Metadata-Version: 2.1
Name: DCASE-models
Version: 0.2.0rc0
Summary: Python library for rapid prototyping of environmental sound analysis systems
Home-page: https://github.com/pzinemanas/DCASE-models
Author: Pablo Zinemanas
Author-email: pablo.zinemanas@upf.edu
License: UNKNOWN
Download-URL: http://github.com/pzinemanas/DCASE-models/releases
Description: <pre>
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         |  _ \ / ___|  / \  / ___|| ____|     _ __ ___   ___   __| | ___| |___ 
         | | | | |     / _ \ \___ \|  _| _____| '_ ` _ \ / _ \ / _` |/ _ \ / __|
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         |____/ \____/_/   \_\____/|_____|    |_| |_| |_|\___/ \__,_|\___|_|___/
                                                                               
        </pre>
        
        [![PyPI](https://img.shields.io/pypi/v/DCASE-models)](https://pypi.org/project/DCASE-models/)
        [![GitHub license](https://img.shields.io/github/license/pzinemanas/DCASE-models)](https://github.com/pzinemanas/DCASE-models/blob/master/LICENSE)
        
        
        `DCASE-models` is an open-source Python library for rapid prototyping of environmental sound analysis systems, with an emphasis on deep–learning models. The library has a flat and light design that allows easy extension and integration with other existing tools. 
        
        Documentation
        -------------
        See [https://dcase-models.readthedocs.io](https://dcase-models.readthedocs.io/en/latest/) for a complete reference manual and introductory tutorials.
        
        ## Installation instructions
        We recommend to install DCASE-models in a dedicated virtual environment. For instance, using [anaconda](https://www.anaconda.com/):
        ```
        conda create -n dcase python=3.6
        conda activate dcase
        ```
        For GPU support:
        ```
        conda install cudatoolkit cudnn
        ```
        DCASE-models uses [SoX](http://sox.sourceforge.net/) for functions related to the datasets. You can install it in your conda environment by:
        ```
        conda install -c conda-forge sox
        ```
        Before installing the library, you must install only one of the Tensorflow variants: CPU-only or GPU.
        ``` 
        pip install "tensorflow<1.14" # for CPU-only version
        pip install "tensorflow-gpu<1.14" # for GPU version
        ```
        
        Then to install the package:
        ```
        pip install DCASE-models
        ```
        To include visualization related dependencies, run the following instead:
        ```
        pip install DCASE-models[visualization]
        ```
        
        ## Usage
        There are several ways to use this library. In this repository, we accompany the library with three types of examples.
        
        > Note that the default parameters for each model, dataset and feature representation, are stored in [`parameters.json`](parameters.json) on the root directory.
        
        ### Python scripts
        The folder [`scripts`](scripts) includes python scripts for data downloading, feature extraction, model training and testing, and fine-tuning. These examples show how to use DCASE-models within a python script.
        
        ### Jupyter notebooks
        The folder [`notebooks`](notebooks) includes a list of notebooks that replicate scientific experiments using DCASE-models.
        
        ### Web applications
        The folder [`visualization`](visualization) includes a user interface to define, train and visualize the models defined in this library.
        
        Go to DCASE-models folder and run:
        ```
        python -m visualization.index
        ```
        Then, open your browser and navigate to:
        ```
        http://localhost:8050/
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: docs
Provides-Extra: tests
Provides-Extra: visualization
