Python Part-of-speech Resources

STYLER: Style Factor Modeling with Rapidity and Robustness via Speech Decomposition for Expressive and Controllable Neural Text to Speech Keon Lee, Ky

This project is part of Eleuther AI's quest to create a massive repository of high quality text data for training language models.

GLOM TensorFlow This Python package attempts to implement GLOM in TensorFlow, which allows advances made by several different groups transformers, neu

GLOM An implementation of Geoffrey Hinton's paper "How to represent part-whole hierarchies in a neural network" for MNIST Dataset. To understand this

Continuous Reforestation Make tree planting a part of your daily workflow. 🌳 A GitHub Action for planting trees within your development workflow usin

Kaldi Speech Recognition Toolkit To build the toolkit: see ./INSTALL. These instructions are valid for UNIX systems including various flavors of Linux

MASR中文语音识别(pytorch版) 开箱即用 自行训练 使用与训练分离(增量训练) 识别率高 说明：因为每个人电脑机器不同，而且有些安装包安装起来比较麻烦，强烈建议直接用我编译好的docker环境跑 目前docker基础环境为ubuntu-cuda10.1-cudnn7-pytorch1.6.

🤪 TensorFlowTTS provides real-time state-of-the-art speech synthesis architectures such as Tacotron-2, Melgan, Multiband-Melgan, FastSpeech, FastSpeech2 based-on TensorFlow 2. With Tensorflow 2, we can speed-up training/inference progress, optimizer further by using fake-quantize aware and pruning, make TTS models can be run faster than real-time and be able to deploy on mobile devices or embedded systems.

The goal is to create a single, flexible, and user-friendly toolkit that can be used to easily develop state-of-the-art speech technologies, including systems for speech recognition, speaker recognition, speech enhancement, multi-microphone signal processing and many others.

[TCSVT] Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization LPN [Paper] NEWs Prerequisites Python 3.6 GPU Memory = 8G Numpy 1.

ASEGAN: Speech Enhancement Generative Adversarial Network Based on Asymmetric AutoEncoder 中文版简介 Readme with English Version 介绍 基于SEGAN模型的改进版本，使用自主设计的非

LightSpeech UnOfficial PyTorch implementation of LightSpeech: Lightweight and Fast Text to Speech with Neural Architecture Search.

iter8-analytics iter8 enables statistically robust continuous experimentation of microservices in your CI/CD pipelines. For in-depth information about

ConferencingSpeech 2021 challenge This repository contains the datasets list and scripts required for the ConferencingSpeech challenge. For more detai

anaGo anaGo is a Python library for sequence labeling(NER, PoS Tagging,...), implemented in Keras. anaGo can solve sequence labeling tasks such as nam

DELTA - A DEep learning Language Technology plAtform What is DELTA? DELTA is a deep learning based end-to-end natural language and speech processing p

NVIDIA NeMo Introduction NeMo is a toolkit for creating Conversational AI applications. NeMo product page. Introductory video. The toolkit comes with

Texar is a toolkit aiming to support a broad set of machine learning, especially natural language processing and text generation tasks. Texar provides

Spark NLP: State of the Art Natural Language Processing Spark NLP is a Natural Language Processing library built on top of Apache Spark ML. It provide

TextBlob: Simplified Text Processing Homepage: https://textblob.readthedocs.io/ TextBlob is a Python (2 and 3) library for processing textual data. It

SpeechRecognition Library for performing speech recognition, with support for several engines and APIs, online and offline. Speech recognition engine/

⚠️ Checkout develop branch to see what is coming in pyannote.audio 2.0: a much smaller and cleaner codebase Python-first API (the good old pyannote-au

Project DeepSpeech DeepSpeech is an open-source Speech-To-Text engine, using a model trained by machine learning techniques based on Baidu's Deep Spee

SpeechPy Official Project Documentation Table of Contents Documentation Which Python versions are supported Citation How to Install? Local Installatio

python_speech_features This library provides common speech features for ASR including MFCCs and filterbank energies. If you are not sure what MFCCs ar

torchaudio: an audio library for PyTorch The aim of torchaudio is to apply PyTorch to the audio domain. By supporting PyTorch, torchaudio follows the

AUDIOMATE Audiomate is a library for easy access to audio datasets. It provides the datastructures for accessing/loading different datasets in a gener

KorBERT를 활용한 한국어 텍스트 기반 위협 상황인지(2020 인공지능 그랜드 챌린지) 본 프로젝트는 ETRI에서 제공된 한국어 korBERT 모델을 활용하여 폭력 기반 한국어 텍스트를 분류하는 다양한 분류 모델들을 제공합니다. 본 개발자들이 참여한 2020 인공지

anaGo anaGo is a Python library for sequence labeling(NER, PoS Tagging,...), implemented in Keras. anaGo can solve sequence labeling tasks such as nam

DELTA - A DEep learning Language Technology plAtform What is DELTA? DELTA is a deep learning based end-to-end natural language and speech processing p

NVIDIA NeMo Introduction NeMo is a toolkit for creating Conversational AI applications. NeMo product page. Introductory video. The toolkit comes with

Texar is a toolkit aiming to support a broad set of machine learning, especially natural language processing and text generation tasks. Texar provides

Spark NLP: State of the Art Natural Language Processing Spark NLP is a Natural Language Processing library built on top of Apache Spark ML. It provide

TextBlob: Simplified Text Processing Homepage: https://textblob.readthedocs.io/ TextBlob is a Python (2 and 3) library for processing textual data. It

PORORO: Platform Of neuRal mOdels for natuRal language prOcessing pororo performs Natural Language Processing and Speech-related tasks. It is easy to

Trankit: A Light-Weight Transformer-based Python Toolkit for Multilingual Natural Language Processing Trankit is a light-weight Transformer-based Pyth

PIKA: a lightweight speech processing toolkit based on Pytorch and (Py)Kaldi PIKA is a lightweight speech processing toolkit based on Pytorch and (Py)

Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech (BVAE-TTS) Yoonhyung Lee, Joongbo Shin, Kyomin Jung Abstract: Although early

PhoNLP is a multi-task learning model for joint part-of-speech (POS) tagging, named entity recognition (NER) and dependency parsing. Experiments on Vietnamese benchmark datasets show that PhoNLP produces state-of-the-art results, outperforming a single-task learning approach that fine-tunes the pre-trained Vietnamese language model PhoBERT for each task independently.

Pseudo-Visual Speech Denoising This code is for our paper titled: Visual Speech Enhancement Without A Real Visual Stream published at WACV 2021. Autho

FullSubNet This Git repository for the official PyTorch implementation of "A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech E

PyTorch implementation of Conformer: Convolution-augmented Transformer for Speech Recognition. Transformer models are good at capturing content-based