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")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("KBLab/kb-whisper-large") model = AutoModelForSpeechSeq2Seq.from_pretrained("KBLab/kb-whisper-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download ggml-model.bin from KBLab/kb-whisper-large: direct link, hf CLI and curl.
- Browser
- Download file 3.1 GB
-
https://huggingface.co/KBLab/kb-whisper-large/resolve/main/ggml-model.bin
- Command line
-
hf download hf://KBLab/kb-whisper-large/ggml-model.bin
-
curl -L -o ggml-model.bin https://huggingface.co/KBLab/kb-whisper-large/resolve/main/ggml-model.bin
3.1 GB
- Xet hash:
- 49881fe5c95da6609bb0c14132d179d1f5c9608fa4ef3d0328ea9512865716c3
- Size of remote file:
- 3.1 GB
- SHA256:
- b66f2dda369a88f6c03fe37326d7cc37aa216f6f34e6fc1be686e497ba9c2f39
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.