Instructions to use hf-tiny-model-private/tiny-random-Wav2Vec2ConformerModel 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-Wav2Vec2ConformerModel 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-Wav2Vec2ConformerModel")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2ConformerModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2ConformerModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bc080419a93d74aa9f7055e57e5132f45a891d5bc7bdf49d3b2610141d4065c5
- Size of remote file:
- 162 kB
- SHA256:
- 7ce5af4e09eb9b4d93767995c959ff4593c8f62e30be9a04e34d6c318f8f00a7
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