Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLab/nb-whisper-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-whisper-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/nb-whisper-base") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/nb-whisper-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download onnx/encoder_model.onnx from NbAiLab/nb-whisper-base: direct link, hf CLI and curl.
- Browser
- Download file 82.5 MB
-
https://huggingface.co/NbAiLab/nb-whisper-base/resolve/main/onnx/encoder_model.onnx
- Command line
-
hf download hf://NbAiLab/nb-whisper-base/onnx/encoder_model.onnx
-
curl -L -o encoder_model.onnx https://huggingface.co/NbAiLab/nb-whisper-base/resolve/main/onnx/encoder_model.onnx
82.5 MB
- Xet hash:
- fdd8bfa7c2abcb123c827510680ff9c83b6191be6f4346596177678a080af9e9
- Size of remote file:
- 82.5 MB
- SHA256:
- 7f9a82b47fd1b82a5262c148d82b025df0c1ae7e1213f7db96f413e498fe2976
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.