try to use arrow mode

Guzheng_Tech99.py

@@ -1,342 +0,0 @@
-import os
-import csv
-import random
-import librosa
-import datasets
-import numpy as np
-from tqdm import tqdm
-from glob import glob
-
-_NAMES = {
-    "chanyin": 0,
-    "dianyin": 6,
-    "shanghua": 2,
-    "xiahua": 3,
-    "huazhi": 4,
-    "guazou": 4,
-    "lianmo": 4,
-    "liantuo": 4,
-    "yaozhi": 5,
-    "boxian": 1,
-}
-
-_NAME = [
-    "chanyin",  # Vibrato
-    "boxian",  # Plucks
-    "shanghua",  # Upward Portamento
-    "xiahua",  # Downward Portamento
-    "huazhi/guazou/lianmo/liantuo",  # Glissando
-    "yaozhi",  # Tremolo
-    "dianyin",  # Point Note
-]
-
-_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",
-    "label": f"{_DOMAIN}/label.zip",
-}
-
-_TIME_LENGTH = 3  # seconds
-_SAMPLE_RATE = 44100
-_HOP_LENGTH = 512  # SAMPLE_RATE * ZHEN_LENGTH // 1000
-
-
-class Guzheng_Tech99(datasets.GeneratorBasedBuilder):
-    def _info(self):
-        return datasets.DatasetInfo(
-            features=(
-                datasets.Features(
-                    {
-                        "audio": datasets.Audio(sampling_rate=44100),
-                        "mel": datasets.Image(),
-                        "label": datasets.Sequence(
-                            feature={
-                                "onset_time": datasets.Value("float32"),
-                                "offset_time": datasets.Value("float32"),
-                                "IPT": datasets.ClassLabel(num_classes=7, names=_NAME),
-                                "note": datasets.Value("int8"),
-                            }
-                        ),
-                    }
-                )
-                if self.config.name == "default"
-                else datasets.Features(
-                    {
-                        "mel": datasets.features.Array3D(
-                            dtype="float32", shape=(128, 258, 1)
-                        ),
-                        "cqt": datasets.features.Array3D(
-                            dtype="float32", shape=(88, 258, 1)
-                        ),
-                        "chroma": datasets.features.Array3D(
-                            dtype="float32", shape=(12, 258, 1)
-                        ),
-                        "label": datasets.features.Array2D(
-                            dtype="float32", shape=(7, 258)
-                        ),
-                    }
-                )
-            ),
-            homepage=_HOMEPAGE,
-            license="CC-BY-NC-ND",
-            version="1.2.0",
-        )
-
-    def _RoW_norm(self, data):
-        common_sum = 0
-        square_sum = 0
-        tfle = 0
-        for i in range(len(data)):
-            tfle += (data[i].sum(-1).sum(0) != 0).astype("float").sum()
-            common_sum += data[i].sum(-1).sum(-1)
-            square_sum += (data[i] ** 2).sum(-1).sum(-1)
-
-        common_avg = common_sum / tfle
-        square_avg = square_sum / tfle
-        std = np.sqrt(square_avg - common_avg**2)
-        return common_avg, std
-
-    def _norm(self, data):
-        size = data.shape
-        avg, std = self._RoW_norm(data)
-        avg = np.tile(avg.reshape((1, -1, 1, 1)), (size[0], 1, size[2], size[3]))
-        std = np.tile(std.reshape((1, -1, 1, 1)), (size[0], 1, size[2], size[3]))
-        return (data - avg) / std
-
-    def _load(self, wav_dir, csv_dir, groups):
-        def files(wav_dir, csv_dir, group):
-            flacs = sorted(glob(os.path.join(wav_dir, group, "*.flac")))
-            if len(flacs) == 0:
-                flacs = sorted(glob(os.path.join(wav_dir, group, "*.wav")))
-
-            csvs = sorted(glob(os.path.join(csv_dir, group, "*.csv")))
-            files = list(zip(flacs, csvs))
-            if len(files) == 0:
-                raise RuntimeError(f"Group {group} is empty")
-
-            result = []
-            for audio_path, csv_path in files:
-                result.append((audio_path, csv_path))
-
-            return result
-
-        def logMel(y, sr=_SAMPLE_RATE):
-            # 帧长为32ms (1000ms/(16000/512) = 32ms), D2的频率是73.418
-            mel = librosa.feature.melspectrogram(
-                y=y,
-                sr=sr,
-                hop_length=_HOP_LENGTH,
-                fmin=27.5,
-            )
-            return librosa.power_to_db(mel, ref=np.max)
-
-        # Returns the CQT of the input audio
-        def logCQT(y, sr=_SAMPLE_RATE):
-            # 帧长为32ms (1000ms/(16000/512) = 32ms), D2的频率是73.418
-            cqt = librosa.cqt(
-                y,
-                sr=sr,
-                hop_length=_HOP_LENGTH,
-                fmin=27.5,
-                n_bins=88,
-                bins_per_octave=12,
-            )
-            return (
-                (1.0 / 80.0) * librosa.core.amplitude_to_db(np.abs(cqt), ref=np.max)
-            ) + 1.0
-
-        def logChroma(y, sr=_SAMPLE_RATE):
-            # 帧长为32ms (1000ms/(16000/512) = 32ms), D2的频率是73.418
-            chroma = librosa.feature.chroma_stft(
-                y=y,
-                sr=sr,
-                hop_length=_HOP_LENGTH,
-            )
-            return (
-                (1.0 / 80.0) * librosa.core.amplitude_to_db(np.abs(chroma), ref=np.max)
-            ) + 1.0
-
-        def chunk_data(f):
-            x = []
-            xdata = np.transpose(f)
-            s = _SAMPLE_RATE * _TIME_LENGTH // _HOP_LENGTH
-            length = int(np.ceil((int(len(xdata) / s) + 1) * s))
-            app = np.zeros((length - xdata.shape[0], xdata.shape[1]))
-            xdata = np.concatenate((xdata, app), 0)
-            for i in range(int(length / s)):
-                data = xdata[int(i * s) : int(i * s + s)]
-                x.append(np.transpose(data[:s, :]))
-
-            return np.array(x)
-
-        def load_all(audio_path, csv_path, hop=_HOP_LENGTH, n_IPTs=7, technique=_NAMES):
-            # Load audio features: The shape of cqt (88, 8520), 8520 is the number of frames on the time axis
-            y, sr = librosa.load(audio_path, sr=_SAMPLE_RATE)
-            mel = logMel(y, sr)
-            cqt = logCQT(y, sr)
-            chroma = logChroma(y, sr)
-            # Load the ground truth label
-            n_steps = cqt.shape[1]
-            IPT_label = np.zeros([n_IPTs, n_steps], dtype=int)
-            with open(csv_path, "r", encoding="utf-8") as f:  # csv file for each audio
-                reader = csv.DictReader(f, delimiter=",")
-                for label in reader:  # each note
-                    onset = float(label["onset_time"])
-                    offset = float(label["offset_time"])
-                    IPT = int(technique[label["IPT"]])
-                    left = int(round(onset * _SAMPLE_RATE / hop))
-                    frame_right = int(round(offset * _SAMPLE_RATE / hop))
-                    frame_right = min(n_steps, frame_right)
-                    IPT_label[IPT, left:frame_right] = 1
-
-            return dict(
-                audio_path=audio_path,
-                csv_path=csv_path,
-                mel=mel,
-                cqt=cqt,
-                chroma=chroma,
-                IPT_label=IPT_label,
-            )
-
-        data = []
-        # print(f"Loading {len(groups)} group{'s' if len(groups) > 1 else ''} ")
-        for group in groups:
-            for input_files in files(wav_dir, csv_dir, group):
-                data.append(load_all(*input_files))
-
-        for i, dic in tqdm(enumerate(data), total=len(data), desc="Feature extracting"):
-            x_mel = chunk_data(dic["mel"])
-            x_cqt = chunk_data(dic["cqt"])
-            x_chroma = chunk_data(dic["chroma"])
-            y_i = dic["IPT_label"]
-            y_i = chunk_data(y_i)
-            if i == 0:
-                Xtr_mel = x_mel
-                Xtr_cqt = x_cqt
-                Xtr_chroma = x_chroma
-                Ytr_i = y_i
-
-            else:
-                Xtr_mel = np.concatenate([Xtr_mel, x_mel], axis=0)
-                Xtr_cqt = np.concatenate([Xtr_cqt, x_cqt], axis=0)
-                Xtr_chroma = np.concatenate([Xtr_chroma, x_chroma], axis=0)
-                Ytr_i = np.concatenate([Ytr_i, y_i], axis=0)
-
-        # Transform the shape of the input
-        Xtr_mel = np.expand_dims(Xtr_mel, axis=3)
-        Xtr_cqt = np.expand_dims(Xtr_cqt, axis=3)
-        Xtr_chroma = np.expand_dims(Xtr_chroma, axis=3)
-        # Normalize
-        Xtr_mel = self._norm(Xtr_mel)
-        Xtr_cqt = self._norm(Xtr_cqt)
-        Xtr_chroma = self._norm(Xtr_chroma)
-        return [list(Xtr_mel), list(Xtr_cqt), list(Xtr_chroma)], list(Ytr_i)
-
-    def _parse_csv_label(self, csv_file):
-        label = []
-        with open(csv_file, mode="r", encoding="utf-8") as file:
-            for row in csv.DictReader(file):
-                label.append(
-                    {
-                        "onset_time": float(row["onset_time"]),
-                        "offset_time": float(row["offset_time"]),
-                        "IPT": _NAME[_NAMES[row["IPT"]]],
-                        "note": int(row["note"]),
-                    }
-                )
-
-        return label
-
-    def _split_generators(self, dl_manager):
-        audio_files = dl_manager.download_and_extract(_URLS["audio"])
-        csv_files = dl_manager.download_and_extract(_URLS["label"])
-        trainset, validset, testset = [], [], []
-        if self.config.name == "default":
-            files = {}
-            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(".flac"):
-                    item_id = fname.split(".")[0]
-                    files[item_id] = {"audio": path}
-
-            for path in dl_manager.iter_files([mel_files]):
-                fname = os.path.basename(path)
-                if fname.endswith(".jpg"):
-                    item_id = fname.split(".")[0]
-                    files[item_id]["mel"] = path
-
-            for path in dl_manager.iter_files([csv_files]):
-                fname = os.path.basename(path)
-                if fname.endswith(".csv"):
-                    item_id = fname.split(".")[0]
-                    files[item_id]["label"] = self._parse_csv_label(path)
-
-            for item in files.values():
-                if "train" in item["audio"]:
-                    trainset.append(item)
-
-                elif "validation" in item["audio"]:
-                    validset.append(item)
-
-                elif "test" in item["audio"]:
-                    testset.append(item)
-
-        else:
-            audio_dir = os.path.join(audio_files, "audio")
-            csv_dir = os.path.join(csv_files, "label")
-            X_train, Y_train = self._load(audio_dir, csv_dir, ["train"])
-            X_valid, Y_valid = self._load(audio_dir, csv_dir, ["validation"])
-            X_test, Y_test = self._load(audio_dir, csv_dir, ["test"])
-            for i in range(len(Y_train)):
-                trainset.append(
-                    {
-                        "mel": X_train[0][i],
-                        "cqt": X_train[1][i],
-                        "chroma": X_train[2][i],
-                        "label": Y_train[i],
-                    }
-                )
-
-            for i in range(len(Y_valid)):
-                validset.append(
-                    {
-                        "mel": X_valid[0][i],
-                        "cqt": X_valid[1][i],
-                        "chroma": X_valid[2][i],
-                        "label": Y_valid[i],
-                    }
-                )
-
-            for i in range(len(Y_test)):
-                testset.append(
-                    {
-                        "mel": X_test[0][i],
-                        "cqt": X_test[1][i],
-                        "chroma": X_test[2][i],
-                        "label": Y_test[i],
-                    }
-                )
-
-        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, path in enumerate(files):
-            yield i, path

README.md

@@ -11,9 +11,70 @@ tags:
 pretty_name: Guzheng Technique 99 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: sequence
+    splits:
+      - name: train
+        num_bytes: 242218
+        num_examples: 79
+      - name: validation
+        num_bytes: 32229
+        num_examples: 10
+      - name: test
+        num_bytes: 31038
+        num_examples: 10
+    download_size: 683115163
+    dataset_size: 305485
+  - config_name: eval
+    features:
+      - name: mel
+        dtype: array3d
+      - name: cqt
+        dtype: array3d
+      - name: chroma
+        dtype: array3d
+      - name: label
+        dtype: array2d
+    splits:
+      - name: train
+        num_bytes: 1190227192
+        num_examples: 2486
+      - name: validation
+        num_bytes: 133098616
+        num_examples: 278
+      - name: test
+        num_bytes: 148898092
+        num_examples: 311
+    download_size: 667607870
+    dataset_size: 1472223900
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: default/train/data-*.arrow
+      - split: validation
+        path: default/validation/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/Guzheng_Tech99/dataPeview)
+
 # Dataset Card for Guzheng Technique 99 Dataset
 ## Original Content
 This dataset is created and used by [[1]](https://arxiv.org/pdf/2303.13272) for frame-level Guzheng playing technique detection. The original dataset encompasses 99 solo compositions for Guzheng, recorded by professional musicians within a studio environment. Each composition is annotated for every note, indicating the onset, offset, pitch, and playing techniques. This is different from the GZ IsoTech, which is annotated at the clip-level. Also, its playing technique categories differ slightly, encompassing a total of seven techniques. They are: _Vibrato (chanyin 颤音), Plucks (boxian 拨弦), Upward Portamento (shanghua 上滑), Downward Portamento (xiahua 下滑), Glissando (huazhi\guazou\lianmo\liantuo 花指\刮奏\连抹\连托), Tremolo (yaozhi 摇指), and Point Note (dianyin 点音)_. This meticulous annotation results in a total of 63,352 annotated labels.
@@ -21,7 +82,7 @@ This dataset is created and used by [[1]](https://arxiv.org/pdf/2303.13272) for
 ## Integration
 In the original dataset, the labels were stored in a separate CSV file. This posed usability challenges, as researchers had to perform time-consuming operations on CSV parsing and label-audio alignment. After our integration, the data structure has been streamlined and optimized. It now contains three columns: audio sampled at 44,100 Hz, pre-processed mel spectrograms, and a dictionary. This dictionary contains onset, offset, technique numeric labels, and pitch. The number of data entries after integration remains 99, with a cumulative duration amounting to 151.08 minutes. The average audio duration is 91.56 seconds.
 
-We performed data processing and constructed the [default subset](#default-subset) of the current integrated version of the dataset, and the details of its data structure can be viewed through the [viewer](https://www.modelscope.cn/datasets/ccmusic-database/Guzheng_Tech99/dataPeview). In light of the fact that the current dataset has been referenced and evaluated in a published article, we transcribe here the details of the dataset processing during the evaluation in the said article: each audio clip is a 3-second segment sampled at 44,100Hz, which is then converted into a log Constant-Q Transform (CQT) spectrogram. A CQT accompanied by a label constitutes a single data entry, forming the first and second columns, respectively. The CQT is a 3-dimensional array with dimensions of 88x258x1, representing the frequency-time structure of the audio. The label, on the other hand, is a 2-dimensional array with dimensions of 7x258, indicating the presence of seven distinct techniques across each time frame. Ultimately, given that the original dataset has already been divided into train, valid, and test sets, we have integrated the feature extraction method mentioned in this article's evaluation process into the API, thereby constructing the [eval subset](#eval-subset), which is not embodied in our paper.
+We performed data processing and constructed the [default subset](#default-subset) of the current integrated version of the dataset, and the details of its data structure can be viewed through the [viewer](https://huggingface.co/datasets/ccmusic-database/Guzheng_Tech99/viewer). In light of the fact that the current dataset has been referenced and evaluated in a published article, we transcribe here the details of the dataset processing during the evaluation in the said article: each audio clip is a 3-second segment sampled at 44,100Hz, which is then converted into a log Constant-Q Transform (CQT) spectrogram. A CQT accompanied by a label constitutes a single data entry, forming the first and second columns, respectively. The CQT is a 3-dimensional array with dimensions of 88x258x1, representing the frequency-time structure of the audio. The label, on the other hand, is a 2-dimensional array with dimensions of 7x258, indicating the presence of seven distinct techniques across each time frame. Ultimately, given that the original dataset has already been divided into train, valid, and test sets, we have integrated the feature extraction method mentioned in this article's evaluation process into the API, thereby constructing the [eval subset](#eval-subset), which is not embodied in our paper.
 
 ## Statistics
 In this part, we present statistics at the label-level. The number of audio clips is equivalent to the count of either onset or offset occurrences. The duration of an audio clip is determined by calculating the offset time minus the onset time. At this level, the number of clips is 15,838, and the total duration is 162.69 minutes. 
@@ -99,8 +160,14 @@ Chinese, English
 ```python
 from datasets import load_dataset
 
-ds = load_dataset("ccmusic-database/Guzheng_Tech99", split="train")
-for item in ds:
+ds = load_dataset("ccmusic-database/Guzheng_Tech99", name="default")
+for item in ds["train"]:
+    print(item)
+
+for item in ds["validation"]:
+    print(item)
+
+for item in ds["test"]:
     print(item)
 ```
 
@@ -121,7 +188,7 @@ for item in ds["test"]:
 
 ## Maintenance
 ```bash
-git clone [email protected]:datasets/ccmusic-database/Guzheng_Tech99
+GIT_LFS_SKIP_SMUDGE=1 git clone [email protected]:datasets/ccmusic-database/Guzheng_Tech99
 cd Guzheng_Tech99
 ```
 

default/dataset_dict.json

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

default/test/dataset_info.json

@@ -0,0 +1,90 @@
+{
+  "builder_name": "guzheng_tech99",
+  "citation": "",
+  "config_name": "default",
+  "dataset_name": "guzheng_tech99",
+  "dataset_size": 305485,
+  "description": "",
+  "download_checksums": {
+    "https://www.modelscope.cn/datasets/ccmusic-database/Guzheng_Tech99/resolve/master/data/audio.zip": {
+      "num_bytes": 667458676,
+      "checksum": null
+    },
+    "https://www.modelscope.cn/datasets/ccmusic-database/Guzheng_Tech99/resolve/master/data/label.zip": {
+      "num_bytes": 149194,
+      "checksum": null
+    },
+    "https://www.modelscope.cn/datasets/ccmusic-database/Guzheng_Tech99/resolve/master/data/mel.zip": {
+      "num_bytes": 15507293,
+      "checksum": null
+    }
+  },
+  "download_size": 683115163,
+  "features": {
+    "audio": {
+      "sampling_rate": 44100,
+      "_type": "Audio"
+    },
+    "mel": {
+      "_type": "Image"
+    },
+    "label": {
+      "feature": {
+        "onset_time": {
+          "dtype": "float32",
+          "_type": "Value"
+        },
+        "offset_time": {
+          "dtype": "float32",
+          "_type": "Value"
+        },
+        "IPT": {
+          "names": [
+            "chanyin",
+            "boxian",
+            "shanghua",
+            "xiahua",
+            "huazhi/guazou/lianmo/liantuo",
+            "yaozhi",
+            "dianyin"
+          ],
+          "_type": "ClassLabel"
+        },
+        "note": {
+          "dtype": "int8",
+          "_type": "Value"
+        }
+      },
+      "_type": "Sequence"
+    }
+  },
+  "homepage": "https://www.modelscope.cn/datasets/ccmusic-database/Guzheng_Tech99",
+  "license": "CC-BY-NC-ND",
+  "size_in_bytes": 683420648,
+  "splits": {
+    "train": {
+      "name": "train",
+      "num_bytes": 242218,
+      "num_examples": 79,
+      "dataset_name": "guzheng_tech99"
+    },
+    "validation": {
+      "name": "validation",
+      "num_bytes": 32229,
+      "num_examples": 10,
+      "dataset_name": "guzheng_tech99"
+    },
+    "test": {
+      "name": "test",
+      "num_bytes": 31038,
+      "num_examples": 10,
+      "dataset_name": "guzheng_tech99"
+    }
+  },
+  "version": {
+    "version_str": "0.0.0",
+    "major": 0,
+    "minor": 0,
+    "patch": 0
+  }
+}

default/test/state.json

@@ -0,0 +1,13 @@
+{
+  "_data_files": [
+    {
+      "filename": "data-00000-of-00001.arrow"
+    }
+  ],
+  "_fingerprint": "f20b0fc5bfe8a075",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": null,
+  "_output_all_columns": false,
+  "_split": "test"
+}

default/train/dataset_info.json

@@ -0,0 +1,90 @@
+{
+  "builder_name": "guzheng_tech99",
+  "citation": "",
+  "config_name": "default",
+  "dataset_name": "guzheng_tech99",
+  "dataset_size": 305485,
+  "description": "",
+  "download_checksums": {
+    "https://www.modelscope.cn/datasets/ccmusic-database/Guzheng_Tech99/resolve/master/data/audio.zip": {
+      "num_bytes": 667458676,
+      "checksum": null
+    },
+    "https://www.modelscope.cn/datasets/ccmusic-database/Guzheng_Tech99/resolve/master/data/label.zip": {
+      "num_bytes": 149194,
+      "checksum": null
+    },
+    "https://www.modelscope.cn/datasets/ccmusic-database/Guzheng_Tech99/resolve/master/data/mel.zip": {
+      "num_bytes": 15507293,
+      "checksum": null
+    }
+  },
+  "download_size": 683115163,
+  "features": {
+    "audio": {
+      "sampling_rate": 44100,
+      "_type": "Audio"
+    },
+    "mel": {
+      "_type": "Image"
+    },
+    "label": {
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eval/dataset_dict.json

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