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:
- 632c7a2fc594031afa102a9ee654d6c6a17ed1c9acd5bc194b1638977f4d703b
- Size of remote file:
- 32.9 MB
- SHA256:
- 450520ee4bbfd8f6991a73e308ef1709feacec3c9cd3ebbe5ee49a1eca091533
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