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")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/nb-whisper-medium") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/nb-whisper-medium", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.bin from NbAiLab/nb-whisper-medium: direct link, hf CLI and curl.
- Browser
- Download file 3.06 GB
-
https://huggingface.co/NbAiLab/nb-whisper-medium/resolve/main/model.bin
- Command line
-
hf download hf://NbAiLab/nb-whisper-medium/model.bin
-
curl -L -o model.bin https://huggingface.co/NbAiLab/nb-whisper-medium/resolve/main/model.bin
3.06 GB
- Xet hash:
- ea0d5cbbcd3ccd3a2f23d60f3a3ea0fbcf9ef8a736e4b650f0e025c54a98ba3d
- Size of remote file:
- 3.06 GB
- SHA256:
- 1721dd1dc26d7a543280aaedbc8ab2832578cef4bf834d4fdd838393f36d278a
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