Instructions to use KBLab/kb-whisper-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KBLab/kb-whisper-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="KBLab/kb-whisper-base")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("KBLab/kb-whisper-base") model = AutoModelForSpeechSeq2Seq.from_pretrained("KBLab/kb-whisper-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- db692588ad80e0b2151571ee8f49616cda0ca3aee2bfd976e586b303769b97a1
- Size of remote file:
- 145 MB
- SHA256:
- fa942ec92ad7747aec2e9ea8c57ad8971a3695f3c9ff440018a3667bb818a5c4
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