Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLab/nb-whisper-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-whisper-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-small")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/nb-whisper-small") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/nb-whisper-small") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1f334665dfc0c20ae89109e3fcd3986aeadda351dc69650731ebb8cb62cc0772
- Size of remote file:
- 967 MB
- SHA256:
- 9efd6bcd79f353d36ac3402e4f0e52a6ab11d8c0b6266be36f27005ddb1c5a99
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.