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3d-convolutional-speaker-recognition

:speaker: Deep Learning & 3D Convolutional Neural Networks for Speaker Verification

acnet

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks

annotated_deep_learning_paper_implementations

🧑‍🏫 50! Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

automatic_speech_recognition

End-to-end Automatic Speech Recognition for Madarian and English in Tensorflow

awesome-asr-contextualization

A curated list of awesome papers on contextualizing E2E ASR outputs

awesome-diarization

A curated list of awesome Speaker Diarization papers, libraries, datasets, and other resources.

awesome-pytorch-list

A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.

awesome-visual-transformer

Collect some papers about transformer with vision. Awesome Transformer with Computer Vision (CV)

chinesecodinginterviewappendix

The source code for the appendix part of the Chinese version of the book Coding Interviews

copynet

An implementation of CopyNet

csbook

deep-learning-with-keras-notebooks

Jupyter notebooks for using & learning Keras

deep-speaker

Deep Speaker: an End-to-End Neural Speaker Embedding System https://arxiv.org/pdf/1705.02304.pdf

deep_speaker-speaker_recognition_system

Keras implementation of ‘’Deep Speaker: an End-to-End Neural Speaker Embedding System‘’ (speaker recognition)

deepspeaker-pytorch

Speaker embedding(verification and recognition) using Pytorch

deepspeech.pytorch

Speech Recognition using DeepSpeech2.

deepspeech2-keras

DeepSpeech, Speech To Text, ASR, Speech recognition, Keras, Tensorflow

demand_forecast

denoiser

Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)We provide a PyTorch implementation of the paper Real Time Speech Enhancement in the Waveform Domain. In which, we present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities.

densenet

DenseNet implementation in Keras

dgc

Dynamic Group Convolution for Accelerating Convolutional Neural Networks (ECCV 2020)

dvectorspeakerrecognition

基于dVector的说话人识别keras

eesen

The official repository of the Eesen project

end2end-asr-pytorch

End-to-End Automatic Speech Recognition on PyTorch

espnet

End-to-End Speech Processing Toolkit

external-attention-pytorch

🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

fat

[Preprint] "In Defense of the Triplet Loss Again: Learning Robust Person Re-Identification with Fast Approximated Triplet Loss and Label Distillation" by Ye Yuan, Wuyang Chen, Yang Yang, Zhangyang Wang

flightdelayprediction

joint-slu-lm

Tensorflow implementation of joint modeling of SLU (intent & slot filling) and LM with RNNs.

jointslu