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:
- ea0d5cbbcd3ccd3a2f23d60f3a3ea0fbcf9ef8a736e4b650f0e025c54a98ba3d
- Size of remote file:
- 3.06 GB
- SHA256:
- 1721dd1dc26d7a543280aaedbc8ab2832578cef4bf834d4fdd838393f36d278a
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