Instructions to use AescF/hubert-base-ls960-finetuned-common_language with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AescF/hubert-base-ls960-finetuned-common_language with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="AescF/hubert-base-ls960-finetuned-common_language")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("AescF/hubert-base-ls960-finetuned-common_language") model = AutoModelForAudioClassification.from_pretrained("AescF/hubert-base-ls960-finetuned-common_language", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c9c2c14b9ed9f0a5a11284cc6150e98ac4d044e3e8b11dce46332e99ff5426d2
- Size of remote file:
- 378 MB
- SHA256:
- ce3956b6cc6253c8d98461c1d2e55c1cd0db5b3facb0a7aac1954833d56c9de8
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