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music_genre.py

@@ -0,0 +1,135 @@
+import os
+import random
+import datasets
+import pandas as pd
+from datasets.tasks import ImageClassification
+
+# Once upload a new piano brand, please register its name here
+_NAMES = [
+    "0_none",
+    "1_classic",
+    "2_non_classic",
+    "3_symphony",
+    "4_opera",
+    "5_solo",
+    "6_chamber",
+    "7_pop",
+    "8_dance_and_house",
+    "9_indie",
+    "10_soul_or_r_and_b",
+    "11_rock"
+]
+
+_DBNAME = os.path.basename(__file__).split('.')[0]
+
+_HOMEPAGE = "https://huggingface.co/datasets/ccmusic-database/" + _DBNAME
+
+_CITATION = """\
+@dataset{zhaorui_liu_2021_5676893,
+  author       = {Zhaorui Liu and Zijin Li},
+  title        = {{Music Data Sharing Platform for Computational Musicology Research (CCMUSIC DATASET)}},
+  month        = nov,
+  year         = 2021,
+  publisher    = {Zenodo},
+  version      = {1.1},
+  doi          = {10.5281/zenodo.5676893},
+  url          = {https://doi.org/10.5281/zenodo.5676893}
+}
+"""
+
+_DESCRIPTION = """\
+This database contains about 1700 musical pieces (.mp3 format, downloaded from NetEase Cloud Music) 
+with lengths of 270-300s that are divided into 17 genres in total. 
+"""
+
+_URL = _HOMEPAGE + "/resolve/main/data/dataset.zip"
+_CSV = _HOMEPAGE + "/resolve/main/data/labels.csv"
+
+
+class music_genre(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "duration": datasets.Value("string"),
+                    "fst_level_label": datasets.features.ClassLabel(names=_NAMES),
+                    "sec_level_label": datasets.features.ClassLabel(names=_NAMES),
+                    "thr_level_label": datasets.features.ClassLabel(names=_NAMES),
+                }
+            ),
+            supervised_keys=("image", "fst_level_label"),
+            homepage=_HOMEPAGE,
+            license="mit",
+            citation=_CITATION,
+            description=_DESCRIPTION,
+            task_templates=[
+                ImageClassification(
+                    task="image-classification",
+                    image_column="image",
+                    label_column="fst_level_label",
+                )
+            ]
+        )
+
+    # def _set_to_label(self, dataset):
+    #     output = []
+    #     for path in dataset:
+    #         id = int(os.path.basename(path)[:-4])
+
+    def _split_generators(self, dl_manager):
+        data_files = dl_manager.download_and_extract(_URL)
+        files = dl_manager.iter_files([data_files])
+        labels = dl_manager.download(_CSV)
+
+        dataset = []
+        for _, path in enumerate(files):
+            dataset.append(path)
+
+        random.shuffle(dataset)
+        data_count = len(dataset)
+        p80 = int(data_count * 0.8)
+        p90 = int(data_count * 0.9)
+
+        # tra_set = dataset[:p80]
+        # val_set = dataset[p80:p90]
+        # tes_set = dataset[p90:]
+
+        # tra_label =
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "files": dataset[:p80],
+                    "labels": labels
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "files": dataset[p80:p90],
+                    "labels": labels
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "files": dataset[p90:],
+                    "labels": labels
+                },
+            ),
+        ]
+
+    def _generate_examples(self, files, labels):
+        label = pd.read_csv(labels, index_col='id')
+        for i, path in enumerate(files):
+            file_name = os.path.basename(path)
+            if file_name.endswith(".png"):
+                yield i, {
+                    "image": path,
+                    "duration": label.iloc[i]['duration'],
+                    "fst_level_label": _NAMES[label.iloc[i]['fst_level_label']],
+                    "sec_level_label": _NAMES[label.iloc[i]['sec_level_label']],
+                    "thr_level_label": _NAMES[label.iloc[i]['thr_level_label']],
+                }