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import os |
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import datasets |
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import datasets.info |
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import pandas as pd |
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from pathlib import Path |
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from renumics import spotlight |
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from datasets import load_dataset |
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from typing import Iterable, Dict, Optional, Union, List |
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_CITATION = """\ |
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@dataset{kota_dohi_2023_7687464, |
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author = {Kota Dohi and |
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Keisuke and |
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Noboru and |
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Daisuke and |
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Yuma and |
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Tomoya and |
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Harsh and |
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Takashi and |
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Yohei}, |
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title = {DCASE 2023 Challenge Task 2 Development Dataset}, |
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month = mar, |
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year = 2023, |
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publisher = {Zenodo}, |
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version = {1.0}, |
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doi = {10.5281/zenodo.7687464}, |
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url = {https://doi.org/10.5281/zenodo.7687464} |
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} |
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""" |
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_LICENSE = "Creative Commons Attribution 4.0 International Public License" |
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_METADATA_REG = r"attributes_\d+.csv" |
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_NUM_TARGETS = 2 |
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_NUM_CLASSES = 7 |
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_TARGET_NAMES = ["normal", "anomaly"] |
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_CLASS_NAMES = ["gearbox", "fan", "bearing", "slider", "ToyCar", "ToyTrain", "valve"] |
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_HOMEPAGE = { |
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"dev": "https://zenodo.org/record/7687464#.Y_96q9LMLmH", |
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"add": None, |
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"eval": None, |
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} |
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DATA_URLS = { |
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"dev": { |
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"train": "data/dev_train.tar.gz", |
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"test": "data/dev_test.tar.gz", |
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"metadata": "data/dev_metadata.csv", |
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}, |
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"add": { |
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"train": "data/add_train.tar.gz", |
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"test": "data/add_test.tar.gz", |
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"metadata": "data/add_metadata.csv", |
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}, |
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"eval": { |
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"train": "data/eval_train.tar.gz", |
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"test": "data/eval_test.tar.gz", |
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"metadata": "data/eval_metadata.csv", |
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}, |
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} |
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STATS = { |
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"name": "Enriched Dataset of 'DCASE 2023 Challenge Task 2'", |
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"configs": { |
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'dev': { |
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'date': "Mar 1, 2023", |
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'version': "1.0.0", |
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'homepage': "https://zenodo.org/record/7687464#.ZABmANLMLmH", |
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"splits": ["train", "test"], |
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}, |
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'add': { |
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'date': None, |
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'version': "0.0.0", |
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'homepage': None, |
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"splits": ["train", "test"], |
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}, |
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'eval': { |
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'date': None, |
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'version': "0.0.0", |
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'homepage': None, |
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"splits": ["test"], |
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}, |
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} |
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} |
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DATASET = { |
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'dev': 'DCASE 2023 Challenge Task 2 Development Dataset', |
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'add': 'DCASE 2023 Challenge Task 2 Additional Train Dataset', |
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'eval': 'DCASE 2023 Challenge Task 2 Evaluation Dataset', |
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} |
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_SPOTLIGHT_LAYOUT = "data/config-spotlight-layout.json" |
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_SPOTLIGHT_RENAME = { |
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"audio": "original_audio", |
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"path": "audio", |
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} |
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class DCASE2023Task2DatasetConfig(datasets.BuilderConfig): |
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"""BuilderConfig for DCASE2023Task2Dataset.""" |
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def __init__(self, name, version, **kwargs): |
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self.release_date = kwargs.pop("release_date", None) |
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self.homepage = kwargs.pop("homepage", None) |
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self.data_urls = kwargs.pop("data_urls", None) |
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self.splits = kwargs.pop("splits", None) |
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self._rename = kwargs.pop("rename", None) |
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self._layout = kwargs.pop("layout", None) |
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description = ( |
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f"Dataset for the DCASE 2023 Challenge Task 2 'First-Shot Unsupervised Anomalous Sound Detection " |
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f"for Machine Condition Monitoring'. released on {self.release_date}. Original data available under" |
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f"{self.homepage}. " |
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f"CONFIG: {name}." |
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) |
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super(DCASE2023Task2DatasetConfig, self).__init__( |
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name=name, |
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version=datasets.Version(version), |
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description=description, |
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) |
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def to_spotlight(self, data: Union[pd.DataFrame, datasets.Dataset]) -> pd.DataFrame: |
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if type(data) == datasets.Dataset: |
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df = data.to_pandas() |
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df["split"] = data.split |
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df["config"] = data.config_name |
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class_names = data.features["class"].names |
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df["class_name"] = df["class"].apply(lambda x: class_names[x]) |
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elif type(data) == pd.DataFrame: |
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df = data |
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else: |
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raise TypeError("type(data) not in Union[pd.DataFrame, datasets.Dataset]") |
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df["file_path"] = df["path"] |
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df.rename(columns=self._rename, inplace=True) |
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return df.copy() |
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def get_layout(self): |
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return self._layout |
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class DCASE2023Task2Dataset(datasets.GeneratorBasedBuilder): |
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"""Dataset for the DCASE 2023 Challenge Task 2 "First-Shot Unsupervised Anomalous Sound Detection |
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for Machine Condition Monitoring".""" |
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VERSION = datasets.Version("0.0.2") |
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DEFAULT_CONFIG_NAME = "dev" |
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BUILDER_CONFIGS = [ |
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DCASE2023Task2DatasetConfig( |
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name=key, |
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version=stats["version"], |
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dataset=DATASET[key], |
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homepage=_HOMEPAGE[key], |
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data_urls=DATA_URLS[key], |
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release_date=stats["date"], |
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splits=stats["splits"], |
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layout=_SPOTLIGHT_LAYOUT, |
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rename=_SPOTLIGHT_RENAME, |
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) |
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for key, stats in STATS["configs"].items() |
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] |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"audio": datasets.Audio(sampling_rate=16_000), |
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"path": datasets.Value("string"), |
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"section": datasets.Value("int64"), |
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"d1p": datasets.Value("string"), |
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"d1v": datasets.Value("string"), |
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"d2p": datasets.Value("string"), |
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"d2v": datasets.Value("string"), |
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"d3p": datasets.Value("string"), |
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"d3v": datasets.Value("string"), |
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"label": datasets.ClassLabel(num_classes=_NUM_TARGETS, names=_TARGET_NAMES), |
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"class": datasets.ClassLabel(num_classes=_NUM_CLASSES, names=_CLASS_NAMES), |
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} |
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) |
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return datasets.DatasetInfo( |
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description=self.config.description, |
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features=features, |
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supervised_keys=datasets.info.SupervisedKeysData("label"), |
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homepage=self.config.homepage, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators( |
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self, |
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dl_manager: datasets.DownloadManager |
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): |
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"""Returns SplitGenerators.""" |
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dl_manager.download_config.ignore_url_params = True |
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audio_path = {} |
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local_extracted_archive = {} |
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split_type = {"train": datasets.Split.TRAIN, "test": datasets.Split.TEST} |
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for split in split_type: |
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audio_path[split] = dl_manager.download(self.config.data_urls[split]) |
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local_extracted_archive[split] = dl_manager.extract( |
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audio_path[split]) if not dl_manager.is_streaming else None |
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return [ |
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datasets.SplitGenerator( |
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name=split_type[split], |
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gen_kwargs={ |
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"split": split, |
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"local_extracted_archive": local_extracted_archive[split], |
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"audio_files": dl_manager.iter_archive(audio_path[split]), |
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"metadata_file": dl_manager.download_and_extract(self.config.data_urls["metadata"]), |
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}, |
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) for split in split_type |
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] |
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def _generate_examples( |
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self, |
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split: str, |
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local_extracted_archive: Union[Dict, List], |
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audio_files: Optional[Iterable], |
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metadata_file: Optional[str], |
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): |
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"""Yields examples.""" |
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metadata = pd.read_csv(metadata_file) |
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data_fields = list(self._info().features.keys()) |
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id_ = 0 |
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for path, f in audio_files: |
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lookup = Path(path).parent.name + "/" + Path(path).name |
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if lookup in metadata["path"].values: |
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path = os.path.join(local_extracted_archive, path) if local_extracted_archive else path |
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audio = {"path": path, "bytes": f.read()} |
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result = {field: None for field in data_fields} |
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result.update(metadata[metadata["path"] == lookup].T.squeeze().to_dict()) |
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result["path"] = path |
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yield id_, {**result, "audio": audio} |
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id_ += 1 |
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if __name__ == "__main__": |
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ds = load_dataset("dcase23-task2-enriched.py", "dev", split="train", streaming=True) |
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