This project contains the code to train a gender classification model that takes raw audio as inputs.
The weights of the model from the article can be found in the models/ directory.
See my Medium article for more discussion.
Make a new virtualenv and install requirements from requirements.txt with
pip install -r requirements.txt
This project was written in Python 2.7.12 so I cannot guarantee it works on any other version.
python -m unittest tests
Get training data here: http://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.
Run run_experiment.py with the default parameters to train the model with the performance discussed in the article.
Run process_audio.py, specifying the model and audio file to use. The audio file must be a .flac file.
This script makes many predictions on different fragments of the target audio file and saves the results to
data/results.csv.
I used this script to produce the data for the video embedded in the Medium article.
I have uploaded two notebooks with this project.
Model_Performance_Investigation gives a breakdown of the performance of the model over the different speakers in the
LibriSpeech dataset.
Interview_Segmentation is where I analysed the results of the process_audio.py script on an interview between Elton
John and Kirsty Wark.