Datasets:
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README.md
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- audio
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size_categories:
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- 1M<n<10M
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- audio
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size_categories:
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- 1M<n<10M
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---
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# AudioSet
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AudioSet is a large-scale dataset comprising approximately 2 million 10-second YouTube audio clips, categorised into 527 sound classes.
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We have pre-processed all audio files to a 16 kHz sampling rate and stored them in the WebDataset format for efficient large-scale training and retrieval.
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## Download
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We recommend using the following commands to download the `confit/audioset-16khz-wds` dataset from HuggingFace.
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The dataset is available in two versions:
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- 20k: A smaller version with 20,550 clips for quick experimentation.
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- 2m: The complete dataset with ~2 million clips.
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```bash
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# For the 20k version
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huggingface-cli download confit/audioset-16khz-wds --include 20k/train/*.tar --repo-type=dataset --local-dir /path/to/store
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huggingface-cli download confit/audioset-16khz-wds --include 20k/test/*.tar --repo-type=dataset --local-dir /path/to/store
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# For the 2m version
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huggingface-cli download confit/audioset-16khz-wds --include 2m/train/*.tar --repo-type=dataset --local-dir /path/to/store
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huggingface-cli download confit/audioset-16khz-wds --include 2m/test/*.tar --repo-type=dataset --local-dir /path/to/store
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```
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## Format and Usage
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The dataset is stored in the WebDataset (WDS) format, which is optimised for distributed training and streaming.
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Each `.tar` archive contains audio files and corresponding metadata.
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To load the dataset in Python using webdataset:
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```python
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train_base_url = '/path/to/20k/train/shard-{i:05d}.tar'
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train_urls = [train_base_url.format(i=i) for i in range(7)]
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test_base_url = '/path/to/20k/test/shard-{i:05d}.tar'
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test_urls = [test_base_url.format(i=i) for i in range(6)]
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raw_datasets = load_dataset(
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"webdataset",
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data_files={"train": train_urls, "test": test_urls},
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streaming=False
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)
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```
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## License and Usage Restrictions
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Please ensure compliance with YouTube's terms of service when using this dataset.
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Some clips may no longer be available if the original videos have been removed or made private.
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