Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use BrainTheos/whisper-tiny-ln-ojpl-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BrainTheos/whisper-tiny-ln-ojpl-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="BrainTheos/whisper-tiny-ln-ojpl-2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("BrainTheos/whisper-tiny-ln-ojpl-2") model = AutoModelForSpeechSeq2Seq.from_pretrained("BrainTheos/whisper-tiny-ln-ojpl-2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 850598c311e450f9bcae996b114a5822e0a27e4042ee2a99e804907e980e6985
- Size of remote file:
- 151 MB
- SHA256:
- 4d3ba96d7ed9e5c20934945c17fda53fe2dcbf5a2b379234e898499858a8bcb6
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