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import os |
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import json |
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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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import numpy as np |
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import tempfile |
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import requests |
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import io |
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from pathlib import Path |
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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_7882613, |
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author = {Kota Dohi and |
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Keisuke Imoto and |
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Noboru Harada and |
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Daisuke Niizumi and |
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Yuma Koizumi and |
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Tomoya Nishida and |
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Harsh Purohit and |
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Takashi Endo and |
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Yohei Kawaguchi}, |
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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 = {3.0}, |
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doi = {10.5281/zenodo.7882613}, |
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url = {https://doi.org/10.5281/zenodo.7882613} |
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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 = 14 |
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_TARGET_NAMES = ["normal", "anomaly"] |
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_CLASS_NAMES = ["gearbox", "fan", "bearing", "slider", "ToyCar", "ToyTrain", "valve", "bandsaw", "grinder", "shaker", "ToyDrone", "ToyNscale", "ToyTank", "Vacuum"] |
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_HOMEPAGE = { |
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"dev": "https://zenodo.org/record/7690157", |
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"add": "", |
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"eval": "", |
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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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"metadata": "data/add_metadata.csv", |
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}, |
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"eval": { |
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"test": "data/eval_test.tar.gz", |
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"metadata": None, |
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}, |
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} |
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EMBEDDING_URLS = { |
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"dev": { |
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"embeddings_ast-finetuned-audioset-10-10-0.4593": { |
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"train": "data/MIT_ast-finetuned-audioset-10-10-0.4593-embeddings_dev_train.npz", |
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"test": "data/MIT_ast-finetuned-audioset-10-10-0.4593-embeddings_dev_test.npz", |
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"size": (1, 768), |
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"dtype": "float32", |
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}, |
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}, |
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"add": { |
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"embeddings_ast-finetuned-audioset-10-10-0.4593": { |
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"train": "data/MIT_ast-finetuned-audioset-10-10-0.4593-embeddings_add_train.npz", |
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"size": (1, 768), |
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"dtype": "float32", |
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}, |
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}, |
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"eval": { |
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"embeddings_ast-finetuned-audioset-10-10-0.4593": { |
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"test": "data/MIT_ast-finetuned-audioset-10-10-0.4593-embeddings_eval_test.npz", |
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"size": (1, 768), |
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"dtype": "float32", |
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}, |
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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': "3.0.0", |
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'homepage': "https://zenodo.org/record/7882613", |
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"splits": ["train", "test"], |
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}, |
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'add': { |
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'date': "Apr 15, 2023", |
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'version': "1.0.0", |
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'homepage': "https://zenodo.org/record/7830345", |
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"splits": ["train"], |
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}, |
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'eval': { |
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'date': "May 1, 2023", |
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'version': "1.0.0", |
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'homepage': "https://zenodo.org/record/7860847", |
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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_LAYOUTS = { |
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"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}}]}]}, |
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"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}}]}]}, |
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"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"}}]}]}]}, |
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} |
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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.embeddings_urls = kwargs.pop("embeddings_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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def get_split(path: str) -> str: |
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fn = os.path.basename(path) |
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if "train" in fn: |
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return "train" |
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elif "test" in fn: |
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return "test" |
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else: |
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raise NotImplementedError |
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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._name if "+" not in data.split._name else df["path"].map(get_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, config: str = "standard") -> str: |
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layout_json = tempfile.mktemp(".json") |
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with open(layout_json, "w") as outfile: |
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json.dump(self.layout[config], outfile) |
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return layout_json |
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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("0.1.0") |
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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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embeddings_urls=EMBEDDING_URLS[key], |
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release_date=stats["date"], |
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splits=stats["splits"], |
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layout=SPOTLIGHT_LAYOUTS, |
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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 = { |
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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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"domain": datasets.ClassLabel(num_classes=2, names=["source", "target"]), |
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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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"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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"dev_train_lof_anomaly": datasets.Value("int64"), |
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"dev_train_lof_anomaly_score": datasets.Value("float32"), |
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"add_train_lof_anomaly": datasets.Value("int64"), |
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"add_train_lof_anomaly_score": datasets.Value("float32"), |
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} |
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if self.config.embeddings_urls is not None: |
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features.update({ |
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emb_name: [datasets.Value(emb["dtype"])] for emb_name, emb in self.config.embeddings_urls.items() |
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}) |
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features = datasets.Features(features) |
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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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embeddings = {split: dict() for split in split_type} |
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for split in split_type: |
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if split in self.config.splits: |
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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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if self.config.embeddings_urls is not None: |
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for emb_name, emb_data in self.config.embeddings_urls.items(): |
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downloaded_embeddings = dl_manager.download(emb_data[split]) |
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if dl_manager.is_streaming: |
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response = requests.get(downloaded_embeddings) |
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response.raise_for_status() |
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downloaded_embeddings = io.BytesIO(response.content) |
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npz_file = np.load(downloaded_embeddings, allow_pickle=True) |
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embeddings[split][emb_name] = npz_file["arr_0"].item() |
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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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"embeddings": embeddings[split], |
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"metadata_file": dl_manager.download_and_extract(self.config.data_urls["metadata"]) if self.config.data_urls["metadata"] is not None else None, |
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"scores_file": dl_manager.download_and_extract("data/scores.csv"), |
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"is_streaming": dl_manager.is_streaming, |
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}, |
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) for split in split_type if split in self.config.splits |
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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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embeddings: Optional[Dict], |
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metadata_file: Optional[str], |
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scores_file: Optional[str], |
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is_streaming: Optional[bool], |
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): |
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"""Yields examples.""" |
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if metadata_file is not None: |
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metadata = pd.read_csv(metadata_file) |
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if scores_file is not None: |
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scores = pd.read_csv(scores_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 metadata_file is None or 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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if is_streaming: |
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audio = {"path": path, "bytes": f.read()} |
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else: |
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audio = {"path": path, "bytes": None} |
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result = {field: None for field in data_fields} |
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if metadata_file is not None: |
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result.update(metadata[metadata["path"] == lookup].T.squeeze().to_dict()) |
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if scores is not None: |
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result.update(scores[scores["path"] == lookup].T.squeeze().to_dict()) |
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for emb_key in embeddings.keys(): |
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result[emb_key] = np.asarray(embeddings[emb_key][lookup]).squeeze().tolist() |
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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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|
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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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