Instructions to use KBLab/kb-whisper-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KBLab/kb-whisper-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="KBLab/kb-whisper-small")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("KBLab/kb-whisper-small") model = AutoModelForSpeechSeq2Seq.from_pretrained("KBLab/kb-whisper-small") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6217176afd81be50d33e45dd718b3c28911c7ad9322383320d4ebc7f5e52fbff
- Size of remote file:
- 484 MB
- SHA256:
- 58bf16e6878108f898c4db7983d0f4ec01c4891500f7a2b3a2c9ce98b3c0029d
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