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:
- cb608ca69e7d451f5b190de54034c5455c3280fcc1cfa8e004d05847b893d7a7
- Size of remote file:
- 3.63 GB
- SHA256:
- 6c38760bcbe1b4b2ef543b37dc352fab146f93bec8ee1e37bbaedcb972e8b70e
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