sync ms

CNPM.py

@@ -1,40 +1,63 @@
 import os
 import random
+import hashlib
 import datasets
 import pandas as pd
 from datasets.tasks import AudioClassification
 
 
-_SYSTEM_TONIC = ["C", "#C/bD", "D", "#D/bE", "E", "F",
-                 "#F/bG", "G", "#G/bA", "A", "#A/bB", "B"]
+_SYSTEM_TONIC = [
+    "C",
+    "#C/bD",
+    "D",
+    "#D/bE",
+    "E",
+    "F",
+    "#F/bG",
+    "G",
+    "#G/bA",
+    "A",
+    "#A/bB",
+    "B",
+]
 
 _PATTERN = ["Gong", "Shang", "Jue", "Zhi", "Yu"]
 
-_TYPE = ["Pentatonic", "Hexatonic_Qingjue", "Hexatonic_Biangong",
-         "Heptatonic_Yayue", "Heptatonic_Qingyue", "Heptatonic_Yanyue"]
+_TYPE = [
+    "Pentatonic",
+    "Hexatonic_Qingjue",
+    "Hexatonic_Biangong",
+    "Heptatonic_Yayue",
+    "Heptatonic_Qingyue",
+    "Heptatonic_Yanyue",
+]
 
-_HOMEPAGE = f"https://huggingface.co/datasets/ccmusic-database/{os.path.basename(__file__).split('.')[0]}"
+_DBNAME = os.path.basename(__file__).split(".")[0]
+
+_DOMAIN = f"https://www.modelscope.cn/api/v1/datasets/ccmusic/{_DBNAME}/repo?Revision=master&FilePath=data"
+
+_HOMEPAGE = f"https://www.modelscope.cn/datasets/ccmusic/{_DBNAME}"
 
 _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 = """\
-Based on the working idea of combining manual labeling with computer in the construction of World Music Database, this database collects and labels the audio of five modes (including five tones, six tones and seven tones) of "Gong, Shang, Jue, Zhi and Yu". At the same time, it makes a detailed analysis of the judgment of Chinese national pentatonic modes, and finds application scenarios and technical models, which can provide raw data for the analysis and retrieval of Chinese national music characteristics.
+Musical pieces collected are mostly composed in pentatonic (five-note) scales, with some of them being hexatonic (six-note) and heptatonic (seven-note) scales. The total recording number is 287 with the average duration being 179.5 s. The expanded dataset is integrated into our database, and each data entry consists of seven columns: the first column denotes the audio recording in .wav format, sampled at 44,100 Hz. The second and third presents the name of the piece and artist. The subsequent columns represent the system, tonic, pattern, and type of the musical piece, respectively. The eighth column contains an additional Chinese name of the mode, while the final column indicates the duration of the audio in seconds. This dataset is applicable for tasks related to Chinese traditional pentatonic mode or computational ethnomusicology research related to Chinese music mode.
 """
 
 _URLS = {
-    'audio': f"{_HOMEPAGE}/resolve/main/data/audio.zip",
-    'label': f"{_HOMEPAGE}/resolve/main/data/label.csv"
+    "audio": f"{_DOMAIN}/audio.zip",
+    "mel": f"{_DOMAIN}/mel.zip",
+    "label": f"{_DOMAIN}/label.csv",
 }
 
 
@@ -42,16 +65,21 @@ class CNPM(datasets.GeneratorBasedBuilder):
     def _info(self):
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
-            features=datasets.Features({
-                "audio": datasets.Audio(sampling_rate=44_100),
-                "System": datasets.features.ClassLabel(names=_SYSTEM_TONIC),
-                "Tonic": datasets.features.ClassLabel(names=_SYSTEM_TONIC),
-                "Pattern": datasets.features.ClassLabel(names=_PATTERN),
-                "Type": datasets.features.ClassLabel(names=_TYPE),
-                "Mode_Name": datasets.Value("string"),
-                "Length": datasets.Value("string")
-            }),
-            supervised_keys=("audio", "Type"),
+            features=datasets.Features(
+                {
+                    "audio": datasets.Audio(sampling_rate=22050),
+                    "mel": datasets.Image(),
+                    "title": datasets.Value("string"),
+                    "artist": datasets.Value("string"),
+                    "system": datasets.features.ClassLabel(names=_SYSTEM_TONIC),
+                    "tonic": datasets.features.ClassLabel(names=_SYSTEM_TONIC),
+                    "pattern": datasets.features.ClassLabel(names=_PATTERN),
+                    "type": datasets.features.ClassLabel(names=_TYPE),
+                    "mode_name": datasets.Value("string"),
+                    "length": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=("audio", "type"),
             homepage=_HOMEPAGE,
             license="mit",
             citation=_CITATION,
@@ -59,33 +87,54 @@ class CNPM(datasets.GeneratorBasedBuilder):
                 AudioClassification(
                     task="audio-classification",
                     audio_column="audio",
-                    label_column="Type"
+                    label_column="type",
                 )
-            ]
+            ],
         )
 
-    def _split_generators(self, dl_manager):
-        audio_files = dl_manager.download_and_extract(_URLS['audio'])
-        label_file = dl_manager.download(_URLS['label'])
-
-        files = dl_manager.iter_files([audio_files])
-        labels = pd.read_csv(
-            label_file, index_col='File_Name', encoding='gbk')
-
-        dataset = []
-        for fpath in files:
-            if os.path.basename(fpath).endswith(".wav"):
-                dataset.append(fpath)
+    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):
+        audio_files = dl_manager.download_and_extract(_URLS["audio"])
+        mel_files = dl_manager.download_and_extract(_URLS["mel"])
+        label_file = dl_manager.download(_URLS["label"])
+
+        labels = pd.read_csv(label_file, index_col="文件名/File Name", encoding="gbk")
+        files = {}
+
+        for fpath in dl_manager.iter_files([audio_files]):
+            fname = os.path.basename(fpath)
+            if fname.endswith(".wav") or fname.endswith(".mp3"):
+                song_id = self._str2md5(fname.split(".")[0])
+                files[song_id] = {"audio": fpath}
+
+        for fpath in dl_manager.iter_files([mel_files]):
+            fname = os.path.basename(fpath)
+            if fname.endswith(".jpg"):
+                song_id = self._str2md5(fname.split(".")[0])
+                files[song_id]["mel"] = fpath
+
+        dataset = list(files.values())
         random.shuffle(dataset)
 
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "files": dataset,
-                    "labels": labels
-                }
+                gen_kwargs={"files": dataset, "labels": labels},
             )
         ]
 
@@ -93,17 +142,26 @@ class CNPM(datasets.GeneratorBasedBuilder):
         try:
             return labels.loc[key][col]
         except KeyError:
-            return ''
+            return ""
 
     def _generate_examples(self, files, labels):
         for i, path in enumerate(files):
-            fname = os.path.basename(path)
+            fname = os.path.basename(path["audio"])
             yield i, {
-                "audio": path,
-                "System": _SYSTEM_TONIC[int(self._val_of_key(labels, fname, 'System'))],
-                "Tonic": _SYSTEM_TONIC[int(self._val_of_key(labels, fname, 'Tonic'))],
-                "Pattern": _PATTERN[int(self._val_of_key(labels, fname, 'Pattern'))],
-                "Type": _TYPE[int(self._val_of_key(labels, fname, 'Type'))],
-                "Mode_Name": self._val_of_key(labels, fname, 'Mode_Name'),
-                "Length": self._val_of_key(labels, fname, 'Length')
+                "audio": path["audio"],
+                "mel": path["mel"],
+                "title": self._val_of_key(labels, fname, "曲名/Title"),
+                "artist": self._val_of_key(labels, fname, "演奏者/Artist"),
+                "system": _SYSTEM_TONIC[
+                    int(self._val_of_key(labels, fname, "同宫系统/System"))
+                ],
+                "tonic": _SYSTEM_TONIC[
+                    int(self._val_of_key(labels, fname, "主音音名/Tonic"))
+                ],
+                "pattern": _PATTERN[
+                    int(self._val_of_key(labels, fname, "样式/Pattern"))
+                ],
+                "type": _TYPE[int(self._val_of_key(labels, fname, "种类/Type"))],
+                "mode_name": self._val_of_key(labels, fname, "调式全称/Mode Name"),
+                "length": self._val_of_key(labels, fname, "时长/Length"),
             }

README.md

@@ -13,16 +13,19 @@ size_categories:
 - n<1K
 viewer: false
 ---
+
 # Dataset Card for Chinese National Pentatonic Mode Dataset
+The raw dataset includes audio recordings and annotations of five modes of Chinese music, encompassing the Gong, Shang, Jue, Zhi, and Yu modes. Musical pieces collected are mostly composed in pentatonic (five-note) scales, with some of them being hexatonic (six-note) and heptatonic (seven-note) scales. The total recording number is 287 with the average duration being 179.5s.
+
 ## Dataset Description
 - **Homepage:** <https://ccmusic-database.github.io>
 - **Repository:** <https://huggingface.co/datasets/ccmusic-database/CNPM>
 - **Paper:** <https://doi.org/10.5281/zenodo.5676893>
-- **Leaderboard:** <https://ccmusic-database.github.io/team.html>
+- **Leaderboard:** <https://www.modelscope.cn/datasets/ccmusic/CNPM>
 - **Point of Contact:** Chinese Ethnic Pentatonic Scale; Database; Music Information Retrieval; Pentatonic Therapy
 
 ### Dataset Summary
-Based on the working idea of combining manual labeling with computer in the construction of World Music Database, this database collects and labels the audio of five modes (including five tones, six tones and seven tones) of "Gong, Shang, Jue, Zhi and Yu". At the same time, it makes a detailed analysis of the judgment of Chinese national pentatonic modes, and finds application scenarios and technical models, which can provide raw data for the analysis and retrieval of Chinese national music characteristics.
+The expanded dataset is integrated into our database, and each data entry consists of seven columns: the first column denotes the audio recording in .wav format, sampled at 22,050 Hz. The second and third presents the name of the piece and artist. The subsequent columns represent the system, tonic, pattern, and type of the musical piece, respectively. The eighth column contains an additional Chinese name of the mode, while the final column indicates the duration of the audio in seconds.
 
 ### Supported Tasks and Leaderboards
 MIR, audio classification
@@ -31,17 +34,22 @@ MIR, audio classification
 Chinese, English
 
 ## Dataset Structure
+|              audio(.wav, 22050Hz)               |          mel(.jpg, 22050Hz)          | title  | artist |  system  |  tonic   | pattern |  type   | mode_name | length |
+| :---------------------------------------------: | :----------------------------------: | :----: | :----: | :------: | :------: | :-----: | :-----: | :-------: | :----: |
+| <audio controls src="./data/刮地风 - 廖莎.mp3"> | <img src="./data/刮地风 - 廖莎.jpg"> | string | string | 12-class | 12-class | 5-class | 6-class |  string   | string |
+|                       ...                       |                 ...                  |  ...   |  ...   |   ...    |   ...    |   ...   |   ...   |    ...    |  ...   |
+
 ### Data Instances
 .zip(.wav), .csv
 
-### Data Fields
-Mode Type, Name, Performer, Album Name, National Mode Name, Tonggong System, Audio Links
+### Data Fields ###
+Mode type, Name, Performer, Album Name, National Mode Name, Tonggong System, Audio Links
 
 ### Data Splits
 train
 
 ## Usage
-```
+```python
 from datasets import load_dataset
 
 dataset = load_dataset("ccmusic-dabase/CNPM", split='train')
@@ -50,6 +58,12 @@ for data in dataset:
     print(data)
 ```
 
+## Maintenance
+```bash
+GIT_LFS_SKIP_SMUDGE=1 git clone git@hf.co:datasets/ccmusic-database/CNPM
+cd CNPM
+```
+
 ## Dataset Creation
 ### Curation Rationale
 Lack of a dataset for Chinese National Pentatonic Mode
@@ -63,7 +77,7 @@ Teachers & students from FD-LAMT, CCOM, SCCM
 
 ### Annotations
 #### Annotation process
-Based on the working idea of combining manual labeling with computer in the construction of World Music Database, this database collects and labels the audio of five modes (including five tones, six tones and seven tones) of "Gong, Shang, Jue, Zhi and Yu". At the same time, it makes a detailed analysis of the judgment of Chinese national pentatonic modes, and finds application scenarios and technical models, which can provide raw data for the analysis and retrieval of Chinese national music characteristics.
+Based on the working idea of combining manual labeling with a computer in the construction of the World Music Database, this database collects and labels the audio of five modes (including five tones, six tones and seven tones) of "Gong, Shang, Jue, Zhi and Yu". At the same time, it makes a detailed analysis of the judgment of Chinese national pentatonic modes and finds application scenarios and technical models, which can provide raw data for the analysis and retrieval of Chinese national music characteristics.
 
 #### Who are the annotators?
 Teachers & students from FD-LAMT, CCOM, SCCM
@@ -73,7 +87,7 @@ Due to copyright reasons, only some of the audio can be released directly. This
 
 ## Considerations for Using the Data
 ### Social Impact of Dataset
-Promoting the development of music AI industry
+Promoting the development of the music AI industry
 
 ### Discussion of Biases
 Only for Traditional Chinese Instruments
@@ -83,9 +97,9 @@ Only for Pentatonic Mode
 
 ## Additional Information
 ### Dataset Curators
-Weixin Ren, Mingjin Che, Zhaowen Wang, Qinyu Li, Jiaye Hu, Fan Xia, Wei Li
+Weixin Ren, Mingjin Che, Zhaowen Wang, Qinyu Li, Jiaye Hu, Fan Xia, Wei Li.
 
-### Evalution
+### Evaluation
 [任伟鑫,车明锦,汪照文,孟文武,李沁雨,胡佳弋,夏凡,李伟.CNPM Database:一个用于计算音乐学的中国民族五声调式数据库[J].复旦学报(自然科学版),2022,61(05):555-563.DOI:10.15943/j.cnki.fdxb-jns.20221017.008.](https://kns.cnki.net/kcms2/article/abstract?v=lD5CuVSaeOtw0E2oWliKSMrLiLDt9iwvkwoTgSclPspwUECyt4uNZ6T7DCLlfwMqohXCQXkFzf_XjAUOQ3CAkhPqNj20H8eG9UfUVuHEey0x7Kqp32fMlJiM9xuPtdVMvC1PB2qW0qI=&uniplatform=NZKPT&src=copy)
 
 ### Licensing Information
@@ -114,18 +128,17 @@ 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}
 }
 ```
 
 ### Contributions
-Provide a dataset for Chinese National Pentatonic Mode
+Provide a dataset for the Chinese National Pentatonic Mode

data/label.csv

@@ -1,70 +0,0 @@
-File_Name,Mode_Name,System,Tonic,Pattern,Type,Length
-shang1.wav,D������,0,2,1,0,0.000648148
-shang10.wav,D������,0,2,1,0,0.0003125
-shang11.wav,G������,5,7,1,0,0.000219907
-shang12.wav,G������,5,7,1,0,0.000405093
-shang13.wav,G������,5,7,1,0,0.00037037
-shang14.wav,E������,2,4,1,0,0.000266204
-shang15.wav,E������,2,4,1,0,0.000578704
-shang16.wav,E������,2,4,1,0,0.000439815
-shang17.wav,E����������,2,4,1,4,0.000972222
-shang18.wav,D�����������,0,2,1,1,0.000324074
-shang19.wav,D������,0,2,1,0,0.000266204
-shang2.wav,D������,0,2,1,0,0.000636574
-shang20.wav,D������,0,2,1,0,0.000590278
-shang22.wav,C�������ӱ乬,10,0,1,2,0.00056713
-shang23.wav,C�������ӱ乬,10,0,1,2,0.000474537
-shang24.wav,C������,10,0,1,0,0.001215278
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-shang3.wav,D������,0,2,1,0,0.000729167
-shang4.wav,D������,0,2,1,0,0.00056713
-shang5.wav,A������,7,9,1,0,0.000300926
-shang6.wav,A������,7,9,1,0,0.000266204
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-shang9.wav,A������,7,9,1,0,0.000243056
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data/{raw_data.zip → 刮地风 - 廖莎.mp3}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:31529ee9866c09164297a01143213e0afe950e01441921c1bbb5f48b4bfa9b38
-size 379378115
+oid sha256:882464f35327ad63aacbd499295a0f40bb11636b966097b6274225dc9a9a1e37
+size 858980