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ltuncay
/
BEST-RQ-2

Feature Extraction
Transformers
Safetensors
audio_embeddings
audio
custom_code
self-supervised-learning
audio-embeddings
best-rq-2
audioset
Model card Files Files and versions
xet
Community

Instructions to use ltuncay/BEST-RQ-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use ltuncay/BEST-RQ-2 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="ltuncay/BEST-RQ-2", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("ltuncay/BEST-RQ-2", trust_remote_code=True, device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
BEST-RQ-2 / audio-embeddings /src
220 kB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 1 commit
ltuncay's picture
ltuncay
Submission to the Interspeech 2026 Audio Encoder Capability Challenge
eca55dc verified 7 months ago
  • callbacks
    Submission to the Interspeech 2026 Audio Encoder Capability Challenge 7 months ago
  • data
    Submission to the Interspeech 2026 Audio Encoder Capability Challenge 7 months ago
  • models
    Submission to the Interspeech 2026 Audio Encoder Capability Challenge 7 months ago
  • utils
    Submission to the Interspeech 2026 Audio Encoder Capability Challenge 7 months ago
  • train.py
    4.22 kB
    Submission to the Interspeech 2026 Audio Encoder Capability Challenge 7 months ago