Speech-Separation-Paper-Tutorial. A must-read paper for speech separation based on neural networks
947Conv-TasNet. Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation Pytorch's Implement
549Dual-Path-RNN-Pytorch. Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation implemented by Pytorch
466TIGER. TIGER: Time-frequency Interleaved Gain Extraction and Reconstruction for Efficient Speech Separation
431Apollo. Music repair method to convert lossy MP3 compressed music to lossless music.
392SonicSim. SonicSim: A customizable simulation platform for speech processing in moving sound source scenarios
277TDANet. An efficient speech separation method
277SPMamba. Python
227AudioTrust. AudioTrust: Benchmarking the Multi-faceted Trustworthiness of Audio Large Language Models
215Dolphin. Python
184Looking-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".
110Calculate-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
44S4M. 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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