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@@ -20,6 +20,9 @@ the music samples, both in the time domain and in the latent space. Using beat-s
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  encourages the model to interpolate between the training samples, but stay within the domain of the training data. The
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  result is generated music that is more diverse while staying faithful to the corresponding style.
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  ## Model Sources
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  - [**🧨 Diffusers Pipeline**](https://huggingface.co/docs/diffusers/api/pipelines/musicldm)
 
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  encourages the model to interpolate between the training samples, but stay within the domain of the training data. The
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  result is generated music that is more diverse while staying faithful to the corresponding style.
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+ This work is licensed under a
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+ [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-nc-sa/4.0/).
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+
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  ## Model Sources
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  - [**🧨 Diffusers Pipeline**](https://huggingface.co/docs/diffusers/api/pipelines/musicldm)