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
MLX verisons of the models
#1
by fuzzbin - opened
Hello!
First, thank you for providing these models. We ar testing different use cases for local transcription.
So to the question: Does anyone know how to convert this model so it can be used with MLX?
Hi,
Glad to read you are finding use cases for it!
Unfortunately, it's not very straightforward. First, you need to convert the HF model to a PyTorch .ptmodel file. We've used this convert_hf_whisper() function with some success in the past. And once you have the .ptfile, you might be abe to convert it to MLX by following the mlx-examples convert.py script, but it hasn't been updated in a while, so I'm not sure if it will work.
Good luck :)