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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6c8f020a29a167e8384dcded19c5d63399809c54fd324a042439941e203d754d
- Size of remote file:
- 197 kB
- SHA256:
- 217061ebd90edde11e695324a285a5c323b7a2afc14b3b2dc8abe705c5a907e3
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