General

• A source code to fine-tune self-supervised learning model (SSL) on NIA-2022-1-13 Non-native L2 Korean Dataset for Automatic Pronunciation Assessment (APA).
• NIA-2022-1-13 Non-native L2 Korean Dataset for Automatic Pronunciation Assessment (APA) will soon be released within 2023.
• More information regarding the usage of the dataset and docker support will be updated with the relase of dataset.

License

• SPDX-FileCopyrightText: © 2023 Hyungshin Ryu <rhss10@snu.ac.kr>
• SPDX-License-Identifier: Apache-2.0

Notes

• NIA-2022-1-13 Non-native L2 Korean Dataset supports proficiency scores of 3 aspects, 'comprehensibility', 'fluency', 'accentedness'.
• The example code is aimed at scoring 'comprehensibility'.
• By changing the data/preprocess_data.py code, you may asess 'fluency' or 'accentedness' scores.

Commands

Prepare Data

# Data processing should be done with the ACTUAL data path
python preprocess_data.py
# create Huggingface-based datasets arrows.
python create_datasets.py

Train

# Example command for training. For more supported arguments, please refer to train.py
python train.py --exp_prefix NIA

Test

# Example
python test.py