110 Open source projects that are alternatives of or similar to ConvolutionaNeuralNetworksToEnhanceCodedSpeech

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

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

🔉 spafe: Simplified Python Audio Features Extraction

Pytorch implementation of subband decomposition

基于DTW与MFCC特征进行数字0-9的语音识别，DTW，MFCC，语音识别，中英数据，端点检测，Digital Voice Recognition。

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

PyTorch implementation of convolutional neural networks-based text-to-speech synthesis models

Implement Wave-U-Net by PyTorch, and migrate it to the speech enhancement.

Python Telemac Tools for post-processing tasks (includes a workflow)

Audio feature extraction and classification

ConvNets for Audio Recognition using Google Commands Dataset

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

Basics of Musical Instruments Classification using Machine Learning

Collection of EM algorithms for blind source separation of audio signals

Shows how to support rendering per-instance motion vectors within Indirect instanced drawing of Unity.

Neural speaker recognition/verification system based on Kaldi and Tensorflow

A pytorch implementation of MFCC.

Emotion recognition by speech in android.

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.

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.

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

iOS application for finding formants in spoken sounds

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

Software to post-process gridded weather forecasts

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

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

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].

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

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

Post Processing Stack

WaveNet vocoder

A program for automatic speaker identification using deep learning techniques.

A minimum unofficial implementation of the "A Convolutional Recurrent Neural Network for Real-Time Speech Enhancement" (CRN) using PyTorch

A simple audio feature extraction library

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

aubio plugins for Vamp

Deep Learning-based Voice Conversion system

This project's main goal is to generate and visualize terrain built using voxels. It was achieved using different approaches and computing technologies just for the sake of performance and implementation comparison.

A light weight neural speaker embeddings extraction based on Kaldi and PyTorch.

Extract mfcc vectors and phones from TIMIT dataset

A modified version of Speech Signal Processing Toolkit (SPTK)

A wavelet audio denoiser done in python

Discriminative Neural Clustering for Speaker Diarisation

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

Speech recognition toolkit for the arduino

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

No description or website provided.

A implementation of Power Normalized Cepstral Coefficients: PNCC

Real-time GCC-NMF Blind Speech Separation and Enhancement

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

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

Open source audio annotation tool for humans™

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 neural network for end-to-end speech denoising

a library for audio and music analysis

Deep neural network based speech enhancement toolkit

Problem Agnostic Speech Encoder

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

A generic post-processing injector for games and video software.

Machine learning, in numpy