Instructions to use hf-internal-testing/tiny-random-Speech2TextModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Speech2TextModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-Speech2TextModel")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-Speech2TextModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-Speech2TextModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9c4d3de6c4a52f9cbcdcce3d292e4ed6a0625eefbe9bd0ba7212a71b9e29510f
- Size of remote file:
- 708 kB
- SHA256:
- a99117b80c4924fc6d8afc7a1a7f57935d82d8f8b5cbac2603c222ce82585654
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.