Instructions to use hf-tiny-model-private/tiny-random-WhisperModel 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-WhisperModel 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-WhisperModel")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-WhisperModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-WhisperModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2a003cf2189b947f2413b6e34f006cdccac3ab39276f3d66a8bc4fa6689e00f4
- Size of remote file:
- 3.29 MB
- SHA256:
- 4df2a67e1dd2ea08d8b3c95099e33f116bc1c9c0e91f0cb7e3ea0c87b07bbc0c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.