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:
- be3bfab20d2db2594e20ea5b3d2b7506e59a65f2cac10f88772fc10aed38cb79
- Size of remote file:
- 2.55 GB
- SHA256:
- 3eb94ea5f21a2a5d8ff855cbbd59ed038ca47026f91dcdb1f343019a8dbe78f2
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