Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabBeta/nb-whisper-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabBeta/nb-whisper-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-large")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-large") model = AutoModelForMultimodalLM.from_pretrained("NbAiLabBeta/nb-whisper-large") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 63449ecbd1010c47d76fcf1a6e506164153bafcaa3332c923eb3f2f0bfc36f6c
- Size of remote file:
- 4.99 GB
- SHA256:
- 4bdf9a2cb1ae48dbb7de0fdc014dd2bc258f02b0e4cae9f53e4ca8aa0b0e589a
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