Update music_genre.py

music_genre.py

@@ -1,10 +1,12 @@
-# import hashlib
 import os
 import random
 import datasets
 from datasets.tasks import ImageClassification
 
-_NAMES_1 = {1: "Classic", 2: "Non_classic"}
+_NAMES_1 = {
+    1: "Classic",
+    2: "Non_classic",
+}
 
 _NAMES_2 = {
     3: "Symphony",
@@ -14,7 +16,7 @@ _NAMES_2 = {
     7: "Pop",
     8: "Dance_and_house",
     9: "Indie",
-    10: "Soul_or_r_and_b",
+    10: "Soul_or_RnB",
     11: "Rock",
 }
 
@@ -30,7 +32,7 @@ _NAMES_3 = {
     16: "Dance_pop",
     17: "Classic_indie_pop",
     18: "Chamber_cabaret_and_art_pop",
-    10: "Soul_or_r_and_b",
+    10: "Soul_or_RnB",
     19: "Adult_alternative_rock",
     20: "Uplifting_anthemic_rock",
     21: "Soft_rock",
@@ -39,13 +41,13 @@ _NAMES_3 = {
 
 _DBNAME = os.path.basename(__file__).split(".")[0]
 
-_HOMEPAGE = f"https://www.modelscope.cn/datasets/ccmusic/{_DBNAME}"
+_HOMEPAGE = f"https://www.modelscope.cn/datasets/ccmusic-database/{_DBNAME}"
 
-_DOMAIN = f"https://www.modelscope.cn/api/v1/datasets/ccmusic/{_DBNAME}/repo?Revision=master&FilePath=data"
+_DOMAIN = f"https://www.modelscope.cn/api/v1/datasets/ccmusic-database/{_DBNAME}/repo?Revision=master&FilePath=data"
 
 _CITATION = """\
 @dataset{zhaorui_liu_2021_5676893,
-  author       = {Monan Zhou, Shenyang Xu, Zhaorui Liu, Zhaowen Wang, Feng Yu, Wei Li and Zijin Li},
+  author       = {Monan Zhou, Shenyang Xu, Zhaorui Liu, Zhaowen Wang, Feng Yu, Wei Li and Baoqiang Han},
   title        = {CCMusic: an Open and Diverse Database for Chinese and General Music Information Retrieval Research},
   month        = {mar},
   year         = {2024},
@@ -68,62 +70,52 @@ _URLS = {
 }
 
 
-class music_genre_Config(datasets.BuilderConfig):
-    def __init__(self, features, **kwargs):
-        super(music_genre_Config, self).__init__(
-            version=datasets.Version("1.2.0"), **kwargs
-        )
-        self.features = features
-
-
 class music_genre(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("1.2.0")
     BUILDER_CONFIGS = [
-        music_genre_Config(
-            name="eval",
-            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())
-                    ),
-                }
-            ),
-        ),
-        music_genre_Config(
-            name="default",
-            features=datasets.Features(
-                {
-                    "audio": datasets.Audio(sampling_rate=22050),
-                    "mel": 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())
-                    ),
-                }
-            ),
-        ),
+        datasets.BuilderConfig(name="default"),
+        datasets.BuilderConfig(name="eval"),
     ]
 
     def _info(self):
         return datasets.DatasetInfo(
-            features=self.config.features,
+            features=(
+                datasets.Features(
+                    {
+                        "audio": datasets.Audio(sampling_rate=22050),
+                        "mel": 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())
+                        ),
+                    }
+                )
+                if self.config.name == "default"
+                else 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())
+                        ),
+                    }
+                )
+            ),
             supervised_keys=("mel", "sec_level_label"),
             homepage=_HOMEPAGE,
-            license="mit",
+            license="CC-BY-NC-ND",
+            version="1.2.0",
             citation=_CITATION,
             description=_DESCRIPTION,
             task_templates=[
@@ -135,25 +127,25 @@ class music_genre(datasets.GeneratorBasedBuilder):
             ],
         )
 
-    # def _str2md5(self, original_string):
-    #     """
-    #     Calculate and return the MD5 hash of a given string.
-    #     Parameters:
-    #     original_string (str): The original string for which the MD5 hash is to be computed.
-    #     Returns:
-    #     str: The hexadecimal representation of the MD5 hash.
-    #     """
-    #     # Create an md5 object
-    #     md5_obj = hashlib.md5()
-    #     # Update the md5 object with the original string encoded as bytes
-    #     md5_obj.update(original_string.encode("utf-8"))
-    #     # Retrieve the hexadecimal representation of the MD5 hash
-    #     md5_hash = md5_obj.hexdigest()
-    #     return md5_hash
-
     def _split_generators(self, dl_manager):
         dataset = []
-        if self.config.name == "eval":
+        if self.config.name == "default":
+            files = {}
+            audio_files = dl_manager.download_and_extract(_URLS["audio"])
+            mel_files = dl_manager.download_and_extract(_URLS["mel"])
+            for path in dl_manager.iter_files([audio_files]):
+                fname: str = os.path.basename(path)
+                if fname.endswith(".mp3"):
+                    files[fname.split(".mp")[0]] = {"audio": path}
+
+            for path in dl_manager.iter_files([mel_files]):
+                fname = os.path.basename(path)
+                if fname.endswith(".jpg"):
+                    files[fname.split(".jp")[0]]["mel"] = path
+
+            dataset = list(files.values())
+
+        else:
             data_files = dl_manager.download_and_extract(_URLS["eval"])
             for path in dl_manager.iter_files([data_files]):
                 if os.path.basename(path).endswith(".jpg") and "mel" in path:
@@ -169,22 +161,6 @@ class music_genre(datasets.GeneratorBasedBuilder):
                         }
                     )
 
-        else:
-            files = {}
-            audio_files = dl_manager.download_and_extract(_URLS["audio"])
-            mel_files = dl_manager.download_and_extract(_URLS["mel"])
-            for path in dl_manager.iter_files([audio_files]):
-                fname: str = os.path.basename(path)
-                if fname.endswith(".mp3"):
-                    files[fname.split(".mp")[0]] = {"audio": path}
-
-            for path in dl_manager.iter_files([mel_files]):
-                fname: str = os.path.basename(path)
-                if fname.endswith(".jpg"):
-                    files[fname.split(".jp")[0]]["mel"] = path
-
-            dataset = list(files.values())
-
         random.shuffle(dataset)
         data_count = len(dataset)
         p80 = int(data_count * 0.8)
@@ -213,7 +189,7 @@ class music_genre(datasets.GeneratorBasedBuilder):
             spect = spect.replace("/", "\\")
             substr_index = dirpath.find(spect)
 
-        labstr: str = dirpath[substr_index + len(spect) :]
+        labstr = dirpath[substr_index + len(spect) :]
         labs = labstr.split("/")
         if len(labs) < 2:
             labs = labstr.split("\\")
@@ -224,12 +200,11 @@ class music_genre(datasets.GeneratorBasedBuilder):
             return int(labs[-1].split("_")[0])
 
     def _generate_examples(self, files):
-        if self.config.name == "eval":
+        if self.config.name == "default":
             for i, path in enumerate(files):
                 yield i, {
+                    "audio": path["audio"],
                     "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)],
@@ -238,8 +213,9 @@ class music_genre(datasets.GeneratorBasedBuilder):
         else:
             for i, path in enumerate(files):
                 yield i, {
-                    "audio": path["audio"],
                     "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)],