Instructions to use hf-tiny-model-private/tiny-random-Speech2TextModel 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-Speech2TextModel 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-Speech2TextModel")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Speech2TextModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-Speech2TextModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 65cb8e34cb6bd9470eb3510d2c57bb4499b3f2b32fa75abfe73a7c66043c1092
- Size of remote file:
- 708 kB
- SHA256:
- c57d43f535a1c61cb876b62c03ada6b3738be2e10b97e16219cfb6743bc00645
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.