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Speech-Separation-Paper-Tutorial. A must-read paper for speech separation based on neural networks
952Conv-TasNet. Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation Pytorch's Implement
550Dual-Path-RNN-Pytorch. Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation implemented by Pytorch
468TIGER. TIGER: Time-frequency Interleaved Gain Extraction and Reconstruction for Efficient Speech Separation
437Apollo. Music repair method to convert lossy MP3 compressed music to lossless music.
396TDANet. An efficient speech separation method
278SonicSim. SonicSim: A customizable simulation platform for speech processing in moving sound source scenarios
277SPMamba. Python
227AudioTrust. AudioTrust: Benchmarking the Multi-faceted Trustworthiness of Audio Large Language Models
215Dolphin. Python
186Looking-to-Listen-at-the-Cocktail-Party. Executable code based on Google articles
166Deep-Clustering-for-Speech-Separation. Pytorch implements Deep Clustering: Discriminative Embeddings For Segmentation And Separation
133AFRCNN-For-Speech-Separation. Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network
131IIANet. This is the demo of our paper "IIANet: An Intra- and Inter-Modality Attention Network for Audio-Visual Speech Separation".
111Calculate-SNR-SDR. Script to calculate SNR and SDR using python
93LRS3-For-Speech-Separation. Multi-modal speech separation task data generation script on LRS3 data set.
88CTCNet. An Audio-Visual Speech Separation Model Inspired by Cortico-Thalamo-Cortical Circuits
82UtterancePIT-Speech-Separation. According to funcwj's uPIT, the training code supporting multi-gpu is written, and the Dataloader is reconstructed.
67Deep-Encoder-Decoder-Conv-TasNet. A PyTorch implementation of " AN EMPIRICAL STUDY OF CONV-TASNET "
51AV-ConvTasNet. Unofficial Time Domain Audio Visual Speech Separation Implementation
45S4M. Official implementation of Efficient Speech Separation Framework Based on Neural State-Space Models
28Swift-Net. Power-Guided Grouped SRU for Real-Time Causal Audio-Visual Speech Separation
26TFACM. Python
23DANet-For-Speech-Separation. Pytorch implement of DANet For Speech Separation
21Look2hear. A toolkit for researchers in the multimodal sound separation.
16awesome-speech-enhancement. speech enhancement\speech seperation\sound source localization
15Apollo-data-preprocess. Apollo training data preprocessing scripts
14speechbrain-docs-zh-cn. SpeechBrain中文文档
12Gull-Codec-Training. Python
12Arxiv-New-Paper-Server. Arxiv automatically obtains the latest article service.
11ExamOnline. This is a complete online exam system
10My-Script-For-Audio-Process. Some convenient scripts for your own use
9WeChatApp. Complete code of WeChat Mini Program
8Grass. Python
7GrabCut. C++
7Deep-Learning. Learn to deep learning the code of your own records.
7jusperlee.github.io. HTML
7JusperLee.
7Accelerator. Openmp Accelerator
7player. Android Homework(3)
7ELF-SR. Python
7Time. My Android Project
7speech_separation. Include some core functions and model to handle speech separation
6Dive-into-DL-PyTorch. 本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet代码实现改为PyTorch实现。
6BasicSR. Basic Super-Resolution Toolbox, including SRResNet, SRGAN, ESRGAN, etc.
5Sound-of-Pixels. Codebase for ECCV18 "The Sound of Pixels"
5pytorch-template. PyTorch deep learning projects made easy.
5book-mate. 一款用于图书推荐、搜索、借阅、交流的微信小程序
4espnet. End-to-End Speech Processing Toolkit
3AdversarialNetsPapers. The classical paper list with code about generative adversarial nets
2avlit. Official source code of the INTERSPEECH 2023 paper: "Audio-Visual Speech Separation in Noisy Environments with a Lightweight Iterative Model" (AVLIT)
2GenerSpeech. PyTorch Implementation of GenerSpeech (NeurIPS'22): a text-to-speech model towards zero-shot style transfer of OOD custom voice.
1FRA-RIR. Python
1voxceleb_trainer. In defence of metric learning for speaker recognition
1RSTnet. Real-time Speech-Text Foundation Model Toolkit
1taichicon. TaichiCon: Taichi (Virtual) Conference
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