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---
license: cc-by-nc-4.0
dataset_info:
  features:
  - name: audio_filename
    dtype:
      audio:
        sampling_rate: 16000
  - name: labels
    sequence: string
  - name: scores
    sequence: float64
  splits:
  - name: chunk_0.5s
    num_bytes: 2335914119.828
    num_examples: 408828
  - name: 500k_part1_chunk_0.5s
    num_bytes: 10729355414.456
    num_examples: 3103548
  - name: chunk_0.25s
    num_bytes: 2570845459.725
    num_examples: 692209
  - name: 500k_part2_chunk_0.5s
    num_bytes: 10547345488.462
    num_examples: 3073862
  download_size: 30054318161
  dataset_size: 26183460482.470993
configs:
- config_name: default
  data_files:
  - split: chunk_0.5s
    path: data/chunk_0.5s-*
  - split: chunk_0.25s
    path: data/chunk_0.25s-*
  - split: 500k_part1_chunk_0.5s
    path: data/500k_part1_chunk_0.5s-*
  - split: 500k_part2_chunk_0.5s
    path: data/500k_part2_chunk_0.5s-*
task_categories:
- audio-classification
pretty_name: AudioSet-Chunk
---

# AudioSet Chunk

Chunk [AudioSet](https://research.google.com/audioset/index.html) every 0.25 seconds and 0.5 seconds and predict using [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593), after that only filter and labels predicted overlapped with the gold labels.