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.

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

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

UniSpeech - Large Scale Self-Supervised Learning for Speech

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

Deep Recurrent Neural Networks for Source Separation

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.

Real-time GCC-NMF Blind Speech Separation and Enhancement

Deep learning based speech source separation using Pytorch