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import os |
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import socket |
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import random |
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import datasets |
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from datasets.tasks import ImageClassification |
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_NAMES = { |
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'all': ['m_bel', 'f_bel', 'm_folk', 'f_folk'], |
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'gender': ['female', 'male'], |
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'singing_method': ['Folk_Singing', 'Bel_Canto'] |
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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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This database contains hundreds of acapella singing clips that are sung in two styles, Bel Conto and Chinese national singing style by professional vocalists. All of them are sung by professional vocalists and were recorded in professional commercial recording studios. |
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""" |
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class bel_canto(datasets.GeneratorBasedBuilder): |
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def _info(self): |
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return datasets.DatasetInfo( |
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features=datasets.Features( |
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{ |
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"mel": datasets.Image(), |
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"cqt": datasets.Image(), |
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"chroma": datasets.Image(), |
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"label": datasets.features.ClassLabel(names=_NAMES['all']), |
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"gender": datasets.features.ClassLabel(names=_NAMES['gender']), |
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"singing_method": datasets.features.ClassLabel(names=_NAMES['singing_method']) |
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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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description=_DESCRIPTION, |
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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/belcanto_data.zip' |
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except (socket.timeout, socket.error): |
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return f"{_HOMEPAGE}/resolve/main/data/belcanto_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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files = dl_manager.iter_files([data_files]) |
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dataset = [] |
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for path in files: |
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if 'mel' in path and os.path.basename(path).endswith(".jpg"): |
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dataset.append(path) |
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random.shuffle(dataset) |
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data_count = len(dataset) |
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p80 = int(data_count * 0.8) |
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p90 = int(data_count * 0.9) |
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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, fpath in enumerate(files): |
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dirname = os.path.basename(os.path.dirname(fpath)) |
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sex = dirname.split('_')[0] |
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method = dirname.split('_')[1] |
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yield i, { |
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"mel": fpath, |
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"cqt": fpath.replace('mel', 'cqt'), |
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"chroma": fpath.replace('mel', 'chroma'), |
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"label": dirname, |
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"gender": 'male' if sex == 'm' else 'female', |
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"singing_method": 'Bel_Canto' if method == 'bel' else 'Folk_Singing' |
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} |
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