6 +33,10 @@ wavs = torch.randn(3, 50000)
 lens = torch.ones((wavs.size(0)))
 
 aug_wavs, aug_lens = augmenter(wavs, lens)
+
+# or just
+
+aug_wavs = augmenter(wavs)
 ```
 
 That's it!
@@ -53,6 +52,9 @@ That's it!
 * 🕳️ **Time Dropout**: Random silence insertion
 * 👥 **Babble Noise**: Multi-speaker background (auto-enabled with sufficient buffer)
 
+**Randomness**: all stochastic ops use PyTorch RNGs. Set a single seed and be done, e.g. torch.manual_seed(0); torch.cuda.manual_seed_all(0)
+
+
 ## 🛠️ Development Installation
 
 ### uv
@@ -109,8 +111,8 @@ pytest -q tests/
 
 * Issues and PRs are welcome and encouraged!
 
-* Bug reports: please open an issue with a minimal repro (env, torch/torchaudio/torchcodec versions, code snippet, expected vs. actual, traceback).
+* Bug reports: please open an issue with a minimal repro (env, dep versions, code snippet, expected vs. actual, traceback, etc.)
 
 * Feature requests: please open an issue with use-case and proposed feature.
 
-* PRs: keep them focused. Add tests when behavior changes. Don't forget to run formatters and tests before submitting!
+* PRs: Add tests for new stuff or when behavior changes. Also, don't forget to run formatters and tests before submitting!