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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 680fedbceb89795de68086c192a32fcfb11d4fd44353c3571858b04ea510f664
- Size of remote file:
- 2.49 GB
- SHA256:
- 1dc734c1a4dd627199f36475e76ae0214cf217b24190577178226f4a8bf78b38
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.