Instructions to use declare-lab/mustango-pretrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use declare-lab/mustango-pretrained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="declare-lab/mustango-pretrained")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("declare-lab/mustango-pretrained", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ebbfcad95c881c782bc226dd5977e6b0d58b1a6a1807e41aa786cac409688b26
- Size of remote file:
- 8.54 MB
- SHA256:
- 8674a4cc9755fafa48350bfa3412cf9b9a0d357d18289dbfd86f0fb34e1ca4db
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