Instructions to use kming/wav2vec2-base-superb-sv-finetuned-ami-ten-percent-train with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kming/wav2vec2-base-superb-sv-finetuned-ami-ten-percent-train with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForAudioXVector processor = AutoProcessor.from_pretrained("kming/wav2vec2-base-superb-sv-finetuned-ami-ten-percent-train") model = AutoModelForAudioXVector.from_pretrained("kming/wav2vec2-base-superb-sv-finetuned-ami-ten-percent-train") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b40dc5003615fbfba75170090c2d41594a734a741c7aa588b27342a938917c6c
- Size of remote file:
- 404 MB
- SHA256:
- 994325f7d3a2bb96c5510d8258342de921c356ccec918b211f997a22eaddbda7
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