Instructions to use bhimrazy/whisper-small-np with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bhimrazy/whisper-small-np with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bhimrazy/whisper-small-np")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("bhimrazy/whisper-small-np") model = AutoModelForSpeechSeq2Seq.from_pretrained("bhimrazy/whisper-small-np", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f714320011923122e9f6fd3183f256ec5ec06703f124461b78b30acf5202b1d8
- Size of remote file:
- 3.5 kB
- SHA256:
- ea9049d3eec2bb40a55fbe60237635bda82e3a6b943e57d1ccfb13aaf2081dc7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.