Instructions to use NhutP/ViWhisper-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NhutP/ViWhisper-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NhutP/ViWhisper-small")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("NhutP/ViWhisper-small") model = AutoModelForMultimodalLM.from_pretrained("NhutP/ViWhisper-small") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 00f85678e3e9470169d114407312cde2b6c678fb6de5d55b7f95d39e58537583
- Size of remote file:
- 967 MB
- SHA256:
- 21471d071da1c5cf95d486bc7bf55262f0739bd05df5569135becaf5187b8eb5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.