The PyTorch-based audio source separation toolkit for researchers

Unofficial PyTorch implementation of Google AI's VoiceFilter system

A PyTorch implementation of Conv-TasNet described in "TasNet: Surpassing Ideal Time-Frequency Masking for Speech Separation" with Permutation Invariant Training (PIT).

Deep Recurrent Neural Networks for Source Separation

Two-talker Speech Separation with LSTM/BLSTM by Permutation Invariant Training method.

A PyTorch implementation of DNN-based source separation.

A PyTorch implementation of Time-domain Audio Separation Network (TasNet) with Permutation Invariant Training (PIT) for speech separation.

An implementation of audio source separation tools.

Adaptive and Focusing Neural Layers for Multi-Speaker Separation Problem

Ultimate Vocal Remover for Google Colab

Real time multilingual face translator

An app for creating audio-based content such as song covers and speech using Retrieval-based Voice Conversion.

Software that performs the separation of vocals from music using neural networks (part of my Bachelor's thesis).

logWMSE, an audio quality metric & loss function with support for digital silence target. Useful for training and evaluating audio source separation systems.

Code and datasets for 'Move2Hear: Active Audio-Visual Source Separation' (ICCV 2021)

A convolutional neural network for blind audio source separation.

eCMU: An Efficient Phase-aware Framework for Music Source Separation with Conformer (IEEE RIVF23)

An exploration of blind source audio separation using spiking neural networks. Latency, power. and intelligibility are primary objectives while bio-plausibility is left as a secondary objective to be addressed in the future.

OpenVINO DevCUP music aeparation & transcription

Download multiple tracks from youtube by a single query - with GUI.