LyriKOR

LyriKOR: English to Korean Song Translation with Syllabic Alignment

🔤2023-1 Natural language processing (COSE461) Term project in Korea Univ.
✅Achieve Outstanding Projects (in Top-10 of 50 teams)
✅위 논문의 한국어 버전이 HCLT2023 (한글 및 한국어 정보처리 학술대회)에 accepted됨
[report]

※ 저자들의 시험 기간 이슈로 일단은 미완성 레포입니다.

TODO:

• commit evaluation.py
• debugging (일부 완료)
• inference code for csv file

Approach

Model structure overview diagram

Model structure & How to train syllabic adjustment model

Example of inference

Results

원문	번역
I will always remember	항상 기억할께요
The day you kissed my lips	입맞춤 해주던
A hopeless romantic all my life	내 평생의 희망 없는 낭만
Surrounded by couples all the time	늘 커플에 둘러싸여 늘

Environment Setup

1. Install KoBART
   Colab에서 위 링크에 소개된 설치 방법(pip install ~)대로 KoBART를 설치할 경우 버전 충돌 때문에 제대로 설치되지 않는 문제 발생.
   그러므로 pip을 이용해서 설치하는 대신,

   git clone https://github.com/SKT-AI/KoBART
   

   로 코드를 내려받은 후 kobart 디렉토리를 다음의 경로에 위치시켜 주세요.
   Use the command above (git clone~ ) and put the kobart directory to the following path.

   LyriKOR
   └─ kobart
   └─ ...
   

2. Install the other required modules
   ! TODO: make requeirements.txt file !
   (colab에 깔려있는 module + transformer + boto3)

   pip install -r requirements.txt
   

How to Train the Syllabic Adjustment model

1. Prepare the Lyrics csv file of Korean songs.
   It must have a column named lyrics. For example:

   lyrics
   이 밤 그날의 반딧불을 당신의 ...

2. Make train dataset file. Use the command below.

   python preprocessing_for_train_data.py --lyrics_dataset_path=lyrics_file_name.csv
   				       --save_dataset_path=train_dataset_file_name.csv
   

3. If there were no abnormalities, the train dataset file would have been created in the dataset directory. If you created a file in a different location, please move the file to the following location.

   LyriKOR
   └─ dataset
   	└─ train_dataset_file_name.csv
   └─ ...
   

4. Use the command to train the model.

   cd Syllabic_adjustment
   python train.py --train_csv_file=train_dataset_file_name.csv
   

5. If you want to load the checkpoint of our model and continue to train, use --checkpoint_path option.

   python train.py --checkpoint_path=path/to/load/model
   

6. If you want to tune the hyperparameter, use those options.

   python train.py ...
   		--batch_size (default=512)
   		--epochs (default=15)
   		--warmup_ratio (default=3e-5)
   		--learning_rate (default=1.0)
   

How to Inference

1. By a single text line

   python inference.py --input=input_text
          --checkpoint_path=path/to/load/model
   

2. By a csv file (multiple lines)
   ! TODO !

Reference

KoBART
KoBART Question Generation