Instructions to use KBLab/kb-whisper-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KBLab/kb-whisper-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="KBLab/kb-whisper-base")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("KBLab/kb-whisper-base") model = AutoModelForSpeechSeq2Seq.from_pretrained("KBLab/kb-whisper-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 58ad94e0e4cc4613b520cd0dfc628af2fa7b257235715ac163f66248cf07237f
- Size of remote file:
- 148 MB
- SHA256:
- f5e3cdb33e537eedfa2a749b5cae28c4c511873a1b13362f87dffbe07891d3fe
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.