Projet SAM

Our project focuses on multimodal approaches for predicting turn-taking changes in natural conversations. The goals of this project enable us to introduce various concepts of textual, visual, and auditory modality, as well as to compare and explore different multimodal processing models and their fusion.

• Projet SAM
• Requirements
• Launch the code
  • Mode
  • Configuration
  • Help
  • Example
• Models
  • Unimodal Models
    • TEXT
    • AUDIO
    • VIDEO
  • Multimodal Models
    • LATE FUSION
    • EARLY FUSION
• Results

Requirements

To run the code you need python (We use python 3.9.13) and packages that is indicate in requirements.txt. You can run the following code to install all packages in the correct versions:

pip install -r requirements.txt

Launch the code

The main.py script is the main entry point for this project. It accepts several command-line arguments to control its behavior:

• --mode or -m: This option allows you to choose a mode between 'train', 'test' and 'latefusion'.
• --config_path or -c: This option allows you to specify the path to the configuration file. The default is config/config.yaml. Use only for the training.
• --path or -p: This option allows you to specify the experiment path for testing.
• --task or -t: This option allows you to specify the task for the model. This will overwrite the task specified in the configuration file for training. It's can be text, audio, video, or multi, that will train a new experiments with this type of data. The task multi will be use the unimodal models that is indicate in the parameter 'load' in the configuration file.

Mode

Here's what each mode does:

• train: Trains a model using the configuration specified in the --config_path and the task specified in --task.
• test: Tests the model specified in the --path. You must specify a path.
• latefusion: test a late fusion with models which is in load in the configuration file. There's no need to train this model, as it has no learnable parameters.

Configuration

The configuration of the model and the training process is done through a YAML file. You can specify the path to this file with the --config_path option. The default path is config/config.yaml.

The configuration file includes various parameters such as the learning rate, batch size, number of epochs, etc.

Help

To get a list of all available options, you can use the -h or --help option:

python main.py --help

This will display a help message with a description of all available options.

Example

Here's an example of how to use the script to train a model:

python main.py --mode train --config_path config/config.yaml --task text

This command will train a model using the configuration specified in config/config.yaml with a task=text.

Here's an example of how to run a test on the experiment separete:

python main.py --mode test --path logs/multi_4

Models

Unimodal Models

TEXT

AUDIO

VIDEO

Multimodal Models

LATE FUSION

EARLY FUSION

Results

Model test results. The LATE and EARLY FUSION do not use the VIDEO model.

Models	Accuracy	Precision	Recall	$f_1$ score
TEXT	82.8	41.3	50.0	45.3
AUDIO	47.1	48.5	47.4	41.5
VIDEO	82.9	41.4	50.0	45.2
LATE FUSION	78.5	50.6	50.1	48.8
EARLY FUSION	82.9	43.6	50.2	45.7