Instructions to use kinit/whisper-small-sk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kinit/whisper-small-sk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="kinit/whisper-small-sk")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("kinit/whisper-small-sk") model = AutoModelForSpeechSeq2Seq.from_pretrained("kinit/whisper-small-sk", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from kinit/whisper-small-sk: direct link, hf CLI and curl.
- Browser
- Download file 6.16 kB
-
https://huggingface.co/kinit/whisper-small-sk/resolve/main/training_args.bin
- Command line
-
hf download hf://kinit/whisper-small-sk/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/kinit/whisper-small-sk/resolve/main/training_args.bin
6.16 kB
- Xet hash:
- 7a93ee340c9c8abebd497602b9fb4c3e07c7ed1c185b7d29f8d8697fec558f56
- Size of remote file:
- 6.16 kB
- SHA256:
- 14835a2a1812bdcec11db8e7ff0b07de13ddfd23ff0c720458c8ee6aa35a5af7
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