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"""GTZAN music genres classification dataset.""" |
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import os |
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import textwrap |
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import datasets |
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import itertools |
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import typing as tp |
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from pathlib import Path |
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from ._gtzan import FILTERED_TRAIN, FILTERED_VALID, FILTERED_TEST, GTZAN_GENRES |
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FOLDER_IN_ARCHIVE = "genres" |
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SAMPLE_RATE = 22_050 |
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_COMPRESSED_FILENAME = 'archive.zip' |
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class GtzanConfig(datasets.BuilderConfig): |
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"""BuilderConfig for GTZAN.""" |
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def __init__(self, features, **kwargs): |
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super(GtzanConfig, self).__init__(version=datasets.Version("0.0.1", ""), **kwargs) |
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self.features = features |
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class GTZAN(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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GtzanConfig( |
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features=datasets.Features( |
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{ |
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"file": datasets.Value("string"), |
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"audio": datasets.Audio(sampling_rate=SAMPLE_RATE), |
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"genre": datasets.Value("string"), |
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"label": datasets.ClassLabel(names=GTZAN_GENRES), |
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} |
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), |
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name="gtzan", |
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description=textwrap.dedent( |
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"""\ |
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Music Genres classifies each audio for its music genre as a multi-class |
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classification, where music are in the same pre-defined set for both training and testing. |
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The evaluation metric is accuracy (ACC). |
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""" |
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), |
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), |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description="", |
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features=self.config.features, |
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supervised_keys=None, |
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homepage="", |
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citation="", |
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task_templates=None, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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archive_path = dl_manager.extract(_COMPRESSED_FILENAME) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, gen_kwargs={"archive_path": archive_path, "split": "train"} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, gen_kwargs={"archive_path": archive_path, "split": "validation"} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, gen_kwargs={"archive_path": archive_path, "split": "test"} |
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), |
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] |
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def _generate_examples(self, archive_path, split=None): |
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extensions = ['.au'] |
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_, _walker = fast_scandir(archive_path, extensions, recursive=True) |
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if split == 'train': |
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_walker = [fileid for fileid in _walker if Path(fileid).stem in FILTERED_TRAIN] |
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elif split == 'validation': |
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_walker = [fileid for fileid in _walker if Path(fileid).stem in FILTERED_VALID] |
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elif split == 'test': |
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_walker = [fileid for fileid in _walker if Path(fileid).stem in FILTERED_TEST] |
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for guid, audio_path in enumerate(_walker): |
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yield guid, { |
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"id": str(guid), |
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"file": audio_path, |
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"audio": audio_path, |
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"genre": Path(audio_path).parent.stem, |
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"label": Path(audio_path).parent.stem, |
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} |
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def fast_scandir(path: str, exts: tp.List[str], recursive: bool = False): |
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subfolders, files = [], [] |
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try: |
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for f in os.scandir(path): |
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try: |
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if f.is_dir(): |
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subfolders.append(f.path) |
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elif f.is_file(): |
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if os.path.splitext(f.name)[1].lower() in exts: |
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files.append(f.path) |
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except Exception: |
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pass |
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except Exception: |
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pass |
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if recursive: |
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for path in list(subfolders): |
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sf, f = fast_scandir(path, exts, recursive=recursive) |
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subfolders.extend(sf) |
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files.extend(f) |
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return subfolders, files |
