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Apache Spark - A unified analytics engine for large-scale data processing
winutils.exe hadoop.dll and hdfs.dll binaries for hadoop windows
This is a TensorFlow implementation of the WaveNet generative neural network architecture https://deepmind.com/blog/wavenet-generative-model-raw-audio/ for text generation.
Clone a voice in 5 seconds to generate arbitrary speech in real-time
This repository has implementation for "Neural Voice Cloning With Few Samples"
Open Source Implementation of Neural Voice Cloning with Few Audio Samples (Baidu Research)
implementation of music transformer with tensorflow-2.0 (ICLR2019)
A dataset of piano midi files organized by genres, sub-genres and artists.
Creative Adversarial Network for generating Dance Music Rhythm Patterns
Using LSTMs and GANs to Generate Music from MIDI Files (APM Fall 2018)
Magenta: Music and Art Generation with Machine Intelligence
Implements data-driven approaches for the detection of coughs in patients with respiratory illnesses.
COVID-19 Coughs files for training AI models
Cough sounds dataset splitted to 5 classes (covid, upper, lower, obstructive, healthy cough) prepared with COUGHVID dataset (https://coughvid.epfl.ch/). 3325 files wav, 16KHz, mono, 1 seconds durat…
This is a deep learning final project. Original cough dataset: https://coughvid.epfl.ch/about/
Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies
tacotronV2 + wavernn 实现中文语音合成(Tensorflow + pytorch)
Automatic Speech Recognition (ASR), Speaker Verification, Speech Synthesis, Text-to-Speech (TTS), Language Modelling, Singing Voice Synthesis (SVS), Voice Conversion (VC)
中文长文本分类、短句子分类、多标签分类、两句子相似度（Chinese Text Classification of Keras NLP, multi-label classify, or sentence classify, long or short），字词句向量嵌入层（embeddings）和网络层（graph）构建基类，FastText，TextCNN，CharCNN，TextRNN,…
keras implement of transformers for humans
总结梳理自然语言处理工程师(NLP)需要积累的各方面知识，包括面试题，各种基础知识，工程能力等等，提升核心竞争力
北京航空航天大学大数据高精尖中心自然语言处理研究团队开展了智能问答的研究与应用总结。包括基于知识图谱的问答（KBQA），基于文本的问答系统（TextQA），基于表格的问答系统（TableQA）、基于视觉的问答系统（VisualQA）和机器阅读理解（MRC）等，每类任务分别对学术界和工业界进行了相关总结。
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