2 arrows

CNPM.py

@@ -1,190 +0,0 @@
-import os
-import random
-import hashlib
-import datasets
-import pandas as pd
-
-
-_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",
-]
-
-_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",
-    "label": f"{_DOMAIN}/label.csv",
-}
-
-
-class CNPM(datasets.GeneratorBasedBuilder):
-    def _info(self):
-        return datasets.DatasetInfo(
-            features=(
-                datasets.Features(
-                    {
-                        "audio": datasets.Audio(sampling_rate=44100),
-                        "mel": datasets.Image(),
-                        "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": datasets.Value("string"),
-                    }
-                )
-                if self.config.name == "default"
-                else (
-                    datasets.Features(
-                        {
-                            "mel": datasets.Image(),
-                            "cqt": datasets.Image(),
-                            "chroma": datasets.Image(),
-                            "label": datasets.features.ClassLabel(names=_PATTERN),
-                        }
-                    )
-                )
-            ),
-            homepage=_HOMEPAGE,
-            license="CC-BY-NC-ND",
-            version="1.2.0",
-        )
-
-    def _str2md5(self, original_string: str):
-        md5_obj = hashlib.md5()
-        md5_obj.update(original_string.encode("utf-8"))
-        return md5_obj.hexdigest()
-
-    def _val_of_key(self, labels: pd.DataFrame, key: str, col: str):
-        try:
-            return labels.loc[key][col]
-        except KeyError:
-            return ""
-
-    def _split_generators(self, dl_manager):
-        label_file = dl_manager.download(_URLS["label"])
-        labels = pd.read_csv(label_file, index_col="文件名/File Name", encoding="gbk")
-        if self.config.name == "default":
-            files = {}
-            dataset = []
-            audio_files = dl_manager.download_and_extract(_URLS["audio"])
-            for fpath in dl_manager.iter_files([audio_files]):
-                fname: str = os.path.basename(fpath)
-                if fname.endswith(".wav") or fname.endswith(".mp3"):
-                    song_id = self._str2md5(fname.split(".")[0])
-                    files[song_id] = {"audio": fpath}
-
-            mel_files = dl_manager.download_and_extract(_URLS["mel"])
-            for fpath in dl_manager.iter_files([mel_files]):
-                fname = os.path.basename(fpath)
-                if fname.endswith(".png"):
-                    song_id = self._str2md5(fname.split(".")[0])
-                    files[song_id]["mel"] = fpath
-
-            for path in files.values():
-                fname = os.path.basename(path["audio"])
-                dataset.append(
-                    {
-                        "audio": path["audio"],
-                        "mel": path["mel"],
-                        "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": self._val_of_key(labels, fname, "调式全称/Mode Name"),
-                    }
-                )
-
-            random.shuffle(dataset)
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    gen_kwargs={"files": dataset},
-                ),
-            ]
-
-        else:
-            files = {key: [] for key in _PATTERN}
-            data_files = dl_manager.download_and_extract(_URLS["eval"])
-            for fpath in dl_manager.iter_files([data_files]):
-                if fpath.endswith(".jpg") and "mel" in fpath:
-                    fname = os.path.basename(fpath).rsplit("_", 1)[0]
-                    label = _PATTERN[
-                        int(self._val_of_key(labels, fname, "样式/Pattern"))
-                    ]
-                    files[label].append(
-                        {
-                            "mel": fpath,
-                            "cqt": fpath.replace("mel", "cqt"),
-                            "chroma": fpath.replace("mel", "chroma"),
-                            "label": label,
-                        }
-                    )
-
-            trainset, validset, testset = [], [], []
-            for cls in files:
-                random.shuffle(files[cls])
-                count = len(files[cls])
-                if count < 3:
-                    raise ValueError(f"Class {cls} has items < 3 !")
-
-                p10 = max(count // 10, 1)
-                p20 = p10 * 2
-                trainset += files[cls][p20:]
-                validset += files[cls][p10:p20]
-                testset += files[cls][:p10]
-
-            random.shuffle(trainset)
-            random.shuffle(validset)
-            random.shuffle(testset)
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    gen_kwargs={"files": trainset},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    gen_kwargs={"files": validset},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    gen_kwargs={"files": testset},
-                ),
-            ]
-
-    def _generate_examples(self, files):
-        for i, item in enumerate(files):
-            yield i, item

README.md

@@ -11,9 +11,68 @@ tags:
 pretty_name: Chinese National Pentatonic Mode Dataset
 size_categories:
 - n<1K
-viewer: false
+dataset_info:
+  - config_name: default
+    features:
+      - name: audio
+        dtype:
+          audio:
+            sampling_rate: 44100
+      - name: mel
+        dtype: image
+      - name: system
+        dtype: int8
+      - name: tonic
+        dtype: int8
+      - name: pattern
+        dtype: int8
+      - name: type
+        dtype: int8
+      - name: mode
+        dtype: string
+    splits:
+      - name: train
+        num_bytes: 116273
+        num_examples: 287
+    download_size: 2876503790
+    dataset_size: 116273
+  - config_name: eval
+    features:
+      - name: mel
+        dtype: image
+      - name: cqt
+        dtype: image
+      - name: chroma
+        dtype: image
+      - name: label
+        dtype: int8
+    splits:
+      - name: train
+        num_bytes: 1294328
+        num_examples: 2182
+      - name: validation
+        num_bytes: 161246
+        num_examples: 271
+      - name: test
+        num_bytes: 161378
+        num_examples: 271
+    download_size: 328570384
+    dataset_size: 1616952
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: default/train/data-*.arrow
+  - 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
 ---
-If you want to view the dataset, please visit [here](https://www.modelscope.cn/datasets/ccmusic-database/CNPM/dataPeview)
+
 # Dataset Card for Chinese National Pentatonic Mode Dataset
 ## Original Content
 The dataset is initially created by [[1]](https://archives.ismir.net/ismir2022/paper/000041.pdf). It is then expanded and used for automatic Chinese national pentatonic mode recognition by [[2]](https://kns.cnki.net/kcms2/article/abstract?v=lD5CuVSaeOtw0E2oWliKSMrLiLDt9iwvkwoTgSclPspwUECyt4uNZ6T7DCLlfwMqohXCQXkFzf_XjAUOQ3CAkhPqNj20H8eG9UfUVuHEey0x7Kqp32fMlJiM9xuPtdVMvC1PB2qW0qI=&uniplatform=NZKPT&src=copy), to which readers can refer for more details along with a brief introduction to the modern theory of Chinese pentatonic mode. This includes the definition of "system", "tonic", "pattern", and "type," which will be included in one unified table during our integration process as described below. The original dataset includes audio recordings and annotations of five modes of Chinese music, encompassing the _Gong (宫), Shang (商), Jue (角), Zhi (徵), and Yu (羽)_ modes. The total recording number is 287.
@@ -21,7 +80,7 @@ The dataset is initially created by [[1]](https://archives.ismir.net/ismir2022/p
 ## Integration
 Similar to the Guzheng Tech99 dataset in Section 1, the labels in this dataset were initially stored in a separate CSV file, which led to certain usability issues. Through our integration, labels are integrated with audio data into a single dictionary. After the integration, the data structure consists of seven columns: the first and second columns denote the audio recording (sampled at 44,100 Hz) and mel spectrogram. The subsequent columns represent the system, tonic, pattern, and type of the musical piece, respectively. The final column contains an additional Chinese name of the mode. The total recording number remains at 287, and the total duration is 858.63 minutes. The average duration is 179.51 seconds.
 
-We have constructed the [default subset](#default-subset) of this integrated version of the dataset, and its data structure can be viewed in the [viewer](https://www.modelscope.cn/datasets/ccmusic-database/CNPM/dataPeview). As this dataset has been cited and used in published articles, no further eval subset needs to be constructed for evaluation. Because the default subset is multi-labelled, it is difficult to maintain the integrity of labels in the split for all label columns, hence only a single split for the training set is provided. Users can perform their own splits on specified columns according to their specific downstream tasks. Building on the default subset, we segmented the audio into 20-second slices and used zero padding to complete segments shorter than 20 seconds. The audio was then converted into mel, CQT, and chroma spectrograms. This process resulted in the construction of the [eval subset](#eval-subset) for dataset evaluation experiments.
+We have constructed the [default subset](#default-subset) of this integrated version of the dataset, and its data structure can be viewed in the [viewer](https://huggingface.co/datasets/ccmusic-database/CNPM/viewer). As this dataset has been cited and used in published articles, no further eval subset needs to be constructed for evaluation. Because the default subset is multi-labelled, it is difficult to maintain the integrity of labels in the split for all label columns, hence only a single split for the training set is provided. Users can perform their own splits on specified columns according to their specific downstream tasks. Building on the default subset, we segmented the audio into 20-second slices and used zero padding to complete segments shorter than 20 seconds. The audio was then converted into mel, CQT, and chroma spectrograms. This process resulted in the construction of the [eval subset](#eval-subset) for dataset evaluation experiments.
 
 ## Statistics
 In this part, we provide statistics for the "pattern" in the dataset, which includes five categories: _Gong, Shang, Jue, Zhi, and Yu_. Our evaluation is also conducted on "pattern." In a Western context, identifying these patterns is analogous to identifying a musical mode, such as determining whether a piece of music is in the Dorian mode or Lydian mode. These patterns form the core of modern Chinese pentatonic mode theory.
@@ -162,7 +221,7 @@ for item in ds["test"]:
 
 ## Maintenance
 ```bash
-git clone [email protected]:datasets/ccmusic-database/CNPM
+GIT_LFS_SKIP_SMUDGE=1 git clone [email protected]:datasets/ccmusic-database/CNPM
 cd CNPM
 ```
 

default/dataset_dict.json

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default/train/data-00000-of-00007.arrow

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default/train/dataset_info.json

@@ -0,0 +1,108 @@
+{
+  "builder_name": "cnpm",
+  "citation": "",
+  "config_name": "default",
+  "dataset_name": "cnpm",
+  "dataset_size": 116273,
+  "description": "",
+  "download_checksums": {
+    "https://www.modelscope.cn/datasets/ccmusic-database/CNPM/resolve/master/data/label.csv": {
+      "num_bytes": 22642,
+      "checksum": null
+    },
+    "https://www.modelscope.cn/datasets/ccmusic-database/CNPM/resolve/master/data/audio.zip": {
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+      "checksum": null
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+      "checksum": null
+    }
+  },
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+  "features": {
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+      "_type": "Audio"
+    },
+    "mel": {
+      "_type": "Image"
+    },
+    "system": {
+      "names": [
+        "C",
+        "#C/bD",
+        "D",
+        "#D/bE",
+        "E",
+        "F",
+        "#F/bG",
+        "G",
+        "#G/bA",
+        "A",
+        "#A/bB",
+        "B"
+      ],
+      "_type": "ClassLabel"
+    },
+    "tonic": {
+      "names": [
+        "C",
+        "#C/bD",
+        "D",
+        "#D/bE",
+        "E",
+        "F",
+        "#F/bG",
+        "G",
+        "#G/bA",
+        "A",
+        "#A/bB",
+        "B"
+      ],
+      "_type": "ClassLabel"
+    },
+    "pattern": {
+      "names": [
+        "Gong",
+        "Shang",
+        "Jue",
+        "Zhi",
+        "Yu"
+      ],
+      "_type": "ClassLabel"
+    },
+    "type": {
+      "names": [
+        "Pentatonic",
+        "Hexatonic_Qingjue",
+        "Hexatonic_Biangong",
+        "Heptatonic_Yayue",
+        "Heptatonic_Qingyue",
+        "Heptatonic_Yanyue"
+      ],
+      "_type": "ClassLabel"
+    },
+    "mode": {
+      "dtype": "string",
+      "_type": "Value"
+    }
+  },
+  "homepage": "https://www.modelscope.cn/datasets/ccmusic-database/CNPM",
+  "license": "CC-BY-NC-ND",
+  "size_in_bytes": 2876620063,
+  "splits": {
+    "train": {
+      "name": "train",
+      "num_bytes": 116273,
+      "num_examples": 287,
+      "dataset_name": "cnpm"
+    }
+  },
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+    "version_str": "0.0.0",
+    "major": 0,
+    "minor": 0,
+    "patch": 0
+  }
+}

default/train/state.json

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+}

eval/dataset_dict.json

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eval/test/dataset_info.json

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+  "description": "",
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+  "license": "CC-BY-NC-ND",
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+}

eval/train/state.json

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+}

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eval/validation/dataset_info.json

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eval/validation/state.json

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