MSP-Podcast Emotion Challenge - Baselines Training and Evaluation

This repository contains scripts to train and evaluate baseline models on various tasks including categorical emotion, multi-task emotional attributes, and single-task emotional attributes for arousal, dominance, and valence.

Link to the baseline experiments: Baseline_Model.pdf

Link to the challenge: Odyssey 2024 - Emotion Recognition Challenge

Link to the submission website

Refer to the links above to sign-up for the challenge, rules, submission deadlines, and file formatting instructions.

Environment Setup

Python version = 3.9.7

To replicate the environment necessary to run the code, you have two options:

Using Conda

Ensure that you have conda installed.
Create a conda environment using the spec-file.txt by running: conda create --name baseline_env --file spec-file.txt
Activate the environment: conda activate baseline_env
Make sure to install the transformers library as it is essential for the code to run: pip install transformers

Using pip

Alternatively, you can use requirements.txt to install the necessary packages in a virtual environment: python -m venv myenv source myenv/bin/activate pip install -r requirements.txt
Make sure to install the transformers library as it is essential for the code to run: pip install transformers

Configuration

Before running the training or evaluation scripts, check intructions below and update the config.json file with the paths to your local audio folder and label CSV file.

Categorical Emotion Recognition Model

Before running training or evaluation of categorical emotion recognition model. Please execute the script process_labels_for_categorical.py to properly format the provided labels_consensus.csv file for categorical emotion recognition. Then place the path of the processed .csv file in the config.json file to run this configuration.

Attributes Emotion Recognition Model

The original labels_consensus.csv file provided with the dataset can be used as-is for attributes emotion recognition. Please place the path to the labels_consensu.csv file in the config.json file to run this configuration.

Training and Evaluation

To train or evaluate the models, use the provided shell scripts. Here's how to use each script:

bash run_cat.sh: Trains or evaluates the categorical emotion recognition baseline.
bash run_dim.sh: Trains or evaluates the multi-task emotional attributes baseline.
bash run_arousal.sh: Trains or evaluates the single-task emotional attribute baseline for arousal.
bash run_dominance.sh: Trains or evaluates the single-task emotional attribute baseline for dominance.
bash run_valence.sh: Trains or evaluates the single-task emotional attribute baseline for valence.

Models

The models are to be saved in the model folder. If you are evaluating the pretrained models, please download the models using the script provided in model folder.

$ bash download_models.sh <categorical|arousal|dominance|valence|multitask|all>

Example 1: bash download_models.sh all to download all the models.
Example 2: bash download_models.sh arousal valence to download arousal and valence models.

If you wish to manually download a pre-trained model. Please visit this website and download the desired model and place them in the model folder.

Pre-trained models file descriptions:

"weight_cat_ser.zip" --> Categorical emotion recognition baseline.
"dim_aro_ser.zip" --> Arousal single-task emotional attribute baseline.
"dim_dom_ser.zip" --> Dominance single-task emotional attribute baseline.
"dim_val_ser.zip" --> Valence single-task emotional attribute baseline.
"dim_ser.zip" --> Multi-task emotional attributes baseline.

Evaluation Only

If you are only evaluating a model and do not wish to train it, comment out the lines related to train_****_.py in the respective .sh file.

Evaluation and saving results for emotional attributes prediction

A custom executable sample file for evaluation and results saving has been provided eval_dim_ser_test3.py. To execute, just download or train the multi-task emotional attributes baseline as a sample. Then execute bash run_dim.sh (NOTE: if you will not be training the entire model again. Please, comment out the training lines in run_dim.sh before evaluation). The saved results will be saved in the correct .csv format and it will be located in a results folder created inside your model path location.

Issues

If you encounter any issues while setting up, training, or evaluating the models, please open an issue on this repository with a detailed description of the problem.

To cite this repository in your works, use the following BibTeX entry:

@misc{SER_Challenge_2024,
  author = {Goncalves, Lucas and Salman, Ali and Reddy, Abinay and Velazquez, Laureano Moro and Thebaud, Thomas and Garcia, Leibny Paola and Dehak, Najim and Sisman, Berrak and Busso, Carlos},
  title = {Odyssey 2024 - Emotion Recognition Challenge},
  year = {2024},
  publisher = {GitHub},
  journal = {MSP-Podcast Challenge},
  howpublished = {\url{https://github.com/MSP-UTD/MSP-Podcast_Challenge}},
}

dariadiatlova/MSP-Podcast_Challenge