speech-processing

A PyTorch-based Speech Toolkit

Reading list for research topics in multimodal machine learning

WaveNet vocoder

Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding

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

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

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

A neural network for end-to-end speech denoising

Speech recognition toolkit for the arduino

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.

Problem Agnostic Speech Encoder

Speech Algorithms Collections

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"

On-device speech-to-text engine powered by deep learning

Tensorflow 2.x implementation of the DTLN real time speech denoising model. With TF-lite, ONNX and real-time audio processing support.

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.

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

VocGAN: A High-Fidelity Real-time Vocoder with a Hierarchically-nested Adversarial Network

Tracking the progress in non-autoregressive generation (translation, transcription, etc.)

Real-time GCC-NMF Blind Speech Separation and Enhancement

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

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

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

General Speech Restoration

UniSpeech - Large Scale Self-Supervised Learning for Speech