import os import random import datasets from datasets.tasks import ImageClassification, AudioClassification _NAMES = [ "PearlRiver", "YoungChang", "Steinway-T", "Hsinghai", "Kawai", "Steinway", "Kawai-G", "Yamaha", ] _DBNAME = os.path.basename(__file__).split(".")[0] _HOMEPAGE = f"https://www.modelscope.cn/datasets/ccmusic/{_DBNAME}" _DOMAIN = f"https://www.modelscope.cn/api/v1/datasets/ccmusic/{_DBNAME}/repo?Revision=master&FilePath=data" _CITATION = """\ @dataset{zhaorui_liu_2021_5676893, author = {Monan Zhou, Shenyang Xu, Zhaorui Liu, Zhaowen Wang, Feng Yu, Wei Li and Zijin Li}, title = {CCMusic: an Open and Diverse Database for Chinese and General Music Information Retrieval Research}, month = {mar}, year = {2024}, publisher = {HuggingFace}, version = {1.2}, url = {https://huggingface.co/ccmusic-database} } """ _DESCRIPTION = """\ Piano-Sound-Quality is a dataset of piano sound. 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). Data was annotated by students from the China Conservatory of Music (CCMUSIC) in Beijing and collected by Monan Zhou. """ _PITCHES = { "009": "A2", "010": "A2#/B2b", "011": "B2", "100": "C1", "101": "C1#/D1b", "102": "D1", "103": "D1#/E1b", "104": "E1", "105": "F1", "106": "F1#/G1b", "107": "G1", "108": "G1#/A1b", "109": "A1", "110": "A1#/B1b", "111": "B1", "200": "C", "201": "C#/Db", "202": "D", "203": "D#/Eb", "204": "E", "205": "F", "206": "F#/Gb", "207": "G", "208": "G#/Ab", "209": "A", "210": "A#/Bb", "211": "B", "300": "c", "301": "c#/db", "302": "d", "303": "d#/eb", "304": "e", "305": "f", "306": "f#/gb", "307": "g", "308": "g#/ab", "309": "a", "310": "a#/bb", "311": "b", "400": "c1", "401": "c1#/d1b", "402": "d1", "403": "d1#/e1b", "404": "e1", "405": "f1", "406": "f1#/g1b", "407": "g1", "408": "g1#/a1b", "409": "a1", "410": "a1#/b1b", "411": "b1", "500": "c2", "501": "c2#/d2b", "502": "d2", "503": "d2#/e2b", "504": "e2", "505": "f2", "506": "f2#/g2b", "507": "g2", "508": "g2#/a2b", "509": "a2", "510": "a2#/b2b", "511": "b2", "600": "c3", "601": "c3#/d3b", "602": "d3", "603": "d3#/e3b", "604": "e3", "605": "f3", "606": "f3#/g3b", "607": "g3", "608": "g3#/a3b", "609": "a3", "610": "a3#/b3b", "611": "b3", "700": "c4", "701": "c4#/d4b", "702": "d4", "703": "d4#/e4b", "704": "e4", "705": "f4", "706": "f4#/g4b", "707": "g4", "708": "g4#/a4b", "709": "a4", "710": "a4#/b4b", "711": "b4", "800": "c5", } _URLS = { "audio": f"{_DOMAIN}/audio.zip", "mel": f"{_DOMAIN}/mel.zip", "eval": f"{_DOMAIN}/eval.zip", } class pianos_Config(datasets.BuilderConfig): def __init__(self, features, supervised_keys, task_templates, **kwargs): super(pianos_Config, self).__init__(version=datasets.Version("0.1.2"), **kwargs) self.features = features self.supervised_keys = supervised_keys self.task_templates = task_templates class pianos(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("0.1.2") BUILDER_CONFIGS = [ pianos_Config( name="eval", features=datasets.Features( { "mel": datasets.Image(), "label": datasets.features.ClassLabel(names=_NAMES), "pitch": datasets.features.ClassLabel( names=list(_PITCHES.values()) ), } ), supervised_keys=("mel", "label"), task_templates=[ ImageClassification( task="image-classification", image_column="mel", label_column="label", ) ], ), pianos_Config( name="default", features=datasets.Features( { "audio": datasets.Audio(sampling_rate=22050), "mel": datasets.Image(), "label": datasets.features.ClassLabel(names=_NAMES), "pitch": datasets.features.ClassLabel( names=list(_PITCHES.values()) ), } ), supervised_keys=("audio", "label"), task_templates=[ AudioClassification( task="audio-classification", audio_column="audio", label_column="label", ) ], ), ] def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=self.config.features, homepage=_HOMEPAGE, license="mit", citation=_CITATION, supervised_keys=self.config.supervised_keys, task_templates=self.config.task_templates, ) def _split_generators(self, dl_manager): dataset = [] if self.config.name == "eval": data_files = dl_manager.download_and_extract(_URLS["eval"]) for path in dl_manager.iter_files([data_files]): fname = os.path.basename(path) if fname.endswith(".jpg"): dataset.append( { "mel": path, "label": os.path.basename(os.path.dirname(path)), "pitch": _PITCHES[fname.split("_")[0]], } ) else: subset = {} audio_files = dl_manager.download_and_extract(_URLS["audio"]) for path in dl_manager.iter_files([audio_files]): fname = os.path.basename(path) if fname.endswith(".wav"): subset[fname.split(".")[0]] = { "audio": path, "label": os.path.basename(os.path.dirname(path)), "pitch": _PITCHES[fname[1:4]], } mel_files = dl_manager.download_and_extract(_URLS["mel"]) for path in dl_manager.iter_files([mel_files]): fname = os.path.basename(path) if fname.endswith(".jpg"): subset[fname.split(".")[0]]["mel"] = path dataset = list(subset.values()) random.shuffle(dataset) count = len(dataset) p80 = int(0.8 * count) p90 = int(0.9 * count) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"files": dataset[:p80]} ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={"files": dataset[p80:p90]} ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"files": dataset[p90:]} ), ] def _generate_examples(self, files): if self.config.name == "eval": for i, path in enumerate(files): yield i, { "mel": path["mel"], "label": path["label"], "pitch": path["pitch"], } else: for i, path in enumerate(files): yield i, { "audio": path["audio"], "mel": path["mel"], "label": path["label"], "pitch": path["pitch"], }