Instructions to use kerasformers/moonshine_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasFormers
How to use kerasformers/moonshine_base with KerasFormers:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Keras
How to use kerasformers/moonshine_base with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://kerasformers/moonshine_base") - Notebooks
- Google Colab
- Kaggle
metadata
pipeline_tag: automatic-speech-recognition
license: mit
library_name: kerasformers
tags:
- keras
- kerasformers
- moonshine
- tf
- jax
- pytorch
moonshine_base (Keras 3)
Pure-Keras 3 weights for kerasformers, mirrored from the GitHub release. License: mit.
from kerasformers.models.moonshine import MoonshineSpeechToText, MoonshineProcessor
model = MoonshineSpeechToText.from_weights("moonshine_base")
processor = MoonshineProcessor.from_weights("moonshine_base")