enormous-system,github

adasoptimizer

ADAS is short for Adaptive Step Size, it's an optimizer that unlike other optimizers that just normalize the derivative, it fine-tunes the step size, truly making step size scheduling obsolete, achieving state-of-the-art training performance

albert_vi

ALBERT for Vietnamese

awesome-embedding-models

A curated list of awesome embedding models tutorials, projects and communities.

awesome-tensorflow

TensorFlow - A curated list of dedicated resources http://tensorflow.org

banditpam

BanditPAM (arxiv.org/abs/2006.06856) C++ implementation and Python package

electra

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

insightface

Face Analysis Project on MXNet

luigi

Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.

machine-learning-interview

Machine Learning Interviews from FAAG, Snapchat, LinkedIn.

models

Models and examples built with TensorFlow

mxnet-mtcnn

mtcnn mxnet C++ implementation

odoo

Odoo. Open Source Apps To Grow Your Business.

phobert

PhoBERT: Pre-trained language models for Vietnamese (EMNLP-2020 Findings)

phonlp

PhoNLP: A BERT-based multi-task learning toolkit for part-of-speech tagging, named entity recognition and dependency parsing

production-level-deep-learning

A guideline for building practical production-level deep learning systems to be deployed in real world applications.

pydantic

Data parsing and validation using Python type hints

sense2vec

🦆 Contextually-keyed word vectors

svoice

We provide a PyTorch implementation of the paper Voice Separation with an Unknown Number of Multiple Speakers In which, we present a new method for separating a mixed audio sequence, in which multiple voices speak simultaneously. The new method employs gated neural networks that are trained to separate the voices at multiple processing steps, while maintaining the speaker in each output channel fixed. A different model is trained for every number of possible speakers, and the model with the largest number of speakers is employed to select the actual number of speakers in a given sample. Our method greatly outperforms the current state of the art, which, as we show, is not competitive for more than two speakers.

enormous-system Goto Github PK

enormous-system's Projects

Recommend Projects

Recommend Topics

Recommend Org