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:
- e28297295f541e0c20bdb991afa5da9f3eaf0a7b57dde49d14123810dd946b6f
- Size of remote file:
- 3.38 kB
- SHA256:
- ae7398b65f658f8e5719f095368d8e690212d763c9683ba18149f60d6ef2bc43
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.