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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"""Vanilla PyTorch FP32 inference for SA3 medium DiT + SAME-L decoder.
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Drop-in alternative to SA3Inference for the "GT PyTorch eager" comparison
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backend in the gradio app. Uses stable_audio_tools directly for everything
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except T5 encoding (we still reuse the TRT T5 engine — it's fast, validated
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equivalent to PT BF16, and avoids re-loading the 538MB T5Gemma weights).
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Public API matches SA3Inference's relevant subset:
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inf = PTInference() # lazy-loads PT model at first .generate()
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pcm, timing = inf.generate(prompt, seconds=120.0, steps=8, seed=1)
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"""
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from __future__ import annotations
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import json
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import math
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import sys
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import time
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from pathlib import Path
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from typing import Optional
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import numpy as np
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import torch
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TRT_REPO = Path("/weka2/cj/clod/sa3s/stable-audio-3/optimized/tensorRT")
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SCRIPTS_DIR = TRT_REPO / "scripts"
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sys.path.insert(0, str(SCRIPTS_DIR))
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# stable_audio_tools is expected to be importable (the venv this gradio runs in
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# must include it). The producer venv at /admin/home-cj/sa/venvs/sa3_torch29
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# has both gradio + tensorrt + stable_audio_tools.
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SAT_PATH = "/admin/home-cj/sa/stable-audio-tools-dev-latest"
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if SAT_PATH not in sys.path:
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sys.path.insert(0, SAT_PATH)
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# Defaults — matches the user's "medium / SAME-L / fp32" scope.
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PT_MODEL_DIR = Path("/weka2/cj/clod/sa3s/models/SA3-M-hf")
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T5_TRT_PATH = TRT_REPO / "models" / "sm_90" / "t5gemma" / "t5gemma_fp16.trt"
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SAMEL_CKPT_DIR = Path("/weka2/cj/clod/sa3s/models/SAME-L") # local SAME-L = TRT decoder's weight source
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SAMES_CKPT_DIR = Path("/weka2/cj/clod/sa3s/models/SAME-S") # local SAME-S = TRT SAME-S decoder's weight source
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SAMPLE_RATE = 44100
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SAMPLES_PER_LATENT = 4096
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T5_MAX_LEN = 256
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SIGMA_MAX = 1.0
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def _patch_pt():
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"""No-op for now. The transformer SDPA fallback in stable_audio_tools is NOT
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numerically equivalent to flash_attn for this model: encode→decode round-trip
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cosine drops from 0.78 (flash_attn) → 0.005 (SDPA) on real music. Keep
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flash_attn enabled."""
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return
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def _resolve_pt_model_dir() -> Path:
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"""Find the SA3-M HF safetensors dir.
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The producer build references either SA3-M-hf or SA3-medium-ARC. Try both;
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return the first match.
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"""
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for name in ("SA3-M-hf", "SA3-M", "SA3-medium-ARC", "SA3-medium"):
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d = Path("/weka2/cj/clod/sa3s/models") / name
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if (d / "model.safetensors").exists() and (d / "model_config.json").exists():
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return d
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raise FileNotFoundError(
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f"Couldn't locate the medium DiT PyTorch checkpoint under "
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f"/weka2/cj/clod/sa3s/models/. Looked for SA3-M-hf / SA3-M / SA3-medium-ARC / SA3-medium."
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)
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def _load_pt_model(model_dir: Path, device="cuda"):
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from stable_audio_tools.models.factory import create_model_from_config
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from stable_audio_tools.models.utils import copy_state_dict
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from safetensors.torch import load_file
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with open(model_dir / "model_config.json") as f:
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cfg = json.load(f)
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# SAT's T5GemmaConditioner only takes model_name; HF cfg adds repo_id/subfolder
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for c in cfg.get("model", {}).get("conditioning", {}).get("configs", []):
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if c.get("type") == "t5gemma":
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cc = c.get("config", {})
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cc.pop("repo_id", None); cc.pop("subfolder", None)
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cc.setdefault("model_name", "google/t5gemma-b-b-ul2")
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model = create_model_from_config(cfg)
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sd = load_file(model_dir / "model.safetensors")
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copy_state_dict(model, sd)
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model = model.float().to(device).eval()
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return model, sd
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def _load_decoder(ckpt_dir: Path, name: str, device="cuda"):
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"""Load a LOCAL SAME-{S,L} AudioAutoencoder — same weights as the TRT decoder.
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Noise sources are disabled to match TRT's deterministic rewrite (the producer's
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ONNX validator disables `bottleneck.noise_regularize` and
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`decoder.layers[3].mask_noise` before comparing — those random kernels would
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inject buzz at inference if left on).
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"""
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from stable_audio_tools.models.factory import create_model_from_config
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from stable_audio_tools.models.utils import load_ckpt_state_dict, copy_state_dict
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with open(ckpt_dir / f"{name}.json") as f:
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cfg = json.load(f)
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model = create_model_from_config(cfg).to(device).eval()
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sd = load_ckpt_state_dict(str(ckpt_dir / f"{name}.ckpt"))
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copy_state_dict(model, sd)
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model.bottleneck.noise_regularize = False
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model.decoder.layers[3].mask_noise = 0
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return model
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def _load_samel_decoder(device="cuda"):
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return _load_decoder(SAMEL_CKPT_DIR, "SAME-L", device=device)
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def _load_sames_decoder(device="cuda"):
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return _load_decoder(SAMES_CKPT_DIR, "SAME-S", device=device)
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def _build_dist_shift():
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"""Import runtime.DistributionShift for the sigma schedule shift."""
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import runtime as rt
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return rt.DistributionShift()
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class PTInference:
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"""Vanilla PyTorch FP32 inference: PT DiT + PT decoder(s), TRT T5.
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Scope: medium DiT + SAME-L / SAME-S decoders + FP32 only.
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The DiT is loaded once; both decoders are loaded so generate(decoder=...)
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can pick between them without reloading anything.
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Other configs raise NotImplementedError.
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Thread-safe via an internal Lock (the underlying PyTorch model state is
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shared mutable; serialize generate() calls).
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"""
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def __init__(self, device: str = "cuda", *,
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load_samel: bool = True, load_sames: bool = True):
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import threading
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self._lock = threading.Lock()
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self.device = device
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print(" PT FP32 GT: patching stable_audio_tools + loading checkpoint...", flush=True)
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_patch_pt()
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t0 = time.time()
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model_dir = _resolve_pt_model_dir()
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self.pt_model, self.sd = _load_pt_model(model_dir, device=device)
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print(f" PT model loaded from {model_dir.name} in {time.time()-t0:.1f}s "
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f"({sum(p.numel() for p in self.pt_model.parameters())/1e6:.0f}M params)",
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flush=True)
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self.pt_dit = self.pt_model.model.model
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# Use the LOCAL SAME-L / SAME-S AudioAutoencoders — same weights as the
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# TRT decoder engines (the engines are an ONNX rewrite of these PT models).
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self.pt_samel = None
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self.pt_sames = None
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if load_samel:
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t0 = time.time()
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self.pt_samel = _load_samel_decoder(device=device)
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print(f" SAME-L decoder loaded from {SAMEL_CKPT_DIR.name} in {time.time()-t0:.1f}s "
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f"(noise sources disabled)", flush=True)
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if load_sames:
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t0 = time.time()
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self.pt_sames = _load_sames_decoder(device=device)
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print(f" SAME-S decoder loaded from {SAMES_CKPT_DIR.name} in {time.time()-t0:.1f}s "
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f"(noise sources disabled)", flush=True)
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# Reuse TRT T5 (fast + validated). Loaded lazily via canon to share
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# the same heavy-imports path the SA3Inference uses.
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import sa3_trt_core as canon
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canon._import_heavy()
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import runtime as rt
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rt.MODELS_DIR = str(TRT_REPO / "models")
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rt.ARCH_DIR = str(TRT_REPO / "models" / "sm_90")
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self.tokenizer = rt.load()["tokenizer"]
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self.t5_runner = canon.TRTRunner(T5_TRT_PATH)
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self.dist_shift = _build_dist_shift()
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self._canon = canon
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# ── conditioning helper (FP32 PT path with the 2π factor baked in) ────
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def _build_cond(self, t5_hidden, t5_mask, seconds_total: float):
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sd = self.sd
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padding_emb = sd["conditioner.conditioners.prompt.padding_embedding"].float().to(self.device)
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sec_w = sd["conditioner.conditioners.seconds_total.embedder.embedding.1.weight"].float().to(self.device)
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sec_b = sd["conditioner.conditioners.seconds_total.embedder.embedding.1.bias"].float().to(self.device)
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half = 128
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ramp = torch.linspace(0, 1, half, device=self.device)
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freqs = torch.exp(ramp * (math.log(10000.0) - math.log(0.5)) + math.log(0.5))
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s = torch.tensor([seconds_total], device=self.device).clamp(0, 384) / 384
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args = s.unsqueeze(-1) * freqs * 2 * math.pi
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ff = torch.cat([args.cos(), args.sin()], dim=-1)
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sec_emb = (ff @ sec_w.T + sec_b).unsqueeze(1)
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m = t5_mask.unsqueeze(-1).bool()
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pe = padding_emb.view(1, 1, -1).expand_as(t5_hidden)
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t5_padded = torch.where(m, t5_hidden, pe)
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return torch.cat([t5_padded, sec_emb], dim=1), sec_emb.squeeze(1)
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@staticmethod
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def resolve_T_lat(seconds: float, decoder: str) -> int:
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"""seconds → T_lat (no even-bump for SAME-L, since SAME-L doesn't care)."""
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T_lat = max(1, math.ceil(seconds * SAMPLE_RATE / SAMPLES_PER_LATENT))
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return T_lat
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def generate(self, prompt: str, *,
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seconds: float = 120.0, steps: int = 8,
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seed: Optional[int] = None,
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decoder: str = "same-l",
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# Future kwargs (NotImplementedError until wired):
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init_noise_level: float = 1.0,
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negative_prompt: Optional[str] = None,
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cfg: float = 1.0,
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init_audio_path: Optional[str] = None,
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inpaint_range: Optional[tuple] = None,
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) -> tuple[np.ndarray, dict]:
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if cfg != 1.0:
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raise NotImplementedError("CFG not yet wired through PTInference")
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if init_audio_path is not None or inpaint_range is not None:
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raise NotImplementedError("audio-to-audio / inpaint not yet wired")
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if init_noise_level != 1.0:
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raise NotImplementedError("non-unity init_noise_level not yet wired")
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if decoder == "same-l":
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pt_decoder = self.pt_samel
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elif decoder == "same-s":
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pt_decoder = self.pt_sames
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else:
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raise ValueError(f"unknown decoder={decoder!r}; valid: same-l, same-s")
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if pt_decoder is None:
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raise RuntimeError(f"decoder {decoder!r} was not loaded — pass load_{decoder.replace('-','_')}=True to PTInference()")
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canon = self._canon
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device = self.device
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T_lat = self.resolve_T_lat(seconds, decoder)
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if not (1 <= T_lat <= 4096):
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raise ValueError(f"T_lat={T_lat} out of [1, 4096]")
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if seed is None:
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import random
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seed = random.randint(0, 2**31 - 1)
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with self._lock:
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t_total = time.time()
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# Match official generate_diffusion_cond: disable TF32 + benchmark for
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# FP32-faithful math.
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torch.backends.cuda.matmul.allow_tf32 = False
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torch.backends.cudnn.allow_tf32 = False
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torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = False
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torch.backends.cudnn.benchmark = False
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# 1. T5 encode via TRT (fast).
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t0 = time.time()
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t5_hidden, t5_mask = canon.t5gemma_encode(self.t5_runner, self.tokenizer, prompt)
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t5_hidden = t5_hidden.to(device).float(); t5_mask = t5_mask.to(device)
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t5_ms = (time.time() - t0) * 1000
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# 2. Conditioning + sigmas.
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cond_ca, cond_g = self._build_cond(t5_hidden, t5_mask, seconds)
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sigmas = torch.linspace(SIGMA_MAX, 0.0, steps + 1, device=device)
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sigmas = self.dist_shift.shift(sigmas, T_lat); sigmas[0] = SIGMA_MAX
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# 3. Sampling.
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g_init = torch.Generator(device=device); g_init.manual_seed(int(seed))
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g_loop = torch.Generator(device=device); g_loop.manual_seed(int(seed) + 1)
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x = torch.randn(1, 256, T_lat, device=device, dtype=torch.float32, generator=g_init)
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local_add_cond = torch.zeros(1, 257, T_lat, device=device)
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t0 = time.time()
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with torch.no_grad():
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for i in range(steps):
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t_curr = sigmas[i]; t_next = sigmas[i + 1]
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v = self.pt_dit._forward(
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x, t_curr.unsqueeze(0).contiguous(),
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cross_attn_cond=cond_ca, global_embed=cond_g,
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local_add_cond=local_add_cond,
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)
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denoised = x - t_curr * v
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if i < steps - 1:
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noise = torch.randn(*x.shape, device=device, dtype=x.dtype, generator=g_loop)
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x = (1.0 - t_next) * denoised + t_next * noise
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else:
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x = denoised
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torch.cuda.synchronize()
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sampling_ms = (time.time() - t0) * 1000
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# 4. Decode FP32 via selected decoder (matches TRT decoder weights).
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t0 = time.time()
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with torch.no_grad():
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audio_fp32 = pt_decoder.decode(x.float())
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torch.cuda.synchronize()
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decode_ms = (time.time() - t0) * 1000
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# 5. FP32 audio → int16 stereo PCM (same formula as TRT pipeline).
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pcm_torch = (audio_fp32.clamp(-1.0, 1.0) * 32767.0).to(torch.int16)
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pcm = pcm_torch.squeeze(0).T.contiguous().cpu().numpy()
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# Trim to requested seconds.
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actual_samples = int(round(seconds * SAMPLE_RATE))
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pcm = pcm[:actual_samples].copy()
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inference_ms = (time.time() - t_total) * 1000
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return pcm, {
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"inference_ms": inference_ms,
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"graph_build_ms": 0.0, # n/a for PT
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"realtime": (seconds * 1000.0) / inference_ms if inference_ms > 0 else 0.0,
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"seed": int(seed),
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"T_lat": T_lat,
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"samples": actual_samples,
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"t5_ms": t5_ms,
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"sampling_ms": sampling_ms,
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"decode_ms": decode_ms,
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}
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# ── module-level singleton (lazy) ─────────────────────────────────────────
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_pt_inference: Optional[PTInference] = None
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def get_pt_inference() -> PTInference:
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"""Return the singleton PTInference, loading on first call (~30 sec)."""
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global _pt_inference
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if _pt_inference is None:
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_pt_inference = PTInference()
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return _pt_inference
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