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speakerrecognition_tutorial's Introduction

SpeakerRecognition_tutorial

A pytorch implementation of d-vector based speaker recognition system.
All the features for training and testing are uploaded.
Korean manual is included ("2019_LG_SpeakerRecognition_tutorial.pdf").

Requirements

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

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 40-dimensional log mel filterbank energy features extracted from the above dataset.
python_speech_features library is used.

* Train

24000 utterances, 240 folders (240 speakers)
Size : 3GB
feat_logfbank_nfilt40 - train

* Enroll & test

20 utterances, 10 folders (10 speakers)
Size : 11MB
feat_logfbank_nfilt40 - test

Usage

1. Training

Background model (ResNet based speaker classifier) is trained.
You can change settings for training in 'train.py' file.

python train.py

2. Enrollment

Extract the speaker embeddings (d-vectors) using 10 enrollment speech files.
They are extracted from the last hidden layer of the background model.
All the embeddings are saved in 'enroll_embeddings' folder.

python enroll.py

3. Testing

For speaker verification, you can change settings in 'verification.py' file.

python verification.py

For speaker identification, you can change settings in 'identification.py' file.

python identification.py

Author

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

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