import os import json import datasets import datasets.info import pandas as pd import numpy as np import tempfile import requests import io 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", "add": "", "eval": "", } DATA_URLS = { "dev": { "train": "data/dev_train.tar.gz", "test": "data/dev_test.tar.gz", "metadata": "data/dev_metadata_extended.csv", }, "add": { "train": "data/add_train.tar.gz", "test": "data/add_test.tar.gz", "metadata": "data/add_metadata_extended.csv", }, "eval": { "test": "data/eval_test.tar.gz", "metadata": "data/eval_metadata_extended.csv", }, } EMBEDDING_URLS = { "dev": { "embeddings_ast-finetuned-audioset-10-10-0.4593": { "train": "data/MIT_ast-finetuned-audioset-10-10-0-4593-embeddings_dev_train.npz", "test": "data/MIT_ast-finetuned-audioset-10-10-0-4593-embeddings_dev_test.npz", "size": (1, 768), "dtype": "float32", }, }, "add": { "embeddings_ast-finetuned-audioset-10-10-0.4593": { "train": "", "test": "", }, }, "eval": { "embeddings_ast-finetuned-audioset-10-10-0.4593": { "train": "", "test": "", }, }, } 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"], }, # 'add': { # 'date': None, # 'version': "0.0.0", # 'homepage': None, # "splits": ["train", "test"], # }, # 'eval': { # 'date': None, # 'version': "0.0.0", # 'homepage': None, # "splits": ["test"], # }, } } DATASET = { 'dev': 'DCASE 2023 Challenge Task 2 Development Dataset', 'add': 'DCASE 2023 Challenge Task 2 Additional Train Dataset', 'eval': 'DCASE 2023 Challenge Task 2 Evaluation Dataset', } SPOTLIGHT_LAYOUTS = { "standard": { "orientation": "vertical", "children": [ { "kind": "split", "weight": 51.96463654223969, "orientation": "horizontal", "children": [ { "kind": "tab", "weight": 30, "children": [ { "kind": "widget", "name": "Table", "type": "table", "config": { "tableView": "full", "visibleColumns": [ "class", "class_name", "config", "d1p", "d1v", "d2p", "d2v", "d3p", "d3v", "file_path", "label", "section", "split" ], "sorting": None, "orderByRelevance": False } } ] }, { "kind": "tab", "weight": 33.970588235294116, "children": [ { "kind": "widget", "name": "Similarity Map (2)", "type": "similaritymap", "config": { "umapNNeighbors": 20, "umapMinDist": 0.15, "colorBy": "label" } } ] }, { "kind": "tab", "weight": 36.029411764705884, "children": [ { "kind": "widget", "name": "Similarity Map", "type": "similaritymap", "config": { "placeBy": None, "reductionMethod": None, "colorBy": "class_name", "sizeBy": None, "filter": False, "umapNNeighbors": 20, "umapMetric": None, "umapMinDist": 0.15, "pcaNormalization": None, "umapMenuLocalGlobalBalance": None, "umapMenuIsAdvanced": False } }, { "kind": "widget", "name": "Scatter Plot", "type": "scatterplot", "config": { "xAxisColumn": None, "yAxisColumn": None, "colorBy": None, "sizeBy": None, "filter": False } }, { "kind": "widget", "name": "Histogram", "type": "histogram", "config": { "columnKey": None, "stackByColumnKey": None, "filter": False } } ] } ] }, { "kind": "tab", "weight": 48.03536345776031, "children": [ { "kind": "widget", "name": "Inspector", "type": "inspector", "config": { "views": [ { "view": "AudioView", "columns": [ "audio" ], "name": "view", "key": "9c37fe2d-6305-436b-b944-30dbda7b1f4d" }, { "view": "SpectrogramView", "columns": [ "audio" ], "name": "view", "key": "9e676bb9-0b21-4214-806f-4e8c0f6db4c3" } ], "visibleColumns": 4 } } ] } ] }, } 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.embeddings_urls = kwargs.pop("embeddings_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: def get_split(path: str) -> str: fn = os.path.basename(path) if "train" in fn: return "train" elif "test" in fn: return "test" else: raise NotImplementedError if type(data) == datasets.Dataset: # retrieve split df = data.to_pandas() df["split"] = data.split._name if "+" not in data.split._name else df["path"].map(get_split) df["config"] = data.config_name # get clearnames for classes 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, config: str = "standard") -> str: layout_json = tempfile.mktemp(".json") with open(layout_json, "w") as outfile: json.dump(self.layout[config], outfile) return layout_json 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("0.0.4") DEFAULT_CONFIG_NAME = "dev" BUILDER_CONFIGS = [ DCASE2023Task2DatasetConfig( name=key, version=stats["version"], dataset=DATASET[key], homepage=_HOMEPAGE[key], data_urls=DATA_URLS[key], embeddings_urls=EMBEDDING_URLS[key], release_date=stats["date"], splits=stats["splits"], layout=SPOTLIGHT_LAYOUTS, rename=SPOTLIGHT_RENAME, ) for key, stats in STATS["configs"].items() ] def _info(self): features = { "audio": datasets.Audio(sampling_rate=16_000), "path": datasets.Value("string"), "section": datasets.Value("int64"), "domain": datasets.ClassLabel(num_classes=2, names=["source", "target"]), "label": datasets.ClassLabel(num_classes=_NUM_TARGETS, names=_TARGET_NAMES), "class": datasets.ClassLabel(num_classes=_NUM_CLASSES, names=_CLASS_NAMES), "d1p": datasets.Value("string"), "d1v": datasets.Value("string"), "d2p": datasets.Value("string"), "d2v": datasets.Value("string"), "d3p": datasets.Value("string"), "d3v": datasets.Value("string"), "anomaly_score_dcase2023_task2_baseline_ae": datasets.Value("float32"), "prediction_dcase2023_task2_baseline_ae": datasets.Value("int64"), "prediction_correct_dcase2023_task2_baseline_ae": datasets.Value("int64"), } if self.config.embeddings_urls is not None: features.update({ emb_name: [datasets.Value(emb["dtype"])] for emb_name, emb in self.config.embeddings_urls.items() }) features = datasets.Features(features) 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} embeddings = {split: dict() for split in split_type} for split in split_type: if split in self.config.splits: 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 if self.config.embeddings_urls is not None: for emb_name, emb_data in self.config.embeddings_urls.items(): downloaded_embeddings = dl_manager.download(emb_data[split]) if dl_manager.is_streaming: response = requests.get(downloaded_embeddings) response.raise_for_status() downloaded_embeddings = io.BytesIO(response.content) npz_file = np.load(downloaded_embeddings, allow_pickle=True) embeddings[split][emb_name] = npz_file["arr_0"].item() 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]), "embeddings": embeddings[split], "metadata_file": dl_manager.download_and_extract(self.config.data_urls["metadata"]), "is_streaming": dl_manager.is_streaming, }, ) for split in split_type if split in self.config.splits ] def _generate_examples( self, split: str, local_extracted_archive: Union[Dict, List], audio_files: Optional[Iterable], embeddings: Optional[Dict], metadata_file: Optional[str], is_streaming: Optional[bool], ): """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 if is_streaming: audio = {"path": path, "bytes": f.read()} else: audio = {"path": path, "bytes": None} result = {field: None for field in data_fields} result.update(metadata[metadata["path"] == lookup].T.squeeze().to_dict()) for emb_key in embeddings.keys(): result[emb_key] = np.asarray(embeddings[emb_key][lookup]).squeeze().tolist() 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)