2 arrow mode

README.md

@@ -12,9 +12,68 @@ tags:
 pretty_name: Bel Conto and Chinese Folk Song Singing Tech
 size_categories:
 - 1K<n<10K
-viewer: false
+dataset_info:
+  - config_name: default
+    features:
+      - name: audio
+        dtype:
+          audio:
+            sampling_rate: 22050
+      - name: mel
+        dtype: image
+      - name: label
+        dtype: int8
+      - name: gender
+        dtype: int8
+      - name: singing_method
+        dtype: int8
+    splits:
+      - name: train
+        num_bytes: 74400
+        num_examples: 203
+    download_size: 1221158882
+    dataset_size: 74400
+  - config_name: eval
+    features:
+      - name: mel
+        dtype: image
+      - name: cqt
+        dtype: image
+      - name: chroma
+        dtype: image
+      - name: label
+        dtype: int8
+      - name: gender
+        dtype: int8
+      - name: singing_method
+        dtype: int8
+    splits:
+      - name: train
+        num_bytes: 4463751
+        num_examples: 7926
+      - name: validation
+        num_bytes: 557511
+        num_examples: 990
+      - name: test
+        num_bytes: 559833
+        num_examples: 994
+    download_size: 889320608
+    dataset_size: 5581095
+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/bel_canto/dataPeview)
+
 # Dataset Card for Bel Conto and Chinese Folk Song Singing Tech
 ## Original Content
 This dataset is created by the authors and encompasses two distinct singing styles: bel canto and Chinese folk singing. Bel canto is a vocal technique frequently employed in Western classical music and opera, symbolizing the zenith of vocal artistry within the broader Western musical heritage. Chinese folk singing, for which there is no official English translation, is referred to here as a classical singing style that originated in China during the 20th century. It is a fusion of traditional Chinese vocal techniques with European bel canto singing and is currently widely utilized in the performance of various forms of Chinese folk music. The purpose of creating this dataset is to fill a gap in the current singing datasets, as none of them includes Chinese folk singing, and by incorporating both bel canto and Chinese folk singing, it provides a valuable resource for researchers to conduct cross-cultural comparative studies in vocal performance. The original dataset contains 203 acapella singing recordings sung in two styles, bel canto and Chinese folk singing style. All of them were sung by professional vocalists and were recorded in the recording studio of the China Conservatory of Music using a Schoeps MK4 microphone. Additionally, apart from singing style labels, gender labels are also included.
@@ -22,7 +81,7 @@ This dataset is created by the authors and encompasses two distinct singing styl
 ## Integration
 Since this is a self-created dataset, we directly carry out the unified integration of the data structure. After integration, the data structure of the dataset is as follows: audio (with a sampling rate of 22,050 Hz), mel spectrograms, 4-class numerical label, gender label and singing style label. The data number remains 203, with a total duration of 5.08 hours. The average duration is 90 seconds.
 
-We have constructed the [default subset](#default-subset) of the current integrated version of the dataset, and its data structure can be viewed in the [viewer](https://www.modelscope.cn/datasets/ccmusic-database/bel_canto/dataPeview). Since the default subset has not been evaluated, to verify its effectiveness, we have built the [eval subset](#eval-subset) based on the default subset for the evaluation of the integrated version of the dataset. The evaluation results can be seen in the [bel_canto](https://huggingface.co/ccmusic-database/bel_canto). Below are the data structures and invocation methods of the subsets.
+We have constructed the [default subset](#default-subset) of the current integrated version of the dataset, and its data structure can be viewed in the [viewer](https://huggingface.co/datasets/ccmusic-database/bel_canto/viewer). Since the default subset has not been evaluated, to verify its effectiveness, we have built the [eval subset](#eval-subset) based on the default subset for the evaluation of the integrated version of the dataset. The evaluation results can be seen in the [bel_canto](https://huggingface.co/ccmusic-database/bel_canto). Below are the data structures and invocation methods of the subsets.
 
 ## Statistics
 | ![](https://www.modelscope.cn/datasets/ccmusic-database/bel_canto/resolve/master/data/bel_pie.jpg) | ![](https://www.modelscope.cn/datasets/ccmusic-database/bel_canto/resolve/master/data/bel_canto.jpg) | ![](https://www.modelscope.cn/datasets/ccmusic-database/bel_canto/resolve/master/data/bel_bar.jpg) |
@@ -129,7 +188,7 @@ for item in ds["test"]:
 
 ## Maintenance
 ```bash
-git clone [email protected]:datasets/ccmusic-database/bel_canto
+GIT_LFS_SKIP_SMUDGE=1 git clone [email protected]:datasets/ccmusic-database/bel_canto
 cd bel_canto
 ```
 

bel_canto.py

@@ -1,150 +0,0 @@
-import os
-import random
-import datasets
-from datasets.tasks import ImageClassification
-
-
-_NAMES = {
-    "all": ["m_bel", "f_bel", "m_folk", "f_folk"],
-    "gender": ["female", "male"],
-    "singing_method": ["Folk_Singing", "Bel_Canto"],
-}
-
-_HOMEPAGE = f"https://www.modelscope.cn/datasets/ccmusic-database/{os.path.basename(__file__)[:-3]}"
-
-_DOMAIN = f"{_HOMEPAGE}/resolve/master/data"
-
-_URLS = {
-    "mel": f"{_DOMAIN}/mel.zip",
-    "eval": f"{_DOMAIN}/eval.zip",
-}
-
-
-class bel_canto(datasets.GeneratorBasedBuilder):
-    def _info(self):
-        return datasets.DatasetInfo(
-            features=(
-                datasets.Features(
-                    {
-                        "audio": datasets.Audio(sampling_rate=22050),
-                        "mel": datasets.Image(),
-                        "label": datasets.features.ClassLabel(names=_NAMES["all"]),
-                        "gender": datasets.features.ClassLabel(names=_NAMES["gender"]),
-                        "singing_method": datasets.features.ClassLabel(
-                            names=_NAMES["singing_method"]
-                        ),
-                    }
-                )
-                if self.config.name == "default"
-                else (
-                    datasets.Features(
-                        {
-                            "mel": datasets.Image(),
-                            "cqt": datasets.Image(),
-                            "chroma": datasets.Image(),
-                            "label": datasets.features.ClassLabel(names=_NAMES["all"]),
-                            "gender": datasets.features.ClassLabel(
-                                names=_NAMES["gender"]
-                            ),
-                            "singing_method": datasets.features.ClassLabel(
-                                names=_NAMES["singing_method"]
-                            ),
-                        }
-                    )
-                )
-            ),
-            supervised_keys=("mel", "label"),
-            homepage=_HOMEPAGE,
-            license="CC-BY-NC-ND",
-            version="1.2.0",
-            task_templates=[
-                ImageClassification(
-                    task="image-classification",
-                    image_column="mel",
-                    label_column="label",
-                )
-            ],
-        )
-
-    def _split_generators(self, dl_manager):
-        dataset = []
-        if self.config.name == "default":
-            data_files = dl_manager.download_and_extract(_URLS["mel"])
-            for fpath in dl_manager.iter_files([data_files]):
-                if fpath.endswith(".wav"):
-                    dataset.append(fpath)
-
-            random.shuffle(dataset)
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN, gen_kwargs={"files": dataset}
-                ),
-            ]
-
-        else:
-            data_files = dl_manager.download_and_extract(_URLS["eval"])
-            for fpath in dl_manager.iter_files([data_files]):
-                fname = os.path.basename(fpath)
-                if "mel" in fpath and fname.endswith(".jpg"):
-                    dataset.append(fpath)
-
-            categories = {}
-            for name in _NAMES["all"]:
-                categories[name] = []
-
-            for fpath in dataset:
-                label = os.path.basename(os.path.dirname(fpath))
-                categories[label].append(fpath)
-
-            testset, validset, trainset = [], [], []
-            for cls in categories:
-                random.shuffle(categories[cls])
-                count = len(categories[cls])
-                p80 = int(count * 0.8)
-                p90 = int(count * 0.9)
-                trainset += categories[cls][:p80]
-                validset += categories[cls][p80:p90]
-                testset += categories[cls][p90:]
-
-            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):
-        if self.config.name == "default":
-            for i, item in enumerate(files):
-                label: str = os.path.basename(os.path.dirname(item))
-                yield i, {
-                    "audio": item,
-                    "mel": item.replace(".wav", ".jpg"),
-                    "label": label,
-                    "gender": ("male" if label.split("_")[0] == "m" else "female"),
-                    "singing_method": (
-                        "Bel_Canto" if label.split("_")[1] == "bel" else "Folk_Singing"
-                    ),
-                }
-
-        else:
-            for i, item in enumerate(files):
-                label = os.path.basename(os.path.dirname(item))
-                yield i, {
-                    "mel": item,
-                    "cqt": item.replace("mel", "cqt"),
-                    "chroma": item.replace("mel", "chroma"),
-                    "label": label,
-                    "gender": ("male" if label.split("_")[0] == "m" else "female"),
-                    "singing_method": (
-                        "Bel_Canto" if label.split("_")[1] == "bel" else "Folk_Singing"
-                    ),
-                }

default/dataset_dict.json

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default/train/data-00000-of-00004.arrow

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default/train/dataset_info.json

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+  "citation": "",
+  "config_name": "default",
+  "dataset_name": "bel_canto",
+  "dataset_size": 74400,
+  "description": "",
+  "download_checksums": {
+    "https://www.modelscope.cn/datasets/ccmusic-database/bel_canto/resolve/master/data/mel.zip": {
+      "num_bytes": 1221158882,
+      "checksum": null
+    }
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+  "features": {
+    "audio": {
+      "sampling_rate": 22050,
+      "_type": "Audio"
+    },
+    "mel": {
+      "_type": "Image"
+    },
+    "label": {
+      "names": [
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+        "f_bel",
+        "m_folk",
+        "f_folk"
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+      "_type": "ClassLabel"
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+        "female",
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+      "_type": "ClassLabel"
+    },
+    "singing_method": {
+      "names": [
+        "Folk_Singing",
+        "Bel_Canto"
+      ],
+      "_type": "ClassLabel"
+    }
+  },
+  "homepage": "https://www.modelscope.cn/datasets/ccmusic-database/bel_canto",
+  "license": "CC-BY-NC-ND",
+  "size_in_bytes": 1221233282,
+  "splits": {
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+      "name": "train",
+      "num_bytes": 74400,
+      "num_examples": 203,
+      "dataset_name": "bel_canto"
+    }
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+  "supervised_keys": {
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+}

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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+  "dataset_name": "bel_canto",
+  "dataset_size": 5581095,
+  "description": "",
+  "download_checksums": {
+    "https://www.modelscope.cn/datasets/ccmusic-database/bel_canto/resolve/master/data/eval.zip": {
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+      "checksum": null
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eval/test/state.json

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eval/train/dataset_info.json

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+  "builder_name": "bel_canto",
+  "citation": "",
+  "config_name": "eval",
+  "dataset_name": "bel_canto",
+  "dataset_size": 5581095,
+  "description": "",
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eval/train/state.json

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eval/validation/data-00000-of-00001.arrow

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eval/validation/dataset_info.json

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+  "builder_name": "bel_canto",
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+  "dataset_name": "bel_canto",
+  "dataset_size": 5581095,
+  "description": "",
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+        "m_folk",
+        "f_folk"
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+      "label_column": "label"
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+}

eval/validation/state.json

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