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_fsd2019.py

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+CLASSES = [
+    "Accelerating_and_revving_and_vroom", 
+    "Accordion", 
+    "Acoustic_guitar", 
+    "Applause", 
+    "Bark", 
+    "Bass_drum", 
+    "Bass_guitar", 
+    "Bathtub_(filling_or_washing)", 
+    "Bicycle_bell", 
+    "Burping_and_eructation", 
+    "Bus", 
+    "Buzz", 
+    "Car_passing_by", 
+    "Cheering", 
+    "Chewing_and_mastication", 
+    "Child_speech_and_kid_speaking", 
+    "Chink_and_clink", 
+    "Chirp_and_tweet", 
+    "Church_bell", 
+    "Clapping", 
+    "Computer_keyboard", 
+    "Crackle", 
+    "Cricket", 
+    "Crowd", 
+    "Cupboard_open_or_close", 
+    "Cutlery_and_silverware", 
+    "Dishes_and_pots_and_pans", 
+    "Drawer_open_or_close", 
+    "Drip", 
+    "Electric_guitar", 
+    "Fart", 
+    "Female_singing", 
+    "Female_speech_and_woman_speaking", 
+    "Fill_(with_liquid)", 
+    "Finger_snapping", 
+    "Frying_(food)", 
+    "Gasp", 
+    "Glockenspiel", 
+    "Gong", 
+    "Gurgling", 
+    "Harmonica", 
+    "Hi-hat", 
+    "Hiss", 
+    "Keys_jangling", 
+    "Knock", 
+    "Male_singing", 
+    "Male_speech_and_man_speaking", 
+    "Marimba_and_xylophone", 
+    "Mechanical_fan", 
+    "Meow", 
+    "Microwave_oven", 
+    "Motorcycle", 
+    "Printer", 
+    "Purr", 
+    "Race_car_and_auto_racing", 
+    "Raindrop", 
+    "Run", 
+    "Scissors", 
+    "Screaming", 
+    "Shatter", 
+    "Sigh", 
+    "Sink_(filling_or_washing)", 
+    "Skateboard", 
+    "Slam", 
+    "Sneeze", 
+    "Squeak", 
+    "Stream", 
+    "Strum", 
+    "Tap", 
+    "Tick-tock", 
+    "Toilet_flush", 
+    "Traffic_noise_and_roadway_noise", 
+    "Trickle_and_dribble", 
+    "Walk_and_footsteps", 
+    "Water_tap_and_faucet", 
+    "Waves_and_surf", 
+    "Whispering", 
+    "Writing", 
+    "Yell", 
+    "Zipper_(clothing)", 
+]

fsd2019.py

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+# coding=utf-8
+
+"""FSDKaggle2019 sound classification dataset."""
+
+
+import os
+import textwrap
+import datasets
+import itertools
+import pandas as pd
+import typing as tp
+from pathlib import Path
+from copy import deepcopy
+
+from ._fsd2019 import CLASSES
+
+SAMPLE_RATE = 44_100
+
+_TRAIN_CURATED_URL = "https://zenodo.org/records/3612637/files/FSDKaggle2019.audio_train_curated.zip"
+_TEST_URL = "https://zenodo.org/records/3612637/files/FSDKaggle2019.audio_test.zip"
+_METADATA_URL = "https://zenodo.org/records/3612637/files/FSDKaggle2019.meta.zip"
+
+
+class FSDKaggle2019Config(datasets.BuilderConfig):
+    """BuilderConfig for FSDKaggle2019."""
+    
+    def __init__(self, features, **kwargs):
+        super(FSDKaggle2019Config, self).__init__(version=datasets.Version("0.0.1", ""), **kwargs)
+        self.features = features
+
+
+class FSDKaggle2019(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        FSDKaggle2019Config(
+            features=datasets.Features(
+                {
+                    "file": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=SAMPLE_RATE),
+                    "sound": datasets.Sequence(datasets.Value("string")),
+                    "label": datasets.Sequence(datasets.features.ClassLabel(names=CLASSES)),
+                }
+            ),
+            name="curated", 
+            description="",
+        ), 
+    ]
+
+    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."""
+        train_archive_path = dl_manager.download_and_extract(_TRAIN_CURATED_URL)
+        test_archive_path = dl_manager.download_and_extract(_TEST_URL)
+        metadata_archive_path = dl_manager.download_and_extract(_METADATA_URL)
+
+        train_df = pd.read_csv(os.path.join(metadata_archive_path, "FSDKaggle2019.meta", "train_curated_post_competition.csv"))
+        test_df = pd.read_csv(os.path.join(metadata_archive_path, "FSDKaggle2019.meta", "test_post_competition.csv"))
+        
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"archive_path": train_archive_path, "split": "train", "metadata": train_df}
+            ), 
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"archive_path": test_archive_path, "split": "test", "metadata": test_df}
+            ), 
+        ]
+
+    def _generate_examples(self, archive_path, split=None, metadata=None):
+        extensions = ['.wav']
+        _, _walker = fast_scandir(archive_path, extensions, recursive=True)
+
+        metadata_df = deepcopy(metadata)
+
+        def default_find_classes(audio_path):
+            fileid = Path(audio_path).name
+            ids = metadata_df.query(f'fname=="{fileid}"')['labels'].values.tolist()
+            ids = str(ids[0]).split(',')
+            # assert False, f"{ids}"
+            return ids
+
+        for guid, audio_path in enumerate(_walker):
+            yield guid, {
+                "id": str(guid),
+                "file": audio_path, 
+                "audio": audio_path, 
+                "audio": default_find_classes(audio_path), 
+                "label": default_find_classes(audio_path), 
+            }
+
+
+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