Instructions to use hf-tiny-model-private/tiny-random-Wav2Vec2Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-Wav2Vec2Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-Wav2Vec2Model")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2Model") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2Model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0c443f240681abd26f68321180114ade5bed32fed24dc9e3ac9db165f5b20d60
- Size of remote file:
- 115 kB
- SHA256:
- 11d51b51dcb0602b6db6e076a727cd2e6e36e2a56b16a327b6b1fd86cc77fcea
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