# coding=utf-8 """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): # Scan files recursively faster than glob # From github.com/drscotthawley/aeiou/blob/main/aeiou/core.py subfolders, files = [], [] try: # hope to avoid 'permission denied' by this try for f in os.scandir(path): try: # 'hope to avoid too many levels of symbolic links' error 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) # type: ignore return subfolders, files