upd script

README.md

@@ -43,7 +43,7 @@ Chinese, English
 m_bel, f_bel, m_folk, f_folk
 
 ### Data Splits
-train, validation, test
+train(4314), validation(539), test(540)
 
 ## Dataset Creation
 ### Curation Rationale

bel_canto.py

@@ -2,7 +2,7 @@ import os
 import socket
 import random
 import datasets
-from datasets.tasks import AudioClassification
+from datasets.tasks import ImageClassification
 
 
 _NAMES = {
@@ -29,9 +29,7 @@ _CITATION = """\
 """
 
 _DESCRIPTION = """\
-This database contains hundreds of acapella singing clips that are sung in two styles, 
-Bel Conto and Chinese national singing style by professional vocalists. 
-All of them are sung by professional vocalists and were recorded in professional commercial recording studios.
+This database contains hundreds of acapella singing clips that are sung in two styles, Bel Conto and Chinese national singing style by professional vocalists. All of them are sung by professional vocalists and were recorded in professional commercial recording studios.
 """
 
 
@@ -42,32 +40,33 @@ class bel_canto(datasets.GeneratorBasedBuilder):
                 {
                     "mel": datasets.Image(),
                     "cqt": datasets.Image(),
+                    "chroma": datasets.Image(),
                     "label": datasets.features.ClassLabel(names=_NAMES['all']),
                     "gender": datasets.features.ClassLabel(names=_NAMES['gender']),
-                    "singing_method": datasets.features.ClassLabel(names=_NAMES['singing_method']),
+                    "singing_method": datasets.features.ClassLabel(names=_NAMES['singing_method'])
                 }
             ),
-            supervised_keys=("audio", "label"),
+            supervised_keys=("mel", "label"),
             homepage=_HOMEPAGE,
             license="mit",
             citation=_CITATION,
             description=_DESCRIPTION,
             task_templates=[
-                AudioClassification(
-                    task="audio-classification",
-                    audio_column="audio",
-                    label_column="label",
+                ImageClassification(
+                    task="image-classification",
+                    image_column="mel",
+                    label_column="label"
                 )
-            ],
+            ]
         )
 
     def _cdn_url(self, ip='127.0.0.1', port=80):
         try:
             # easy for local test
             with socket.create_connection((ip, port), timeout=5):
-                return f'http://{ip}/{_NAME}/data/data.zip'
+                return f'http://{ip}/{_NAME}/data/belcanto_data.zip'
         except (socket.timeout, socket.error):
-            return f"{_HOMEPAGE}/resolve/main/data/data.zip"
+            return f"{_HOMEPAGE}/resolve/main/data/belcanto_data.zip"
 
     def _split_generators(self, dl_manager):
         data_files = dl_manager.download_and_extract(self._cdn_url())
@@ -87,32 +86,33 @@ class bel_canto(datasets.GeneratorBasedBuilder):
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "files": dataset[:p80],
-                },
+                    "files": dataset[:p80]
+                }
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
                 gen_kwargs={
-                    "files": dataset[p80:p90],
-                },
+                    "files": dataset[p80:p90]
+                }
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
-                    "files": dataset[p90:],
-                },
-            ),
+                    "files": dataset[p90:]
+                }
+            )
         ]
 
     def _generate_examples(self, files):
-        for i, path in enumerate(files):
-            file_name = os.path.basename(path)
-            sex = file_name.split('_')[2].split(' (')[0]
-            method = file_name.split('_')[1].lower()
+        for i, fpath in enumerate(files):
+            dirname = os.path.basename(os.path.dirname(fpath))
+            sex = dirname.split('_')[0]
+            method = dirname.split('_')[1]
             yield i, {
-                "mel": path,
-                "cqt": path.replace('/mel/', '/cqt/').replace('\\mel\\', '\\cqt\\'),
-                "label": f'{sex}_{method}',
+                "mel": fpath,
+                "cqt": fpath.replace('mel', 'cqt'),
+                "chroma": fpath.replace('mel', 'chroma'),
+                "label": dirname,
                 "gender": 'male' if sex == 'm' else 'female',
-                "singing_method": 'Bel_Canto' if method == 'bel' else 'Folk_Singing',
+                "singing_method": 'Bel_Canto' if method == 'bel' else 'Folk_Singing'
             }