A GAN that uses style transfer to generate harsh noise music.

Indraji Ushantha Wanigaratne f6fbac8061 Update 'README.md' 4 weeks ago
.dvc dfaae6d237 Initial commit 1 month ago
notebooks dfaae6d237 Initial commit 1 month ago
references dfaae6d237 Initial commit 1 month ago
reports dfaae6d237 Initial commit 1 month ago
src dfaae6d237 Initial commit 1 month ago
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Makefile dfaae6d237 Initial commit 1 month ago
README.md f6fbac8061 Update 'README.md' 4 weeks ago
eval.dvc dfaae6d237 Initial commit 1 month ago
process_data.dvc dfaae6d237 Initial commit 1 month ago
raw_data.dvc dfaae6d237 Initial commit 1 month ago
requirements.txt dfaae6d237 Initial commit 1 month ago
setup.py dfaae6d237 Initial commit 1 month ago
tox.ini dfaae6d237 Initial commit 1 month ago
train.dvc dfaae6d237 Initial commit 1 month ago

Data Pipeline

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README.md

rave-against-the-machine

A GAN that uses style transfer to generate harsh noise music.

Instructions

  1. Clone the repo.
  2. Run make dirs to create the missing parts of the directory structure described below.
  3. Optional: Run make virtualenv to create a python virtual environment. Skip if using conda or some other env manager.
    1. Run source env/bin/activate to activate the virtualenv.
  4. Run make requirements to install required python packages.
  5. Put the raw data in data/raw.
  6. To save the raw data to the DVC cache, run dvc commit raw_data.dvc
  7. Edit the code files to your heart's desire.
  8. Process your data, train and evaluate your model using dvc repro eval.dvc or make reproduce
  9. When you're happy with the result, commit files (including .dvc files) to git.

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make dirs` or `make clean`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── eval.dvc           <- The end of the data pipeline - evaluates the trained model on the test dataset.
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── process_data.dvc   <- Process the raw data and prepare it for training.
├── raw_data.dvc       <- Keeps the raw data versioned.
│
├── 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
│   └── metrics.txt    <- Relevant metrics after evaluating the model.
│   └── training_metrics.txt    <- Relevant metrics from training the model.
│
├── 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_dataset.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
├── tox.ini            <- tox file with settings for running tox; see tox.testrun.org
└── train.dvc          <- Traing a model on the processed data.

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