Instructions to use TopSlayer/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TopSlayer/model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="TopSlayer/model")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("TopSlayer/model") model = AutoModelForCTC.from_pretrained("TopSlayer/model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 23f55ba71a2655613cde5a4dd9f616fb8c035a4532e23f2be2ac9b83c98d7dbd
- Size of remote file:
- 5.71 kB
- SHA256:
- b8bf9f8adf0256e5209e6d28f4fcd568074a6d07243619be9f8c19cc3dbfab16
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.