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:
- 332aea57e83e6c8d34c82bc85cbc4146c2121ebbd32590d0fe4cc6260a851953
- Size of remote file:
- 1.18 GB
- SHA256:
- a39147bedbb75ed1083344aeaac03d28c94347bda418cc1f21057b94a93d283c
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