Música com IA
ACE-Step
ACE-Step — the open foundation model for music generation: full songs with vocals and lyrics in 19 languages, up to 4 minutes in about 20 seconds, Apache-2.0
★ 5,0 mil estrelasApache-2.0
Ver o guia →DiffRhythm é um projeto de música com IA de código aberto de ASLP-lab (ASLP-lab/DiffRhythm no GitHub). Tem 2.350 estrelas no GitHub e 279 forks, é publicado sob a licença Apache-2.0 e foi atualizado pela última vez no GitHub em 27 de novembro de 2025.
DiffRhythm — blazingly fast, embarrassingly simple full-length song generation with latent diffusion: complete songs with vocals from lyrics and a style prompt, Apache-2.0
Clone the repo and install espeak-ng (apt/brew, or the .msi on Windows) — inference depends on it, per the README.
conda create -n diffrhythm python=3.10 (or a plain venv), activate it, then pip install -r requirements.txt. On Windows, set the eSpeak NG path in your environment variables first, per the README.
Run the inference script with your lyrics and either a reference WAV file or a text style prompt; or use the Docker compose route in the docker folder.
Generates complete songs with vocals in one latent-diffusion model instead of stitched sections.
Condition on lyrics and a style described in text, or steer the sound with a reference audio file.
One repo, one requirements file, one inference script — the project pitch is that song generation does not need a pipeline empire.
DiffRhythm+ and DiffRhythm 2 extend the line, with papers, demos and a Hugging Face Space to try in the browser.
Get the code first.
git clone https://github.com/ASLP-lab/DiffRhythm.git
cd DiffRhythmThe README inference setup: espeak-ng, then conda + requirements.
sudo apt-get install espeak-ng
conda create -n diffrhythm python=3.10
conda activate diffrhythm
pip install -r requirements.txtThe README docker route: edit the compose bindings in the docker folder, compose up, then exec into the container.
docker compose up -d
docker exec -it DiffRhythm bashASLP-lab (the Audio, Speech and Language Processing lab team behind the repo) with named authors on the papers — ASLP-lab/DiffRhythm on GitHub.
Yes — Apache-2.0 licensed. Check rights for any reference audio or lyrics you feed it before releasing output commercially.
A Python 3.10 environment, the requirements file, espeak-ng installed, and a GPU for practical generation speeds — or use the Hugging Face Space demo to try it with none of that.
Against ACE-Step (also added this week), DiffRhythm is the simpler, single-purpose song generator — lyrics and style in, full song out — while ACE-Step is the broader foundation model with editing, cloning and repaint controls. Against Suno, DiffRhythm is open, self-hosted and Apache-2.0.
Clone, install espeak-ng, conda create -n diffrhythm python=3.10, pip install -r requirements.txt, then run inference with your lyrics and a style prompt.
ACE-Step — the open foundation model for music generation: full songs with vocals and lyrics in 19 languages, up to 4 minutes in about 20 seconds, Apache-2.0
Local AI song generator with an editable score — YuE2 on your GPU: full songs with vocals, sheet music, covers, exact replay. Native Windows app, no Python, …
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ComfyUI nodes for multilingual AI songs with lyrics via HeartMuLa.
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Os outros projetos de Música com IA com mais estrelas no catálogo do LibreAI — a lista realista de alternativas se DiffRhythm não for o ideal para você.
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🎵 The Ultimate Open Source Suno Alternative - Professional UI for ACE-Step 1.5 AI Music Generation. Free, local, unlimited. Stop paying for Suno!
ACE-Step — the open foundation model for music generation: full songs with vocals and lyrics in 19 languages, up to 4 minutes in about 20 seconds, Apache-2.0
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The hub for audio AI research: papers, open models, benchmarks & datasets across audio LLMs, speech recognition, TTS, music & audio generation.
Grátis e aberto: pegue o código-fonte, dê uma estrela no repositório e comece a criar.