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# coding=utf-8
"""Watkins Marine Mammal Sound Database."""
import os
import random
import textwrap
import datasets
import itertools
import typing as tp
from pathlib import Path
from collections import defaultdict
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', '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']
_remove_class = 'Weddell_Seal' # only 2 samples in the dataset
_, _walker = fast_scandir(archive_path, extensions, recursive=True)
filepaths = [f for f in _walker if default_find_classes(f) != _remove_class]
labels = [default_find_classes(f) for f in filepaths]
# Step 1: Organize samples by class
class_to_files = defaultdict(list)
for filepath, label in zip(filepaths, labels):
class_to_files[label].append(filepath)
# Step 2: Select exactly n samples per class for the test set
n_shot = 5
test_files, test_labels = [], []
train_files, train_labels = [], []
for label, files in class_to_files.items():
if len(files) < n_shot:
raise ValueError(f"Not enough samples for class {label}") # Ensure each class has at least n_shot samples
random.Random(914).shuffle(files) # Shuffle to ensure randomness
test_files.extend(files[:n_shot]) # Pick first n_shot for test
test_labels.extend([label] * n_shot)
train_files.extend(files[n_shot:]) # Remaining go to train
train_labels.extend([label] * (len(files) - n_shot))
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"audio_paths": train_files, "split": "train"}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"audio_paths": test_files, "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 |