Instructions to use keras/whisper_base_multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/whisper_base_multi with KerasHub:
import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://keras/whisper_base_multi") - Keras
How to use keras/whisper_base_multi with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://keras/whisper_base_multi") - Notebooks
- Google Colab
- Kaggle
| { | |
| "module": "keras_hub.src.models.whisper.whisper_audio_converter", | |
| "class_name": "WhisperAudioConverter", | |
| "config": { | |
| "name": "whisper_audio_converter", | |
| "trainable": true, | |
| "dtype": { | |
| "module": "keras", | |
| "class_name": "DTypePolicy", | |
| "config": { | |
| "name": "float32" | |
| }, | |
| "registered_name": null | |
| }, | |
| "num_mels": 80, | |
| "num_fft_bins": 400, | |
| "stride": 160, | |
| "sampling_rate": 16000, | |
| "max_audio_length": 30 | |
| }, | |
| "registered_name": "keras_hub>WhisperAudioConverter" | |
| } |