cantable-diffuguesion

Maintainer: andreasjansson

Total Score

5

Last updated 5/17/2024
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Model overview

Cantable Diffuguesion is an AI model developed by andreasjansson that can generate and harmonize Bach chorales. It is a diffusion model trained on a dataset of four-part Bach chorales, similar to models like Riffusion and DeepBach. The model can be used to generate new Bach-style chorales unconditionally, or to harmonize melodies or parts of melodies provided as input.

Model inputs and outputs

The Cantable Diffuguesion model takes in a few key inputs to control the generation process:

Inputs

  • Seed: A random seed value to control the randomness of the generated output. Setting this to a specific value allows for reproducible results.
  • Tempo: The tempo of the generated chorale in quarter notes per minute.
  • Melody: A melody in "tinyNotation" format, which allows specifying sections to be inpainted by the model.
  • Duration: The total duration of the generated chorale in quarter notes.
  • Return mp3 and Return midi: Flags to control whether the model should return the generated chorale as an mp3 audio file or a midi file.

Outputs

  • Mp3: If requested, the generated chorale as an mp3 audio file.
  • Midi: If requested, the generated chorale as a midi file.

Capabilities

The Cantable Diffuguesion model is capable of generating high-quality, Bach-style four-part chorales both unconditionally and by harmonizing user-provided melodies. The model leverages diffusion techniques similar to those used in Stable Diffusion, but applied to the domain of music generation and harmonization.

What can I use it for?

The Cantable Diffuguesion model could be used for a variety of applications, such as:

  • Generating new Bach-style chorales for use in musical compositions, performances, or educational materials.
  • Harmonizing melodies or parts of melodies to create full four-part chorale arrangements.
  • Experimenting with different musical styles and techniques by adjusting the model inputs like tempo or melody.
  • Integrating the model into larger music generation or composition systems.

Things to try

Some interesting things to try with the Cantable Diffuguesion model include:

  • Experimenting with different melodic inputs, including both familiar and novel melodies, to see how the model harmonizes them.
  • Trying the model's inpainting capabilities by providing partially completed melodies and letting the model fill in the missing sections.
  • Investigating the model's ability to capture the stylistic nuances of Bach's chorale writing by generating multiple samples and analyzing their musical characteristics.
  • Exploring ways to combine the Cantable Diffuguesion model with other music-related AI models, such as those for music transcription, analysis, or generation, to create more sophisticated musical applications.


This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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