Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLab/nb-whisper-medium-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-whisper-medium-beta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-medium-beta")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/nb-whisper-medium-beta") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/nb-whisper-medium-beta") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 925efca0273810eda4bcec52f28b4079208d6eb9986b6deb7216f9c7bd5cf1e0
- Size of remote file:
- 3.06 GB
- SHA256:
- 6433d6f2f8c5adec5a0c7102d6edf357c36879c6b2a9dc2cce3b5b57829b75fd
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