Add suggestions from the review from Polina and Sanchit

README.md

@@ -376,47 +376,50 @@ Abkhaz, Arabic, Armenian, Assamese, Asturian, Azerbaijani, Basaa, Bashkir, Basqu
 
 ### How to use
 
-To get started, you should be able to plug-and-play this dataset in your existing Machine Learning workflow
+The 'datasets' library allows you to load and pre-process your dataset in pure Python at scale. There's no need to rely on hacky shell scripts and decades old C/C++ pre-processing scripts anymore.
 
-The entire dataset (or a particular split) can be downloaded to your local drive by using the `load_dataset` function.
+The minimalistic API ensure that you can plug-and-play this dataset in your existing Machine Learning workflow with just a few lines of code.
+
+The entire dataset (or a particular split) can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. To download the Hindi split, simply specify the corresponding dataset config name:
 ```python
 from datasets import load_dataset
 
-CV_11_hi_train = load_dataset("mozilla-foundation/common_voice_11_0", "hi", split="train")
+cv_11 = load_dataset("mozilla-foundation/common_voice_11_0", "hi", split="train")
 ```
 
-Using the datasets library, you can stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode allows one to iterate over the dataset without downloading it on disk.
+Using the datasets library, you can stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
 ```python
 from datasets import load_dataset
 
-CV_11_hi_train_stream = load_dataset("mozilla-foundation/common_voice_11_0", "hi", split="train", streaming=True)
+cv_11 = load_dataset("mozilla-foundation/common_voice_11_0", "hi", split="train", streaming=True)
 
-# Iterate through the stream and fetch individual data points as you need them
-print(next(iter(CV_11_hi_train_stream)))
+print(next(iter(cv_11)))
 ```
 
-*Bonus*: Create a PyTorch dataloader with directly with the downloaded/ streamed datasets.
+*Bonus*: Create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) with directly with the downloaded/ streamed datasets.
 ```python
 from datasets import load_dataset
 from torch.utils.data.sampler import BatchSampler, RandomSampler
 
-ds = load_dataset("mozilla-foundation/common_voice_11_0", "hi", split="train")
-batch_sampler = BatchSampler(RandomSampler(ds), batch_size=32, drop_last=False)
-dataloader = DataLoader(ds, batch_sampler=batch_sampler)
+cv_11 = load_dataset("mozilla-foundation/common_voice_11_0", "hi", split="train")
+batch_sampler = BatchSampler(RandomSampler(cv_11), batch_size=32, drop_last=False)
+dataloader = DataLoader(cv_11, batch_sampler=batch_sampler)
 ```
 
-ofcourse, you can do the same with streaming datasets as well.
+ofcourse, you can also create a dataloader with streaming datasets as well.
 ```python
 from datasets import load_dataset
 from torch.utils.data import DataLoader
 
-ds = load_dataset("mozilla-foundation/common_voice_11_0", "hi", split="train")
-dataloader = DataLoader(ds, batch_size=32)
+cv_11 = load_dataset("mozilla-foundation/common_voice_11_0", "hi", split="train")
+dataloader = DataLoader(cv_11, batch_size=32)
 ```
 
+To find out more about loading and preparing audio datasets, head over to [hf.coblog/audio-datasets](https://huggingface.co/blog/audio-datasets).
+
 ### Example scripts
 
-Train your own CTC or Seq2Seq Automatic Speech Recognition models with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition).
+Train your own CTC or Seq2Seq Automatic Speech Recognition models on Common Voice 11 with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition).
 
 ## Dataset Structure