Name:
Description: Rhythm generator using Variational Autoencoder (VAE). Based on M4L.RhythmVAE by Nao Tokui, modded and extended to support simple and compound meter rhythms, with minimal amount of training data. Similarly to RhythmVAE, the goal of R-VAE is the exploration of latent spaces of musical rhythms. Unlike most previous work in rhythm modeling, R-VAE can be trained with small datasets, enabling rapid customization and exploration by individual users. R-VAE employs a data representation that encodes simple and compound meter rhythms. Models and latent space visualizations for R-VAE are available on the project's GitHub page: https://github.com/vigliensoni/R-VAE-models.
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Website:
Input types: Audio MIDI Text None Genre Metadata Image
Output types: Audio MIDI
Output length:
Technology: Not Specified Latent Consistency Model Latent Diffusion LSTM VAE Sequence-to-sequence neural network Transformer Suite of AI tools Diffusion Hierarchical Recurrent Neural Network (RNN) Autoregressive Convolutional Neural Network
Dataset:
License type:
Has real time inference: Yes No Not known
Is free: Yes No Yes and No, depending on the plan Not known
Is open source: Yes No Not known
Are checkpoints available: Yes No Not known
Can finetune: Yes No Not known
Can train from scratch: Yes No Not known
Tags: text-to-audio MIDI text-prompt small-dataset open-source low-resource free checkpoints proprietary no-input image-to-audio
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