Instructions to use kming/wav2vec2-base-superb-sv-finetuned-ami-ten-percent-train-new 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-new with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForAudioXVector processor = AutoProcessor.from_pretrained("kming/wav2vec2-base-superb-sv-finetuned-ami-ten-percent-train-new") model = AutoModelForAudioXVector.from_pretrained("kming/wav2vec2-base-superb-sv-finetuned-ami-ten-percent-train-new") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ec6c240d3bd9b6c3637ccc2a3ea3359c313a51ac2afcdad6e35f1b2c1de36efb
- Size of remote file:
- 404 MB
- SHA256:
- 6203e2f30b7a1f4a1c7d7aae88e58ed9320321704c049f02da9bbca2460cc7d6
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