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 pytorch_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/pytorch_model.bin
- Command line
-
hf download hf://NbAiLab/nb-whisper-medium/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/NbAiLab/nb-whisper-medium/resolve/main/pytorch_model.bin
3.06 GB
- Xet hash:
- 3f03d3f4bde4c3e61fca7afc1268ec1ca8b5ed9e48515a7c96918f10a1908df5
- Size of remote file:
- 3.06 GB
- SHA256:
- b339e8050c288da93c36c93769555b674f5bc1e9e456dcaf67347cf5af439117
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