Victor Costa Beraldo's starred repositories
build-your-own-x
Master programming by recreating your favorite technologies from scratch.
Real-Time-Voice-Cloning
Clone a voice in 5 seconds to generate arbitrary speech in real-time
pyannote-audio
Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
machine-learning
:earth_americas: machine learning tutorials (mainly in Python3)
Resemblyzer
A python package to analyze and compare voices with deep learning
repo2docker
Turn repositories into Jupyter-enabled Docker images
awesome-python-scientific-audio
Curated list of python software and packages related to scientific research in audio
jupyterlab-vim
:neckbeard: Vim notebook cell bindings for JupyterLab
torch-audiomentations
Fast audio data augmentation in PyTorch. Inspired by audiomentations. Useful for deep learning.
PyTorch_Speaker_Verification
PyTorch implementation of "Generalized End-to-End Loss for Speaker Verification" by Wan, Li et al.
VGG-Speaker-Recognition
Utterance-level Aggregation For Speaker Recognition In The Wild
pytorch_xvectors
Deep speaker embeddings in PyTorch, including x-vectors. Code used in this work: https://arxiv.org/abs/2007.16196
You-Only-Speak-Once
Deep Learning - one shot learning for speaker recognition using Filter Banks
NeuralPlda
Implementation of Neural PLDA (NPLDA) model (A discriminative backend for Speaker Verification)
SpeakerEmbeddingLossComparison
Companion repository for the paper "A Comparison of Metric Learning Loss Functions for End-to-End Speaker Verification" published at SLSP 2020
AM-SincNet
The Additive Margin SincNet (AM-SincNet) is a new approach for speaker recognition problems which is based in the neural network architecture SincNet and the additive margin softmax (AM-Softmax) loss function. It uses the architecture of the SincNet, but with an improved AM-Softmax layer.
banknoteBrazil
Classification of Brazilian paper money.
voice-unlocker
Detecção e identificação de usuários atravéz da fala dentro de um contexto de extração de features de sinais de áudio nos domínios do tempo, frequência e tempo-frequência.
speech-emotion-recogntion-ser2022
Speech Emotion Recognition SE&R 2022