test jpg dataset

data/labels.csv

@@ -1,1709 +0,0 @@
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data/{dataset.zip → mel.zip}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0edf6aceaf2dc1eeafe373e8017e4aee00b1b6594fd2531d594648f67e142277
-size 246916559
+oid sha256:956087bc37239e131a0984af02c33a6d812b7ef09f7571ef6de3c516f4c0f952
+size 77204448

music_genre.py

@@ -1,7 +1,6 @@
 import os
 import random
 import datasets
-import pandas as pd
 from datasets.tasks import ImageClassification
 
 _NAMES = [
@@ -50,10 +49,7 @@ This database contains about 1700 musical pieces (.mp3 format)
 with lengths of 270-300s that are divided into 17 genres in total. 
 """
 
-_URLS = {
-    'zip': _HOMEPAGE + "/resolve/main/data/dataset.zip",
-    'csv': _HOMEPAGE + "/resolve/main/data/labels.csv"
-}
+_URL = _HOMEPAGE + "/resolve/main/data/mel.zip"
 
 
 class music_genre(datasets.GeneratorBasedBuilder):
@@ -83,14 +79,12 @@ class music_genre(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        data_files = dl_manager.download_and_extract(_URLS['zip'])
+        data_files = dl_manager.download_and_extract(_URL)
         files = dl_manager.iter_files([data_files])
-        csv_file = dl_manager.download(_URLS['csv'])
-        labels = pd.read_csv(csv_file, index_col='id')
 
         dataset = []
         for path in files:
-            if os.path.basename(path).endswith(".png"):
+            if os.path.basename(path).endswith(".jpg"):
                 dataset.append(path)
 
         random.shuffle(dataset)
@@ -102,38 +96,34 @@ class music_genre(datasets.GeneratorBasedBuilder):
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "files": dataset[:p80],
-                    "labels": labels
+                    "files": dataset[:p80]
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
                 gen_kwargs={
-                    "files": dataset[p80:p90],
-                    "labels": labels
+                    "files": dataset[p80:p90]
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
-                    "files": dataset[p90:],
-                    "labels": labels
+                    "files": dataset[p90:]
                 },
             ),
         ]
 
-    def _generate_examples(self, files, labels):
-        for path in files:
-            i = int(os.path.basename(path)[:-4])
-            sec_level_label = int(labels.iloc[i]['sec_level_label'])
-            thr_level_label = int(labels.iloc[i]['thr_level_label'])
-            if thr_level_label == 0:
-                thr_level_label = sec_level_label
+    def _generate_examples(self, files):
+        for i, path in enumerate(files):
+            # i = int(os.path.basename(path)[:-4])
+            # sec_level_label = int(labels.iloc[i]['sec_level_label'])
+            # thr_level_label = int(labels.iloc[i]['thr_level_label'])
+            # if thr_level_label == 0:
+            #     thr_level_label = sec_level_label
 
             yield i, {
                 "image": path,
-                "fst_level_label": _NAMES[int(labels.iloc[i]['fst_level_label'])],
-                "sec_level_label": _NAMES[sec_level_label],
-                "thr_level_label": _NAMES[thr_level_label],
-                "duration": labels.iloc[i]['duration'],
+                "fst_level_label": os.path.dirname(path),
+                "sec_level_label": os.path.dirname(path),
+                "thr_level_label": os.path.dirname(path)
             }