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:
- 6e116160c49b68d008ad11566ecede197d0f9a18a86914558772e3c3cdc9399c
- Size of remote file:
- 378 MB
- SHA256:
- 5ca5d569f7a42cac3a8324053e163394af8f96e3f22601ed32862e78f1549c0d
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