dcase23-task2-enriched / dcase23-task2-enriched.py
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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",
},
"embeddings_dcase2023_task2_baseline_ae": {
"train": "data/PCA_REDUCED_dcase2023_task2_baseline_ae-embeddings_dev_train.npz",
"test": "data/PCA_REDUCED_dcase2023_task2_baseline_ae-embeddings_dev_test.npz",
# "size": (1, 2976),
"size": (1, 512),
"dtype": "float32",
},
},
"add": {
"embeddings_ast-finetuned-audioset-10-10-0.4593": {
"train": "",
"test": "",
},
"embeddings_dcase2023_task2_baseline_ae": {
"train": "",
"test": "",
},
},
"eval": {
"embeddings_ast-finetuned-audioset-10-10-0.4593": {
"train": "",
"test": "",
},
"embeddings_dcase2023_task2_baseline_ae": {
"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
}
}
]
}
]
},
"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","label","section","domain"],"sorting":None,"orderByRelevance":False}}]},{"kind":"tab","weight":33.869200490640154,"children":[{"kind":"widget","name":"Similarity map with baseline anomaly score","type":"similaritymap","config":{"placeBy":None,"reductionMethod":None,"colorBy":"anomaly_score_dcase2023_task2_baseline_ae","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","label","section","domain"],"sorting":None,"orderByRelevance":False}}]},{"kind":"tab","weight":33.869200490640154,"children":[{"kind":"widget","name":"Similarity map with baseline anomaly score","type":"similaritymap","config":{"placeBy":None,"reductionMethod":None,"colorBy":"anomaly_score_dcase2023_task2_baseline_ae","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":"domain","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":62.314308053166535,"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":37.685691946833465,"children":[{"kind":"widget","name":"Histogram","type":"histogram","config":{"columnKey":"anomaly_score_dcase2023_task2_baseline_ae","stackByColumnKey":"prediction_correct_dcase2023_task2_baseline_ae","filter":False}},{"kind":"widget","name":"Scatter Plot (2)","type":"scatterplot","config":{"xAxisColumn":"anomaly_score_dcase2023_task2_baseline_ae","yAxisColumn":"anomaly_score_lof","colorBy":"label"}}]}]}]},
}
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"),
"anomaly_score_embedding_lof": 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"]),
"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)