Instructions to use Barani1-t/hubert-base-ls960 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Barani1-t/hubert-base-ls960 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Barani1-t/hubert-base-ls960")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Barani1-t/hubert-base-ls960") model = AutoModelForAudioClassification.from_pretrained("Barani1-t/hubert-base-ls960", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ed383ad168eaaec7070c5d0c582865cbf3bfa7aadbb00f0c26945849e25a89ad
- Size of remote file:
- 378 MB
- SHA256:
- fa4577bdae503e90582d577be81399ff5afe1335043f7d44cf6e474b1c4ac7d1
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