Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLab/nb-whisper-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-whisper-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-large")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/nb-whisper-large") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/nb-whisper-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 65eccce5f6de0fb5a97f56098f6ecca07196acfd185ab906252a6e371e2199d4
- Size of remote file:
- 3.1 GB
- SHA256:
- 0f2f66f22e11a7c7da3c582d8e5c89cb2c0011753ba9c7c9731e320a4ba33e76
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