Upload 2 files

data/data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7484d529402647aaff937494e3254bb4a46ef2f95ef4804e3ab9d5e6566a5b0b
+size 4358927179

music_genre.py

@@ -55,11 +55,7 @@ This database contains about 1700 musical pieces (.mp3 format)
 with lengths of 270-300s that are divided into 17 genres in total. 
 """
 
-_URLS = {
-    "mel": _HOMEPAGE + "/resolve/main/data/mel.zip",
-    "cqt": _HOMEPAGE + "/resolve/main/data/cqt.zip",
-    "chroma": _HOMEPAGE + "/resolve/main/data/chroma.zip"
-}
+_URL = _HOMEPAGE + "/resolve/main/data/data.zip"
 
 
 class music_genre(datasets.GeneratorBasedBuilder):
@@ -68,6 +64,8 @@ class music_genre(datasets.GeneratorBasedBuilder):
             features=datasets.Features(
                 {
                     "mel": datasets.Image(),
+                    "cqt": datasets.Image(),
+                    "chroma": datasets.Image(),
                     "fst_level_label": datasets.features.ClassLabel(names=list(_NAMES_1.values())),
                     "sec_level_label": datasets.features.ClassLabel(names=list(_NAMES_2.values())),
                     "thr_level_label": datasets.features.ClassLabel(names=list(_NAMES_3.values()))
@@ -88,29 +86,43 @@ class music_genre(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        splits = []
-        for spect in _URLS.keys():
-            data_files = dl_manager.download_and_extract(_URLS[spect])
-            files = dl_manager.iter_files([data_files])
-
-            dataset = []
-            for path in files:
-                if os.path.basename(path).endswith(".jpg"):
-                    dataset.append(path)
-
-            random.shuffle(dataset)
-
-            splits.append(
-                datasets.SplitGenerator(
-                    name=spect,
-                    gen_kwargs={
-                        "files": dataset,
-                        "spect": f"/{spect}/"
-                    }
-                )
-            )
-
-        return splits
+        data_files = dl_manager.download_and_extract(_URL)
+        files = dl_manager.iter_files([data_files])
+
+        dataset = []
+        for path in files:
+            if os.path.basename(path).endswith(".jpg"):
+                dataset.append({
+                    'mel': path,
+                    'cqt': path.replace('\\mel\\', '\\cqt\\').replace('/mel/', '/cqt/'),
+                    'chroma': path.replace('\\mel\\', '\\chroma\\').replace('/mel/', '/chroma/')
+                })
+
+        random.shuffle(dataset)
+        data_count = len(dataset)
+        p80 = int(data_count * 0.8)
+        p90 = int(data_count * 0.9)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "files": dataset[:p80]
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "files": dataset[p80:p90]
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "files": dataset[p90:]
+                },
+            ),
+        ]
 
     def _calc_label(self, path, depth, substr='/mel/'):
         spect = substr
@@ -130,11 +142,13 @@ class music_genre(datasets.GeneratorBasedBuilder):
         else:
             return 0
 
-    def _generate_examples(self, files, spect):
+    def _generate_examples(self, files):
         for i, path in enumerate(files):
             yield i, {
-                "mel": path,
-                "fst_level_label": _NAMES_1[self._calc_label(path, 1, spect)],
-                "sec_level_label": _NAMES_2[self._calc_label(path, 2, spect)],
-                "thr_level_label": _NAMES_3[self._calc_label(path, 3, spect)]
+                "mel": path['mel'],
+                "cqt": path['cqt'],
+                "chroma": path['chroma'],
+                "fst_level_label": _NAMES_1[self._calc_label(path['mel'], 1)],
+                "sec_level_label": _NAMES_2[self._calc_label(path['mel'], 2)],
+                "thr_level_label": _NAMES_3[self._calc_label(path['mel'], 3)]
             }