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zencastr
/
laughter-detection

Audio Classification
Transformers
ONNX
Safetensors
multilingual
eat-laughter
audio-frame-classification
audio
sound-event-detection
laughter-detection
podcast
custom_code
Model card Files Files and versions
xet
Community

Instructions to use zencastr/laughter-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use zencastr/laughter-detection with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("audio-classification", model="zencastr/laughter-detection", trust_remote_code=True)
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoModelForAudioFrameClassification
    model = AutoModelForAudioFrameClassification.from_pretrained("zencastr/laughter-detection", trust_remote_code=True, device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
laughter-detection / eval
3.76 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 1 commit
alexcannan's picture
alexcannan
v0.1
849623f 9 days ago
  • configs
    v0.1 9 days ago
  • probs
    v0.1 9 days ago
  • MANIFEST.json
    15.7 kB
    v0.1 9 days ago
  • ami-eval-v1.clips.tsv
    253 kB
    v0.1 9 days ago
  • fsd50k-panel.clips.tsv
    221 kB
    v0.1 9 days ago
  • podcastfillers-eval.clips.tsv
    1.26 MB
    v0.1 9 days ago
  • podcastfillers-relabel.tsv
    9.34 kB
    v0.1 9 days ago
  • public-vocalsound.clips.tsv
    352 kB
    v0.1 9 days ago