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
whisper-large-sme / runs /Dec15_08-28-50_dante /1671089463.6206663 /events.out.tfevents.1671089463.dante.1342144.1
- Xet hash:
- 594964d71e8c3e8ca9e48343ba3705b27bb305d4b071c86569c8e4c24d4e6645
- Size of remote file:
- 5.75 kB
- SHA256:
- 990df0968a4b5dcdc55e63c87dc503db15472ccda388226ddb13bd3de5507238
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