AI vocal pitch shifter

This project is a singing voice conversion tool based on VITS generative modeling. It transforms the identity of a singing voice to a target speaker while preserving the original melody, lyrics, and intonation. The system distinguishes itself through hybrid voice synthesis, allowing for the blending of multiple speaker identities via linear model interpolation. It utilizes cluster-based feature retrieval to increase target voice similarity and employs a diffusion probabilistic model as a post-processor to remove electronic artifacts and improve vocal clarity. The software covers a broad rang

This project is a comprehensive software suite for voice synthesis and model management, providing a framework for training custom acoustic models and performing voice conversion. It utilizes deep-learning-based acoustic modeling to map source audio characteristics to target voice identities, enabling the transformation of input audio into specific vocal profiles. The system distinguishes itself through a feature-retrieval-based inference mechanism, which employs vector index files to perform nearest-neighbor searches on acoustic features for high-fidelity timbre matching. Users can manage th

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 system is a hybrid spectrogram waveform separator that combines spectral and waveform analysis. This approach allows the software to process audio in both frequency and time domains to achieve high-fidelity source separation. The tool provides capabilities for audio source separation, including acapella track extraction

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

This project is a deep learning toolkit designed for audio source separation and music information retrieval. It provides a framework for decomposing polyphonic audio signals into distinct components, such as vocals, drums, and bass, by processing raw waveforms through neural network architectures. The library enables users to train custom separation models or fine-tune existing ones to improve accuracy on specific audio datasets. It supports the entire model lifecycle, including the conversion of raw audio into structured, indexed formats to optimize data loading and training efficiency. Th

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

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

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-ui is an AI music production workspace and interface for generating, editing, and organizing synthetic audio tracks and vocals. It provides a technical control panel for managing prompts, seeds, and style parameters to produce high-quality audio. The project includes a digital audio workstation interface for trimming and fading files, alongside an audio stem separation tool that splits mixed tracks into individual components such as drums, bass, and vocals. It also features a music video creator for generating visual content and procedural album art to accompany generated music. The

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

Whisper-diarization is a system for identifying and separating different speakers in audio recordings by combining OpenAI Whisper for transcription with automated speaker attribution. It functions as a pipeline that isolates vocal tracks from background noise and assigns transcribed segments to specific individuals. The project uses forced alignment to synchronize transcribed text timestamps with audio signals, improving the accuracy of speaker attribution. It employs voice activity detection to separate speech from silence and noise, ensuring precise boundaries for identification. The syste

Sonic Pi is a live coding music environment and algorithmic composition tool. It functions as a Ruby-based audio engine that allows users to compose and perform music in real time by writing and executing code during playback. The platform serves as an educational coding platform, using sound creation as a play-based environment to teach software logic and programming principles. It also acts as a MIDI and OSC controller for synchronizing external hardware and software. The system covers a broad range of audio capabilities, including software-based sound synthesis, audio sample manipulation,