Instructions to use superb/hubert-base-superb-er with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use superb/hubert-base-superb-er with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="superb/hubert-base-superb-er")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("superb/hubert-base-superb-er") model = AutoModelForAudioClassification.from_pretrained("superb/hubert-base-superb-er", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from superb/hubert-base-superb-er: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/superb/hubert-base-superb-er/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://superb/hubert-base-superb-er/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/superb/hubert-base-superb-er/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 8cb984974da45051cf176c043d47d6f389d01ab67f9e27b1d3d2b3429866c827
- Size of remote file:
- 378 MB
- SHA256:
- 9e10f7a1672ad8f262012d3698bfbcdc232fe9483f9fbee3d9a1de405d134a2a
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