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:
- 2e332609e8ef14580f8b2c0cb928a793d7564af40db18930eaa963e710d6ee41
- Size of remote file:
- 378 MB
- SHA256:
- ddb2af4c5fdb51f755c5000a5e83dd21d1c96b2b29f8f618a33d6bfedb8122df
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