71 Open source projects that are alternatives of or similar to Sptk

a simple speech recognition app using the Web Speech API Interfaces

A wavelet audio denoiser done in python

(1st place at HopHacks) A dynamic webVR memory palace for speech training, utilizing natural language processing and Google Streetview API

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

A React-Native Bridge for the Google Dialogflow (API.AI) SDK

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.

🔉 spafe: Simplified Python Audio Features Extraction

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

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.

ttslearn: Library for Pythonで学ぶ音声合成 (Text-to-speech with Python)

A Convolutional Neural Network based Voice Activity Detector for Smartphones

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

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

IEEE ICASSP 21 - Quantum Convolution Neural Networks for Speech Processing and Automatic Speech Recognition

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

ACLEW Diarization Virtual Machine

HiFi-GAN: High Fidelity Denoising and Dereverberation Based on Speech Deep Features in Adversarial Networks

In this work we propose two postprocessing approaches applying convolutional neural networks (CNNs) either in the time domain or the cepstral domain to enhance the coded speech without any modification of the codecs. The time domain approach follows an end-to-end fashion, while the cepstral domain approach uses analysis-synthesis with cepstral d…

iOS application for finding formants in spoken sounds

Text-to-Speech Toolkit of the Speech and Language Technologies Group at the University of Stuttgart. Objectives of the development are simplicity, modularity, controllability and multilinguality.

Scripts for LIUM SpkDiarization tools

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

Speech recognition toolkit for the arduino

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

Combining Weighted Multi-resolution STFT Loss and Distance Fusion to Optimize Speech Enhancement Generative Adversarial Networks

Spokestack: give your iOS app a voice interface!

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

Library to build speech synthesis systems designed for easy and fast prototyping.

This is the main repository of open-sourced speech technology by Huawei Noah's Ark Lab.

Natural Language Processing to generate new speeches for the President of Turkey.

Trained speaker embedding deep learning models and evaluation pipelines in pytorch and tesorflow for speaker recognition.

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

A curated list of awesome Speech Enhancement papers, libraries, datasets, and other resources.

Keras (tensorflow) implementation of SincNet (Mirco Ravanelli, Yoshua Bengio - https://github.com/mravanelli/SincNet)

Pitch shifter using WSOLA and resampling implemented by Python3

Tracking the progress in end-to-end speech translation

This repository is a curated list of awesome Speech Keyword Spotting (Wake-Up Word Detection).

Open source audio annotation tool for humans™

Spokestack: give your React Native app a voice interface!

a neural network toolbox for animal vocalizations and bioacoustics

Pytorch implementation of subband decomposition

Discriminative Neural Clustering for Speaker Diarisation

Bob is a free signal-processing and machine learning toolbox originally developed by the Biometrics group at Idiap Research Institute, in Switzerland. - Mirrored from https://gitlab.idiap.ch/bob/bob

[wip]Speech recognition tool-box written by Nim. Based on Arraymancer.

A data annotation pipeline to generate high-quality, large-scale speech datasets with machine pre-labeling and fully manual auditing.

A neural network for end-to-end speech denoising

Real-time GCC-NMF Blind Speech Separation and Enhancement

Reading list for research topics in multimodal machine learning

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

A implementation of Power Normalized Cepstral Coefficients: PNCC

Deep neural network based speech enhancement toolkit

UniSpeech - Large Scale Self-Supervised Learning for Speech

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].

Problem Agnostic Speech Encoder

python speech signal processing library

ConvNets for Audio Recognition using Google Commands Dataset

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

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

Novoic's audio feature extraction library

Book Library of P&W Studio