Instructions to use Benjaminpwh/sst-en_de_13 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Benjaminpwh/sst-en_de_13 with Transformers:
# Load model directly from transformers import AutoProcessor, DynamicWav2Vec2ForCTC processor = AutoProcessor.from_pretrained("Benjaminpwh/sst-en_de_13") model = DynamicWav2Vec2ForCTC.from_pretrained("Benjaminpwh/sst-en_de_13") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 693ec4b3922b0bd306bf7b4989e115ffbfeb7b0c08b31bc6d956818c6bb07f61
- Size of remote file:
- 11.4 MB
- SHA256:
- aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.