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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 88b2fa54966fbbfab20ec0d094c16ece0e47c2e2d5a728e18cefeeaf9d0b33b4
- Size of remote file:
- 290 MB
- SHA256:
- 3258c2c7698b96c74a5395a7bc1f5845788386c72be3297ecbcaa8fd5f0db4bc
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