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ALM
/
wav2vec2-base-audioset

Audio Classification
Transformers
PyTorch
wav2vec2
pretraining
music
audio
speech
audio-representation-learning
arch-benchmark
general-audio
Model card Files Files and versions
xet
Community
2

Instructions to use ALM/wav2vec2-base-audioset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use ALM/wav2vec2-base-audioset with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("audio-classification", model="ALM/wav2vec2-base-audioset")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForPreTraining
    
    processor = AutoProcessor.from_pretrained("ALM/wav2vec2-base-audioset")
    model = AutoModelForPreTraining.from_pretrained("ALM/wav2vec2-base-audioset")
  • Notebooks
  • Google Colab
  • Kaggle
wav2vec2-base-audioset
380 MB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 7 commits
morenolq's picture
morenolq
Update README.md
192789f verified 5 months ago
  • .gitattributes
    1.52 kB
    initial commit almost 3 years ago
  • README.md
    3.76 kB
    Update README.md 5 months ago
  • config.json
    2.1 kB
    Adding model almost 3 years ago
  • preprocessor_config.json
    215 Bytes
    Adding model almost 3 years ago
  • pytorch_model.bin
    380 MB
    xet
    Adding model almost 3 years ago
  • special_tokens_map.json
    96 Bytes
    Adding model almost 3 years ago
  • tokenizer_config.json
    606 Bytes
    Adding model almost 3 years ago
  • vocab.json
    358 Bytes
    Adding model almost 3 years ago