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|
| | """Watkins Marine Mammal Sound Database.""" |
| |
|
| |
|
| | import os |
| | import textwrap |
| | import datasets |
| | import itertools |
| | import typing as tp |
| | from pathlib import Path |
| | from sklearn.model_selection import train_test_split |
| |
|
| | SAMPLE_RATE = 16_000 |
| |
|
| | _COMPRESSED_FILENAME = 'watkins.zip' |
| |
|
| | CLASSES = ['Atlantic_Spotted_Dolphin', 'Bearded_Seal', 'Beluga,_White_Whale', 'Bottlenose_Dolphin', 'Bowhead_Whale', 'Clymene_Dolphin', 'Common_Dolphin', 'False_Killer_Whale', 'Fin,_Finback_Whale', 'Frasers_Dolphin', 'Grampus,_Rissos_Dolphin', 'Harp_Seal', 'Humpback_Whale', 'Killer_Whale', 'Leopard_Seal', 'Long-Finned_Pilot_Whale', 'Melon_Headed_Whale', 'Minke_Whale', 'Narwhal', 'Northern_Right_Whale', 'Pantropical_Spotted_Dolphin', 'Ross_Seal', 'Rough-Toothed_Dolphin', 'Short-Finned_Pacific_Pilot_Whale', 'Southern_Right_Whale', 'Sperm_Whale', 'Spinner_Dolphin', 'Striped_Dolphin', 'Walrus', 'Weddell_Seal', 'White-beaked_Dolphin', 'White-sided_Dolphin'] |
| |
|
| |
|
| | class WmmsConfig(datasets.BuilderConfig): |
| | """BuilderConfig for WMMS.""" |
| | |
| | def __init__(self, features, **kwargs): |
| | super(WmmsConfig, self).__init__(version=datasets.Version("0.0.1", ""), **kwargs) |
| | self.features = features |
| |
|
| |
|
| | class WMMS(datasets.GeneratorBasedBuilder): |
| |
|
| | BUILDER_CONFIGS = [ |
| | WmmsConfig( |
| | features=datasets.Features( |
| | { |
| | "file": datasets.Value("string"), |
| | "audio": datasets.Audio(sampling_rate=SAMPLE_RATE), |
| | "species": datasets.Value("string"), |
| | "label": datasets.ClassLabel(names=CLASSES), |
| | } |
| | ), |
| | name="wmms", |
| | description='', |
| | ), |
| | ] |
| |
|
| | def _info(self): |
| | return datasets.DatasetInfo( |
| | description="Database can be downloaded from https://archive.org/details/watkins_202104", |
| | 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) |
| | extensions = ['.wav'] |
| | _, _walker = fast_scandir(archive_path, extensions, recursive=True) |
| |
|
| | train_walker, val_test_walker = train_test_split( |
| | _walker, test_size=0.3, random_state=914, stratify=[default_find_classes(f) for f in _walker] |
| | ) |
| | val_walker, test_walker = train_test_split( |
| | val_test_walker, test_size=0.5, random_state=914, stratify=[default_find_classes(f) for f in val_test_walker] |
| | ) |
| | |
| | return [ |
| | datasets.SplitGenerator( |
| | name=datasets.Split.TRAIN, gen_kwargs={"audio_paths": train_walker, "split": "train"} |
| | ), |
| | datasets.SplitGenerator( |
| | name=datasets.Split.VALIDATION, gen_kwargs={"audio_paths": val_walker, "split": "validation"} |
| | ), |
| | datasets.SplitGenerator( |
| | name=datasets.Split.TEST, gen_kwargs={"audio_paths": test_walker, "split": "test"} |
| | ), |
| | ] |
| |
|
| | def _generate_examples(self, audio_paths, split=None): |
| | for guid, audio_path in enumerate(audio_paths): |
| | yield guid, { |
| | "id": str(guid), |
| | "file": audio_path, |
| | "audio": audio_path, |
| | "species": default_find_classes(audio_path), |
| | "label": default_find_classes(audio_path), |
| | } |
| |
|
| |
|
| | def default_find_classes(audio_path): |
| | return 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 |