dawg/models

Machine learning models for use in Vusic

ML Models for Vusic

Introduction

This repo is used to develop the machine learning models used for automated track Separation and music Transcription in Vusic. The track separation modell will use deep learning and blind source separation techniques to automate the process of separating vocals from their accompaniment. The second machine learning model will be used to automate the process of converting piano track to MIDI files that users can apply effects to or convert to sheet music.

Tasks

Here is an incomplete list of the major tasks that need to be completed. 🏃 indicates active development.

• Load and preprocess MUSDB18 dataset.
• Implement an STFT algorithm thats optimized for PyTorch.
• Create spectograms from the STFTs.
• Load and preprocess MAPS dataset
• Implement RNN Encoder/Decoder for Track Seperation
• Implement Denoiser for Track Seperation
• Implement the BiLTSM for Onset and frame detection (Music Transcription)
• Implement Kelz CNN
• Test and evaluate transcription model.
• Integrate the machine learning models in Vusic

Contributing

Prerequisites

1. Install the dependencies using the following command:

   pipenv install
   

2. Run the following setup script in the project root dirctory to fetch the packages:

   python setup.py install
   

Environment

1. Setup your environment using the following script:

   ./environment.sh
   

2. Install the requirements using the following script:

   ./requirements.sh
   

Jupyter Notebook instructions

1. Install all dependencies and enter the pipenv shell

   pipenv install
   pipenv shell
   

2. Create a kernel for your virtual environment

   python -m ipykernel install --user --name=my-virtualenv-name
   

3. Launch the jupyter notebook from your pipenv shell

   jupyter notebook
   

4. Change the notebook kernel to your virtual environment's kernel

Formatting

Follow these instructions to set up black in PyCharm.