wmms.py

# 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