WaveNet vocoder

Open source audio annotation tool for humans™

SincNet is a neural architecture for efficiently processing raw audio samples.

A curated list of awesome Speaker Diarization papers, libraries, datasets, and other resources.

A neural network for end-to-end speech denoising

Problem Agnostic Speech Encoder

Novoic's audio feature extraction library

A python wrapper for Speech Signal Processing Toolkit (SPTK).

This repository has implementation for "Neural Voice Cloning With Few Samples"

Real-time GCC-NMF Blind Speech Separation and Enhancement

Implementation of Neural Voice Cloning with Few Samples Research Paper by Baidu

PyTorch implementation of VQ-VAE + WaveNet by [Chorowski et al., 2019] and VQ-VAE on speech signals by [van den Oord et al., 2017]

Deep neural network based speech enhancement toolkit

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.

RETIRED - OpenSTT is now retired. If you would like more information on Mycroft AI's open source STT projects, please visit:

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].

Deep Learning-based Voice Conversion system

Collection of EM algorithms for blind source separation of audio signals

Implement [Wave-U-Net](https://arxiv.org/abs/1806.03185) by PyTorch, and migrate it to the speech enhancement area.

Time delay neural network (TDNN) implementation in Pytorch using unfold method

Emotion recognition by speech in android.

Implement A Convolutional Recurrent Neural Network for Real-Time Speech Enhancement by PyTorch.

A modified version of Speech Signal Processing Toolkit (SPTK)

Voice Activity Detector

A suite of speech signal processing tools for UNIX environments