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:
- fbc6b8629177d9b4f4fc936d5b4b35c4733a8a1683ba6fb85091e96bb915df1c
- Size of remote file:
- 75.5 MB
- SHA256:
- 6edbc6036ceb79f12c30c5c5c2290383eac7a0bfec0be3163c480e79b26b16b9
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