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:
- 22cc6ccb1c63f04727ab67a906d09a9b84edf8df2f9e4bf78a5ac04bd166fa7a
- Size of remote file:
- 1.07 GB
- SHA256:
- b1858dcfc0851968293c6dbe6cec885f52050ae8fe54f7f90a2e49045ef64f9c
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