Instructions to use KBLab/kb-whisper-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KBLab/kb-whisper-medium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="KBLab/kb-whisper-medium")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("KBLab/kb-whisper-medium") model = AutoModelForSpeechSeq2Seq.from_pretrained("KBLab/kb-whisper-medium", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a1c94c1bd7a9b84f94e9b4d789346139976629878cee95d72985a3696a64a85b
- Size of remote file:
- 1.53 GB
- SHA256:
- 1b7842bc1c3f79fb3bf043a0a3590961d625a49ef3ccbdceb00e738c5dd8b015
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