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) or implementations from the following sites:
- TensorFlow. (n.d.). Generate music with an RNN | TensorFlow Core - Tensorflow tutorial where pretty-midi is used
- Han, X. (2020, September 1). VAE with TensorFlow: 6 Ways - VAE explanation and code
- Li, C. (2019, April 15). Less pain, more gain: A simple method for VAE training with less of that KL-vanishing agony. Microsoft Research. - Microsoft article on the KL training schedule which was applied in this model
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
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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
[TODO: SONG MAP IMAGE]
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).
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- Hours used: [More Information Needed]
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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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Model Card Authors [optional]
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Model Card Contact
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