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")# pip install -U transformers accelerate # 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
Download onnx/decoder_model_merged.onnx from KBLab/kb-whisper-tiny: direct link, hf CLI and curl.
- Browser
- Download file 119 MB
-
https://huggingface.co/KBLab/kb-whisper-tiny/resolve/main/onnx/decoder_model_merged.onnx
- Command line
-
hf download hf://KBLab/kb-whisper-tiny/onnx/decoder_model_merged.onnx
-
curl -L -o decoder_model_merged.onnx https://huggingface.co/KBLab/kb-whisper-tiny/resolve/main/onnx/decoder_model_merged.onnx
119 MB
- Xet hash:
- 6ed5f534fcad16448f524f7ad58d5fdf366c11357141990b63eca1d5b7fcabe8
- Size of remote file:
- 119 MB
- SHA256:
- d5f313c3f6e8dae8b6703cbfaa4a68e6205dc8be4b5f47d3ea61ceacff8ce42a
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