Datasets:

ccmusic-database
/

music_genre

@@ -12,14 +12,81 @@ tags:
 pretty_name: Music Genre Dataset
 size_categories:
 - 10K<n<100K
-viewer: false
 ---
 # Dataset Card for Music Genre
 The Default dataset comprises approximately 1,700 musical pieces in .mp3 format, sourced from the NetEase music. The lengths of these pieces range from 270 to 300 seconds. All are sampled at the rate of 22,050 Hz. As the website providing the audio music includes style labels for the downloaded music, there are no specific annotators involved. Validation is achieved concurrently with the downloading process. They are categorized into a total of 16 genres.
 ## Dataset Structure
-<https://www.modelscope.cn/datasets/ccmusic-database/music_genre/dataPeview>
 <style>
   .genres td {
     vertical-align: middle !important;
@@ -123,21 +190,6 @@ Audio classification
 Multilingual
 ## Usage
-### Default Subset
-```python
-from datasets import load_dataset
-ds = load_dataset("ccmusic-database/music_genre")
-for item in ds["train"]:
-    print(item)
-for item in ds["validation"]:
-    print(item)
-for item in ds["test"]:
-    print(item)
-```
 ### Eval Subset
 ```python
 from datasets import load_dataset
@@ -155,7 +207,7 @@ for item in ds["test"]:
 ## Maintenance
 ```bash
-git clone [email protected]:datasets/ccmusic-database/music_genre
 cd music_genre
 ```

 pretty_name: Music Genre Dataset
 size_categories:
 - 10K<n<100K
+dataset_info:
+  - config_name: eval
+    features:
+      - name: mel
+        dtype: image
+      - name: cqt
+        dtype: image
+      - name: chroma
+        dtype: image
+      - name: fst_level_label
+        dtype:
+          class_label:
+            names:
+                '0': Classic
+                '1': Non_classic
+      - name: sec_level_label
+        dtype:
+          class_label:
+            names:
+                '0': Symphony
+                '1': Opera
+                '2': Solo
+                '3': Chamber
+                '4': Pop
+                '5': Dance_and_house
+                '6': Indie
+                '7': Soul_or_RnB
+                '8': Rock
+      - name: thr_level_label
+        dtype:
+          class_label:
+            names:
+                '0': Symphony
+                '1': Opera
+                '2': Solo
+                '3': Chamber
+                '4': Pop_vocal_ballad
+                '5': Adult_contemporary
+                '6': Teen_pop
+                '7': Contemporary_dance_pop
+                '8': Dance_pop
+                '9': Classic_indie_pop
+                '10': Chamber_cabaret_and_art_pop
+                '11': Soul_or_RnB
+                '12': Adult_alternative_rock
+                '13': Uplifting_anthemic_rock
+                '14': Soft_rock
+                '15': Acoustic_pop
+    splits:
+      - name: train
+        num_bytes: 19661943
+        num_examples: 29100
+      - name: validation
+        num_bytes: 2453757
+        num_examples: 3637
+      - name: test
+        num_bytes: 2456508
+        num_examples: 3638
+    download_size: 4436653005
+    dataset_size: 24572208
+configs:
+  - config_name: eval
+    data_files:
+      - split: train
+        path: eval/train/data-*.arrow
+      - split: validation
+        path: eval/validation/data-*.arrow
+      - split: test
+        path: eval/test/data-*.arrow
 ---
 # Dataset Card for Music Genre
 The Default dataset comprises approximately 1,700 musical pieces in .mp3 format, sourced from the NetEase music. The lengths of these pieces range from 270 to 300 seconds. All are sampled at the rate of 22,050 Hz. As the website providing the audio music includes style labels for the downloaded music, there are no specific annotators involved. Validation is achieved concurrently with the downloading process. They are categorized into a total of 16 genres.
 ## Dataset Structure
 <style>
   .genres td {
     vertical-align: middle !important;
 Multilingual
 ## Usage
 ### Eval Subset
 ```python
 from datasets import load_dataset
 ## Maintenance
 ```bash
+GIT_LFS_SKIP_SMUDGE=1 git clone [email protected]:datasets/ccmusic-database/music_genre
 cd music_genre
 ```

eval/dataset_dict.json ADDED Viewed

	@@ -0,0 +1 @@


1	+ {"splits": ["train", "validation", "test"]}

eval/test/dataset_info.json ADDED Viewed

	@@ -0,0 +1,108 @@

+{
+  "builder_name": "music_genre",
+  "citation": "",
+  "config_name": "eval",
+  "dataset_name": "music_genre",
+  "dataset_size": 24572208,
+  "description": "",
+  "download_checksums": {
+    "https://www.modelscope.cn/datasets/ccmusic-database/music_genre/resolve/master/data/eval.zip": {
+      "num_bytes": 4436653005,
+      "checksum": null
+    }
+  },
+  "download_size": 4436653005,
+  "features": {
+    "mel": {
+      "_type": "Image"
+    },
+    "cqt": {
+      "_type": "Image"
+    },
+    "chroma": {
+      "_type": "Image"
+    },
+    "fst_level_label": {
+      "names": [
+        "Classic",
+        "Non_classic"
+      ],
+      "_type": "ClassLabel"
+    },
+    "sec_level_label": {
+      "names": [
+        "Symphony",
+        "Opera",
+        "Solo",
+        "Chamber",
+        "Pop",
+        "Dance_and_house",
+        "Indie",
+        "Soul_or_RnB",
+        "Rock"
+      ],
+      "_type": "ClassLabel"
+    },
+    "thr_level_label": {
+      "names": [
+        "Symphony",
+        "Opera",
+        "Solo",
+        "Chamber",
+        "Pop_vocal_ballad",
+        "Adult_contemporary",
+        "Teen_pop",
+        "Contemporary_dance_pop",
+        "Dance_pop",
+        "Classic_indie_pop",
+        "Chamber_cabaret_and_art_pop",
+        "Soul_or_RnB",
+        "Adult_alternative_rock",
+        "Uplifting_anthemic_rock",
+        "Soft_rock",
+        "Acoustic_pop"
+      ],
+      "_type": "ClassLabel"
+    }
+  },
+  "homepage": "https://www.modelscope.cn/datasets/ccmusic-database/music_genre",
+  "license": "CC-BY-NC-ND",
+  "size_in_bytes": 4461225213,
+  "splits": {
+    "train": {
+      "name": "train",
+      "num_bytes": 19661943,
+      "num_examples": 29100,
+      "dataset_name": "music_genre"
+    },
+    "validation": {
+      "name": "validation",
+      "num_bytes": 2453757,
+      "num_examples": 3637,
+      "dataset_name": "music_genre"
+    },
+    "test": {
+      "name": "test",
+      "num_bytes": 2456508,
+      "num_examples": 3638,
+      "dataset_name": "music_genre"
+    }
+  },
+  "supervised_keys": {
+    "input": "mel",
+    "output": "sec_level_label"
+  },
+  "task_templates": [
+    {
+      "task": "image-classification",
+      "image_column": "mel",
+      "label_column": "sec_level_label"
+    }
+  ],
+  "version": {
+    "version_str": "0.0.0",
+    "major": 0,
+    "minor": 0,
+    "patch": 0
+  }
+}

eval/test/state.json ADDED Viewed

	@@ -0,0 +1,13 @@

+{
+  "_data_files": [
+    {
+      "filename": "data-00000-of-00001.arrow"
+    }
+  ],
+  "_fingerprint": "39a75b20be049dd1",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": null,
+  "_output_all_columns": false,
+  "_split": "test"
+}

eval/train/dataset_info.json ADDED Viewed

	@@ -0,0 +1,108 @@

+{
+  "builder_name": "music_genre",
+  "citation": "",
+  "config_name": "eval",
+  "dataset_name": "music_genre",
+  "dataset_size": 24572208,
+  "description": "",
+  "download_checksums": {
+    "https://www.modelscope.cn/datasets/ccmusic-database/music_genre/resolve/master/data/eval.zip": {
+      "num_bytes": 4436653005,
+      "checksum": null
+    }
+  },
+  "download_size": 4436653005,
+  "features": {
+    "mel": {
+      "_type": "Image"
+    },
+    "cqt": {
+      "_type": "Image"
+    },
+    "chroma": {
+      "_type": "Image"
+    },
+    "fst_level_label": {
+      "names": [
+        "Classic",
+        "Non_classic"
+      ],
+      "_type": "ClassLabel"
+    },
+    "sec_level_label": {
+      "names": [
+        "Symphony",
+        "Opera",
+        "Solo",
+        "Chamber",
+        "Pop",
+        "Dance_and_house",
+        "Indie",
+        "Soul_or_RnB",
+        "Rock"
+      ],
+      "_type": "ClassLabel"
+    },
+    "thr_level_label": {
+      "names": [
+        "Symphony",
+        "Opera",
+        "Solo",
+        "Chamber",
+        "Pop_vocal_ballad",
+        "Adult_contemporary",
+        "Teen_pop",
+        "Contemporary_dance_pop",
+        "Dance_pop",
+        "Classic_indie_pop",
+        "Chamber_cabaret_and_art_pop",
+        "Soul_or_RnB",
+        "Adult_alternative_rock",
+        "Uplifting_anthemic_rock",
+        "Soft_rock",
+        "Acoustic_pop"
+      ],
+      "_type": "ClassLabel"
+    }
+  },
+  "homepage": "https://www.modelscope.cn/datasets/ccmusic-database/music_genre",
+  "license": "CC-BY-NC-ND",
+  "size_in_bytes": 4461225213,
+  "splits": {
+    "train": {
+      "name": "train",
+      "num_bytes": 19661943,
+      "num_examples": 29100,
+      "dataset_name": "music_genre"
+    },
+    "validation": {
+      "name": "validation",
+      "num_bytes": 2453757,
+      "num_examples": 3637,
+      "dataset_name": "music_genre"
+    },
+    "test": {
+      "name": "test",
+      "num_bytes": 2456508,
+      "num_examples": 3638,
+      "dataset_name": "music_genre"
+    }
+  },
+  "supervised_keys": {
+    "input": "mel",
+    "output": "sec_level_label"
+  },
+  "task_templates": [
+    {
+      "task": "image-classification",
+      "image_column": "mel",
+      "label_column": "sec_level_label"
+    }
+  ],
+  "version": {
+    "version_str": "0.0.0",
+    "major": 0,
+    "minor": 0,
+    "patch": 0
+  }
+}

eval/train/state.json ADDED Viewed

	@@ -0,0 +1,34 @@

+{
+  "_data_files": [
+    {
+      "filename": "data-00000-of-00008.arrow"
+    },
+    {
+      "filename": "data-00001-of-00008.arrow"
+    },
+    {
+      "filename": "data-00002-of-00008.arrow"
+    },
+    {
+      "filename": "data-00003-of-00008.arrow"
+    },
+    {
+      "filename": "data-00004-of-00008.arrow"
+    },
+    {
+      "filename": "data-00005-of-00008.arrow"
+    },
+    {
+      "filename": "data-00006-of-00008.arrow"
+    },
+    {
+      "filename": "data-00007-of-00008.arrow"
+    }
+  ],
+  "_fingerprint": "cf206ff81eed2816",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": null,
+  "_output_all_columns": false,
+  "_split": "train"
+}

eval/validation/dataset_info.json ADDED Viewed

	@@ -0,0 +1,108 @@

+{
+  "builder_name": "music_genre",
+  "citation": "",
+  "config_name": "eval",
+  "dataset_name": "music_genre",
+  "dataset_size": 24572208,
+  "description": "",
+  "download_checksums": {
+    "https://www.modelscope.cn/datasets/ccmusic-database/music_genre/resolve/master/data/eval.zip": {
+      "num_bytes": 4436653005,
+      "checksum": null
+    }
+  },
+  "download_size": 4436653005,
+  "features": {
+    "mel": {
+      "_type": "Image"
+    },
+    "cqt": {
+      "_type": "Image"
+    },
+    "chroma": {
+      "_type": "Image"
+    },
+    "fst_level_label": {
+      "names": [
+        "Classic",
+        "Non_classic"
+      ],
+      "_type": "ClassLabel"
+    },
+    "sec_level_label": {
+      "names": [
+        "Symphony",
+        "Opera",
+        "Solo",
+        "Chamber",
+        "Pop",
+        "Dance_and_house",
+        "Indie",
+        "Soul_or_RnB",
+        "Rock"
+      ],
+      "_type": "ClassLabel"
+    },
+    "thr_level_label": {
+      "names": [
+        "Symphony",
+        "Opera",
+        "Solo",
+        "Chamber",
+        "Pop_vocal_ballad",
+        "Adult_contemporary",
+        "Teen_pop",
+        "Contemporary_dance_pop",
+        "Dance_pop",
+        "Classic_indie_pop",
+        "Chamber_cabaret_and_art_pop",
+        "Soul_or_RnB",
+        "Adult_alternative_rock",
+        "Uplifting_anthemic_rock",
+        "Soft_rock",
+        "Acoustic_pop"
+      ],
+      "_type": "ClassLabel"
+    }
+  },
+  "homepage": "https://www.modelscope.cn/datasets/ccmusic-database/music_genre",
+  "license": "CC-BY-NC-ND",
+  "size_in_bytes": 4461225213,
+  "splits": {
+    "train": {
+      "name": "train",
+      "num_bytes": 19661943,
+      "num_examples": 29100,
+      "dataset_name": "music_genre"
+    },
+    "validation": {
+      "name": "validation",
+      "num_bytes": 2453757,
+      "num_examples": 3637,
+      "dataset_name": "music_genre"
+    },
+    "test": {
+      "name": "test",
+      "num_bytes": 2456508,
+      "num_examples": 3638,
+      "dataset_name": "music_genre"
+    }
+  },
+  "supervised_keys": {
+    "input": "mel",
+    "output": "sec_level_label"
+  },
+  "task_templates": [
+    {
+      "task": "image-classification",
+      "image_column": "mel",
+      "label_column": "sec_level_label"
+    }
+  ],
+  "version": {
+    "version_str": "0.0.0",
+    "major": 0,
+    "minor": 0,
+    "patch": 0
+  }
+}

eval/validation/state.json ADDED Viewed

	@@ -0,0 +1,13 @@

+{
+  "_data_files": [
+    {
+      "filename": "data-00000-of-00001.arrow"
+    }
+  ],
+  "_fingerprint": "1af7cf18053a82fb",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": null,
+  "_output_all_columns": false,
+  "_split": "validation"
+}

music_genre.py DELETED Viewed

@@ -1,195 +0,0 @@
-import os
-import random
-import datasets
-from datasets.tasks import ImageClassification
-_NAMES_1 = {
-    1: "Classic",
-    2: "Non_classic",
-}
-_NAMES_2 = {
-    3: "Symphony",
-    4: "Opera",
-    5: "Solo",
-    6: "Chamber",
-    7: "Pop",
-    8: "Dance_and_house",
-    9: "Indie",
-    10: "Soul_or_RnB",
-    11: "Rock",
-}
-_NAMES_3 = {
-    3: "Symphony",
-    4: "Opera",
-    5: "Solo",
-    6: "Chamber",
-    12: "Pop_vocal_ballad",
-    13: "Adult_contemporary",
-    14: "Teen_pop",
-    15: "Contemporary_dance_pop",
-    16: "Dance_pop",
-    17: "Classic_indie_pop",
-    18: "Chamber_cabaret_and_art_pop",
-    10: "Soul_or_RnB",
-    19: "Adult_alternative_rock",
-    20: "Uplifting_anthemic_rock",
-    21: "Soft_rock",
-    22: "Acoustic_pop",
-}
-_HOMEPAGE = f"https://www.modelscope.cn/datasets/ccmusic-database/{os.path.basename(__file__)[:-3]}"
-_DOMAIN = f"{_HOMEPAGE}/resolve/master/data"
-_URLS = {
-    "audio": f"{_DOMAIN}/audio.zip",
-    "mel": f"{_DOMAIN}/mel.zip",
-    "eval": f"{_DOMAIN}/eval.zip",
-}
-class music_genre(datasets.GeneratorBasedBuilder):
-    def _info(self):
-        return datasets.DatasetInfo(
-            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 == "raw"
-                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="CC-BY-NC-ND",
-            version="1.2.0",
-            task_templates=[
-                ImageClassification(
-                    task="image-classification",
-                    image_column="mel",
-                    label_column="sec_level_label",
-                )
-            ],
-        )
-    def _split_generators(self, dl_manager):
-        dataset = []
-        if self.config.name == "raw":
-            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:
-                    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
-        dirpath: str = os.path.dirname(path)
-        substr_index = dirpath.find(spect)
-        if substr_index < 0:
-            spect = spect.replace("/", "\\")
-            substr_index = dirpath.find(spect)
-        labstr = dirpath[substr_index + len(spect) :]
-        labs = labstr.split("/")
-        if len(labs) < 2:
-            labs = labstr.split("\\")
-        if depth <= len(labs):
-            return int(labs[depth - 1].split("_")[0])
-        else:
-            return int(labs[-1].split("_")[0])
-    def _generate_examples(self, files):
-        if self.config.name == "raw":
-            for i, path in enumerate(files):
-                yield i, {
-                    "audio": path["audio"],
-                    "mel": path["mel"],
-                    "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)],
-                }
-        else:
-            for i, path in enumerate(files):
-                yield i, {
-                    "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)],
-                }