speech-separation

A PyTorch-based Speech Toolkit

End-to-End Speech Processing Toolkit

An AI-Powered Speech Processing Toolkit and Open Source SOTA Pretrained Models, Supporting Speech Enhancement, Separation, and Target Speaker Extraction, etc.

The PyTorch-based audio source separation toolkit for researchers

💎 A list of accessible speech corpora for ASR, TTS, and other Speech Technologies

Unofficial PyTorch implementation of Google AI's VoiceFilter system

A must-read paper for speech separation based on neural networks

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

PyTorch implementation of "FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement."

Deep Xi: A deep learning approach to a priori SNR estimation implemented in TensorFlow 2/Keras. For speech enhancement and robust ASR.

Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation Pytorch's Implement

UniSpeech - Large Scale Self-Supervised Learning for Speech

This repo summarizes the tutorials, datasets, papers, codes and tools for speech separation and speaker extraction task. You are kindly invited to pull requests.

Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation implemented by Pytorch

The dataset of Speech Recognition

Tools for Speech Enhancement integrated with Kaldi

The SpeechBrain project aims to build a novel speech toolkit fully based on PyTorch. With SpeechBrain users can easily create speech processing systems, ranging from speech recognition (both HMM/DNN and end-to-end), speaker recognition, speech enhancement, speech separation, multi-microphone speech processing, and many others.

Deep Recurrent Neural Networks for Source Separation

Code for SuDoRm-Rf networks for efficient audio source separation. SuDoRm-Rf stands for SUccessive DOwnsampling and Resampling of Multi-Resolution Features which enables a more efficient way of separating sources from mixtures.

Real-time GCC-NMF Blind Speech Separation and Enhancement