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:
- 88c07cde51499b53985d9d2c890780e16a4a6a262099f5df4d0ed4b54bbec0bf
- Size of remote file:
- 378 MB
- SHA256:
- 29342a2153a97366167671c5f5e71cdc1a2cd34bf7fb962617f40a4f207a7657
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