Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
Northern Sami
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use NbAiLab/whisper-large-sme with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/whisper-large-sme with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/whisper-large-sme")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/whisper-large-sme") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/whisper-large-sme", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 753362e0f22c1314ed8217b164ae6d7385538c1bccef67044a58791ef2b30343
- Size of remote file:
- 4.23 kB
- SHA256:
- 934055a9ed4dcef74ea80212d9a344604166dfec270733090109903019ff16d9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.