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:
- 010817743694ed10ab644b946359a14344a2d3d55f0a9e60d9d8da9ce24ecb63
- Size of remote file:
- 4.16 kB
- SHA256:
- dc6e2c5a24be289e397b40b0a38498f911f6f9473ab0a635e90a6ce80021f583
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