https://github.com/mtg/dcase-models
Python library for rapid prototyping of environmental sound analysis systems
https://github.com/mtg/dcase-models
audio-classification audio-tagging deep-learning python sound-event-detection
Last synced: 9 months ago
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Python library for rapid prototyping of environmental sound analysis systems
- Host: GitHub
- URL: https://github.com/mtg/dcase-models
- Owner: MTG
- License: mit
- Created: 2020-04-22T15:23:57.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2022-05-20T11:31:46.000Z (about 4 years ago)
- Last Synced: 2025-06-17T22:18:14.759Z (about 1 year ago)
- Topics: audio-classification, audio-tagging, deep-learning, python, sound-event-detection
- Language: Jupyter Notebook
- Homepage:
- Size: 133 MB
- Stars: 43
- Watchers: 5
- Forks: 5
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
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[](https://codecov.io/gh/MTG/DCASE-models)
[](https://pypi.org/project/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
```
When installing the library, you must select the tensorflow variant: version 1 (CPU-only or GPU) or version 2.
```
pip install DCASE-models[keras_tf] # for tensorflow 1 CPU-only version
pip install DCASE-models[keras_tf_gpu] # for tensorflow 1 GPU version
pip install DCASE-models[tf2] # for tensorflow 2
```
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/
```