modified config.splits form url
Browse files- dcase23-task2-enriched.py +38 -3
dcase23-task2-enriched.py
CHANGED
@@ -3,6 +3,7 @@ import datasets
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import datasets.info
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import pandas as pd
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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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@@ -49,8 +50,16 @@ DATA_URLS = {
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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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}
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STATS = {
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@@ -105,7 +114,7 @@ class DCASE2023Task2DatasetConfig(datasets.BuilderConfig):
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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"
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)
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super(DCASE2023Task2DatasetConfig, self).__init__(
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name=name,
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@@ -113,6 +122,27 @@ class DCASE2023Task2DatasetConfig(datasets.BuilderConfig):
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description=description,
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)
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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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@@ -214,3 +244,8 @@ class DCASE2023Task2Dataset(datasets.GeneratorBasedBuilder):
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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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import datasets.info
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import pandas as pd
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from pathlib import Path
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from renumics import spotlight
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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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"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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"test": "data/add_test.tar.gz",
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"metadata": "data/add_metadata.csv",
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},
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"eval": {
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"train": "data/eval_train.tar.gz",
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"test": "data/eval_test.tar.gz",
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"metadata": "data/eval_metadata.csv",
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},
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}
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STATS = {
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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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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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if type(data) == datasets.Dataset:
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df = data.to_pandas()
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df["split"] = data.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):
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return self._layout
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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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result["path"] = path
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yield id_, {**result, "audio": audio}
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id_ += 1
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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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