Making README more robust and verbose

1. Updates old URLs to SpeechBench.
2. Adds code snippet for playing with the datasets.
3. Adds example scripts to further leverage this dataset.

README.md

@@ -366,7 +366,7 @@ Take a look at the [Languages](https://commonvoice.mozilla.org/en/languages) pag
 ### Supported Tasks and Leaderboards
 
 The results for models trained on the Common Voice datasets are available via the 
-[🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench)
+[🤗 Autoevaluate Leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=mozilla-foundation%2Fcommon_voice_11_0&only_verified=0&task=automatic-speech-recognition&config=ar&split=test&metric=wer)
 
 ### Languages
 
@@ -374,6 +374,50 @@ The results for models trained on the Common Voice datasets are available via th
 Abkhaz, Arabic, Armenian, Assamese, Asturian, Azerbaijani, Basaa, Bashkir, Basque, Belarusian, Bengali, Breton, Bulgarian, Cantonese, Catalan, Central Kurdish, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Danish, Dhivehi, Dutch, English, Erzya, Esperanto, Estonian, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Hakha Chin, Hausa, Hill Mari, Hindi, Hungarian, Igbo, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Kurmanji Kurdish, Kyrgyz, Latvian, Lithuanian, Luganda, Macedonian, Malayalam, Maltese, Marathi, Meadow Mari, Moksha, Mongolian, Nepali, Norwegian Nynorsk, Odia, Persian, Polish, Portuguese, Punjabi, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Santali (Ol Chiki), Saraiki, Sardinian, Serbian, Slovak, Slovenian, Sorbian, Upper, Spanish, Swahili, Swedish, Taiwanese (Minnan), Tamil, Tatar, Thai, Tigre, Tigrinya, Toki Pona, Turkish, Twi, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Votic, Welsh
 ```
 
+### How to use
+
+You should be able to plug and play this dataset in your existing Machine Learning workflow as follows:
+
+You can download the entire dataset (or a particular split) 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.
+```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
+print(next(iter(CV_11_hi_train_stream)))
+```
+
+Bonus: You can 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
+
+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)
+```
+
+and, for streaming datasets
+```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)
+```
+
+### Example scripts
+
+
+
 ## Dataset Structure
 
 ### Data Instances