Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLab/nb-whisper-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-whisper-medium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-medium")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/nb-whisper-medium") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/nb-whisper-medium") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3f03d3f4bde4c3e61fca7afc1268ec1ca8b5ed9e48515a7c96918f10a1908df5
- Size of remote file:
- 3.06 GB
- SHA256:
- b339e8050c288da93c36c93769555b674f5bc1e9e456dcaf67347cf5af439117
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.