/olkavs-avspeech

The Introduction of the OLKAVS Dataset

Primary LanguagePython

OLKAVS: An Open Large-Scale Korean Audio-Visual Speech Dataset

This repository contains code scripts for training and evaluation of the OLKAVS dataset described in the paper OLKAVS: An Open Large-Scale Korean Audio-Visual Speech Dataset.

Datasets
Sample #1 : {A,B,D,F,H}
"그때 되게 한여름이어서 되게 뜨거웠거든요."
Sample #2 : {A,C,E,G,I}
"그래서 도서관엘 다시 들어갔어요 공부하기 위해서"

The OLKAVS contains below.

  • a total of 1,150 hours of audio
  • a total of 5,750 hours of synced video from 9 different viewpoints

Those are from 1,107 Korean speakers in a studio setup with corresponding Korean transcriptions.

Yo can download The OLKAVS datasets from AIHub: 립리딩(입모양) 음성인식 데이터.

Dataset Structure

Directory

The folder structure of the OLKAVS dataset is as follows:

{root}/{group}/{subgroup}/{noise}/{specificity}/{gender_group}/{gender_subgroup}/{session_idx}.{extension}
  • root : Root directory
  • group : Data grouped by usage. (e.g. Train, Validation, Test)
  • subgroup : Data are separated in subgroups randomly.
  • noise : Noise condition.
  • specificity : Specificity of speaker.
  • gender_group : Gender.
  • gender_subgroup : Data of each gender are separted in gender_subgroups randomly.
  • session_idx : Index of the 5-minutes-long recording session. This index follows the name rule at here.
  • extension : File extension. mp4, wav, json for video, audio and label, respectively.

example

./원천데이터/TS1/소음환경1/C(일반인)/F(여성)/F(여성)_1/lip_J_1_F_02_C032_A_010.wav
Name Rule

The rule of naming file is as follows:

lip_{video_env}_{audio_noise}_{gender}_{age}_{specificity}{speakerID}_{video_angle}_{index}
  • video_env J: indoor, K:outdoor
  • audio_noise 1: No noise, 2: Indoor noise, 3: Indoor ambiance, 4: Traffic noise, 5:Construction-site noise, 6: Natural outdoor noise
  • gender F : Female, M : Male
  • age 1 : 10 - 19, 2 : 20 - 29, 3: 30 - 39, 4: 40 - 49, 5: 50 - 59, 6: 60 over
  • specificity C : Common speaker , E: Expert
  • speakerID Identified number for speaker
  • video_angle A : Frontal, B : Upper left, C : Left, D : Lower left, E : Lower center, F : Lower right, G : Right, H : Upper right, I : Upper center
  • index Index of the 5-minute-long recording session

Label Structure (json)

├── dataSet
│   ├── description
│   ├── url
│   ├── version
│   └── year
│
├── Video_info
│   ├── video_Name
│   ├── video_Format
│   ├── video_Duration
│   ├── FPS
│   └── Resolution
│
├── Audio_info
│   ├── Audio_Name
│   ├── Audio_Format
│   ├── Audio_Duration
│   ├── Sampling_rate
│   └── Channel(s)
│
├── Audio_env
│   └── Noise
│
├── Video_env
│   ├── env
│   └── Angle
│
├── Sentence_info
│   ├── ID
│   ├── topic
│   ├── sentence_text
│   ├── start_time
│   └── end_time
│
├── speaker_info
│   ├── speaker_ID
│   ├── Specificity
│   ├── Gender
│   ├── Age
│   └── Accent
│
└── Bounding_box_info
    ├── Face_bounding_box
    └── Lip_bounding_box

Dependency

pip install -r requirements.txt

Pre-process

Preprocess the data. Crop the lengths of audio and video by the temporal label. (start, end)
Then crop the video to the shape (96 96), by bounding box.
Finally generate label scripts for training or evaluation.

Preparation

Data folder should comply with this structure

Run Script

python preprocess.py --root_dir {ROOT_DIR} --src_dir {SOURCE_DIR} --label_dir {LABEL_DIR}

Generated Label Samples

{Video_filepath}\t{Audio_filepath}\t{Transcription}\t{Tokenized_Numbers}

./save/원천데이터/TS1/소음환경1/C(일반인)/F(여성)/F(여성)_1/lip_J_1_F_02_C032_A_011/2.mp4	./save/원천데이터/TS1/소음환경1/C(일반인)/F(여성)/F(여성)_1/lip_J_1_F_02_C032_A_011/2.wav	건강이 안 좋아지니 죄착 죄책감이 드네	5 28 48 5 24 65 16 44 4 16 24 48 4 17 32 71 16 24 17 44 7 44 4 17 35 19 24 45 4 17 35 19 25 45 5 24 60 16 44 4 8 42 7 29
./save/원천데이터/TS1/소음환경1/C(일반인)/F(여성)/F(여성)_1/lip_J_1_F_02_C032_A_011/3.mp4	./save/원천데이터/TS1/소음환경1/C(일반인)/F(여성)/F(여성)_1/lip_J_1_F_02_C032_A_011/3.wav	요즘에 불면증이 심해진 것 같아	16 36 17 42 60 16 29 4 12 37 52 11 30 48 17 42 65 16 44 4 14 44 60 23 25 17 44 48 4 5 28 63 4 5 24 69 16 24
...

Extract Lip Feature (Optional)

To reduce the required memory resource, we extracted lip features by pre-trained model from here.

We used its visual front-end, the details of using pre-trained model are in the paper.

Evaluation

Inference

Run

python inference.py -c {CONFIG_FILE_PATH}

Results

Model # of params Eval view Eval noise CER WER sWER pt
AV-model 62M* View A All 3.64 10.82 8.18 here
A-model 38M* View A All 3.57 10.61 8.11
V-model 34M* View A All 26.64 47.89 50.00
F-model 45M View A Clean 41.24 71.39 72.44
All-model 45M View A Clean 32.16 57.35 58.00 here

(* Do not include pre-trained visual front-end parameters.)

Release

  • v1.0.0
    • release baseline
  • v1.0.1
    • update result table
    • ICASSP 2024 accepted

License

The dataset itself is released under custom terms and conditions.

The OLKAVS Scripts are released under MIT license.

Citation

@INPROCEEDINGS{10446901,
  author={Park, Jeongkyun and Hwang, Jung-Wook and Choi, Kwanghee and Lee, Seung-Hyeon and Ahn, Jun Hwan and Park, Rae-Hong and Park, Hyung-Min},
  booktitle={ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={OLKAVS: An Open Large-Scale Korean Audio-Visual Speech Dataset}, 
  year={2024},
  volume={},
  number={},
  pages={6385-6389},
  keywords={Training;Lips;Mouth;Speech recognition;Signal processing;Predictive models;Speaker recognition;Audio-visual speech datasets;multi-view datasets;lip reading;audio-visual speech recognition;eep learning},
  doi={10.1109/ICASSP48485.2024.10446901}}

Contacts

park32323@gmail.com @Park323