Instructions to use vasista22/ccc-wav2vec2-base-SUPERB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vasista22/ccc-wav2vec2-base-SUPERB with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForPreTraining processor = AutoProcessor.from_pretrained("vasista22/ccc-wav2vec2-base-SUPERB") model = AutoModelForPreTraining.from_pretrained("vasista22/ccc-wav2vec2-base-SUPERB") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 12061d92b3eab92e3e5c8785175a1dc89e46f528070e5188c42558f1a838a4bf
- Size of remote file:
- 380 MB
- SHA256:
- a6fc8f5386a70ed864b4cb088cefd2e02f89a66147a5f2fc6f355fbadf794072
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