Instructions to use Rifky/whisper-tiny-ko with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rifky/whisper-tiny-ko with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Rifky/whisper-tiny-ko")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Rifky/whisper-tiny-ko") model = AutoModelForSpeechSeq2Seq.from_pretrained("Rifky/whisper-tiny-ko", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 547c2ad5ed2c13c16c710af9779fc79b6c54503fec849f6920c540929df51a20
- Size of remote file:
- 151 MB
- SHA256:
- f4c99cb01b564750232079a2c8ded7632bf320dfc2f3a620353bac3126810ef5
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