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similarities

Similarities: a toolkit for similarity calculation and semantic search. 语义相似度计算、匹配搜索工具包，支持文本和图像，开箱即用。

sklearn-porter

Transpile trained scikit-learn estimators to C, Java, JavaScript and others.

sklearn2pmml

Python library for converting Scikit-Learn pipelines to PMML

snoring-project

Snoring Detection

snoring_detection

一个简单的基于esp8266的鼾声检测和反馈

soja-box

A little useful toolbox for python.

soundnet

SoundNet: Learning Sound Representations from Unlabeled Video. NIPS 2016

spaces

端到端的长本文摘要模型（法研杯2020司法摘要赛道）

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].

sublimeserver

[DEAD PROJECT]Make Sublime as a HTTP server, compatible with sublime 3

tanxinkeji-xunlianying

学习贪心科技计算机视觉训练营

tensorflow-2.x-tutorials

TensorFlow 2.x version's Tutorials and Examples, including CNN, RNN, GAN, Auto-Encoders, FasterRCNN, GPT, BERT examples, etc. TF 2.0版入门实例代码，实战教程。

test

text-classification

文本分类 法研杯 textcnn rcnn capsule attention

text-summarization-with-amazon-reviews

A seq2seq model that can generate summaries from fine food reviews on Amazon.

text2vec

text2vec, text to vector. 文本向量表征工具，把文本转化为向量矩阵，实现了Word2Vec、RankBM25、Sentence-BERT、CoSENT等文本表征、文本相似度计算模型，开箱即用。

tf-faster-rcnn

Tensorflow Faster RCNN for Object Detection

tf_tutorial_plus

Tutorials for TensorFlow APIs the official documentation doesn't cover

thunder-factorization

algorithms for scalable matrix factorization

transformers

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

tutorials

Tutorials for creating and using ONNX models

udacity-course-statistics101

Introduction to Statistics

unlocker

VMware Workstation macOS

urban-sound-classification

Urban sound classification using Deep Learning

urbansound8k-jams

JAMS annotation files for the original and augmented UrbanSound8K dataset

vision

Datasets, Transforms and Models specific to Computer Vision

vue-awesome-swiper

🏆 Swiper component for @vuejs

webrtc-

利用webRTC对语音进行处理，实现VAD和降噪处理

websocket-client

WebSocket client for Python

wenshu