Datasets:
Tasks:
Audio Classification
Size Categories:
1K<n<10K
ArXiv:
Tags:
anomaly detection
anomalous sound detection
acoustic condition monitoring
sound machine fault diagnosis
machine learning
unsupervised learning
License:
| 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_7882613, | |
| author = {Kota Dohi and | |
| Keisuke Imoto and | |
| Noboru Harada and | |
| Daisuke Niizumi and | |
| Yuma Koizumi and | |
| Tomoya Nishida and | |
| Harsh Purohit and | |
| Takashi Endo and | |
| Yohei Kawaguchi}, | |
| title = {DCASE 2023 Challenge Task 2 Development Dataset}, | |
| month = mar, | |
| year = 2023, | |
| publisher = {Zenodo}, | |
| version = {3.0}, | |
| doi = {10.5281/zenodo.7882613}, | |
| url = {https://doi.org/10.5281/zenodo.7882613} | |
| } | |
| """ | |
| _LICENSE = "Creative Commons Attribution 4.0 International Public License" | |
| _METADATA_REG = r"attributes_\d+.csv" | |
| _NUM_TARGETS = 2 | |
| _NUM_CLASSES = 14 | |
| _TARGET_NAMES = ["normal", "anomaly"] | |
| _CLASS_NAMES = ["gearbox", "fan", "bearing", "slider", "ToyCar", "ToyTrain", "valve", "bandsaw", "grinder", "shaker", "ToyDrone", "ToyNscale", "ToyTank", "Vacuum"] | |
| _HOMEPAGE = { | |
| "dev": "https://zenodo.org/record/7690157", | |
| "add": "", | |
| "eval": "", | |
| } | |
| DATA_URLS = { | |
| "dev": { | |
| "train": "data/dev_train.tar.gz", | |
| "test": "data/dev_test.tar.gz", | |
| "metadata": "data/dev_metadata.csv", | |
| }, | |
| "add": { | |
| "train": "data/add_train.tar.gz", | |
| "metadata": "data/add_metadata.csv", | |
| }, | |
| "eval": { | |
| "test": "data/eval_test.tar.gz", | |
| "metadata": None, | |
| }, | |
| } | |
| 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": "data/MIT_ast-finetuned-audioset-10-10-0.4593-embeddings_add_train.npz", | |
| "size": (1, 768), | |
| "dtype": "float32", | |
| }, | |
| }, | |
| "eval": { | |
| "embeddings_ast-finetuned-audioset-10-10-0.4593": { | |
| "test": "data/MIT_ast-finetuned-audioset-10-10-0.4593-embeddings_eval_test.npz", | |
| "size": (1, 768), | |
| "dtype": "float32", | |
| }, | |
| }, | |
| } | |
| STATS = { | |
| "name": "Enriched Dataset of 'DCASE 2023 Challenge Task 2'", | |
| "configs": { | |
| 'dev': { | |
| 'date': "Mar 1, 2023", | |
| 'version': "3.0.0", | |
| 'homepage': "https://zenodo.org/record/7882613", | |
| "splits": ["train", "test"], | |
| }, | |
| 'add': { | |
| 'date': "Apr 15, 2023", | |
| 'version': "1.0.0", | |
| 'homepage': "https://zenodo.org/record/7830345", | |
| "splits": ["train"], | |
| }, | |
| 'eval': { | |
| 'date': "May 1, 2023", | |
| 'version': "1.0.0", | |
| 'homepage': "https://zenodo.org/record/7860847", | |
| "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":{"placeBy":None,"reductionMethod":None,"colorBy":"label","sizeBy":None,"filter":False,"umapNNeighbors":20,"umapMetric":None,"umapMinDist":0.15,"pcaNormalization":None,"umapMenuLocalGlobalBalance":None,"umapMenuIsAdvanced":False}}]},{"kind":"tab","weight":36.029411764705884,"children":[{"kind":"widget","name":"Similarity Map","type":"similaritymap","config":{"placeBy":None,"reductionMethod":None,"colorBy":"class","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":["path"],"name":"view","key":"43a5beff-9423-41c9-a5ba-285a7ece7a02"},{"view":"SpectrogramView","columns":["path"],"name":"view","key":"5f035027-dd02-4587-ba77-defdf823c124"}],"visibleColumns":4}}]}]}, | |
| "simple": {"orientation":"vertical","children":[{"kind":"split","weight":60.575296108291035,"orientation":"horizontal","children":[{"kind":"tab","weight":31.52260461369049,"children":[{"kind":"widget","name":"Table","type":"table","config":{"tableView":"filtered","visibleColumns":["class","d1p","d1v","d2p","d2v","d3p","d3v","dev_train_lof_anomaly","dev_train_lof_anomaly_score","domain","label","section"],"sorting":None,"orderByRelevance":False}}]},{"kind":"tab","weight":33.869200490640154,"children":[{"kind":"widget","name":"Similarity map with AST-lof anomaly score","type":"similaritymap","config":{"placeBy":None,"reductionMethod":None,"colorBy":"dev_train_lof_anomaly_score","sizeBy":"label","filter":False,"umapNNeighbors":20,"umapMetric":None,"umapMinDist":0.15,"pcaNormalization":None,"umapMenuLocalGlobalBalance":None,"umapMenuIsAdvanced":False}}]},{"kind":"tab","weight":34.60819489566936,"children":[{"kind":"widget","name":"Similarity map with classes","type":"similaritymap","config":{"placeBy":None,"reductionMethod":None,"colorBy":"class","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":"domain","stackByColumnKey":"prediction_correct_dcase2023_task2_baseline_ae","filter":False}}]}]},{"kind":"tab","weight":39.424703891708965,"children":[{"kind":"widget","name":"Inspector","type":"inspector","config":{"views":[{"view":"AudioView","columns":["path"],"name":"view","key":"dea9a175-9582-412e-9f49-be729e8838fb"},{"view":"SpectrogramView","columns":["path"],"name":"view","key":"676bd937-226b-4632-ae2d-ec8bc37bcc5d"},{"view":"ScalarView","columns":["label"],"name":"view","key":"dbfcc0b1-9e96-4d31-8856-f0bd7f0b8144"},{"view":"ScalarView","columns":["domain"],"name":"view","key":"3e79654f-e017-402c-b136-6a13c4409ae4"}],"visibleColumns":4}}]}]}, | |
| "extended": {"orientation":"vertical","children":[{"kind":"split","weight":54.145516074450086,"orientation":"horizontal","children":[{"kind":"tab","weight":31.52260461369049,"children":[{"kind":"widget","name":"Table","type":"table","config":{"tableView":"filtered","visibleColumns":["class","d1p","d1v","d2p","d2v","d3p","d3v","dev_train_lof_anomaly","dev_train_lof_anomaly_score","domain","label","section"],"sorting":None,"orderByRelevance":False}}]},{"kind":"tab","weight":33.869200490640154,"children":[{"kind":"widget","name":"Similarity map with AST-lof anomaly score","type":"similaritymap","config":{"placeBy":None,"reductionMethod":None,"colorBy":"dev_train_lof_anomaly_score","sizeBy":"label","filter":False,"umapNNeighbors":20,"umapMetric":None,"umapMinDist":0.15,"pcaNormalization":None,"umapMenuLocalGlobalBalance":None,"umapMenuIsAdvanced":False}}]},{"kind":"tab","weight":34.60819489566936,"children":[{"kind":"widget","name":"Similarity map with classes","type":"similaritymap","config":{"placeBy":None,"reductionMethod":None,"colorBy":"class","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":"split","weight":45.854483925549914,"orientation":"horizontal","children":[{"kind":"tab","weight":58.581483486735245,"children":[{"kind":"widget","name":"Inspector","type":"inspector","config":{"views":[{"view":"AudioView","columns":["path"],"name":"view","key":"dea9a175-9582-412e-9f49-be729e8838fb"},{"view":"SpectrogramView","columns":["path"],"name":"view","key":"676bd937-226b-4632-ae2d-ec8bc37bcc5d"},{"view":"ScalarView","columns":["label"],"name":"view","key":"dbfcc0b1-9e96-4d31-8856-f0bd7f0b8144"},{"view":"ScalarView","columns":["domain"],"name":"view","key":"3e79654f-e017-402c-b136-6a13c4409ae4"}],"visibleColumns":4}}]},{"kind":"tab","weight":41.418516513264755,"children":[{"kind":"widget","name":"Histogram","type":"histogram","config":{"columnKey":"class","stackByColumnKey":"dev_train_lof_anomaly"}}]}]}]}, | |
| } | |
| 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("0.1.0") | |
| 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"), | |
| "dev_train_lof_anomaly": datasets.Value("int64"), | |
| "dev_train_lof_anomaly_score": datasets.Value("float32"), | |
| "add_train_lof_anomaly": datasets.Value("int64"), | |
| "add_train_lof_anomaly_score": datasets.Value("float32"), | |
| } | |
| 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"]) if self.config.data_urls["metadata"] is not None else None, | |
| "scores_file": dl_manager.download_and_extract("data/scores.csv"), | |
| "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], | |
| scores_file: Optional[str], | |
| is_streaming: Optional[bool], | |
| ): | |
| """Yields examples.""" | |
| if metadata_file is not None: | |
| metadata = pd.read_csv(metadata_file) | |
| if scores_file is not None: | |
| scores = pd.read_csv(scores_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 metadata_file is None or 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} | |
| if metadata_file is not None: | |
| result.update(metadata[metadata["path"] == lookup].T.squeeze().to_dict()) | |
| if scores is not None: | |
| result.update(scores[scores["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) | |