Update README.md

README.md

@@ -62,6 +62,59 @@ configs:
 ---
 # speechocean762: A non-native English corpus for pronunciation scoring task
 
+## How to use?
+
+you can load data using
+
+```py
+speechocean762_dataset = load_dataset('seba3y/speechocean762')
+```
+```py
+>> speechocean762_dataset
+DatasetDict({
+    train: Dataset({
+        features: ['spk', 'age', 'gender', 'utt_name', 'audio', 'utt_text', 'utt_accuracy', 'utt_completeness', 'utt_fluency', 'utt_prosodic', 'utt_total', 'words', 'words_accuracy', 'words_stress', 'words_total', 'phones', 'phones_godness'],
+        num_rows: 2500
+    })
+    test: Dataset({
+        features: ['spk', 'age', 'gender', 'utt_name', 'audio', 'utt_text', 'utt_accuracy', 'utt_completeness', 'utt_fluency', 'utt_prosodic', 'utt_total', 'words', 'words_accuracy', 'words_stress', 'words_total', 'phones', 'phones_godness'],
+        num_rows: 2500
+    })
+})
+```
+Features are ordered as following
+1- Demographic    featurs: `'spk', 'age', 'gender', 'utt_name'`
+2- Sentence-level featurs: `'audio', 'utt_text', 'utt_accuracy', 'utt_completeness', 'utt_fluency', 'utt_prosodic', 'utt_total'`
+3- Word-level     featurs: `'words', 'words_accuracy', 'words_stress', 'words_total'`
+4- Phoneme-level  featurs: `'phones', 'phones_godness'`
+
+```py
+>> speechocean762_dataset['train'][0]
+```
+```py
+{'spk': 1,
+ 'age': 6,
+ 'gender': 'm',
+ 'utt_name': 10011,
+ 'audio': {'path': '000010011.WAV',
+  'array': array([-9.46044922e-04, -2.38037109e-03, -1.31225586e-03, ...,
+         -9.15527344e-05,  3.05175781e-04, -2.44140625e-04]),
+  'sampling_rate': 16000},
+ 'utt_text': 'WE CALL IT BEAR',
+ 'utt_accuracy': 8,
+ 'utt_completeness': 10.0,
+ 'utt_fluency': 9,
+ 'utt_prosodic': 9,
+ 'utt_total': 8,
+ 'words': "['WE', 'CALL', 'IT', 'BEAR']",
+ 'words_accuracy': '[10, 10, 10, 6]',
+ 'words_stress': '[10, 10, 10, 10]',
+ 'words_total': '[10, 10, 10, 6]',
+ 'phones': "[['W', 'IY0'], ['K', 'AO0', 'L'], ['IH0', 'T'], ['B', 'EH0', 'R']]",
+ 'phones_godness': '[[2.0, 2.0], [2.0, 1.8, 1.8], [2.0, 2.0], [2.0, 1.0, 1.0]]'}
+```
+For word-level features, the 'words' in each sample is a list containing words, while 'words_accuracy', 'words_stress', and 'words_total' are lists of the same length as the words. The mapping is such that the first word corresponds to the first value in 'words_accuracy', and so on. On the other hand, for phoneme-level features, the 'phones' in each sample is a 2D list, with each sublist corresponding to a single word
+
 ## Introduction
 Pronunciation scoring is a crucial technology in computer-assisted language learning (CALL) systems. The pronunciation quality scores might be given at phoneme-level, word-level, and sentence-level for a typical pronunciation scoring task.