boggis30's Stars
coells/100days
100 days of algorithms
realpython/materials
Bonus materials, exercises, and example projects for our Python tutorials
kedro-org/kedro
Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
muratsensoy/muratsensoy.github.io
shivamms/books
dgrtwo/tidy-text-mining
Manuscript of the book "Tidy Text Mining with R" by Julia Silge and David Robinson
facebookresearch/fairseq
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
gangiman/CountryRank
Using your preferences rank all countries based on existing rankings.
kael-shipman/libgwiki
A simple single-page app that creates a basic wiki interface out of a Google Drive folder
dask/dask
Parallel computing with task scheduling
jtibshirani/text-embeddings
marcotcr/lime
Lime: Explaining the predictions of any machine learning classifier
KeithGalli/Pandas-Data-Science-Tasks
Set of real world data science tasks completed using the Python Pandas library
google/sentencepiece
Unsupervised text tokenizer for Neural Network-based text generation.
huggingface/transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
mgrankin/ru_transformers
snakers4/open_stt
Open STT
VKCOM/YouTokenToMe
Unsupervised text tokenizer focused on computational efficiency
google-research/adapter-bert
kpe/bert-for-tf2
A Keras TensorFlow 2.0 implementation of BERT, ALBERT and adapter-BERT.
librosa/librosa
Python library for audio and music analysis
LvanderGoten/AttentionIsAllYouNeed
Implementation of "Attention Is All You Need" (aka The Transformer)
jonathanio/update-systemd-resolved
Helper script for OpenVPN to directly update the DNS settings of a link through systemd-resolved via DBus.
tensorflow/ranking
Learning to Rank in TensorFlow
gionanide/Speech_Signal_Processing_and_Classification
Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].
jameslyons/python_speech_features
This library provides common speech features for ASR including MFCCs and filterbank energies.
MycroftAI/mycroft-precise
A lightweight, simple-to-use, RNN wake word listener
philipperemy/keras-attention
Keras Attention Layer (Luong and Bahdanau scores).
thushv89/attention_keras
Keras Layer implementation of Attention for Sequential models
scikit-learn/scikit-learn
scikit-learn: machine learning in Python