Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLab/nb-whisper-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-whisper-medium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-medium")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/nb-whisper-medium") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/nb-whisper-medium") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ddf9f2f50cb9bad5429baa8498ee33e63fe4413c5c2a8f611cb22bfcdbba64a5
- Size of remote file:
- 6.33 kB
- SHA256:
- 4ac35cd18a35dbd0f51fa9c7d7c8dda0b3e50e8ba95dcb67677c5933c7be6afd
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