update readme with config for individual accents

README.md

@@ -101,9 +101,9 @@ afrispeech = load_dataset("tobiolatunji/afrispeech-200", "all")
 
 The entire dataset is ~120GB and may take about 2hrs to download depending on internet speed/bandwidth. If you have disk space or bandwidth limitations, you can use `streaming` mode described below to work with smaller subsets of the data. 
 
-In future updates, you will be able to pass a config to the `load_dataset` function and download only a subset of the data corresponding to a specific accent of interest. The example provided below is `isizulu`.
+Alterntively you are able to pass a config to the `load_dataset` function and download only a subset of the data corresponding to a specific accent of interest. The example provided below is `isizulu`.
 
-For example, to download the isizulu config, simply specify the corresponding language config name. The list of supported accents is provided in `accent list` section below:
+For example, to download the isizulu config, simply specify the corresponding accent config name. The list of supported accents is provided in the `accent list` section below:
 ```python
 from datasets import load_dataset
 
@@ -141,10 +141,13 @@ afrispeech = load_dataset("tobiolatunji/afrispeech-200", "isizulu", split="train
 dataloader = DataLoader(afrispeech, batch_size=32)
 ```
 
+### Caveats
+Note that till the end of the ongoing [AfriSpeech ASR Challenge event](https://zindi.africa/competitions/intron-afrispeech-200-automatic-speech-recognition-challenge) (Feb - May 2023), the transcripts in the validation set are hidden and the test set will be unreleased till May 19, 2023.
+
 
 ### Fine-tuning Colab tutorial
 
-To walk through a complete colab tutorial that finetunes a wav2vec2 model on the afrispeech-200 dataset with `transformers`, take a look at this colab notebook [afrispeech/wav2vec2-colab-tutorial](https://colab.research.google.com/drive/1uZYew6pcgN6UE6sFDLohxD_HKivvDXzD?usp=sharing#scrollTo=_UEjJqGsQw24).
+To walk through a complete colab tutorial that finetunes a wav2vec2 model on the afrispeech-200 dataset with `transformers`, take a look at this colab notebook [afrispeech/wav2vec2-colab-tutorial](https://colab.research.google.com/drive/1uZYew6pcgN6UE6sFDLohxD_HKivvDXzD?usp=sharing).
 
 
 ### Supported Tasks and Leaderboards