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import os |
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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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"PearlRiver": [2.33, 2.53, 2.37, 2.41], |
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"YoungChang": [2.53, 2.63, 2.97, 2.71], |
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"Steinway-T": [3.6, 3.63, 3.67, 3.63], |
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"Hsinghai": [3.4, 3.27, 3.2, 3.29], |
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"Kawai": [3.17, 2.5, 2.93, 2.87], |
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"Steinway": [4.23, 3.67, 4, 3.97], |
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"Kawai-G": [3.37, 2.97, 3.07, 3.14], |
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"Yamaha": [3.23, 3.03, 3.17, 3.14], |
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} |
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_HOMEPAGE = f"https://www.modelscope.cn/datasets/ccmusic-database/{os.path.basename(__file__)[:-3]}" |
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_DOMAIN = f"{_HOMEPAGE}/resolve/master/data" |
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_PITCHES = { |
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"009": "A2", |
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"010": "A2#/B2b", |
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"011": "B2", |
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"100": "C1", |
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"101": "C1#/D1b", |
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"102": "D1", |
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"103": "D1#/E1b", |
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"104": "E1", |
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"105": "F1", |
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"106": "F1#/G1b", |
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"107": "G1", |
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"108": "G1#/A1b", |
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"109": "A1", |
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"110": "A1#/B1b", |
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"111": "B1", |
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"200": "C", |
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"201": "C#/Db", |
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"202": "D", |
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"203": "D#/Eb", |
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"204": "E", |
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"205": "F", |
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"206": "F#/Gb", |
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"207": "G", |
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"208": "G#/Ab", |
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"209": "A", |
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"210": "A#/Bb", |
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"211": "B", |
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"300": "c", |
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"301": "c#/db", |
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"302": "d", |
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"303": "d#/eb", |
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"304": "e", |
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"305": "f", |
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"306": "f#/gb", |
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"307": "g", |
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"308": "g#/ab", |
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"309": "a", |
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"310": "a#/bb", |
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"311": "b", |
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"400": "c1", |
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"401": "c1#/d1b", |
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"402": "d1", |
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"403": "d1#/e1b", |
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"404": "e1", |
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"405": "f1", |
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"406": "f1#/g1b", |
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"407": "g1", |
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"408": "g1#/a1b", |
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"409": "a1", |
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"410": "a1#/b1b", |
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"411": "b1", |
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"500": "c2", |
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"501": "c2#/d2b", |
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"502": "d2", |
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"503": "d2#/e2b", |
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"504": "e2", |
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"505": "f2", |
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"506": "f2#/g2b", |
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"507": "g2", |
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"508": "g2#/a2b", |
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"509": "a2", |
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"510": "a2#/b2b", |
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"511": "b2", |
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"600": "c3", |
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"601": "c3#/d3b", |
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"602": "d3", |
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"603": "d3#/e3b", |
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"604": "e3", |
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"605": "f3", |
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"606": "f3#/g3b", |
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"607": "g3", |
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"608": "g3#/a3b", |
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"609": "a3", |
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"610": "a3#/b3b", |
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"611": "b3", |
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"700": "c4", |
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"701": "c4#/d4b", |
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"702": "d4", |
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"703": "d4#/e4b", |
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"704": "e4", |
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"705": "f4", |
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"706": "f4#/g4b", |
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"707": "g4", |
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"708": "g4#/a4b", |
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"709": "a4", |
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"710": "a4#/b4b", |
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"711": "b4", |
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"800": "c5", |
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} |
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_URLS = { |
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"audio": f"{_DOMAIN}/audio.zip", |
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"mel": f"{_DOMAIN}/mel.zip", |
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"eval": f"{_DOMAIN}/eval.zip", |
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} |
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class pianos(datasets.GeneratorBasedBuilder): |
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def _info(self): |
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names = list(_NAMES.keys()) |
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if self.config.name == "default": |
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names = names[:-1] |
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return datasets.DatasetInfo( |
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features=( |
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datasets.Features( |
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{ |
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"audio": datasets.Audio(sampling_rate=44100), |
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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( |
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names=list(_PITCHES.values()) |
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), |
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"bass_score": datasets.Value("float32"), |
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"mid_score": datasets.Value("float32"), |
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"treble_score": datasets.Value("float32"), |
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"avg_score": datasets.Value("float32"), |
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} |
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) |
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if self.config.name != "eval" |
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else 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( |
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names=list(_PITCHES.values()) |
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), |
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"bass_score": datasets.Value("float32"), |
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"mid_score": datasets.Value("float32"), |
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"treble_score": datasets.Value("float32"), |
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"avg_score": datasets.Value("float32"), |
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} |
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) |
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), |
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homepage=_HOMEPAGE, |
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license="CC-BY-NC-ND", |
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version="1.2.0", |
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supervised_keys=("mel", "label"), |
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task_templates=ImageClassification( |
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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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def _split_generators(self, dl_manager): |
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dataset = [] |
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if self.config.name != "eval": |
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subset = {} |
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audio_files = dl_manager.download_and_extract(_URLS["audio"]) |
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for path in dl_manager.iter_files([audio_files]): |
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fname = os.path.basename(path) |
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if fname.endswith(".wav"): |
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lebal = os.path.basename(os.path.dirname(path)) |
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if self.config.name == "default" and lebal == "Yamaha": |
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continue |
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subset[fname.split(".")[0]] = { |
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"audio": path, |
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"label": lebal, |
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"pitch": _PITCHES[fname[1:4]], |
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"bass_score": _NAMES[lebal][0], |
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"mid_score": _NAMES[lebal][1], |
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"treble_score": _NAMES[lebal][2], |
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"avg_score": _NAMES[lebal][3], |
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} |
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mel_files = dl_manager.download_and_extract(_URLS["mel"]) |
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for path in dl_manager.iter_files([mel_files]): |
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fname = os.path.basename(path) |
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pname = fname.split(".")[0] |
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if fname.endswith(".jpg") and pname in subset: |
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subset[pname]["mel"] = path |
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dataset = list(subset.values()) |
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else: |
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data_files = dl_manager.download_and_extract(_URLS["eval"]) |
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for path in dl_manager.iter_files([data_files]): |
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fname: str = os.path.basename(path) |
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if fname.endswith(".jpg"): |
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lebal = os.path.basename(os.path.dirname(path)) |
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dataset.append( |
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{ |
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"mel": path, |
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"label": lebal, |
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"pitch": _PITCHES[fname.split("_")[0]], |
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"bass_score": _NAMES[lebal][0], |
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"mid_score": _NAMES[lebal][1], |
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"treble_score": _NAMES[lebal][2], |
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"avg_score": _NAMES[lebal][3], |
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} |
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) |
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names = list(_NAMES.keys()) |
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if self.config.name == "default": |
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names = names[:-1] |
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categories = {} |
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for name in names: |
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categories[name] = [] |
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for data in dataset: |
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categories[data["label"]].append(data) |
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testset, validset, trainset = [], [], [] |
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for cls in categories: |
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random.shuffle(categories[cls]) |
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count = len(categories[cls]) |
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p80 = int(count * 0.8) |
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p90 = int(count * 0.9) |
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trainset += categories[cls][:p80] |
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validset += categories[cls][p80:p90] |
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testset += categories[cls][p90:] |
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random.shuffle(trainset) |
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random.shuffle(validset) |
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random.shuffle(testset) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, gen_kwargs={"files": trainset} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, gen_kwargs={"files": validset} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, gen_kwargs={"files": testset} |
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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, path |
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