2 arrows

GZ_IsoTech.py

@@ -1,185 +0,0 @@
-import os
-import random
-import hashlib
-import datasets
-from datasets.tasks import ImageClassification
-
-_HOMEPAGE = f"https://www.modelscope.cn/datasets/ccmusic-database/{os.path.basename(__file__)[:-3]}"
-
-_DOMAIN = f"{_HOMEPAGE}/resolve/master/data"
-
-_NAMES = {
-    "vibrato": ["颤音", "chan4_yin1"],
-    "upward_portamento": ["上滑音", "shang4_hua2_yin1"],
-    "downward_portamento": ["下滑音", "xia4_hua2_yin1"],
-    "returning_portamento": ["回滑音", "hui2_hua2_yin1"],
-    "glissando": ["刮奏, 花指", "gua1_zou4/hua1_zhi3"],
-    "tremolo": ["摇指", "yao2_zhi3"],
-    "harmonics": ["泛音", "fan4_yin1"],
-    "plucks": ["勾, 打, 抹, 托, ...", "gou1/da3/mo3/tuo1/etc"],
-}
-
-_URLS = {
-    "audio": f"{_DOMAIN}/audio.zip",
-    "mel": f"{_DOMAIN}/mel.zip",
-    "eval": f"{_DOMAIN}/eval.zip",
-}
-
-
-class GZ_IsoTech(datasets.GeneratorBasedBuilder):
-    def _info(self):
-        return datasets.DatasetInfo(
-            features=(
-                datasets.Features(
-                    {
-                        "audio": datasets.Audio(sampling_rate=44100),
-                        "mel": datasets.Image(),
-                        "label": datasets.features.ClassLabel(
-                            names=list(_NAMES.keys())
-                        ),
-                        "name": datasets.Value("string"),
-                        "cname": datasets.Value("string"),
-                        "pinyin": 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=list(_NAMES.keys())
-                            ),
-                        }
-                    )
-                )
-            ),
-            supervised_keys=("mel", "label"),
-            homepage=_HOMEPAGE,
-            license="CC-BY-NC-ND",
-            version="1.2.0",
-            task_templates=[
-                ImageClassification(
-                    task="image-classification",
-                    image_column="image",
-                    label_column="label",
-                )
-            ],
-        )
-
-    def _str2md5(self, original_string: str):
-        md5_obj = hashlib.md5()
-        md5_obj.update(original_string.encode("utf-8"))
-        return md5_obj.hexdigest()
-
-    def _split_generators(self, dl_manager):
-        if self.config.name == "default":
-            audio_files = dl_manager.download_and_extract(_URLS["audio"])
-            mel_files = dl_manager.download_and_extract(_URLS["mel"])
-            train_files, files = {}, {}
-            for path in dl_manager.iter_files([audio_files]):
-                fname: str = os.path.basename(path)
-                dirname = os.path.dirname(path)
-                splt = os.path.basename(os.path.dirname(dirname))
-                if fname.endswith(".wav"):
-                    cls = f"{splt}/{os.path.basename(dirname)}/"
-                    item_id = self._str2md5(cls + fname.split(".wa")[0])
-                    if splt == "train":
-                        train_files[item_id] = {"audio": path}
-
-                    else:
-                        files[item_id] = {"audio": path}
-
-            for path in dl_manager.iter_files([mel_files]):
-                fname = os.path.basename(path)
-                dirname = os.path.dirname(path)
-                splt = os.path.basename(os.path.dirname(dirname))
-                if fname.endswith(".jpg"):
-                    cls = f"{splt}/{os.path.basename(dirname)}/"
-                    item_id = self._str2md5(cls + fname.split(".jp")[0])
-                    if splt == "train":
-                        train_files[item_id]["mel"] = path
-
-                    else:
-                        files[item_id]["mel"] = path
-
-            trainset = list(train_files.values())
-            testset = list(files.values())
-            random.shuffle(trainset)
-            random.shuffle(testset)
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    gen_kwargs={"files": trainset},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    gen_kwargs={"files": testset},
-                ),
-            ]
-
-        else:
-            data_files = dl_manager.download_and_extract(_URLS["eval"])
-            trainset, validset, testset = [], [], []
-            files = {key: [] for key in _NAMES}
-            for path in dl_manager.iter_files([data_files]):
-                clsdir = os.path.dirname(path)
-                cls = os.path.basename(clsdir)
-                splt = os.path.basename(os.path.dirname(clsdir))
-                if path.endswith(".jpg") and "mel" in path:
-                    if splt == "train":
-                        trainset.append(path)
-                    else:
-                        files[cls].append(path)
-
-            for cls in _NAMES:
-                count = len(files[cls])
-                if count < 2:
-                    raise ValueError(f"Class {cls} in test data has items < 2 !")
-
-                random.shuffle(files[cls])
-                half = max(count // 2, 1)
-                validset += files[cls][:half]
-                testset += files[cls][half:]
-
-            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, path in enumerate(files):
-                pt = os.path.basename(os.path.dirname(path["audio"]))
-                yield i, {
-                    "audio": path["audio"],
-                    "mel": path["mel"],
-                    "label": pt,
-                    "name": pt,
-                    "cname": _NAMES[pt][0],
-                    "pinyin": _NAMES[pt][1],
-                }
-
-        else:
-            for i, path in enumerate(files):
-                yield i, {
-                    "mel": path,
-                    "cqt": path.replace("mel", "cqt"),
-                    "chroma": path.replace("mel", "chroma"),
-                    "label": os.path.basename(os.path.dirname(path)),
-                }

README.md

@@ -11,9 +11,71 @@ tags:
 pretty_name: GZ_IsoTech 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: label
+        dtype: int8
+      - name: name
+        dtype: string
+      - name: cname
+        dtype: string
+      - name: pinyin
+        dtype: string
+    splits:
+      - name: train
+        num_bytes: 1102596
+        num_examples: 2328
+      - name: test
+        num_bytes: 223896
+        num_examples: 496
+    download_size: 273681660
+    dataset_size: 1326492
+  - 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: 1560776
+        num_examples: 2389
+      - name: validation
+        num_bytes: 155960
+        num_examples: 253
+      - name: test
+        num_bytes: 158410
+        num_examples: 257
+    download_size: 249961089
+    dataset_size: 1875146
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: default/train/data-*.arrow
+      - split: test
+        path: default/test/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/GZ_IsoTech/dataPeview)
+
 # Dataset Card for GZ_IsoTech Dataset
 ## Original Content
 The dataset is created and used for Guzheng playing technique detection by [[1]](https://archives.ismir.net/ismir2022/paper/000037.pdf). The original dataset comprises 2,824 variable-length audio clips showcasing various Guzheng playing techniques. Specifically, 2,328 clips were sourced from virtual sound banks, while 496 clips were performed by a professional Guzheng artist.
@@ -23,7 +85,7 @@ The clips are annotated in eight categories, with a Chinese pinyin and Chinese c
 ## Integration
 In the original dataset, the labels were represented by folder names, which provided Italian and Chinese pinyin labels. During the integration process, we added the corresponding Chinese character labels to ensure comprehensiveness. Lastly, after integration, the data structure has six columns: audio clip sampled at a rate of 44,100 Hz, mel spectrogram, numerical label, Italian label, Chinese character label, and Chinese pinyin label. The data number after integration remains at 2,824 with a total duration of 63.98 minutes. The average duration is 1.36 seconds.
 
-Based on the aforementioned original dataset, we conducted data processing to construct the [default subset](#default-subset) of the current integrated version of the dataset. Due to the pre-existing split in the original dataset, wherein the data has been partitioned approximately in a 4:1 ratio for training and testing sets, we uphold the original data division approach for the default subset. The data structure of the default subset can be viewed in the [viewer](https://www.modelscope.cn/datasets/ccmusic-database/GZ_IsoTech/dataPeview). In addition, we have retained the [eval subset](#eval-subset) used in the experiment for easy replication.
+Based on the aforementioned original dataset, we conducted data processing to construct the [default subset](#default-subset) of the current integrated version of the dataset. Due to the pre-existing split in the original dataset, wherein the data has been partitioned approximately in a 4:1 ratio for training and testing sets, we uphold the original data division approach for the default subset. The data structure of the default subset can be viewed in the [viewer](https://huggingface.co/datasets/ccmusic-database/CNPM/viewer). In addition, we have retained the [eval subset](#eval-subset) used in the experiment for easy replication.
 
 ## Default Subset Structure
 <style>
@@ -111,6 +173,12 @@ for item in ds["test"]:
     print(item)
 ```
 
+## Maintenance
+```bash
+GIT_LFS_SKIP_SMUDGE=1 git clone [email protected]:datasets/ccmusic-database/CNPM
+cd CNPM
+```
+
 ## Dataset Creation
 ### Curation Rationale
 The Guzheng is a kind of traditional Chinese instrument with diverse playing techniques. Instrument playing techniques (IPT) play an important role in musical performance. However, most of the existing works for IPT detection show low efficiency for variable-length audio and do not assure generalization as they rely on a single sound bank for training and testing. In this study, we propose an end-to-end Guzheng playing technique detection system using Fully Convolutional Networks that can be applied to variable-length audio. Because each Guzheng playing technique is applied to a note, a dedicated onset detector is trained to divide an audio into several notes and its predictions are fused with frame-wise IPT predictions. During fusion, we add the IPT predictions frame by frame inside each note and get the IPT with the highest probability within each note as the final output of that note. We create a new dataset named GZ_IsoTech from multiple sound banks and real-world recordings for Guzheng performance analysis. Our approach achieves 87.97% in frame-level accuracy and 80.76% in note-level F1 score, outperforming existing works by a large margin, which indicates the effectiveness of our proposed method in IPT detection.

default/dataset_dict.json

@@ -0,0 +1 @@
+{"splits": ["train", "test"]}

default/test/data-00000-of-00001.arrow

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+size 56693320

default/test/dataset_info.json

@@ -0,0 +1,86 @@
+{
+  "builder_name": "gz_iso_tech",
+  "citation": "",
+  "config_name": "default",
+  "dataset_name": "gz_iso_tech",
+  "dataset_size": 1326492,
+  "description": "",
+  "download_checksums": {
+    "https://www.modelscope.cn/datasets/ccmusic-database/GZ_IsoTech/resolve/master/data/audio.zip": {
+      "num_bytes": 265012844,
+      "checksum": null
+    },
+    "https://www.modelscope.cn/datasets/ccmusic-database/GZ_IsoTech/resolve/master/data/mel.zip": {
+      "num_bytes": 8668816,
+      "checksum": null
+    }
+  },
+  "download_size": 273681660,
+  "features": {
+    "audio": {
+      "sampling_rate": 44100,
+      "_type": "Audio"
+    },
+    "mel": {
+      "_type": "Image"
+    },
+    "label": {
+      "names": [
+        "vibrato",
+        "upward_portamento",
+        "downward_portamento",
+        "returning_portamento",
+        "glissando",
+        "tremolo",
+        "harmonics",
+        "plucks"
+      ],
+      "_type": "ClassLabel"
+    },
+    "name": {
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+      "_type": "Value"
+    },
+    "cname": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "pinyin": {
+      "dtype": "string",
+      "_type": "Value"
+    }
+  },
+  "homepage": "https://www.modelscope.cn/datasets/ccmusic-database/GZ_IsoTech",
+  "license": "CC-BY-NC-ND",
+  "size_in_bytes": 275008152,
+  "splits": {
+    "train": {
+      "name": "train",
+      "num_bytes": 1102596,
+      "num_examples": 2328,
+      "dataset_name": "gz_iso_tech"
+    },
+    "test": {
+      "name": "test",
+      "num_bytes": 223896,
+      "num_examples": 496,
+      "dataset_name": "gz_iso_tech"
+    }
+  },
+  "supervised_keys": {
+    "input": "mel",
+    "output": "label"
+  },
+  "task_templates": [
+    {
+      "task": "image-classification",
+      "label_column": "label"
+    }
+  ],
+  "version": {
+    "version_str": "0.0.0",
+    "major": 0,
+    "minor": 0,
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+}

default/test/state.json

@@ -0,0 +1,13 @@
+{
+  "_data_files": [
+    {
+      "filename": "data-00000-of-00001.arrow"
+    }
+  ],
+  "_fingerprint": "c760a28217f0ba3a",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": null,
+  "_output_all_columns": false,
+  "_split": "test"
+}

default/train/data-00000-of-00001.arrow

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+size 291029112

default/train/dataset_info.json

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+  "citation": "",
+  "config_name": "default",
+  "dataset_name": "gz_iso_tech",
+  "dataset_size": 1326492,
+  "description": "",
+  "download_checksums": {
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+      "num_bytes": 265012844,
+      "checksum": null
+    },
+    "https://www.modelscope.cn/datasets/ccmusic-database/GZ_IsoTech/resolve/master/data/mel.zip": {
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+      "checksum": null
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+  "features": {
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+    },
+    "mel": {
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+    },
+    "label": {
+      "names": [
+        "vibrato",
+        "upward_portamento",
+        "downward_portamento",
+        "returning_portamento",
+        "glissando",
+        "tremolo",
+        "harmonics",
+        "plucks"
+      ],
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+    "cname": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "pinyin": {
+      "dtype": "string",
+      "_type": "Value"
+    }
+  },
+  "homepage": "https://www.modelscope.cn/datasets/ccmusic-database/GZ_IsoTech",
+  "license": "CC-BY-NC-ND",
+  "size_in_bytes": 275008152,
+  "splits": {
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+      "name": "train",
+      "num_bytes": 1102596,
+      "num_examples": 2328,
+      "dataset_name": "gz_iso_tech"
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+      "num_bytes": 223896,
+      "num_examples": 496,
+      "dataset_name": "gz_iso_tech"
+    }
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+  "supervised_keys": {
+    "input": "mel",
+    "output": "label"
+  },
+  "task_templates": [
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+      "label_column": "label"
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+  ],
+  "version": {
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+    "major": 0,
+    "minor": 0,
+    "patch": 0
+  }
+}

default/train/state.json

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+  "_format_kwargs": {},
+  "_format_type": null,
+  "_output_all_columns": false,
+  "_split": "train"
+}

eval/dataset_dict.json

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

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eval/test/dataset_info.json

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+  "dataset_name": "gz_iso_tech",
+  "dataset_size": 1875146,
+  "description": "",
+  "download_checksums": {
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+      "checksum": null
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+        "harmonics",
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+  "license": "CC-BY-NC-ND",
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+      "dataset_name": "gz_iso_tech"
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+      "dataset_name": "gz_iso_tech"
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+}

eval/test/state.json

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eval/train/dataset_info.json

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+  "builder_name": "gz_iso_tech",
+  "citation": "",
+  "config_name": "eval",
+  "dataset_name": "gz_iso_tech",
+  "dataset_size": 1875146,
+  "description": "",
+  "download_checksums": {
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+    }
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+      "dataset_name": "gz_iso_tech"
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