Instructions to use declare-lab/mustango with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use declare-lab/mustango with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="declare-lab/mustango")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("declare-lab/mustango", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e68749fdc5900e7d1d3b69a402cf7aef3023f6feeab6743ece3fe42444950ca3
- Size of remote file:
- 7.05 GB
- SHA256:
- 785214ae81f41860195db0acbf6fc5129aa6d09beb32c455c3ba44bd4028f185
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