chartsing's repositories
AnoGAN
Tensorflow Implementation of AnoGAN (Anomaly GAN)
anomaly-detection-resources
Anomaly detection related books, papers, videos, and toolboxes
asteroid
The PyTorch-based audio source separation toolkit for researchers
awesome-deep-learning-papers
The most cited deep learning papers
awesome-diarization
A curated list of awesome Speaker Diarization papers, libraries, datasets, and other resources.
berkeley-stat-157
Homepage for STAT 157 at UC Berkeley
checklist
Beyond Accuracy: Behavioral Testing of NLP models with CheckList
CTCNet
An Audio-Visual Speech Separation Model Inspired by Cortico-Thalamo-Cortical Circuits
CycleGAN-tensorflow
Tensorflow implementation for learning an image-to-image translation without input-output pairs. https://arxiv.org/pdf/1703.10593.pdf
d2l-zh
《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被60多个国家的400多所大学用于教学。
darknet
Convolutional Neural Networks
Deep-Reinforcement-Learning-Algorithms-with-PyTorch
PyTorch implementations of deep reinforcement learning algorithms and environments
Dual-Path-RNN-Pytorch
Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation implemented by Pytorch
eat_tensorflow2_in_30_days
Tensorflow2.0 🍎🍊 is delicious, just eat it! 😋😋
keras-retinanet
Keras implementation of RetinaNet object detection.
knowledge-distillation-pytorch
A PyTorch implementation for exploring deep and shallow knowledge distillation (KD) experiments with flexibility
leeml-notes
李宏毅《机器学习》笔记，在线阅读地址：https://datawhalechina.github.io/leeml-notes
lihang_book_algorithm
致力于将李航博士《统计学习方法》一书中所有算法实现一遍
looking-to-listen
Deep neural network (DNN) for noise reduction, removal of background music, and speech separation
Mastering-Quantum-Computing-with-IBM-QX
Mastering Quantum Computing with IBM QX, published by Packt
PythonDataScienceHandbook
Python Data Science Handbook: full text in Jupyter Notebooks
pytorch-tutorial
PyTorch Tutorial for Deep Learning Researchers
Sound-of-Pixels
Codebase for ECCV18 "The Sound of Pixels"
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.
whisperX
WhisperX: Automatic Speech Recognition with Word-level Timestamps (& Diarization)