This is the code base for Acoustic span embeddings for multilingual query-by-example search that will appear in SLT 2021.
@article{hu2020acoustic,
title={Acoustic span embeddings for multilingual query-by-example search},
author={Hu, Yushi and Settle, Shane and Livescu, Karen},
journal={arXiv preprint arXiv:2011.11807},
year={2020}
}
python 3.7, pytorch 1.4, h5py 2.8.0, numpy, scipy, tensorboard 1.14.0
The conda environment is provided in pt1.4.yml
Download the processed QUESST 2015 QbE task data from the link https://drive.google.com/file/d/1WEGVWtyC5x5V-0QMdNyRetGZgZ-2Z7Un/view?usp=sharing. Unzip and put the quesst2015 folder in the directory to run the code.
There are two versions of acoustic span embeddings (ASE-concat and ASE-mean). The trained checkpoints are in expts folder
Run query-by-example search with ASE (mean) on QUESST 2015 dev set. Results will be stored in expts/span-mean/2015results/
cd code
# Fast run. About 1s per query. Requires 32GB RAM. maxTWV 0.238 minCnxe 0.711
python qbe_main_2015.py --dir ../expts/span-mean/ --mode mean --inc 20
# Result in paper. About 5s per query. Requires 64GB RAM. maxTWV 0.255 minCnxe 0.706
python qbe_main_2015.py --dir ../expts/span-mean/ --mode mean --inc 5
Run query-by-example search with ASE (concat) on QUESST 2015 dev set. Results will be stored in expts/span-concat/2015results/
cd code
# Fast run. About 1s per query. Requires 32GB RAM. maxTWV 0.171 minCnxe 0.779
python qbe_main_2015.py --dir ../expts/span-concat/ --mode concat --inc 20
# Result in paper. About 5s per query. Requires 64GB RAM. maxTWV 0.193 minCnxe 0.753
python qbe_main_2015.py --dir ../expts/span-concat/ --mode concat --inc 5
To get the best result in paper, fusion the score output from the ASE-mean and ASE-concat systems.
Best result on dev: maxTWV 0.323 , minCnxe 0.670
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent