timoncool/ACE-Step-Studio

Portable AI music generator — full songs with vocals, covers, music videos. One-click install, 100% offline, NVIDIA GPU.

What it solves

ACE-Step Studio is a desktop application that allows users to generate full-length songs with vocals on their own hardware, removing the dependency on cloud services like Suno. It provides a comprehensive environment for music creation, including songwriting assistance, model management, and post-production tools.

How it works

The software is built on acestep.cpp, a native C++/GGML port of the ACE-Step 1.5 model. The user interface is developed using Rust and React within a Tauri window. It utilizes a language model (the ACE-Step planner, local Gemma, or OpenRouter) to plan the song's structure, tempo, and lyrics, which is then rendered into audio by a Diffusion Transformer (DiT) on the user's GPU (supporting CUDA for NVIDIA, Vulkan for AMD/Intel, or CPU fallback).

Who it’s for

Musicians, content creators, and AI music enthusiasts who want to run high-quality vocal music generation locally on Windows (with experimental Linux support) and have a GPU with at least 4 GB of VRAM.

Highlights

  • Local Generation: Generates full songs with vocals offline on NVIDIA cards starting from 4 GB VRAM.
  • Integrated Songwriting: Includes a writing assistant to generate captions and lyrics based on official songwriting rules.
  • LoRA Support: Features a curated catalogue of 131 LoRAs and the ability to train custom LoRAs on local hardware using HOT-Step.
  • Comprehensive Toolset: Includes stem separation (HT-Demucs), audio-to-MIDI conversion (MuScriptor), a MIDI editor, and a visualizer for creating music videos.
  • Agentic Control: Provides an MCP server allowing AI agents (like Claude Code or Cursor) to fully drive the studio's functions.
  • Customization: Offers a Winamp 2 skin mode, a ten-band equalizer, and MilkDrop visualizers.

Related

  • Project
    ace-step/ACE-Step-1.5

    ACE‑Step 1.5 is an open‑source music‑generation foundation model that combines a language‑model planner with a Diffusion‑Transformer audio decoder. It can create full songs (10 s‑10 min) in under 10 seconds on a RTX 3090, runs with <4 GB VRAM for base models, and supports LoRA fine‑tuning from a handful of tracks. The repo provides Gradio and REST interfaces, launch scripts for all major OSes, multiple model sizes (including a 4 B XL version), and extensive control over lyrics, style, tempo, and instrumentation.

  • Project
    timoncool/YuE2-Studio

    YuE2 Studio is a standalone Windows application for generating, editing, and training AI music models, providing a complete workflow for creating songs from lyrics and scores without needing Python.

  • Project
    debpalash/VoiceStudio

    VoiceStudio is an open‑source desktop app (Electron) for voice cloning, voice design, video dubbing, dictation, transcription and audiobook creation in hundreds of languages. It runs locally on macOS, Windows or Linux, supports GPU acceleration, offers a model manager, and exposes a local API/MCP for integration with AI agents.

  • Project
    ServeurpersoCom/acestep.cpp

    acestep.cpp is a C++‑based local server that generates 48 kHz stereo music from text prompts. It uses GGML‑quantized models (LM, text encoder, diffusion DiT, VAE) and runs on CPU, CUDA, Vulkan or Metal. After downloading the four GGUF model files, you build the binary (CPU‑only, CUDA, Vulkan, or all‑in‑one) and start the server. A web UI at http://localhost:8085 lets you write a caption, select a model/LoRA adapter, and generate, play, or download tracks. The server also offers a tiny REST API (POST /lm, /synth, /understand, /vae) and CLI tools for scripting. It is a genuine open‑source AI music‑generation project, not a tutorial list.

  • Project
    AkshitIreddy/AI-Powered-Video-Tutorial-Generator

    AI‑Powered Video Tutorial Generator is a Windows‑only desktop app that turns a question or topic into a fully editable video lesson. It handles research, script writing, storyboard creation, narration, illustration, presenter animation and final export, using either local diffusion models (ComfyUI/SDXL) or cloud LLM/image providers. The open‑source stack combines a React/Tauri UI, a Rust core, and a Python pipeline, and it ships with bundled runtimes (Python, Node, Chromium, FFmpeg).