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import io |
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import os |
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import wave |
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import zipfile |
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import datasets |
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import requests |
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from datasets.tasks import AudioClassification |
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_NAMES = [ |
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"1_PearlRiver", |
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"2_YoungChang", |
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"3_Steinway-T", |
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"4_Hsinghai", |
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"5_Kawai", |
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"6_Steinway", |
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"7_Kawai-G", |
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"8_Yamaha", |
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] |
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_DBNAME = os.path.basename(__file__).split('.')[0] |
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_HOMEPAGE = "https://huggingface.co/datasets/ccmusic-database/" + _DBNAME |
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_CITATION = """\ |
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@dataset{zhaorui_liu_2021_5676893, |
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author = {Zhaorui Liu and Zijin Li}, |
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title = {{Music Data Sharing Platform for Computational Musicology Research (CCMUSIC DATASET)}}, |
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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-Database is a dataset of piano sound. |
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It consists of 8 kinds of pianos including |
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PearlRiver, YoungChang, Steinway-T, Hsinghai, Kawai, Steinway, Kawai-G, Yamaha. |
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Data was annotated by students from the China Conservatory of Music (CCMUSIC) in Beijing |
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and collected by George Chou. |
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""" |
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_URLS = {piano: _HOMEPAGE + "/resolve/main/data/" + |
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piano + ".zip" for piano in _NAMES} |
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_PITCHES = {"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", |
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"406": "f1#/g1b", "407": "g1", "408": "g1#/a1b", "409": "a1", "410": "a1#/b1b", "411": "b1", "500": "c2", |
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"501": "c2#/d2b", "502": "d2", "503": "d2#/e2b", "504": "e2", "505": "f2", "506": "f2#/g2b", "507": "g2", |
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"508": "g2#/a2b", "509": "a2", "510": "a2#/b2b", "511": "b2", "600": "c3", "601": "c3#/d3b", "602": "d3", |
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"603": "d3#/e3b", "604": "e3", "605": "f3", "606": "f3#/g3b", "607": "g3", "608": "g3#/a3b", "609": "a3", |
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"610": "a3#/b3b", "611": "b3", "700": "c4", "701": "c4#/d4b", "702": "d4", "703": "d4#/e4b", "704": "e4", |
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"705": "f4", "706": "f4#/g4b", "707": "g4", "708": "g4#/a4b", "709": "a4", "710": "a4#/b4b", "711": "b4", |
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"800": "c5"} |
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class piano_sound_quality(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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"audio": datasets.Audio(sampling_rate=44_100), |
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"label": datasets.features.ClassLabel(names=_NAMES), |
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"pitch": datasets.Value("string"), |
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"duration": datasets.Value("float64"), |
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} |
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), |
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supervised_keys=("audio", "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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AudioClassification( |
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task="audio-classification", |
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audio_column="audio", |
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label_column="label", |
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) |
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], |
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) |
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def _get_wav_duration(self, file_bytes): |
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with wave.open(io.BytesIO(file_bytes), 'r') as wav_file: |
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frames = wav_file.getnframes() |
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rate = wav_file.getframerate() |
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duration = frames / float(rate) |
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return round(duration, 3) |
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def _read_zip(self, zip_url, wav_file_path): |
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resp = requests.get(zip_url) |
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with zipfile.ZipFile(io.BytesIO(resp.content)) as zip_file: |
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with zip_file.open(wav_file_path) as file: |
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file_data = file.read() |
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return self._get_wav_duration(file_data) |
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def _split_generators(self, dl_manager): |
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data_files = dl_manager.download_and_extract(_URLS) |
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split_generator = [] |
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for index in _URLS.keys(): |
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split_generator.append( |
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datasets.SplitGenerator( |
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name=index.replace('-', '_'), |
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gen_kwargs={ |
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"files": dl_manager.iter_files([data_files[index]]), |
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}, |
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) |
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) |
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return split_generator |
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def _generate_examples(self, files): |
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for i, path in enumerate(files): |
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file_name = os.path.basename(path) |
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if file_name.endswith(".wav"): |
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yield i, { |
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"audio": path, |
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"label": os.path.basename(os.path.dirname(path)), |
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"pitch": _PITCHES[file_name[1:4]], |
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"duration": self._read_zip(path.split('::')[1], path.split('::')[0].split('//')[1]), |
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} |
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