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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, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

Adding `safetensors` variant of this model

#13 opened about 1 year ago by
SFconvertbot

License

👍 2
#12 opened about 1 year ago by
mrfakename

Adding `safetensors` variant of this model

#11 opened about 1 year ago by
SFconvertbot

Adding `safetensors` variant of this model

#10 opened over 1 year ago by
SFconvertbot

Adding `safetensors` variant of this model

#8 opened over 1 year ago by
SFconvertbot

Adding `safetensors` variant of this model

#7 opened about 2 years ago by
SFconvertbot

passing device_map argument breaks the model

#6 opened over 2 years ago by
x75

Are there any plans to distribute the checkpoint files trained on each downstream task?

1
#5 opened over 2 years ago by
itsuki1021

Confusion about the use of the Encodec model

1
#4 opened over 2 years ago by
xtluo

can you provide fairseq checkpoint?

2
#1 opened about 3 years ago by
lzl1456
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