metadata

license: mit
language:
  - en
tags:
  - music
  - autoencoder
  - variational autoencoder
  - music generation

Pivaenist

Pivaenist is a two-minute, random piano music generator with a VAE architecture.

By the use of the aforementioned autoencoder, it allows the user to encode piano music pieces and to generate new ones.

Model Details

Model Description

Developed by: TomRB22
Model type: Variational autoencoder
License: MIT

Sources

Code: Some of the code of this repository includes modifications (not the entire code, due to the differences in the architecture) from the following sites:

There might be acknowledgments missing. If you find some other resemblance to a site's code, please notify me and I will make sure of including it.

Uses

Using pivaenist in colab

If you preferred directly using or testing the model without the need to install it, you can use the following colab notebook and follow its instructions. Moreover, this serves as an example of use. [colab link]

[More Information Needed]

Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

Installation

To install the model, you will need to change your working directory to the desired installation location and execute the following commands:

Windows

git clone https://huggingface.co/TomRB22/pivaenist
sudo apt install -y fluidsynth
pip install -r ./pivaenist/requirements.txt

Mac

git clone https://huggingface.co/TomRB22/pivaenist
brew install fluidsynth
pip install -r ./pivaenist/requirements.txt

The first one will clone the repository. Then, fluidsynth, a real-time MIDI synthesizer, is also set up in order to be used by the pretty-midi library. With the last line, you will make sure to have all dependencies on your system.

[More Information Needed]

Training Details

Pivaenist was trained on the MAESTRO v2.0.0 dataset, which contains 1282 midi files [check it in colab]. Their preprocessing involves splitting each note in pitch, duration and step, which compose a column of a 3xN matrix (which we call song map), where N is the number of notes and a row represents sequentially the different pitches, durations and steps. The VAE's objective is to reconstruct these matrices, making it then possible to generate random maps by sampling from the distribution, and then convert them to a MIDI file.

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

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Training Hyperparameters

Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: [More Information Needed]
Hours used: [More Information Needed]
Cloud Provider: [More Information Needed]
Compute Region: [More Information Needed]
Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

BibTeX:

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APA:

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Glossary [optional]

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More Information [optional]

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Model Card Authors [optional]

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Model Card Contact

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