初始提交：3 款纯音频游戏 + 共享引擎 + 完整文档与资产管线
- 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
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SoundGame Dev
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# SAME autoencoder benchmarks
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Eight configurations — `{SAME-S, SAME-L} × {16-bit, fp8} × {chunked, single-shot}` — across
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32 latent lengths from L=1 to L=8192. 16-bit means fp16 on SAME-L and bf16 on SAME-S, the
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precision each model actually ships.
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Open [`same-ae-sweep.html`](same-ae-sweep.html) for the interactive version — hover any point
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for its value. It is self-contained (no external assets), so it works offline. The same six
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charts are below.
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### Encoder
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### Decoder
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## Reproducing
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```bash
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python ../scripts/bench_autoencoders.py --out same-ae-sweep.json --music /path/to/music
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python ../scripts/make_ae_charts.py same-ae-sweep.json same-ae-sweep.html
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```
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`--music` takes a directory, a file, or a `.npy` of shape `(2, samples)` at 44.1 kHz. **Use
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real music, generated music, or SFX — never noise.** These autoencoders are strongly
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content-dependent and noise fabricates numbers that do not hold on music. The figures below
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come from seven mastered tracks (rock, jazz, classical, electronic, hip hop, piano, folk)
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concatenated to 762 s: L=8192 is 761 s, longer than any single song, and tiling one track
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would be artificially self-similar.
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Measured on one H200 (sm_90).
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## Results
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| model | precision | dec scratch chunked | single-shot | dec ms @L=4096 chunked | single-shot | SNR L≥16 |
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|---|---|---|---|---|---|---|
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| SAME-L | 16-bit | 485 MB | 7,766 MB | 250.0 | 195.6 | 4.885–4.887 dB |
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| SAME-L | fp8 | 485 MB | 7,766 MB | 216.4 | 172.6 | 4.871–4.873 dB |
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| SAME-S | 16-bit | 346 MB | 5,512 MB | 39.3 | 23.7 | 3.836–3.856 dB |
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| SAME-S | fp8 | 327 MB | 5,204 MB | 36.7 | 20.4 | 3.784–3.803 dB |
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**VRAM is flat in L.** TensorRT commits a context's scratch from its *profile ceiling*, not
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from the shape you bind, so the low band costs the same at L=1 as at L=8192. That is the
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whole point of the two-profile build: 16× less scratch, for 2–5% end-to-end (the DiT
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dominates a render).
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**Chunked and single-shot are identical in accuracy** — to three decimals, at every length,
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on both models and both precisions. Since single-shot has no seams at all, that is the direct
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evidence the windowed overlap is exact.
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**fp8 costs 0.014 dB** on SAME-L and 0.052 dB on SAME-S.
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**Single-shot stops at L=4096**, the wide band's ceiling. Chunked keeps going because it
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windows.
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## Two measurement traps
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**Accuracy must be measured with content held fixed.** The obvious construction — take the
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first L latents, round-trip them, score against the original — turns the L axis into a
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content walk, because a longer L is a *different, longer piece of music*. Measured: at a
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fixed L, moving the excerpt swings SNR by **9.4–15.0 dB**, while the whole L axis at a fixed
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offset moves **2.95 dB**. Content dominates by ~4×, so that chart plots the music, not the
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model, and it looks like a length-dependent defect that is not there. The default
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`--accuracy fixed` instead reassembles one region from L-sized blocks so only the processing
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length changes; it comes out flat to 0.002 dB (SAME-L) above L=16. `--accuracy absolute`
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keeps the naive construction when the absolute number is what you want.
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There is one real effect below L≈16: SNR falls from 4.89 to 4.21 dB on SAME-L. That is not a
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seam — it is 0.09 s blocks encoded independently, losing context at every boundary.
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**Waveform SNR understates a perceptual autoencoder.** The same pipeline scores ~16 dB on
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generated audio and ~5 dB on real masters, and published figures for this family span roughly
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−23 dB (solo piano) to −4 dB (dense circus). Compare configurations to each other at a given
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L; do not read the absolute height as a quality verdict, and do not let any of it substitute
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for listening.
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The level is not the limiter, though it looks like a suspect: the decoders bake a 0.977
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ceiling and real masters here peak at 1.019. Re-measured with the input scaled from 0.9 down
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to 0.225 the SNR moves 0.03 dB, and an optimal scalar gain-match recovers 0.27 dB.
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The shipped SAME-S decoder also has a `RandomNormalLike` at its bottleneck, so its round trip
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carries run-to-run variance belonging to neither precision nor chunking.
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