Instructions to use facebook/hubert-base-ls960 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/hubert-base-ls960 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="facebook/hubert-base-ls960")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("facebook/hubert-base-ls960") model = AutoModel.from_pretrained("facebook/hubert-base-ls960", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/hubert-base-ls960: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/facebook/hubert-base-ls960/resolve/refs%2Fpr%2F2/pytorch_model.bin
- Command line
-
hf download hf://facebook/hubert-base-ls960@refs/pr/2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/hubert-base-ls960/resolve/refs%2Fpr%2F2/pytorch_model.bin
378 MB
- Xet hash:
- f5f62511734b7081da910419d3f9658b22b3baa779fe4ef514e214c0ae796e77
- Size of remote file:
- 378 MB
- SHA256:
- 062249fffb353eab67547a2fbc129f7c31a2f459faf641b19e8fb007cc5c48ad
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