GUI for a Vocal Remover that uses Deep Neural Networks.

Gradio WebUI for creators and developers, featuring key TTS (Edge-TTS, kokoro) and zero-shot Voice Cloning (E2 & F5-TTS, CosyVoice), with Whisper audio processing, YouTube download, Demucs vocal isolation, and multilingual translation.

Isolate vocals, drums, bass, and other instrumental stems from any song

an extremely simple tool for separating vocals and background music, completely localized for web operation, using 2stems/4stems/5stems models 这是一个极简的人声和背景音乐分离工具，本地化网页操作，无需连接外网

Omniscient Mozart, being able to transcribe everything in the music, including vocal, drum, chord, beat, instruments, and more.

Vocal Remover using Deep Neural Networks

All in One Version : Youtube WAV Download, Separating Vocal, Splitting Audio, Training, and Inference Using Google Colab

Easy to use stem (e.g. instrumental/vocals) separation from CLI or as a python package, using a variety of amazing pre-trained models (primarily from UVR)

YARG (a.k.a. Yet Another Rhythm Game) is a free, open-source, plastic guitar game that is still in development. It supports guitar (five fret), drums (plastic or e-kit), vocals, pro-keys, and more!

Ultimate Vocal Remover 5 with Gradio UI. Separate an audio file into various stems, using multiple models

Self-hostable web app for isolating the vocal, accompaniment, bass, and drums of any song. Supports Spleeter, Demucs, BS-RoFormer. Built with React and Django.

Separate stems (vocals, bass, drums, other) from audio. Recombine, tempo match, slice/crop audio

AI based tool to convert vocals lyrics and pitch from music to autogenerate Ultrastar Deluxe, Midi and notes. It automatic tapping, adding text, pitch vocals and creates karaoke files.

DALI: a large Dataset of synchronised Audio, LyrIcs and vocal notes.

Gender recognition by voice and speech analysis

No description available

A powerful, beautiful, and simple podcast client for the modern free desktop.

Vocaluxe is an open source singing game inspired by SingStar™ and Ultrastar Deluxe.

A vocal pitch correction web application (like Autotune)

Speech-to-speech AI assistant with natural conversation flow, mid-speech interruption, vision capabilities and AI-initiated follow-ups. Features low-latency audio streaming, dynamic visual feedback, and works with local LLM/TTS services via OpenAI-compatible endpoints.

A simple javascript plugin to show people how to say your name correctly.

A cli tool for split vocal timbre.

All in One Version : Youtube WAV Download, Separating Vocal, Splitting Audio, Training, and Inference Using Google Colab

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].

Free, open-source, 100% offline voice dictation for Linux. Speak and type anywhere via whisper.cpp, Whisper & VOSK engines, GPU-accelerated, works on X11 + Wayland!

Objective-C shim layer for Speech Recognition

Go Vocal is a digital democracy platform that facilitates community participation and co-creation. Participants can post ideas, contribute to discussions, or choose to vote and prioritize community projects.

:sound: :boy: :girl:Voice based gender recognition using Mel-frequency cepstrum coefficients (MFCC) and Gaussian mixture models (GMM)

A JUCE-based Open Source DAW

Vocalization sign language iOS App with deep learning using CoreML.