Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLab/nb-whisper-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-whisper-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-large")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/nb-whisper-large") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/nb-whisper-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "test_clean_stortinget_no": { | |
| "cer": 6.058845345530968, | |
| "exact_cer": 6.583421031328422, | |
| "exact_wer": 12.74077681724096, | |
| "wer": 9.46120008749119 | |
| }, | |
| "test_nst": { | |
| "cer": 0.6508931076405126, | |
| "exact_cer": 0.7522526246176737, | |
| "exact_wer": 2.831065201945674, | |
| "wer": 2.175219981417719 | |
| } | |
| } |