Metadata-Version: 2.1
Name: torchsynth
Version: 0.0.1
Summary: A modular synthesizer in pytorch, GPU-optional and differentiable
Home-page: UNKNOWN
Author: 
Author-email: 
License: Apache-2.0
Description: # torchsynth
        
        {\tt torchsynth} is based upon traditional modular synthesis, but is GPU-enabled and is differentiable.
        
        [![codecov.io](https://codecov.io/gh/turian/torchsynth/branch/main/graphs/badge.svg?logoWidth=18)](https://codecov.io/github/turian/torchsynth?branch=master)
        [![Total alerts](https://img.shields.io/lgtm/alerts/g/turian/torchsynth.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/turian/torchsynth/alerts/)
        
        ## Development Installation
        
        ```
        git clone https://github.com/turian/torchsynth
        cd torchsynth
        pip install -e .
        ```
        
        Note that torchsynth requires PyTorch version 1.7 or greater.
        
        ### Examples
        
        We recommend that you run examples through Jupyter notebooks, and
        that you have
        [jupytext](https://towardsdatascience.com/introducing-jupytext-9234fdff6c57)
        installed. It's a little fiddly to install, and those instructions
        are the best. jupytext makes it easy to put demo notebooks into
        the repo as Python files. (Larger assets like ipynb files we should
        avoid.)
        
        To run examples, you should also do:
        ```
        pip install -e ".[dev]"
        ```
        
        Unfortunately, Python 3.9 (e.g. OSX Big Sur) won't work, because
        librosa repends upon numba which isn't packaged for 3.9 yet. In
        which case you'll have to create a Python 3.7 conda environment.
        (You might also need to downgrade LLVM to 10 or 9.):
        ```
        conda install -c conda-forge ipython librosa matplotlib numpy matplotlib scipy jupytext
        conda install -c anaconda ipykernel
        python -m ipykernel install --user --name=envname
        ```
        and change the kernel to `envname`.
        
        ### Tests
        Unit testing is performed using `pytest`.
        
        `pytest` and other project development dependencies can be installed as follows: 
        ```
        pip install -e ".[dev]"
        ```
        
        To run tests, run `pytest` from the project root:
        ```
        pytest
        ```
        
        To run tests with a coverage report:
        ```
        pytest --cov=./src
        ```
        
        
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: dev
