This repository contains code to build deep learning models to identify different speakers based on audio samples containg their voice.
The eventual aim is for this repository to become a pip-installable python package for quickly and easily performing speaker identification related tasks.
This tensorflow/Keras/python2.7 branch is discontinued. Work is continuing on the pytorch-python-3.6 branch which will become the master branch.
Make a new virtualenv and install requirements from requirements.txt
with the following command.
pip install -r requirements.txt
This project was written in Python 2.7.12 so I cannot guarantee it works on any other version.
Get training data here: https://www.openslr.org/12
- train-clean-100.tar.gz
- train-clean-360.tar.gz
- dev-clean.tar.gz
Place the unzipped training data into the data/
folder so the file
structure is as follows:
data/
LibriSpeech/
dev-clean/
train-clean-100/
train-clean-360/
SPEAKERS.TXT
Please use the SPEAKERS.TXT
supplied in the repo as I've made a few
corrections to the one found at openslr.org.
This requires the LibriSpeech data.
python -m unittest tests.tests
This package contains re-usable code for defining network architectures, interacting with datasets and many utility functions.
This package contains experiments in the form of python scripts.
This folder contains Jupyter notebooks used for interactive visualisation and analysis.