Instructions to use Akashpb13/ailabs_proj with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Akashpb13/ailabs_proj with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Akashpb13/ailabs_proj")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Akashpb13/ailabs_proj") model = AutoModelForCTC.from_pretrained("Akashpb13/ailabs_proj") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8daaaef0502e2966920e460e2fca72081cfc3f9e4e20a03ad15cb6e0f89d2858
- Size of remote file:
- 1.26 GB
- SHA256:
- 4e5c42b061a3b623d508312e4cc63088e5996a2174625bada2f02017a29548ca
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.