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https://github.com/atriantafyllopoulos/enrollment-personalization

https://github.com/atriantafyllopoulos/enrollment-personalization

Science Score: 23.0%

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    CITATION.cff file
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    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
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    DOI references
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    1 of 2 committers (50.0%) from academic institutions
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    Low similarity (8.7%) to scientific vocabulary
Last synced: 11 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: ATriantafyllopoulos
  • Language: Python
  • Default Branch: main
  • Size: 850 KB
Statistics
  • Stars: 2
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 2 years ago · Last pushed about 2 years ago
Metadata Files
Readme

README.md

Personalization using enrollment

This repository contains the code needed to reproduce the experiments in (Triantafyllopoulos and Schuller, 2024).

Method Overview

List of python files

  1. main.py: code used to start training, utilizing hydra configuration files in configs
  2. models.py: code to create models
  3. data.py: implementation of datasets
  4. evaluate.py: code to compute Gini index and CIs
  5. iswf.py: code to create ISWF plots
  6. predict_test_msp.py: code to evalute on MSP test set
  7. speaker_plot.py: code to plot speaker-level UAR (not used in paper)
  8. training.py: code implementing training

Usage

  1. Download MSP-Podcast and FAU-AIBO manually. To do this, you need an EULA with the dataset owners.
  2. Run main.py. You will be asked to provide the root for the data and the results-root to store your results.
  3. (Optional) Run evaluate.py and iswf.py to run the detailed evaluations of the paper.

Adaptation data

Adaptation CSVs are included under adaptation-sets for each dataset/task. Only filenames are included (need to request datasets from respective owners).

Reference

Triantafyllopoulos, A., Schuller, B., (2024), "Enrolment-based personalisation for improving individual-level fairness in speech emotion recognition," Proc. INTERSPEECH, Kos Island, Greece, (accepted).

@inproceedings{Triantafyllopoulos24-EPF author={Triantafyllopoulos, Andreas, and Schuller, Björn}, title={Enrolment-based personalisation for improving individual-level fairness in speech emotion recognition}, year={2024}, booktitle={Proc. INTERSPEECH}, address={Kos Island, Greece} }

Owner

  • Name: Andreas Triantafyllopoulos
  • Login: ATriantafyllopoulos
  • Kind: user

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Dependencies

requirements.txt pypi
  • audmetric ==1.1.6
  • audtorch ==0.6.4
  • hydra-core ==1.2.0
  • pandas ==1.5.2
  • seaborn ==0.12.2
  • torch ==1.13.0
  • transformers ==4.31.0