Update README.md

README.md

@@ -376,26 +376,26 @@ Abkhaz, Arabic, Armenian, Assamese, Asturian, Azerbaijani, Basaa, Bashkir, Basqu
 
 ### How to use
 
-You should be able to plug and play this dataset in your existing Machine Learning workflow as follows:
+To get started, you should be able to plug-and-play this dataset in your existing Machine Learning workflow
 
-You can download the entire dataset (or a particular split) to your local drive by using the `load_dataset` function.
+The entire dataset (or a particular split) can be downloaded to your local drive by using the `load_dataset` function.
 ```python
 from datasets import load_dataset
 
 CV_11_hi_train = load_dataset("mozilla-foundation/common_voice_11_0", "hi", split="train")
 ```
 
-Using datasets, you can also 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 allows one to iterate over the dataset without downloading it on 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)
 
-# You'll now be able to iterate through the stream and fetch individual data points as you need them
+# Iterate through the stream and fetch individual data points as you need them
 print(next(iter(CV_11_hi_train_stream)))
 ```
 
-Bonus: You can create a pytorch dataloader with directly with the downloaded/ streamed datasets.
+*Bonus*: Create a PyTorch dataloader with directly with the downloaded/ streamed datasets.
 ```python
 from datasets import load_dataset
 from torch.utils.data.sampler import BatchSampler, RandomSampler
@@ -405,7 +405,7 @@ batch_sampler = BatchSampler(RandomSampler(ds), batch_size=32, drop_last=False)
 dataloader = DataLoader(ds, batch_sampler=batch_sampler)
 ```
 
-and, for streaming datasets
+ofcourse, you can do the same with streaming datasets as well.
 ```python
 from datasets import load_dataset
 from torch.utils.data import DataLoader
@@ -416,7 +416,7 @@ dataloader = DataLoader(ds, batch_size=32)
 
 ### 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).
 
 ## Dataset Structure