Instructions to use KBLab/kb-whisper-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KBLab/kb-whisper-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="KBLab/kb-whisper-large")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("KBLab/kb-whisper-large") model = AutoModelForSpeechSeq2Seq.from_pretrained("KBLab/kb-whisper-large") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3fa2362ec9a2736143d95ceb1ab2ef8dd5ba92c42df79d767ebf64f735bf8555
- Size of remote file:
- 1.14 MB
- SHA256:
- 61d30cdcd184acdecf7c51ae7bf00d04b95f9b85f8db4600a820074a95a0467f
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