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Demucs is a deep learning stem splitter and AI music de-mixing software used to isolate vocals and instruments from a single audio file. It functions as a PyTorch audio source separation tool that splits mixed tracks into individual stems such as drums, bass, and vocals.
The main features of facebookresearch/demucs are: Source Separation Tools, Audio Source Separation Models, Hybrid Spectral-Waveform Separators, Audio De-mixing Software, Hybrid Domain Audio Processing, Audio Stem Extractors, Vocal Isolation, Vocal-to-Instrumental Converters.
Projects with overlapping indexed features include: anjok07/ultimatevocalremovergui — Ultimate Vocal Remover is a desktop application designed for AI-driven audio source separation. It utilizes deep… jianchang512/vocal-separate — Vocal-separate is an audio processing tool designed to isolate vocal and instrumental tracks from audio and video… deezer/spleeter — Spleeter is an AI audio source separation library and deep learning toolkit designed to split mixed music files into… ace-step/ace-step-1.5 — ACE Step 1.5 is a local text-to-music generation and audio editing system that runs on consumer hardware. It… boy1dr/spleetergui — SpleeterGui is a graphical interface for the Spleeter machine learning library, serving as an AI source separation… fspecii/ace-step-ui — ace-step-ui is an AI music production workspace and interface for generating, editing, and organizing synthetic audio…
Ultimate Vocal Remover is a desktop application designed for AI-driven audio source separation. It utilizes deep learning models to isolate vocals, drums, and other individual instruments from mixed audio files, providing a utility for professional production and creative editing workflows. The software distinguishes itself by leveraging GPU-accelerated tensor computation to perform complex signal processing tasks, significantly reducing the time required for high-fidelity audio extraction. It incorporates a modular plugin architecture that integrates external utilities to support a wide rang
Vocal-separate is an audio processing tool designed to isolate vocal and instrumental tracks from audio and video files. It functions as a local artificial intelligence engine that performs source separation directly on the user's machine, ensuring data privacy by eliminating the need for external server connectivity. The system provides a browser-based control interface for managing media uploads and monitoring processing tasks. To handle intensive signal decomposition, it utilizes hardware-accelerated tensor processing, which offloads complex mathematical calculations to dedicated graphics
ACE Step 1.5 is a local text-to-music generation and audio editing system that runs on consumer hardware. It transforms plain-language descriptions into full-length songs with lyrics, and can edit existing audio through cover generation, vocal removal, track separation, and selective repainting. The system supports multilingual prompts and lyrics in over 50 languages, and provides precise control over musical structure including duration, BPM, key, and time signature. The project distinguishes itself through a dual-stream diffusion architecture that processes separate latent streams for vocal
Spleeter is an AI audio source separation library and deep learning toolkit designed to split mixed music files into individual audio stems, such as vocals and drums. It provides a suite of pretrained models for isolating different instruments and voices from a recording. The toolkit includes capabilities for training and evaluating custom audio separation models using labeled datasets and configuration files. It also features utilities for measuring model performance by comparing separation outputs against reference datasets. The system manages audio processing through spectral representati