SER_project_ML_Approach

This repo consists of :

• SER_classification_task.ipynb Jupyter notebook which is a Speech Emotion Recognition (SER) study on the Crema-D dataset.

  The data can be obtained from the following Kaggle link : https://www.kaggle.com/ejlok1/cremad

  In order to view the plotly plots of the ipynb file you may use "Jupyter nbviewer" : https://nbviewer.org/

• report.pdf which is a detailed report of the sections described in the ipynb file

• presentation.pdf which is a short summary of the study's main conclusions

• demo folder contains code to test new speech samples. The new sample must belong in one of the following classes : "happy", "neutral", "sad", "angry".

  The folder contains the following files :

  - demo.py : code to test new samples
  - model.pickle : contains the learned model and the audio features to choose from the test sample
  - scaler.pickle : contains the learned standard scaler
  - "test_sample" folder : folder to put the wav file to test
  

  In order to test a new sample you may insert the wav file in the "test_sample" folder and then run the "demo.py" file.

• requirements.txt

• pickles folder which contains all pickle files generated after running the ipynb file. They are included in the repo for saving purposes.