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:
- 98bea4dd55264ae644ccedc4da0b7dd4855dd874e3b7e6cf8bad8a91f341fcc3
- Size of remote file:
- 967 MB
- SHA256:
- c50b15ce99e46a8fb9609c35e8edb9f9df69bcc7f566edfe972dfd0d33a9996d
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