Instructions to use Inferencelab/whisper-small-urdu-int8-ct2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Inferencelab/whisper-small-urdu-int8-ct2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Inferencelab/whisper-small-urdu-int8-ct2")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Inferencelab/whisper-small-urdu-int8-ct2", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7774909802c889b6f4419df88053855772b55565b49fa9d732e702c75212b9ba
- Size of remote file:
- 248 MB
- SHA256:
- 55ac3dff2a991329def20902f58df0d717cf7ec32da5dfd650357c713ab1d878
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.