maky-hnou / Urban_Sounds_Classification

Urban_Sounds_Classification

About this repo:

An Urban sound classifier built and trained using Tensorflow 2.

Content of the repo:

The project has been organized as follows:

• model/: the folder containing the trained model.
• requirements.txt: a text file containing the needed packages to run the repo.
• main.py: the script used to launch the training or testing.
• prepocessing.py: the code used to extract the spectrogram from the audio files and save them to npy file.
• test_model.py: the code used to load the saved model and classify the audio files.
• train_model.py: the code used to build the model and train it with the training data.

How to run:

N.B: use Python 3.8

1. Clone the repo:
on your terminal, run git clone https://github.com/maky-hnou/Urban_Sounds_Classification.git
Then get into the project folder: cd Urban_Sounds_Classification/
We need to install some dependencies:
sudo apt install python3-pip libpq-dev python3-dev

2. Install requirements:
Before running the app, we need to install some packages.

• Optional Create a virtual environment: To do things in a clean way, let's create a virtual environment to keep things isolated.
  Install the virtual environment wrapper: pip3 install virtualenvwrapper
  Add the following lines to ~/.bashrc:

export WORKON_HOME=$HOME/.virtualenvs
export PROJECT_HOME=$HOME
export VIRTUALENVWRAPPER_PYTHON=/usr/bin/python3
export VIRTUALENVWRAPPER_VIRTUALENV=~/.local/bin/virtualenv
source ~/.local/bin/virtualenvwrapper.sh

Run source ~/.bashrc
Run mkvirtualenv sound_classfier
Activate the virtual environment: workon sound_classfier (To deactivate the virtual environment, run deactivate)

• Install requirements: To install the packages needed to run the application, run pip3 install -r requirements.txt

N.B: If you don't have GPU, or don't have Cuda and Cudnn installed, replace tensorflow-gpu by tensorflow in requirements.txt.

3- Download the dataset:
The dataset is available on Google Drive.
Download the train and test files then extract them in the repository.

4- Run the training or testing:
To run the training or the test the pre-trained model, run:

python3 main.py --mode <train/test>

Then follow the steps; you'll be asked to give the path to files and folders based on the chosen mode.