Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLab/nb-whisper-base-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-whisper-base-beta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-base-beta")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/nb-whisper-base-beta") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/nb-whisper-base-beta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0da0b54e543285b50add8c9e20faa7dd1d8c07494fc7a102da4c84fc8851b69c
- Size of remote file:
- 148 MB
- SHA256:
- 66cafa5e804eb931d6efd5bc4f96b39d9707c1573d3ca2d82addefd950633b68
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