Projects sharing features with Rnnoise

DeepFilterNet is a deep learning noise suppression framework and real-time audio filter designed to isolate speech and remove background noise from audio signals. It functions as a speech enhancement tool and audio processing middleware that cleans microphone input and speaker output for communication applications. The system operates as an audio server interceptor, delivering noise-suppressed streams between hardware devices and software applications. It provides capabilities for microphone signal cleaning and audio output filtering, including the creation of virtual noise-suppressed microph

Librosa is a Python audio analysis library and digital signal processing framework. It functions as a feature extraction suite and music information retrieval tool designed to analyze the structural and sonic characteristics of audio signals. The library provides specialized capabilities for music analysis, including dynamic tempo tracking to identify rhythmic pulses and spectral feature extraction to compute harmonic spectra, chroma variants, and onset points. It also serves as a time-series audio processor for synchronizing audio streams. The system covers a broad range of audio processing

ClearerVoice-Studio is a speech processing studio and framework designed for speech enhancement, audio super-resolution, and targeted voice extraction. It provides a suite of tools to remove background noise, increase the sampling rate of low-resolution recordings, and quantify audio clarity through objective quality evaluation metrics. The project features a target speaker extraction tool that isolates specific voices from mixed audio using acoustic, visual, or neural reference signals. It also includes capabilities for overlapping speech separation by capturing temporal patterns and long-ra

mlx-audio is an audio processing toolkit built on Apple MLX that provides speech transcription, text-to-speech synthesis, voice cloning, and audio source separation using local models. It offers an OpenAI-compatible REST API and web interface for running audio generation and transcription tasks, enabling drop-in integration with existing tools that follow that endpoint structure. The toolkit supports text-prompted audio source separation, allowing specific sounds to be isolated from mixed recordings based on natural language descriptions. It also provides voice cloning from a short reference

ESPnet is a comprehensive speech processing toolkit and PyTorch-based trainer designed for building end-to-end speech recognition, synthesis, and translation models. It provides a structured framework for developing automatic speech recognition systems using transducer and encoder-decoder architectures, alongside engines for text-to-speech synthesis and speech translation pipelines. The project distinguishes itself through a recipe-based workflow execution system that ensures experimental reproducibility by running standardized sequences of scripts for data preparation and model training. It

A library for audio and music analysis, feature extraction.

SimplE Lossless Audio

A C library for reading and writing sound files containing sampled audio data.

C library for cross-platform real-time audio input and output

🎵 Music notation engraving library for MEI with MusicXML and Humdrum support and various toolkits (JavaScript, Python)

Easy and efficient audio synthesis in C++

NI Media is a C++ library for reading and writing audio streams.

SpeechBrain is an all-in-one deep learning toolkit designed for speech and audio processing. Built as a modular library, it provides a structured environment for developing, training, and deploying neural network models across a wide range of tasks, including automatic speech recognition, speaker identification, and audio enhancement. The framework distinguishes itself through a configuration-driven approach that separates model architecture and training hyperparameters from application logic. By utilizing externalized configuration files and standardized recipes, it enables reproducible rese

Minimalistic MP3 decoder single header library

Tensorflow 2.x implementation of the stacked dual-signal transformation LSTM network (DTLN) for real-time noise suppression. This repository provides the code for training, infering and serving the DTLN model in python. It also provides pretrained models in SavedModel, TF-lite and ONNX format,…

A simple C++ library for reading and writing audio files.

A powerful cross-platform audio engine, optimized for games.

Free, easy, portable audio engine for games

C++ Audio and Music DSP Library

wav2letter is an automatic speech recognition toolkit and deep learning framework designed to convert audio speech signals into written text. It functions as a distributed training system and an inference engine for building and deploying neural network architectures. The system enables the training of large-scale speech models across multiple compute nodes using custom architecture files and structured recipes. It includes an inference engine that allows these trained models to be executed within Python workflows to transform audio sequences into text. The framework covers the full speech r

NoiseTorch is a cross-platform audio processor and real-time noise filter designed to suppress ambient sound from audio streams. It functions as a virtual microphone noise suppressor and routing tool, capturing system audio sources and directing filtered signals into virtual input or output devices. The application uses a recurrent neural network to distinguish between human speech and ambient noise. It provides a virtual denoising microphone that removes background noise from a selected input, alongside tools for filtering audio output streams. The system includes capabilities for audio dev

This project is an AI voice training framework and singing voice conversion tool. It uses VITS and SoftVC technologies to transform the timbre of singing and spoken audio recordings, allowing a user to change the vocal characteristics of a recording to match a specific target speaker. The system provides a web-based voice converter interface for managing model checkpoints and performing timbre transformation and pitch shifting. It supports exporting trained models to the ONNX format for use in external interfaces and lightweight runtimes. The framework covers the full production pipeline, in

ProjectM is a cross-platform music visualization library and pixel shader rendering engine. It functions as an audio signal analysis tool that extracts beat and frequency data from audio streams to drive real-time graphical changes. The engine is built for compatibility with the Milkdrop visualization standard, allowing it to parse and load external preset files to define visual styles. It supports the organization of these presets through playlist-driven management to automate transitions between different visual effects. The system can be integrated into external host applications as a sta

This project is a collection of deep learning research implementations and a reproduction kit designed to translate theoretical AI papers into working code. It provides a library of neural network architectures and reference implementations for reproducing seminal research concepts through interactive notebooks. The repository distinguishes itself through the implementation of AI theory and scaling laws, covering complexity dynamics, information theory, and the simulation of universal AI agents. It also includes a benchmarking suite for synthetic reasoning, allowing for the evaluation of mode

This project is an educational codebase and reference library that translates theoretical deep learning concepts into executable PyTorch code. It serves as a practical implementation of a deep learning textbook, providing a course-like structure of guided exercises and architectural examples for learning purposes. The repository includes a library of standard neural network architectures, including linear, convolutional, recurrent, and transformer models. It specifically implements a variety of deep learning patterns such as multilayer perceptrons, VGG networks, gated recurrent units, and lon

EasyEffects is a real-time audio processor and system-wide effects manager designed for PipeWire audio streams. It functions as a comprehensive suite for applying filters, equalizers, and limiters to both input and output audio across the entire system. The project distinguishes itself through its use of deep learning for neural network noise suppression and voice isolation, as well as its ability to simulate physical acoustic environments using impulse-response convolution. It includes a sophisticated preset management system that allows users to associate specific audio configurations with