Instructions to use NbAiLab/salmon-whisper-medium-smj with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/salmon-whisper-medium-smj with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/salmon-whisper-medium-smj")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLab/salmon-whisper-medium-smj") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLab/salmon-whisper-medium-smj") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b29665d600ccc4157428aa925ca1913a53a099d8ac436dc0a37257786e341ff1
- Size of remote file:
- 3.06 GB
- SHA256:
- cea46eaf9284b7198eef5282e059c5864b22b9742a8b0d9df5f58709a59d60a0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.