This is an experimental python machine learning pipeline that extracts MFCC voice features from youtube videos and trains a Gradient Boosting Classifier algorithm. For this project, the training data is ~1800 seconds of donald trump speaks in different scenarios + equivalent time of random people (men and women) talk about random stuff. The project was inspired by this youtube video where this guy explain some unique features in his talk. I wanted to know if computers are able to analyze these things as well and maybe even more.
The pipeline is relatively simple and doesn't include sophisticated pre-processing methods. I was quite happy with the results so far and in the future I'll spend more time improving the model so it could work on potentially any voice, given enough input.
- scikit-learn for the ML algorithm
- YAAFE for feature extraction
- Flask for the web server
- youtube-dl to handle youtube downloads
- postgresql - as the database
- redis + celery to handle the recognition background job
If you want to try it locally you need to have these environment variables:
# Postgres
export PGPASSWORD="password"
export POSTGRES_CRED="dbname=trumplearn user=app password=$PGPASSWORD"
# YAAFE
export LD_LIBRARY_PATH=/usr/local/lib/
# Flask
export DEBUG=True
# Celery
export REDIS_SERVER='redis://127.0.0.1:6379'On an ubuntu machine, it's possible to run bootstap.sh to provision the environment.