sync ms

README.md

@@ -14,12 +14,15 @@ size_categories:
 - 10K<n<100K
 viewer: false
 ---
+
 # Dataset Card for Music Genre
+The raw 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 Description
 - **Homepage:** <https://ccmusic-database.github.io>
 - **Repository:** <https://huggingface.co/datasets/ccmusic-database/music_genre>
 - **Paper:** <https://doi.org/10.5281/zenodo.5676893>
-- **Leaderboard:** <https://ccmusic-database.github.io/team.html>
+- **Leaderboard:** <https://www.modelscope.cn/datasets/ccmusic/music_genre>
 - **Point of Contact:** <https://huggingface.co/ccmusic-database/music_genre>
 
 ### Dataset Summary
@@ -34,23 +37,42 @@ Multilingual
 ## Maintenance
 ```bash
 GIT_LFS_SKIP_SMUDGE=1 git clone git@hf.co:datasets/ccmusic-database/music_genre
+cd music_genre
 ```
 
 ## Usage
-When doing classification task, only one colum of fst_level_label, sec_level_label and thr_level_label can be used, not for mixing.
+### Eval Subset
 ```python
 from datasets import load_dataset
 
-dataset = load_dataset("ccmusic-database/music_genre")
+dataset = load_dataset("ccmusic-database/music_genre", name="eval")
+for item in ds["train"]:
+    print(item)
 
-for item in dataset["train"]:
+for item in ds["validation"]:
     print(item)
 
-for item in dataset["test"]:
+for item in ds["test"]:
     print(item)
 ```
 
+### Raw Subset
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("ccmusic-database/music_genre", name="default")
+for item in ds["train"]:
+    print(item)
+
+for item in ds["validation"]:
+    print(item)
+
+for item in ds["test"]:
+    print(item)
+``` 
+
 ## Dataset Structure
+### Eval Subset
 <style>
   #genres td {
     vertical-align: middle !important;
@@ -62,17 +84,17 @@ for item in dataset["test"]:
 </style>
 <table id="genres">
     <tr>
-        <td>mel(.jpg, 11.4s)</td>
-        <td>cqt(.jpg, 11.4s)</td>
-        <td>chroma(.jpg, 11.4s)</td>
+        <td>mel(.jpg, 11.4s, 48000Hz)</td>
+        <td>cqt(.jpg, 11.4s, 48000Hz)</td>
+        <td>chroma(.jpg, 11.4s, 48000Hz)</td>
         <td>fst_level_label(2-class)</td>
         <td>sec_level_label(9-class)</td>
         <td>thr_level_label(16-class)</td>
     </tr>
     <tr>
-        <td><img src="https://cdn-uploads.huggingface.co/production/uploads/655e0a5b8c2d4379a71882a9/PqdpQP__ls-xo6lz93Q4y.jpeg"></td>
-        <td><img src="https://cdn-uploads.huggingface.co/production/uploads/655e0a5b8c2d4379a71882a9/EZfYLng40hh_FUudB9vvx.jpeg"></td>
-        <td><img src="https://cdn-uploads.huggingface.co/production/uploads/655e0a5b8c2d4379a71882a9/zviZ-rEKAvBCVFvKFml4R.jpeg"></td>
+        <td><img src="./data/PqdpQP__ls-xo6lz93Q4y.jpeg"></td>
+        <td><img src="./data/EZfYLng40hh_FUudB9vvx.jpeg"></td>
+        <td><img src="./data/zviZ-rEKAvBCVFvKFml4R.jpeg"></td>
         <td>1_Classic / 2_Non_classic</td>
         <td>3_Symphony / 4_Opera / 5_Solo / 6_Chamber / 7_Pop / 8_Dance_and_house / 9_Indie / 10_Soul_or_r_and_b / 11_Rock</td>
         <td>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_r_and_b / 19_Adult_alternative_rock / 20_Uplifting_anthemic_rock / 21_Soft_rock / 22_Acoustic_pop</td>
@@ -87,6 +109,31 @@ for item in dataset["test"]:
     </tr>
 </table>
 
+### Raw Subset
+<table>
+    <tr>
+        <th>audio(.wav, 22050Hz)</th>
+        <th>mel(spectrogram, .jpg, 22050Hz)</th>
+        <th>fst_level_label(2-class)</th>
+        <th>sec_level_label(9-class)</th>
+        <th>thr_level_label(16-class)</th>
+    </tr>
+    <tr>
+        <td><audio controls src="./data/8bb58041d6b9d35db688bcedfde0fe39.mp3"></audio></td>
+        <td><img src="./data/8bb58041d6b9d35db688bcedfde0fe39.jpg"></td>
+        <td>1_Classic / 2_Non_classic</td>
+        <td>3_Symphony / 4_Opera / 5_Solo / 6_Chamber / 7_Pop / 8_Dance_and_house / 9_Indie / 10_Soul_or_r_and_b / 11_Rock</td>
+        <td>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_r_and_b / 19_Adult_alternative_rock / 20_Uplifting_anthemic_rock / 21_Soft_rock / 22_Acoustic_pop</td>
+    </tr>
+    <tr>
+        <td>...</td>
+        <td>...</td>
+        <td>...</td>
+        <td>...</td>
+        <td>...</td>
+    </tr>
+</table>
+
 ### Data Instances
 .zip(.jpg)
 
@@ -122,11 +169,12 @@ for item in dataset["test"]:
 ```
 
 ### Data Splits
-|      total      | 36375 |
-| :-------------: | :---: |
-|   train(80%)    | 29100 |
-| validation(10%) | 3637  |
-|    test(10%)    | 3638  |
+|      Split      | Eval  |  Raw  |
+| :-------------: | :---: | :---: |
+|      total      | 36375 | 1713  |
+|   train(80%)    | 29100 | 1370  |
+| validation(10%) | 3637  |  171  |
+|    test(10%)    | 3638  |  172  |
 
 ## Dataset Creation
 ### Curation Rationale
@@ -137,7 +185,7 @@ Promoting the development of AI in the music industry
 Zhaorui Liu, Monan Zhou
 
 #### Who are the source language producers?
-Composers of the songs in dataset
+Composers of the songs in the dataset
 
 ### Annotations
 #### Annotation process
@@ -192,16 +240,15 @@ SOFTWARE.
 ```
 
 ### Citation Information
-```
+```bibtex
 @dataset{zhaorui_liu_2021_5676893,
-  author       = {Zhaorui Liu, Monan Zhou, Shenyang Xu, Yuan Wang, Zhaowen Wang, Wei Li and Zijin Li},
-  title        = {CCMUSIC DATABASE: A Music Data Sharing Platform for Computational Musicology Research},
-  month        = {nov},
-  year         = {2021},
-  publisher    = {Zenodo},
-  version      = {1.1},
-  doi          = {10.5281/zenodo.5676893},
-  url          = {https://doi.org/10.5281/zenodo.5676893}
+  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},
+  publisher    = {HuggingFace},
+  version      = {1.2},
+  url          = {https://huggingface.co/ccmusic-database}
 }
 ```
 

data/8bb58041d6b9d35db688bcedfde0fe39.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d8406e26bbd885e74e0e6775d5150863d030a8fb9133063df503f4481dfa7687
+size 4089932

music_genre.py

@@ -1,13 +1,10 @@
+# import hashlib
 import os
-import socket
 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",
@@ -18,7 +15,7 @@ _NAMES_2 = {
     8: "Dance_and_house",
     9: "Indie",
     10: "Soul_or_r_and_b",
-    11: "Rock"
+    11: "Rock",
 }
 
 _NAMES_3 = {
@@ -37,44 +34,93 @@ _NAMES_3 = {
     19: "Adult_alternative_rock",
     20: "Uplifting_anthemic_rock",
     21: "Soft_rock",
-    22: "Acoustic_pop"
+    22: "Acoustic_pop",
 }
 
-_DBNAME = os.path.basename(__file__).split('.')[0]
+_DBNAME = os.path.basename(__file__).split(".")[0]
+
+_HOMEPAGE = f"https://www.modelscope.cn/datasets/ccmusic/{_DBNAME}"
 
-_HOMEPAGE = f"https://huggingface.co/datasets/ccmusic-database/{_DBNAME}"
+_DOMAIN = f"https://www.modelscope.cn/api/v1/datasets/ccmusic/{_DBNAME}/repo?Revision=master&FilePath=data"
 
 _CITATION = """\
 @dataset{zhaorui_liu_2021_5676893,
-  author       = {Zhaorui Liu, Monan Zhou, Shenyang Xu, Yuan Wang, Zhaowen Wang, Wei Li and Zijin Li},
-  title        = {CCMUSIC DATABASE: A Music Data Sharing Platform for Computational Musicology Research},
-  month        = {nov},
-  year         = {2021},
-  publisher    = {Zenodo},
-  version      = {1.1},
-  doi          = {10.5281/zenodo.5676893},
-  url          = {https://doi.org/10.5281/zenodo.5676893}
+  author       = {Monan Zhou, Shenyang Xu, Zhaorui Liu, Zhaowen Wang, Feng Yu, Wei Li and Zijin Li},
+  title        = {CCMusic: an Open and Diverse Database for Chinese and General Music Information Retrieval Research},
+  month        = {mar},
+  year         = {2024},
+  publisher    = {HuggingFace},
+  version      = {1.2},
+  url          = {https://huggingface.co/ccmusic-database}
 }
 """
 
 _DESCRIPTION = """\
-This database contains about 1700 musical pieces (.mp3 format) with lengths of 270-300s that are divided into 17 genres in total. 
+The raw 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 48,000 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.
+
+For the pre-processed version, audio is cut into an 11.4-second segment, resulting in 36,375 files, which are then transformed into Mel, CQT and Chroma. In the end, the data entry has six columns: the first three columns represent the Mel, CQT, and Chroma spectrogram slices in .jpg format, respectively, while the last three columns contain the labels for the three levels. The first level comprises two categories, the second level consists of nine categories, and the third level encompasses 16 categories. The entire dataset is shuffled and split into training, validation, and test sets in a ratio of 8:1:1. This dataset can be used for genre classification.
 """
 
+_URLS = {
+    "audio": f"{_DOMAIN}/audio.zip",
+    "mel": f"{_DOMAIN}/mel.zip",
+    "eval": f"{_DOMAIN}/eval.zip",
+}
+
+
+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):
-    def _info(self):
-        return datasets.DatasetInfo(
+    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()))
+                    "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())
+                    ),
+                }
+            ),
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=self.config.features,
             supervised_keys=("mel", "sec_level_label"),
             homepage=_HOMEPAGE,
             license="mit",
@@ -86,29 +132,58 @@ class music_genre(datasets.GeneratorBasedBuilder):
                     image_column="mel",
                     label_column="sec_level_label",
                 )
-            ]
+            ],
         )
 
-    def _cdn_url(self, ip='127.0.0.1', port=80):
-        try:
-            # easy for local test
-            with socket.create_connection((ip, port), timeout=5):
-                return f'http://{ip}/{_DBNAME}/data/genre_data.zip'
-        except (socket.timeout, socket.error):
-            return f"{_HOMEPAGE}/resolve/main/data/genre_data.zip"
+    # 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):
-        data_files = dl_manager.download_and_extract(self._cdn_url())
-        files = dl_manager.iter_files([data_files])
-
         dataset = []
-        for path in 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/')
-                })
+        if self.config.name == "eval":
+            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/"
+                            ),
+                        }
+                    )
+
+        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)
@@ -118,49 +193,54 @@ class music_genre(datasets.GeneratorBasedBuilder):
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "files": dataset[:p80]
-                },
+                gen_kwargs={"files": dataset[:p80]},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "files": dataset[p80:p90]
-                },
+                gen_kwargs={"files": dataset[p80:p90]},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
-                gen_kwargs={
-                    "files": dataset[p90:]
-                },
+                gen_kwargs={"files": dataset[p90:]},
             ),
         ]
 
-    def _calc_label(self, path, depth, substr='/mel/'):
+    def _calc_label(self, path, depth, substr="/mel/"):
         spect = substr
-        dirpath = os.path.dirname(path)
+        dirpath: str = os.path.dirname(path)
         substr_index = dirpath.find(spect)
         if substr_index < 0:
-            spect = spect.replace('/', '\\')
+            spect = spect.replace("/", "\\")
             substr_index = dirpath.find(spect)
 
-        labstr = dirpath[substr_index + len(spect):]
-        labs = labstr.split('/')
+        labstr: str = dirpath[substr_index + len(spect) :]
+        labs = labstr.split("/")
         if len(labs) < 2:
-            labs = labstr.split('\\')
+            labs = labstr.split("\\")
 
         if depth <= len(labs):
-            return int(labs[depth - 1].split('_')[0])
+            return int(labs[depth - 1].split("_")[0])
         else:
-            return int(labs[-1].split('_')[0])
+            return int(labs[-1].split("_")[0])
 
     def _generate_examples(self, files):
-        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)]
-            }
+        if self.config.name == "eval":
+            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)],
+                }
+
+        else:
+            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)],
+                }