Instructions to use KBLab/kb-whisper-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KBLab/kb-whisper-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="KBLab/kb-whisper-large")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("KBLab/kb-whisper-large") model = AutoModelForSpeechSeq2Seq.from_pretrained("KBLab/kb-whisper-large") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- af68790857fffb732bfdad9e0f4c647be725ea1450f6ca60d3bcfe219f9bc3dd
- Size of remote file:
- 3.21 GB
- SHA256:
- f68e9cc022a382f6d3e605d1b6feb360d4ea782a30b9acf974f0f95096cca86f
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