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
47 lines
2.3 KiB
Markdown
47 lines
2.3 KiB
Markdown
# Optimized runtimes
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Platform-specific runtimes for **Stable Audio 3** — each a self-contained,
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minimal-dependency way to run SA3 on a given accelerator. Pick by hardware:
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| Runtime | Platforms | Accelerator | Inference | Web UI | Training |
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| **[mlx](mlx)** | Apple Silicon Macs | Metal GPU (MLX) | ✅ | ✅ `./sa3-gradio` | ✅ LoRA (pure-MLX) |
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| **[tflite](tflite)** | macOS / Linux / Windows, x86 & ARM | CPU (LiteRT / XNNPACK) | ✅ | ✅ `./sa3-gradio` | LoRA *inference* only (weight-patching) |
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| **[tensorRT](tensorRT)** | Linux + NVIDIA GPU | CUDA / TensorRT | ✅ | — | — |
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All three read the same weights from
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[`stabilityai/stable-audio-3-optimized`](https://huggingface.co/stabilityai/stable-audio-3-optimized)
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(auto-downloaded on first use) and cover text-to-audio, audio-to-audio,
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inpainting, and CFG / negative-prompt guidance.
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## One-liners
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```bash
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# Apple Silicon (Metal GPU)
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curl -LsSf https://raw.githubusercontent.com/Stability-AI/stable-audio-3/main/optimized/mlx/bootstrap.sh | bash
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# Any CPU (macOS / Linux / Windows — needs Git Bash or WSL on Windows for curl|bash)
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curl -LsSf https://raw.githubusercontent.com/Stability-AI/stable-audio-3/main/optimized/tflite/bootstrap.sh | bash
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# Linux + NVIDIA
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curl -LsSf https://raw.githubusercontent.com/Stability-AI/stable-audio-3/main/optimized/tensorRT/bootstrap.sh | bash
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```
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## Highlights
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- **Web UI** — the **mlx** and **tflite** runtimes each ship a gradio app
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(`./sa3-gradio`) with every generation mode wired (text-to-audio, CFG /
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negative prompt, audio-to-audio, inpainting), tinted mel-spectrogram
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previews, model / precision hot-swap, and LoRA support.
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- **LoRA training** — the **mlx** runtime has a complete pure-MLX LoRA trainer
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(pre-encode → train → generate) that matches
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[underfit](https://github.com/dada-bots/underfit)'s conventions and
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checkpoint format, so it doubles as underfit's Apple-Silicon backend. See
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[mlx → LoRA training](mlx/README.md#lora-training). For a full training
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**dashboard** on a Mac, use underfit.
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- **LoRA inference** — every runtime loads `.safetensors` adapters (mlx/tflite
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merge or patch them into the graph; per-adapter strength + sampling-step
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gating on mlx).
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See each runtime's `README.md` for install, usage, flags, and benchmarks.
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