Instructions to use superb/hubert-base-superb-ks with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use superb/hubert-base-superb-ks with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="superb/hubert-base-superb-ks")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("superb/hubert-base-superb-ks") model = AutoModelForAudioClassification.from_pretrained("superb/hubert-base-superb-ks", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from superb/hubert-base-superb-ks: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/superb/hubert-base-superb-ks/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://superb/hubert-base-superb-ks/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/superb/hubert-base-superb-ks/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- e3d4ccc46bb52ace40d4f05396a4f068ddca683eb2d755ffc9066a38306a5823
- Size of remote file:
- 378 MB
- SHA256:
- 8bd806a9b8e966f16878c5e3b04c360a33627ac4d6c0cab576425bf8a7b3c139
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.