speech-separation

End-to-End Speech Processing Toolkit

A PyTorch-based Speech Toolkit

Unofficial PyTorch implementation of Google AI's VoiceFilter system

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

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

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

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

Deep Recurrent Neural Networks for Source Separation

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.

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

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

Deep learning based speech source separation using Pytorch

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

Real-time GCC-NMF Blind Speech Separation and Enhancement

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

UniSpeech - Large Scale Self-Supervised Learning for Speech

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

A PyTorch implementation of "TasNet: Surpassing Ideal Time-Frequency Masking for Speech Separation" (see recipes in aps framework https://github.com/funcwj/aps)

A PyTorch implementation of DNN-based source separation.

Speech Enhancement based on DNN (Spectral-Mapping, TF-Masking), DNN-NMF, NMF

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.

The dataset of Speech Recognition

Executable code based on Google articles

Deep neural network (DNN) for noise reduction, removal of background music, and speech separation

deep clustering method for single-channel speech separation

A personal toolkit for single/multi-channel speech recognition & enhancement & separation.

Speech separation with utterance-level PIT experiments

Pytorch implements Deep Clustering: Discriminative Embeddings For Segmentation And Separation