streaming embeddings
Browse files
dcase23-task2-enriched.py
CHANGED
@@ -5,6 +5,8 @@ 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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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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@@ -395,10 +397,15 @@ class DCASE2023Task2Dataset(datasets.GeneratorBasedBuilder):
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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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return [
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datasets.SplitGenerator(
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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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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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