sopila-transcriptor

Automated music processing of traditional Croatian instrument - sopila

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── real_data      <- Data for transciption of real music pieces.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│   └── sheets         <- Generated PDF music sheet predictions
│   └── statistics     <- Accuracy, precision, recall and F1 score for training, validation and test data.
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
|   |   ├── make_alternate_data.py
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   ├── create_processed_data <- Creates processed data for all models
│   │   └── alternate_data_create.py <- Creates processed real music recording for model prediction
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
|   |   ├── rf         <- Random forest train and predict scripts
│   │   └── cnn        <- Convolitional Neural Network train and predict scripts
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── make_sheets.py
│
├── tox.ini            <- tox file with settings for running tox; see tox.testrun.org
└── settings.py        <- Project specific settings

Environment instructions:

1. conda env create -f sopela_env.yml Different env for using visualization/make_sheets.py due to python version incompatibility
2. conda env create -f make_sheets_env.yml
3. conda activate sopela_env
4. pip install -r requirements.txt

   Project based on the cookiecutter data science project template. #cookiecutterdatascience