End-to-End Speech Processing Toolkit
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Updated
Jul 16, 2024 - Python
End-to-End Speech Processing Toolkit
A PyTorch-based Speech Toolkit
The PyTorch-based audio source separation toolkit for researchers
Noise supression using deep filtering
Tensorflow 2.x implementation of the DTLN real time speech denoising model. With TF-lite, ONNX and real-time audio processing support.
PyTorch implementation of "FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement."
A tutorial for Speech Enhancement researchers and practitioners. The purpose of this repo is to organize the world’s resources for speech enhancement and make them universally accessible and useful.
A must-read paper for speech separation based on neural networks
Real-time GCC-NMF Blind Speech Separation and Enhancement
deep learning based speech enhancement using keras or pytorch, make it easy to use
General Speech Restoration
Deep Xi: A deep learning approach to a priori SNR estimation implemented in TensorFlow 2/Keras. For speech enhancement and robust ASR.
Voice Conversion Tool Kit
AI powered speech denoising and enhancement
Unofficial implementation of PercepNet: A Perceptually-Motivated Approach for Low-Complexity, Real-Time Enhancement of Fullband Speech
Tools for Speech Enhancement integrated with Kaldi
Two-talker Speech Separation with LSTM/BLSTM by Permutation Invariant Training method.
Python implementation of performance metrics in Loizou's Speech Enhancement book
simple delaysum, MVDR and CGMM-MVDR
The dataset of Speech Recognition
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