Update README.md

README.md

@@ -163,6 +163,35 @@ Take down: We will comply to legitimate requests by removing the affected source
 | zh | Mandarin Chinese | 44 |
 
 
+### Usage
+
+Although webdataset can be used in a streaming fashion, it is recommended to first make a local copy fo the dataset using git clone.
+
+    git lfs install
+    git clone git@hf.co:datasets/TalTechNLP/voxlingua107_wds
+
+Then you can use the Python webdataset library to create an iterable dataset out of it:
+
+    import webdataset as wds
+    import random
+    train_files = glob.glob("voxlingua107_wds/train/**/*.tar") # you can also limit the training data to selected languages    
+    dev_files = glob.glob("voxlingua107_wds/dev/*.tar")
+    
+    random.shuffle(train_files)
+    
+    def mapper(sample):
+        # "audio" field represents 16 kHz raw audio
+        return {"audio": sample[0], "lang": sample[1]["lang"]}
+        
+    # Since each shard contains 500 samples for a single language, it is good to use a reasonably large buffer size to get nicely shuffled samples
+    buffer_size = 100000   
+    dataset = wds.WebDataset(train_urls, shardshuffle=2000).shuffle(buffer_size, initial=buffer_size).decode(wds.torch_audio).to_tuple("wav","json").map(mapper)
+    dev_dataset = wds.WebDataset(dev_urls).decode(wds.torch_audio).to_tuple("wav","json").map(mapper)
+    
+    train_iter = iter(dataset)
+    print(next(train_iter))
+    {'audio': (tensor([[-9.7656e-04, -8.5449e-04, -3.0518e-05,  ...,  2.7466e-03,
+          3.7842e-03,  5.1880e-03]]), 16000), 'lang': 'kn'}