README.md

---
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

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

### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses 
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### Using pivaenist in colab

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

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]

[More Information Needed]

### Downstream Use [optional]

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[More Information Needed]

### Out-of-Scope Use

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## Bias, Risks, and Limitations

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[More Information Needed]

### Recommendations

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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:

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

<!-- REQUIREMENTS PATH IS WRONG -->

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](https://magenta.tensorflow.org/datasets/maestro){: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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[More Information Needed]

### Training Procedure 

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### 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

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **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]

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**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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