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") - Notebooks
- Google Colab
- Kaggle
Upload fixed & optimized fp16/q4f16 ONNX weights
#1
by Xenova HF Staff - opened
onnx/decoder_model_merged_fp16.onnx
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onnx/decoder_model_merged_q4f16.onnx
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