AI音楽
torch-log-wmse
An audio quality metric and loss for training cleaner models.
★ 50 スターApache-2.0
ガイドを見る →Stable Audio Toolsは、Stability-AIが公開しているオープンソースのAI音楽プロジェクトです（GitHub: Stability-AI/stable-audio-tools）。GitHubスターは3,892、フォークは488で、MITライセンスの下で公開されています。GitHubでの最終更新は2026年10月24日です。
Stable Audio Tools — training and inference code for Stability's audio generation models, including Stable Audio Open, MIT
Clone, then sync for inference only — or add the train and UI extras for the full toolkit.
For Stable Audio Open 1.0, accept the model terms on its Hugging Face page before downloading.
Launch the interface pointed at the pretrained model and generate audio from prompts.
Dataset training, inference and a Gradio UI live together, instead of three separate projects.
Generate music and sound with the open model, on your own hardware.
PyTorch 2.5+ with Flash Attention and Flex Attention for faster generation.
Prefer pip over uv? The package installs directly with its train extra.
The README install route with uv.
git clone https://github.com/Stability-AI/stable-audio-tools.git
cd stable-audio-tools
uv syncTraining plus the Gradio UI in one sync.
uv sync --extra train --extra uiPointed at Stable Audio Open 1.0 after accepting its Hugging Face terms.
uv run python run_gradio.py --pretrained-name stabilityai/stable-audio-open-1.0Stability AI, as the training and inference code for its audio generation models.
The code is MIT licensed; the pretrained Stable Audio Open model has its own Hugging Face terms to accept before download.
Python 3.10, PyTorch 2.5+ (for Flash/Flex Attention), and uv for the recommended install.
Yes — training is a first-class part of the repo, with dataset configs and a train extra in the install.
Clone, uv sync, accept the Stable Audio Open terms on Hugging Face, and run the Gradio interface.
An audio quality metric and loss for training cleaner models.
The hub for audio AI research: papers, open models, benchmarks & datasets across audio LLMs, speech recognition, TTS, music & audio generation.
Loss functions for training audio source separation models.
A Python inference package for music source separation models.
A curated MIDI dataset built for training precise music AI models.
Research code for instruction-based text-to-music editing of music models.
LibreAIカタログに掲載されたAI音楽プロジェクトの中で最もスターの多いもの — Stable Audio Toolsが合わなかった場合に検討したい、現実的な候補リストです。
YuE2: frontier music generation with symbolic planning, zero-shot covers, and agentic music editing.
🎵 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
Official DiffSinger code: diffusion-based singing voice synthesis and text-to-speech (AAAI 2022)
Use API to call the music generation AI of suno.ai, and easily integrate it into agents like GPTs.
カタログの中でStable Audio Toolsとスター数が近いAI音楽プロジェクト — 前後の棚もそのまま見て回れます。
Official DiffSinger code: diffusion-based singing voice synthesis and text-to-speech (AAAI 2022)
Use API to call the music generation AI of suno.ai, and easily integrate it into agents like GPTs.
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
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
🎵 The Ultimate Open Source Suno Alternative - Professional UI for ACE-Step 1.5 AI Music Generation. Free, local, unlimited. Stop paying for Suno!
A WebUI app for Music-Source-Separation-Training and we bundled UVR together!