Feature Extraction
Transformers
Safetensors
audio_embeddings
audio
custom_code
self-supervised-learning
audio-embeddings
best-rq-2
audioset
Instructions to use ltuncay/BEST-RQ-2.2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ltuncay/BEST-RQ-2.2-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ltuncay/BEST-RQ-2.2-base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ltuncay/BEST-RQ-2.2-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
4 days ago
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Document 2M X-ARES and HEAR results and link the BEST-RQ-2 paper
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29 days ago
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Clarify Audio-JEPA training differences from the ICME checkpoint
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Add Audio-JEPA results and link the Transformers release
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Link pretrained baselines to their Hugging Face model pages
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Link comparison-table entries to published Hugging Face models
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Add the project X-ARES comparison table
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Release BEST-RQ-2.2-base encoder trained for 200k steps
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initial commit
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