README updates to include code snippets and correct leaderboard URL

README.md

@@ -33,6 +33,7 @@ task_categories:
   - [Dataset Summary](#dataset-summary)
   - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
   - [Languages](#languages)
+  - [How to use](#how-to-use)
 - [Dataset Structure](#dataset-structure)
   - [Data Instances](#data-instances)
   - [Data Fields](#data-fields)
@@ -57,7 +58,7 @@ task_categories:
 - **Homepage:** [MultiLingual LibriSpeech ASR corpus](http://www.openslr.org/94)
 - **Repository:** [Needs More Information]
 - **Paper:** [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://arxiv.org/abs/2012.03411)
-- **Leaderboard:** [Paperswithcode Leaderboard](https://paperswithcode.com/dataset/multilingual-librispeech)
+- **Leaderboard:** [🤗 Autoevaluate Leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=facebook%2Fmultilingual_librispeech&only_verified=0&task=automatic-speech-recognition&config=-unspecified-&split=-unspecified-&metric=wer)
 
 ### Dataset Summary
 
@@ -75,6 +76,55 @@ MLS dataset is a large multilingual corpus suitable for speech research. The dat
 
 The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish
 
+### How to use
+
+The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. 
+
+For example, to download the German config, simply specify the corresponding language config name (i.e., "german" for German):
+```python
+from datasets import load_dataset
+
+mls = load_dataset("facebook/multilingual_librispeech", "german", split="train")
+```
+
+Using the datasets library, 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 loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
+```python
+from datasets import load_dataset
+
+mls = load_dataset("facebook/multilingual_librispeech", "german", split="train", streaming=True)
+
+print(next(iter(mls)))
+```
+
+*Bonus*: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed).
+
+Local:
+
+```python
+from datasets import load_dataset
+from torch.utils.data.sampler import BatchSampler, RandomSampler
+
+mls = load_dataset("facebook/multilingual_librispeech", "german", split="train")
+batch_sampler = BatchSampler(RandomSampler(mls), batch_size=32, drop_last=False)
+dataloader = DataLoader(mls, batch_sampler=batch_sampler)
+```
+
+Streaming:
+
+```python
+from datasets import load_dataset
+from torch.utils.data import DataLoader
+
+mls = load_dataset("facebook/multilingual_librispeech", "german", split="train")
+dataloader = DataLoader(mls, batch_size=32)
+```
+
+To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets).
+
+### Example scripts
+
+Train your own CTC or Seq2Seq Automatic Speech Recognition models on MultiLingual Librispeech with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition).
+
 ## Dataset Structure
 
 ### Data Instances