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 pytorch_model.bin from NbAiLab/nb-whisper-base: direct link, hf CLI and curl.
- Browser
- Download file 290 MB
-
https://huggingface.co/NbAiLab/nb-whisper-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://NbAiLab/nb-whisper-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/NbAiLab/nb-whisper-base/resolve/main/pytorch_model.bin
290 MB
- Xet hash:
- 163456162ac466c5828abf6ae380e40cac1d90c6b9c8d3fe4549d7804c9c1924
- Size of remote file:
- 290 MB
- SHA256:
- 3a3383d53a63f48c5e53231c8aac583366fbfc829a581f7da710f1ce3e398778
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