Volodymyr Holubets''s starred repositories
PWMMotorControl
Arduino library to control brushed DC motors by PWM. Uses optional attached encoders to drive fixed distances.
ydata-profiling
1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
pr-updater-action
GitHub Action that keeps your pull requests up to date
spec_augment
🔦 A Pytorch implementation of GoogleBrain's SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
cross_browser
This is a project for a browser fingerprinting technique that can track users not only within a single browser but also across different browsers on the same machine.
keycloak-nginx
Example for using NGINX as reverse proxy for Keycloak.
neural-style-tf
TensorFlow (Python API) implementation of Neural Style
ffmpeg-normalize
Audio Normalization for Python/ffmpeg
scipy-2017-codegen-tutorial
SymPy code generation tutorial at SciPy 2017
FreeSWITCH-1.8
FreeSWITCH 1.8, published by Packt
Reinforcement-Learning
Learn Deep Reinforcement Learning in 60 days! Lectures & Code in Python. Reinforcement Learning + Deep Learning
learning_invariances_in_speech_recognition
In this work I investigate the speech command task developing and analyzing deep learning models. The state of the art technology uses convolutional neural networks (CNN) because of their intrinsic nature of learning correlated represen- tations as is the speech. In particular I develop different CNNs trained on the Google Speech Command Dataset and tested on different scenarios. A main problem on speech recognition consists in the differences on pronunciations of words among different people: one way of building an invariant model to variability is to augment the dataset perturbing the input. In this work I study two kind of augmentations: the Vocal Tract Length Perturbation (VTLP) and the Synchronous Overlap and Add (SOLA) that locally perturb the input in frequency and time respectively. The models trained on augmented data outperforms in accuracy, precision and recall all the models trained on the normal dataset. Also the design of CNNs has impact on learning invariances: the inception CNN architecture in fact helps on learning features that are invariant to speech variability using different kind of kernel sizes for convolution. Intuitively this is because of the implicit capability of the model on detecting different speech pattern lengths in the audio feature.
wide-language-index
An index of public broadcasts tagged by their primary language.
inaSpeechSegmenter
CNN-based audio segmentation toolkit. Allows to detect speech, music, noise and speaker gender. Has been designed for large scale gender equality studies based on speech time per gender.
py-webrtcvad
Python interface to the WebRTC Voice Activity Detector