This repository contains code for preprocessing the Sep28k corpus and training a model for both binary (fluency/disfluency) and multiclass ('Prolongation', 'Block', 'SoundRep', 'WordRep', 'Interjection', 'NoStutteredWords') classification.
The user is assumed to have downloaded the relevant files from the original Sep28k repo.
Install required libraries:
pip install requirements.txt
The first step is to run the following:
python preprocess.py
This returns a dictionary containing F0, MFB, and wav2vec 2.0 features, and dictionaries for the labels and audio file paths.
To train a model on these features:
python train.py --model --batch_size --num_epochs
This train.py allows one to select a model from the models.py file and generates the dataset from the dataset.py file.
Available models include: ConvLSTM(), LSTM_base(), ResNet()
Following training the utils.py contains a plotting function to show the binary and multiclass losses and F1 scores.