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Python code that creates the dataset.

down_on_copy.py

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+import datasets
+import os
+import shutil
+#from datasets import load_dataset
+train0, validation0, test0  = datasets.load_dataset("superb", "ks", split=["train","validation","test"])
+
+labels = train0.features["label"].names
+label2id = {x: labels.index(x) for x in labels}
+# id2label = {str(id): label for label, id in label2id.items()}
+
+down_id = label2id['down']
+on_id = label2id['on']
+
+train0_down = train0.filter(lambda example: example['label'] == down_id)
+train0_on = train0.filter(lambda example: example['label'] == on_id)
+
+shutil.rmtree('/home/mr249/ac_h/down_on/data/train/down',ignore_errors=True)
+os.mkdir('/home/mr249/ac_h/down_on/data/train/down')
+shutil.rmtree('/home/mr249/ac_h/down_on/data/train/on',ignore_errors=True)
+os.mkdir('/home/mr249/ac_h/down_on/data/train/on')
+
+for e in train0_down:
+    p = e['audio']['path']
+    newpath =  '/home/mr249/ac_h/down_on/data/train/down/{}'.format(p.split('/')[len(p.split('/')) - 1])
+    os.link(p,newpath)
+
+for e in train0_on:
+    p = e['audio']['path']
+    newpath =  '/home/mr249/ac_h/down_on/data/train/on/{}'.format(p.split('/')[len(p.split('/')) - 1])
+    os.link(p,newpath)    
+
+validation0_down = validation0.filter(lambda example: example['label'] == down_id)
+validation0_on = validation0.filter(lambda example: example['label'] == on_id)
+
+shutil.rmtree('/home/mr249/ac_h/down_on/data/validation/down',ignore_errors=True)
+os.mkdir('/home/mr249/ac_h/down_on/data/validation/down')
+shutil.rmtree('/home/mr249/ac_h/down_on/data/validation/on',ignore_errors=True)
+os.mkdir('/home/mr249/ac_h/down_on/data/validation/on')
+
+for e in validation0_down:
+    p = e['audio']['path']
+    newpath =  '/home/mr249/ac_h/down_on/data/validation/down/{}'.format(p.split('/')[len(p.split('/')) - 1])
+    os.link(p,newpath)
+
+for e in validation0_on:
+    p = e['audio']['path']
+    newpath =  '/home/mr249/ac_h/down_on/data/validation/on/{}'.format(p.split('/')[len(p.split('/')) - 1])
+    os.link(p,newpath)    
+
+test0_down = test0.filter(lambda example: example['label'] == down_id)
+test0_on = test0.filter(lambda example: example['label'] == on_id)
+
+shutil.rmtree('/home/mr249/ac_h/down_on/data/test/down',ignore_errors=True)
+os.mkdir('/home/mr249/ac_h/down_on/data/test/down')
+shutil.rmtree('/home/mr249/ac_h/down_on/data/test/on',ignore_errors=True)
+os.mkdir('/home/mr249/ac_h/down_on/data/test/on')
+
+for e in test0_down:
+    p = e['audio']['path']
+    newpath =  '/home/mr249/ac_h/down_on/data/test/down/{}'.format(p.split('/')[len(p.split('/')) - 1])
+    os.link(p,newpath)
+
+for e in test0_on:
+    p = e['audio']['path']
+    newpath =  '/home/mr249/ac_h/down_on/data/test/on/{}'.format(p.split('/')[len(p.split('/')) - 1])
+    os.link(p,newpath)    
+

down_on_create.py

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+import datasets
+#from datasets import load_dataset
+train0, validation0, test0  = datasets.load_dataset("superb", "ks", split=["train","validation","test"])
+
+labels = train0.features["label"].names
+label2id = {x: labels.index(x) for x in labels}
+id2label = {str(id): label for label, id in label2id.items()}
+
+down_id = label2id['down']
+on_id = label2id['on']
+
+# This filters 51094 rows to 3706 rows, with features ['file', 'audio', 'label']
+train1 = train0.filter(lambda example: example['label'] == down_id or example['label'] == on_id)
+
+# 521 rows
+validation1 = validation0.filter(lambda example: example['label'] == down_id or example['label'] == on_id)
+# 499 rows
+test1 = test0.filter(lambda example: example['label'] == down_id or example['label'] == on_id)
+
+train1.to_csv('/home/mr249/ac_h/do1/tmp/train1.csv')
+validation1.to_csv('/home/mr249/ac_h/do1/tmp/validation1.csv')
+test1.to_csv('/home/mr249/ac_h/do1/tmp/test1.csv')
+
+# Fix the labels. TODO Put this in this python program.
+# This is kindof in the wrong location?
+# /home/mr249/ac_h/do1
+# cat tmp/train1.csv | awk -f ../script/fix-down-on-labels.awk > train.csv
+# cat tmp/validation1.csv | awk -f ../script/fix-down-on-labels.awk > validation.csv
+# cat tmp/test1.csv | awk -f ../script/fix-down-on-labels.awk > test.csv
+
+# Create new datasets from the csv files
+
+train2 = datasets.Dataset.from_csv('/home/mr249/ac_h/do1/train.csv','train')
+validation2 = datasets.Dataset.from_csv('/home/mr249/ac_h/do1/validation.csv','validation')
+test2 = datasets.Dataset.from_csv('/home/mr249/ac_h/do1/test.csv','test')
+ 
+# Above, the second argument names the split. It may not be relevant.
+# validation2.split 
+# NamedSplit('validation')
+
+# Add the label names. It does not change the examples, which have ints as labels.
+new_features = train2.features.copy()
+new_features["label"] = datasets.ClassLabel(names=['down', 'on'],id=None)
+train2 = train2.cast(new_features)
+validation2 = validation2.cast(new_features)
+test2 = test2.cast(new_features)
+
+# Combine them into a DatasetDict.
+down_on = datasets.DatasetDict({
+    "train": train2,
+    "validation": validation2,
+    "test": test2,
+})
+
+# Save to disk
+down_on.save_to_disk('/home/mr249/ac_h/down_on')
+
+# Load from disk
+# However, we prefer to load from the hub.
+# down_on2 = datasets.load_from_disk('/home/mr249/ac_h/down_on')
+
+# Push to hub
+# See https://huggingface.co/docs/datasets/upload_dataset
+from huggingface_hub import login
+login()
+# It prompts for the token
+down_on.push_to_hub("MatsRooth/down_on",private=False,embed_external_files=True)
+
+# Now the dataset can be loaded from the hub!
+train3 = load_dataset("MatsRooth/down_on", split="train")
+
+# When running training, the names are wrong
+# FileNotFoundError: [Errno 2] No such file or directory: "{'bytes': None, 'path':
+# '/home/mr249/.cache/huggingface/datasets/downloads/extracted/5836831ec57281eff9b
+# 1882385bf370d016058e6cba1fd7ff1dcb68cd8cddefd/down/28ed6bc9_nohash_4.wav'}"
+
+
+# train3.features['label']
+# ClassLabel(names=['down', 'on'], id=None)
+

down_on_hub.py

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+# See https://huggingface.co/docs/datasets/audio_dataset
+from datasets import load_dataset
+
+# Upload the down_on dataset to huggingfacehub
+
+# This magically does the right thing to create the dataset, including
+# setting the label names and IDs.
+ds = load_dataset("audiofolder", data_dir="down_on/data")
+
+from huggingface_hub import login
+login()
+ds.push_to_hub("MatsRooth/down_on",private=False,embed_external_files=True)
+
+