Instructions to use Salimshakeel/whisper-small-ur with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Salimshakeel/whisper-small-ur with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Salimshakeel/whisper-small-ur")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Salimshakeel/whisper-small-ur") model = AutoModelForSpeechSeq2Seq.from_pretrained("Salimshakeel/whisper-small-ur", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 28e0cb78e0c0fb555cc8759432a193c3bafb392668821b09065096affc9b2494
- Size of remote file:
- 5.24 kB
- SHA256:
- 36726b988b3b06e6f340112d872bf0faace4db993858c684cef50a79566a8ac4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.