Instructions to use superb/hubert-large-superb-ic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use superb/hubert-large-superb-ic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="superb/hubert-large-superb-ic")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("superb/hubert-large-superb-ic") model = AutoModelForAudioClassification.from_pretrained("superb/hubert-large-superb-ic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from superb/hubert-large-superb-ic: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/superb/hubert-large-superb-ic/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://superb/hubert-large-superb-ic/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/superb/hubert-large-superb-ic/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- 339d2959cb68d35e5cb083860c4bbf23b6dbd0c38ca13a56141901628d04d493
- Size of remote file:
- 1.26 GB
- SHA256:
- ca41df74e40e83719818d86da732302fe6181c96d3c3096391095f5c3b8e1adf
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