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:
- c1ddead6386346f3bfa0542ffd7843f830b02ce592fb83511bfe5dd34f690950
- Size of remote file:
- 1.18 GB
- SHA256:
- c17060c77fea999e9802f30d754f30cd52d224b1e210b3f1bf21fe40162c84d1
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