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
Download CODE_LICENSE from ltuncay/BEST-RQ-2: direct link, hf CLI and curl.
- Browser
- Download file 1.09 kB
-
https://huggingface.co/ltuncay/BEST-RQ-2/resolve/main/CODE_LICENSE
- Command line
-
hf download hf://ltuncay/BEST-RQ-2/CODE_LICENSE
-
curl -L -o CODE_LICENSE https://huggingface.co/ltuncay/BEST-RQ-2/resolve/main/CODE_LICENSE
1.09 kB
| MIT License | |
| Copyright (c) 2026 audio-embeddings contributors | |
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| SOFTWARE. | |