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import os |
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import random |
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import socket |
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import datasets |
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from datasets.tasks import ImageClassification |
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_NAMES = [ |
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"PearlRiver", |
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"YoungChang", |
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"Steinway-T", |
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"Hsinghai", |
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"Kawai", |
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"Steinway", |
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"Kawai-G", |
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"Yamaha", |
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] |
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_NAME = os.path.basename(__file__).split('.')[0] |
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_HOMEPAGE = f"https://huggingface.co/datasets/ccmusic-database/{_NAME}" |
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_CITATION = """\ |
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@dataset{zhaorui_liu_2021_5676893, |
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author = {Zhaorui Liu, Monan Zhou, Shenyang Xu, Yuan Wang, Zhaowen Wang, Wei Li and Zijin Li}, |
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title = {CCMUSIC DATABASE: A Music Data Sharing Platform for Computational Musicology Research}, |
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month = {nov}, |
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year = {2021}, |
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publisher = {Zenodo}, |
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version = {1.1}, |
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doi = {10.5281/zenodo.5676893}, |
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url = {https://doi.org/10.5281/zenodo.5676893} |
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} |
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""" |
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_DESCRIPTION = """\ |
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Piano-Sound-Quality is a dataset of piano sound. |
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It consists of 8 kinds of pianos including PearlRiver, YoungChang, Steinway-T, Hsinghai, Kawai, Steinway, Kawai-G, Yamaha(recorded by Shaohua Ji with SONY PCM-D100). |
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Data was annotated by students from the China Conservatory of Music (CCMUSIC) in Beijing and collected by Monan Zhou. |
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""" |
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_PITCHES = { |
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"009": "A2", "010": "A2#/B2b", "011": "B2", "100": "C1", "101": "C1#/D1b", "102": "D1", "103": "D1#/E1b", |
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"104": "E1", "105": "F1", "106": "F1#/G1b", "107": "G1", "108": "G1#/A1b", "109": "A1", "110": "A1#/B1b", |
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"111": "B1", "200": "C", "201": "C#/Db", "202": "D", "203": "D#/Eb", "204": "E", "205": "F", "206": "F#/Gb", |
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"207": "G", "208": "G#/Ab", "209": "A", "210": "A#/Bb", "211": "B", "300": "c", "301": "c#/db", "302": "d", |
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"303": "d#/eb", "304": "e", "305": "f", "306": "f#/gb", "307": "g", "308": "g#/ab", "309": "a", "310": "a#/bb", |
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"311": "b", "400": "c1", "401": "c1#/d1b", "402": "d1", "403": "d1#/e1b", "404": "e1", "405": "f1", "406": "f1#/g1b", |
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"407": "g1", "408": "g1#/a1b", "409": "a1", "410": "a1#/b1b", "411": "b1", "500": "c2", "501": "c2#/d2b", |
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"502": "d2", "503": "d2#/e2b", "504": "e2", "505": "f2", "506": "f2#/g2b", "507": "g2", "508": "g2#/a2b", |
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"509": "a2", "510": "a2#/b2b", "511": "b2", "600": "c3", "601": "c3#/d3b", "602": "d3", "603": "d3#/e3b", |
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"604": "e3", "605": "f3", "606": "f3#/g3b", "607": "g3", "608": "g3#/a3b", "609": "a3", "610": "a3#/b3b", |
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"611": "b3", "700": "c4", "701": "c4#/d4b", "702": "d4", "703": "d4#/e4b", "704": "e4", "705": "f4", |
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"706": "f4#/g4b", "707": "g4", "708": "g4#/a4b", "709": "a4", "710": "a4#/b4b", "711": "b4", "800": "c5" |
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} |
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class pianos(datasets.GeneratorBasedBuilder): |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"mel": datasets.Image(), |
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"label": datasets.features.ClassLabel(names=_NAMES), |
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"pitch": datasets.features.ClassLabel(names=list(_PITCHES.values())), |
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} |
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), |
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supervised_keys=("mel", "label"), |
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homepage=_HOMEPAGE, |
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license="mit", |
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citation=_CITATION, |
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task_templates=[ |
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ImageClassification( |
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task="image-classification", |
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image_column="mel", |
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label_column="label", |
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) |
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], |
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) |
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def _cdn_url(self, ip='127.0.0.1', port=80): |
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try: |
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with socket.create_connection((ip, port), timeout=5): |
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return f'http://{ip}/{_NAME}/data/{_NAME}_data.zip' |
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except (socket.timeout, socket.error): |
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return f"{_HOMEPAGE}/resolve/main/data/{_NAME}_data.zip", |
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def _split_generators(self, dl_manager): |
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data_files = dl_manager.download_and_extract(self._cdn_url()) |
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dataset = [] |
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for path in dl_manager.iter_files([data_files]): |
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fname = os.path.basename(path) |
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if fname.endswith(".jpg"): |
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dataset.append({ |
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'mel': path, |
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'label': os.path.basename(os.path.dirname(path)), |
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'pitch': _PITCHES[fname.split('_')[0]] |
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}) |
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random.shuffle(dataset) |
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count = len(dataset) |
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p80 = int(0.8 * count) |
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p90 = int(0.9 * count) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"files": dataset[:p80] |
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} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"files": dataset[p80:p90] |
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} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"files": dataset[p90:] |
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} |
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), |
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] |
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def _generate_examples(self, files): |
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for i, path in enumerate(files): |
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yield i, { |
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"mel": path['mel'], |
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"label": path['label'], |
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"pitch": path['pitch'] |
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} |
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