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 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)# pip install -U transformers accelerate # 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
Commit History
13 days ago
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Document 2M X-ARES and HEAR results and link the BEST-RQ-2 paper
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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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Recommend BEST-RQ-2.2 and add the project X-ARES comparison table
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Add Transformers loading for the existing AECC 2026 encoder
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Upload the technical report
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Submission to the Interspeech 2026 Audio Encoder Capability Challenge
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initial commit
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