Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabBeta/nb-whisper-base-verbatim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabBeta/nb-whisper-base-verbatim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-base-verbatim")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-base-verbatim") model = AutoModelForMultimodalLM.from_pretrained("NbAiLabBeta/nb-whisper-base-verbatim") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6bb6f61b290169faa3ae6128431722ab316305e398d9096383ed7cdba3c8c15d
- Size of remote file:
- 148 MB
- SHA256:
- b3418e3ef22b8d88148dac4f431da9e9e56c01493e1f55edf56585acd59d80db
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.