Automatic Speech Recognition
Transformers
PyTorch
JAX
Kannada
whisper
whisper-event
Eval Results (legacy)
Instructions to use vasista22/whisper-kannada-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vasista22/whisper-kannada-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="vasista22/whisper-kannada-tiny")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("vasista22/whisper-kannada-tiny") model = AutoModelForSpeechSeq2Seq.from_pretrained("vasista22/whisper-kannada-tiny") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d2aedd635fed3725addb670099c2093c299fddd3320322df2f07300ade537d33
- Size of remote file:
- 151 MB
- SHA256:
- 8f76e7f026515fbf09bbd624648bed9c0d06585b14c7bd10f4f0548daec1f2ea
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