Instructions to use lnxdx/19_2000_1e-5_hp-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lnxdx/19_2000_1e-5_hp-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="lnxdx/19_2000_1e-5_hp-base")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("lnxdx/19_2000_1e-5_hp-base") model = AutoModelForCTC.from_pretrained("lnxdx/19_2000_1e-5_hp-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d157dfb123cd16a35e7270d351371e04962f490605ff318967b2d4f50e3a9792
- Size of remote file:
- 4.66 kB
- SHA256:
- a3a6e76ce66457592fc27ec5dca1633c2db4d07fda9f003b4d738557d0a30d6d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.