Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Javanese
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use OwLim/whisper-java with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OwLim/whisper-java with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="OwLim/whisper-java")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("OwLim/whisper-java") model = AutoModelForSpeechSeq2Seq.from_pretrained("OwLim/whisper-java") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fd071a2340fd79cab96bce88119f73e12c4ff5a304f596efd12dfab3fad36b42
- Size of remote file:
- 5.5 kB
- SHA256:
- a488ef5aa14c3a26db425b40716bba4ce5d8453a565702eb71d876434ac6290f
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