VAD_tutorial

A pytorch implementation of full-connected DNN based voice activity detection (VAD).
All the features for training and testing are uploaded.
Korean manual is included ("190225_LG-AI_VAD.pdf").

Requirements

python 3.5+
pytorch 1.0.0
pandas 0.23.4
numpy 1.13.3
pickle 4.0
matplotlib 2.1.0
sklearn 0.20.2

Datasets

We used the dataset collected through the following task.

• No. 10063424, 'development of distant speech recognition and multi-task dialog processing technologies for in-door conversational robots'

Specification

• Korean read speech corpus (ETRI read speech)
• Clean speech at a distance of 1m and a direction of 0 degrees
• 16kHz, 16bits

We uploaded multi-resolution cochleagram (MRCG) features extracted from the above dataset.
python based MRCG feature extraction toolkit is used.

* Train

10000 utterances, 100 folders (100 speakers)
Size : 4.4GB
feat_MRCG_nfilt96 - train

* Test

20 utterances, 10 folders (10 speakers)
Size : 18MB
feat_MRCG_nfilt96 - test

Usage

1. Training

python train.py

2. Testing

python test.py

Author

Youngmoon Jung ([email protected]) at KAIST, South Korea