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)# 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 requirements.txt from ltuncay/BEST-RQ-2: direct link, hf CLI and curl.
- Browser
- Download file 75 Bytes
-
https://huggingface.co/ltuncay/BEST-RQ-2/resolve/main/requirements.txt
- Command line
-
hf download hf://ltuncay/BEST-RQ-2/requirements.txt
-
curl -L -o requirements.txt https://huggingface.co/ltuncay/BEST-RQ-2/resolve/main/requirements.txt
75 Bytes
| torch>=2.9.1 | |
| torchaudio>=2.9.1 | |
| timm>=0.9 | |
| einops>=0.7 | |
| transformers>=4.57,<6 | |