SoundGame Dev
f0f0a9b50e
- 共享引擎（app/src/main）：GestureEngine 6 手势识别、AudioEngine 三层音频 （ExoPlayer BGM 交叉淡入 / 对白 ducking / SoundPool 低延迟）、Game/GameEngine 状态机接口、MainActivity 全屏封面 + TalkBack - 《声命线》storyline：末世互动广播剧，3 幕 8 抉择点 4 结局 - 《声东击西》reflex：听声辨位反应，12 波战斗 + BGM 三档 BPM 变速 - 《暗室》escape：3x3 网格密室逃脱，钥匙→铁柜→保险丝→电箱→大门谜题链 - 文档：setup（从零搭建）/ architecture（架构）/ engine-api（引擎 API）/ asset-pipeline（资产流水线）/ games/*（三款游戏设计文档） - 资产管线：Qwen3-TTS 对白 + 程序合成音效/BGM + Mureka BGM 的 gen 脚本与台账， 全部 ogg 资产入库可复现 - env.d 环境脚本：android/tts 入库；sfx/mureka 含密钥不入库，仅提交 .example.sh 模板
Optimized runtimes
Platform-specific runtimes for Stable Audio 3 — each a self-contained, minimal-dependency way to run SA3 on a given accelerator. Pick by hardware:
| Runtime | Platforms | Accelerator | Inference | Web UI | Training |
|---|---|---|---|---|---|
| mlx | Apple Silicon Macs | Metal GPU (MLX) | ✅ | ✅ ./sa3-gradio |
✅ LoRA (pure-MLX) |
| tflite | macOS / Linux / Windows, x86 & ARM | CPU (LiteRT / XNNPACK) | ✅ | ✅ ./sa3-gradio |
LoRA inference only (weight-patching) |
| tensorRT | Linux + NVIDIA GPU | CUDA / TensorRT | ✅ | — | — |
All three read the same weights from
stabilityai/stable-audio-3-optimized
(auto-downloaded on first use) and cover text-to-audio, audio-to-audio,
inpainting, and CFG / negative-prompt guidance.
One-liners
# Apple Silicon (Metal GPU)
curl -LsSf https://raw.githubusercontent.com/Stability-AI/stable-audio-3/main/optimized/mlx/bootstrap.sh | bash
# Any CPU (macOS / Linux / Windows — needs Git Bash or WSL on Windows for curl|bash)
curl -LsSf https://raw.githubusercontent.com/Stability-AI/stable-audio-3/main/optimized/tflite/bootstrap.sh | bash
# Linux + NVIDIA
curl -LsSf https://raw.githubusercontent.com/Stability-AI/stable-audio-3/main/optimized/tensorRT/bootstrap.sh | bash
Highlights
- Web UI — the mlx and tflite runtimes each ship a gradio app
(
./sa3-gradio) with every generation mode wired (text-to-audio, CFG / negative prompt, audio-to-audio, inpainting), tinted mel-spectrogram previews, model / precision hot-swap, and LoRA support. - LoRA training — the mlx runtime has a complete pure-MLX LoRA trainer (pre-encode → train → generate) that matches underfit's conventions and checkpoint format, so it doubles as underfit's Apple-Silicon backend. See mlx → LoRA training. For a full training dashboard on a Mac, use underfit.
- LoRA inference — every runtime loads
.safetensorsadapters (mlx/tflite merge or patch them into the graph; per-adapter strength + sampling-step gating on mlx).
See each runtime's README.md for install, usage, flags, and benchmarks.