dcase23-task2-enriched.py

import os
import datasets
import datasets.info
import pandas as pd
from pathlib import Path
from datasets import load_dataset
from typing import Iterable, Dict, Optional, Union, List


_CITATION = """\
@dataset{kota_dohi_2023_7687464,
  author       = {Kota Dohi and
                  Keisuke and
                  Noboru and
                  Daisuke and
                  Yuma and
                  Tomoya and
                  Harsh and
                  Takashi and
                  Yohei},
  title        = {DCASE 2023 Challenge Task 2 Development Dataset},
  month        = mar,
  year         = 2023,
  publisher    = {Zenodo},
  version      = {1.0},
  doi          = {10.5281/zenodo.7687464},
  url          = {https://doi.org/10.5281/zenodo.7687464}
}
"""
_LICENSE = "Creative Commons Attribution 4.0 International Public License"

_METADATA_REG = r"attributes_\d+.csv"

_NUM_TARGETS = 2
_NUM_CLASSES = 7

_TARGET_NAMES = ["normal", "anomaly"]
_CLASS_NAMES = ["gearbox", "fan", "bearing", "slider", "ToyCar", "ToyTrain", "valve"]

_HOMEPAGE = {
    "dev": "https://zenodo.org/record/7687464#.Y_96q9LMLmH",
}

DATA_URLS = {
    "dev": {
        "train": "data/dev_train.tar.gz",
        "test": "data/dev_test.tar.gz",
        "metadata": "data/dev_metadata.csv",
    },
}

STATS = {
    "name": "Enriched Dataset of 'DCASE 2023 Challenge Task 2'",
    "configs": {
        'dev': {
            'date': "Mar 1, 2023",
            'version': "1.0.0",
            'homepage': "https://zenodo.org/record/7687464#.ZABmANLMLmH",
            "splits": ["train", "test"],
        },
    }
}

DATASET = {
    'dev': 'DCASE 2023 Challenge Task 2 Development Dataset',
}

_SPOTLIGHT_LAYOUT = "data/config-spotlight-layout.json"

_SPOTLIGHT_RENAME = {
    "audio": "original_audio",
    "path": "audio",
}


class DCASE2023Task2DatasetConfig(datasets.BuilderConfig):
    """BuilderConfig for DCASE2023Task2Dataset."""

    def __init__(self, name, version, **kwargs):
        self.release_date = kwargs.pop("release_date", None)
        self.homepage = kwargs.pop("homepage", None)
        self.data_urls = kwargs.pop("data_urls", None)
        self.splits = kwargs.pop("splits", None)
        self.rename = kwargs.pop("rename", None)
        self.layout = kwargs.pop("layout", None)
        description = (
            f"Dataset for the DCASE 2023 Challenge Task 2 'First-Shot Unsupervised Anomalous Sound Detection "
            f"for Machine Condition Monitoring'. released on {self.release_date}. Original data available under"
            f"{self.homepage}. "
            f"CONFIG: {name}."
        )
        super(DCASE2023Task2DatasetConfig, self).__init__(
            name=name,
            version=datasets.Version(version),
            description=description,
        )

    def to_spotlight(self, data: Union[pd.DataFrame, datasets.Dataset]) -> pd.DataFrame:
        if type(data) == datasets.Dataset:
            df = data.to_pandas()
            df["split"] = data.split
            df["config"] = data.config_name

            class_names = data.features["class"].names
            df["class_name"] = df["class"].apply(lambda x: class_names[x])
        elif type(data) == pd.DataFrame:
            df = data
        else:
            raise TypeError("type(data) not in Union[pd.DataFrame, datasets.Dataset]")

        df["file_path"] = df["path"]
        df.rename(columns=self.rename, inplace=True)

        return df.copy()

    def get_layout(self):
        return self.layout


class DCASE2023Task2Dataset(datasets.GeneratorBasedBuilder):
    """Dataset for the DCASE 2023 Challenge Task 2 "First-Shot Unsupervised Anomalous Sound Detection
    for Machine Condition Monitoring"."""

    VERSION = datasets.Version("0.0.2")

    DEFAULT_CONFIG_NAME = "dev"

    BUILDER_CONFIGS = [
        DCASE2023Task2DatasetConfig(
            name=key,
            version=stats["version"],
            dataset=DATASET[key],
            homepage=_HOMEPAGE[key],
            data_urls=DATA_URLS[key],
            release_date=stats["date"],
            splits=stats["splits"],
            layout=_SPOTLIGHT_LAYOUT,
            rename=_SPOTLIGHT_RENAME,
        )
        for key, stats in STATS["configs"].items()
    ]

    def _info(self):
        features = datasets.Features(
                {
                    "audio": datasets.Audio(sampling_rate=16_000),
                    "path": datasets.Value("string"),
                    "section": datasets.Value("int64"),
                    "d1p": datasets.Value("string"),
                    "d1v": datasets.Value("string"),
                    "d2p": datasets.Value("string"),
                    "d2v": datasets.Value("string"),
                    "d3p": datasets.Value("string"),
                    "d3v": datasets.Value("string"),
                    "label": datasets.ClassLabel(num_classes=_NUM_TARGETS, names=_TARGET_NAMES),
                    "class": datasets.ClassLabel(num_classes=_NUM_CLASSES, names=_CLASS_NAMES),
                }
            )

        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=self.config.description,
            features=features,
            supervised_keys=datasets.info.SupervisedKeysData("label"),
            homepage=self.config.homepage,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(
            self,
            dl_manager: datasets.DownloadManager
    ):
        """Returns SplitGenerators."""
        dl_manager.download_config.ignore_url_params = True
        audio_path = {}
        local_extracted_archive = {}
        split_type = {"train": datasets.Split.TRAIN, "test": datasets.Split.TEST}

        for split in split_type:
            audio_path[split] = dl_manager.download(self.config.data_urls[split])
            local_extracted_archive[split] = dl_manager.extract(
                audio_path[split]) if not dl_manager.is_streaming else None

        return [
            datasets.SplitGenerator(
                name=split_type[split],
                gen_kwargs={
                    "split": split,
                    "local_extracted_archive": local_extracted_archive[split],
                    "audio_files": dl_manager.iter_archive(audio_path[split]),
                    "metadata_file": dl_manager.download_and_extract(self.config.data_urls["metadata"]),
                },
            ) for split in split_type
        ]

    def _generate_examples(
        self,
        split: str,
        local_extracted_archive: Union[Dict, List],
        audio_files: Optional[Iterable],
        metadata_file: Optional[str],
    ):
        """Yields examples."""
        metadata = pd.read_csv(metadata_file)
        data_fields = list(self._info().features.keys())

        id_ = 0
        for path, f in audio_files:
            lookup = Path(path).parent.name + "/" + Path(path).name
            if lookup in metadata["path"].values:
                path = os.path.join(local_extracted_archive, path) if local_extracted_archive else path
                audio = {"path": path, "bytes": f.read()}
                result = {field: None for field in data_fields}
                result.update(metadata[metadata["path"] == lookup].T.squeeze().to_dict())
                result["path"] = path
                yield id_, {**result, "audio": audio}
                id_ += 1


if __name__ == "__main__":
    ds = load_dataset("dcase23-task2-enriched.py", "dev", split="train", streaming=True)