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
Model Sources [optional]
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Uses
Using pivaenist in colab
If you prefered 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]
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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 execute the following commands:
git clone https://huggingface.co/TomRB22/pivaenist
sudo apt install -y fluidsynth
pip install -r /content/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.
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Training Details
Pivaenist was trained on the MAESTRO v2.0.0 dataset{:target="_blank"}, 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
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
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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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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]
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