Forked from EIHW/ComParE2023

ComParE23 - The Hume-Prosody Corpus (HP-C)

This repository provides the code for running my participation for The Hume-Prosody Corpus (HP-C) subchallenge of ComParE2023 (excluding feature extraction).

Getting the code

Clone this repository and checkout the correct branch:

git clone https://github.com/bagustris/ComParE2023

Adding the data

Drop the data into ./data (~40GB), creating this directory structure:

data
├── features
│  ├── audeep
│  ├── deepspectrum
│  ├── opensmile
│  └── wav2vec
├── lab
├── raw
│  └── wav
└── wav

You can make a soft link (like in this repo) if your data is located elsewhere (e.g., in /data/).

ln -sf /data/14_ComParE23_HPC_AIST-SPRT/data ./data

Creating Virtual Environments via Miniconda

Create a virtual environment with Python 3.9:

conda create -n ComParE2023 python=3.9

Install dependencies:
pip install -r requirements.txt

Run the experiments:
python3 src/ml/svm.py wav2vec

Calculate the results' score:
python3 src/ml/metrics.py wav2vec

Extracting features

To extract features from Hugging Face, you can use feat_extract.py with arguments name [output directory] and Hugging Face model name [e.g. facebook/wav2vec2-large-xlsr-53].

Format:
./feat_extract.py [output directory] [Hugging Face model name] [device]

Example:

./feat_extract.py xlsr-53 jonatasgrosman/wav2vec2-large-xlsr-53-english

You need to change permission (chmod +x feat_extract.py) to run the script directly.