Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLab/nb-whisper-small-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-whisper-small-beta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-small-beta")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/nb-whisper-small-beta") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/nb-whisper-small-beta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8ff44978d991341168fe9019baa0eab22f8b7312213b5f27da56520c4caa8155
- Size of remote file:
- 967 MB
- SHA256:
- 697608d9bf720c73b96ebd7b90d2ab22474746f8ab70091437b4e930015ca435
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