Instructions to use kinit/whisper-tiny-sk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kinit/whisper-tiny-sk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="kinit/whisper-tiny-sk")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("kinit/whisper-tiny-sk") model = AutoModelForSpeechSeq2Seq.from_pretrained("kinit/whisper-tiny-sk", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c5d7862d90ebb4b70e1adbbed544a785c3d8f7c7b20f585028f00d70580da1c4
- Size of remote file:
- 6.16 kB
- SHA256:
- fcd8a08fea734bcb83f2c82d4024f71dda611cb2208c4f682d46385adb815ac4
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