Instructions to use bartelds/gos-gpu6-cp0_adp0_4x168m-silver_24-orig_1e-4_cp-12000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bartelds/gos-gpu6-cp0_adp0_4x168m-silver_24-orig_1e-4_cp-12000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bartelds/gos-gpu6-cp0_adp0_4x168m-silver_24-orig_1e-4_cp-12000")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("bartelds/gos-gpu6-cp0_adp0_4x168m-silver_24-orig_1e-4_cp-12000") model = AutoModelForCTC.from_pretrained("bartelds/gos-gpu6-cp0_adp0_4x168m-silver_24-orig_1e-4_cp-12000") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d018d3b513fdf4bc74a7ec94c682d19be9a71016906749e91f93b55d221a0685
- Size of remote file:
- 1.26 GB
- SHA256:
- 7af08dde7ad512af6e9d772def1032721e11b905a5d0a162fb11d8c12ef57335
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.