Instructions to use KBLab/kb-whisper-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KBLab/kb-whisper-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="KBLab/kb-whisper-tiny")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("KBLab/kb-whisper-tiny") model = AutoModelForSpeechSeq2Seq.from_pretrained("KBLab/kb-whisper-tiny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 17a9ed2727ee2abcc40e3652684b4e04ac1879ed2924fa476383a56e184f3f2f
- Size of remote file:
- 109 MB
- SHA256:
- cfed2716e70b1537fa8430b557dc22609eebad8b0ee0264a668b6bbbe21c1c9e
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