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