This project aims at creating a DL system for Parkinson's recognition from speech. It leverages self-supervised learning models to transfer knowledge acquired by foundational models to the task of Parkinson's recognition. The project is based on the PC-GITA dataset and the proposed model is evaluated using 10-fold cross-validation.
Table of Contents
The project is based on Python 3.11 and PyTorch 2. The following command can be used to install the dependencies:
pip install -r requirements.txtBy default, the project leverages comet_ml for logging. To use it, you need to create an account on comet.ml. Then, you need to set the following environment variables:
export COMET_API_KEY=<your_api_key>export COMET_WORKSPACE=<your_project_name>Disable logging:
To disable the logging, you can set the training.use_comet key to false in the configs/*.yaml files.
To make the results reproducible and comparable with the ones reported in the paper we make available the data splits used for 10-fold cross-validation. The splits are available in the pcgita_splits folder. The data is organized as follows:
pcgita_splits/
├── TRAIN_TEST_1
│ ├── test.csv
│ └── train.csv
├── TRAIN_TEST_2
│ ├── test.csv
│ └── train.csv
├── ...
└── TRAIN_TEST_10
├── test.csv
└── train.csv
The train.csv and test.csv files contain the list of the audio files used for training and testing, respectively. The path to the audio files is stored in the following format:
/PC_GITA_ROOT_PATH/monologue/sin_normalizar/pd/AVPEPUDEA0042-Monologo-NR.wav
where PC_GITA_ROOT_PATH is the root path to the PC-GITA dataset. We also provide a python script set_root_path.py to set the root path to the PC-GITA dataset. The script can be used as follows:
python set_root_path.py --old_root_path <old_root_path> --new_root_path <new_root_path>where <old_root_path> can be set to PC_GITA_ROOT_PATH and <new_root_path> is the new root path to your instance of the PC-GITA dataset.
Note: The splits are generated to ensure that the same speaker does not appear in both the training and testing sets and the classes are balanced across the splits.
To train the proposed model it is first needed to install the requirements and set the root path to the PC-GITA dataset. Then, the following command can be used to train the model:
python train.py --config <config_file>where <config_file> is the path to the configuration file (e.g., configs/W_config.yaml). There are several configuration parameter that can be set in the configuration file. The one reported in the paper is configs/W_config.yaml.
The training file creates 10 models, one for each fold, and compute the metrics for each fold. The metrics are reported on terminal and logged on comet.ml (if activated). Please be sure to update the following path in the configuration file:
training:
checkpoint_path: <path_to_save_checkpoints>data:
fold_root_path: <path_to_pcgita_splits>where <path_to_save_checkpoints> is the path where the checkpoints will be saved and <path_to_pcgita_splits> is the path to the pcgita_splits folder.
Pre-trained models are available on the Hugging Face model hub (SSL4PR WavLM Base, SSL4PR HuBERT Base). To use them, please clone the repository running the following command:
# SSL4PR WavLM Base
git clone https://huggingface.co/morenolq/SSL4PR-wavlm-base
# SSL4PR HuBERT Base
git clone https://huggingface.co/morenolq/SSL4PR-hubert-baseEnsure you have git lfs installed. Each repository contains the pre-trained models, one per fold, named fold_1.pt, fold_2.pt, ..., fold_10.pt.
The paper presents the results of the proposed model on an extended dataset. The extended dataset is available in the extended_dataset folder. The script infer_extended.py can be used to infer the model on the extended dataset. The script can be used as follows:
python infer_extended.py --config <config_file> --training.ext_model_path <path_to_model> --ext_root_path <path_to_extended_dataset>where <config_file> is the path to the configuration file (e.g., configs/W_config.yaml), <path_to_model> is the path to the model checkpoint and <path_to_extended_dataset> is the path to the extended_dataset folder.
Speech Enhancement: The proposed model can be used on the extended dataset in combination with speech enhancement preprocessing. To preprocess data following the same process as in the paper, the speech_enhancement folder contains the instructions to apply, VAD, dereverberation and noise reduction to the audio files.
If you use this code, results from this project or you want to refer to the paper, please cite the following paper:
@inproceedings{laquatra24_interspeech,
title = {{Exploiting Foundation Models and Speech Enhancement for Parkinson's Disease Detection from Speech in Real-World Operative Conditions}},
author = {Moreno {La Quatra} and Maria Francesca Turco and Torbjørn Svendsen and Giampiero Salvi and Juan Rafael Orozco-Arroyave and Sabato Marco Siniscalchi},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {1405--1409},
doi = {10.21437/Interspeech.2024-522},
issn = {2958-1796},
}