Instructions to use UsefulSensors/moonshine-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UsefulSensors/moonshine-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="UsefulSensors/moonshine-base")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("UsefulSensors/moonshine-base") model = AutoModelForMultimodalLM.from_pretrained("UsefulSensors/moonshine-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1045965d8f030413d4f761e17fe5868f0929f9a2ddd217de14acb3c7f154ca1c
- Size of remote file:
- 246 MB
- SHA256:
- e020c79d0a979a7ec099f718ff1cd2f19e92aead230d69654bca5975a8e1b862
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