Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use NbAiLab/whisper-tiny-nob with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/whisper-tiny-nob with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/whisper-tiny-nob")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/whisper-tiny-nob") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/whisper-tiny-nob", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 342c78ff7a4491b37205a3c5589498e6865430b119c601f0f8aee75b6c2b092c
- Size of remote file:
- 3.64 kB
- SHA256:
- 402de2c4bd980e7aa9dfbd8a32ec503e2002f70e226ac0ad0b28d4f63153cc68
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.