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:
- bdb7290a2aed953ebe98ec252e0ebc1147e64b3b5d652fbb2104491ae3e4dee2
- Size of remote file:
- 77.7 MB
- SHA256:
- 054187c95948ee0455d428db0c0d6c84d6c6157dab72e86857ced13233118b03
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