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m-a-p
/
MERT-v1-330M

Audio Classification
Transformers
PyTorch
mert_model
feature-extraction
music
custom_code
Model card Files Files and versions
xet
Community
13

Instructions to use m-a-p/MERT-v1-330M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use m-a-p/MERT-v1-330M with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("audio-classification", model="m-a-p/MERT-v1-330M", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("m-a-p/MERT-v1-330M", trust_remote_code=True, device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
MERT-v1-330M
2.52 GB
Ctrl+K
Ctrl+K
  • 3 contributors
History: 13 commits
SFconvertbot's picture
SFconvertbot
Adding `safetensors` variant of this model
e0f6ee1 verified over 2 years ago
  • .gitattributes
    1.48 kB
    initial commit over 3 years ago
  • README.md
    6.73 kB
    Update README.md about 3 years ago
  • config.json
    2.03 kB
    Update config.json over 3 years ago
  • configuration_MERT.py
    5.27 kB
    Upload model over 3 years ago
  • model.safetensors
    1.26 GB
    xet
    Adding `safetensors` variant of this model over 2 years ago
  • modeling_MERT.py
    18 kB
    Upload model over 3 years ago
  • preprocessor_config.json
    212 Bytes
    Create preprocessor_config.json over 3 years ago
  • pytorch_model.bin
    1.26 GB
    xet
    Upload model over 3 years ago