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
This commit is contained in:
SoundGame Dev
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import pytest
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import torch
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from tests.conftest import HAS_ACCEL
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from tests.utils.audio import assert_audio_valid, sine_wave
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DURATION_SEC = 10
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STEPS = 8
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STEPS_BASE = 50
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def test_text_to_audio_base(sa3_base_model, maybe_save_audio):
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model = sa3_base_model
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sr = model.model_config["sample_rate"]
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prompt = "trap drums, hip hop beat, 120bpm"
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audio = model.generate(
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prompt=prompt,
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duration=DURATION_SEC,
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steps=STEPS_BASE,
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cfg_scale=7.0,
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seed=1234,
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)
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maybe_save_audio(audio, sr, prompt)
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assert_audio_valid(audio, DURATION_SEC, sr)
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def test_text_to_audio(sa3_model, maybe_save_audio):
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model = sa3_model
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sr = model.model_config["sample_rate"]
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prompt = "trap drums, hip hop beat, 120bpm"
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audio = model.generate(
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prompt=prompt,
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negative_prompt="low-quality",
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duration=DURATION_SEC,
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steps=STEPS,
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seed=1234,
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)
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maybe_save_audio(audio, sr, prompt)
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assert_audio_valid(audio, DURATION_SEC, sr)
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def test_inpainting(sa3_model, maybe_save_audio):
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model = sa3_model
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sr = model.model_config["sample_rate"]
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channels = model.model_config.get("io_channels", 2)
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inpaint_duration = 10.0
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prompt = "big trumpet solo, jazz big band, 90bpm"
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base_audio = sine_wave(
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inpaint_duration,
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sr,
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channels=channels,
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device=str(model.device),
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half=model.model_half,
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)
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audio = model.generate(
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prompt=prompt,
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duration=inpaint_duration,
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steps=STEPS,
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inpaint_audio=(sr, base_audio),
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inpaint_mask_start_seconds=2.0,
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inpaint_mask_end_seconds=7.0,
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)
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maybe_save_audio(audio, sr, prompt)
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assert_audio_valid(audio, inpaint_duration, sr)
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def test_inpainting_multiple_regions(sa3_model, maybe_save_audio):
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model = sa3_model
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sr = model.model_config["sample_rate"]
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channels = model.model_config.get("io_channels", 2)
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inpaint_duration = 20.0
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prompt = "jazz piano trio, upbeat swing, 120bpm"
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base_audio = sine_wave(
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inpaint_duration,
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sr,
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channels=channels,
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device=str(model.device),
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half=model.model_half,
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)
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audio = model.generate(
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prompt=prompt,
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duration=inpaint_duration,
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steps=STEPS,
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inpaint_audio=(sr, base_audio),
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inpaint_mask_start_seconds=[2.0, 12.0],
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inpaint_mask_end_seconds=[11.0, 19.0],
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)
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maybe_save_audio(audio, sr, prompt)
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assert_audio_valid(audio, inpaint_duration, sr)
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def test_continuation(sa3_model, maybe_save_audio):
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model = sa3_model
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sr = model.model_config["sample_rate"]
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channels = model.model_config.get("io_channels", 2)
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init_duration = 5.0
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total_duration = 15.0
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prompt = "thunderstorm with heavy rain"
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base_audio = sine_wave(
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init_duration,
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sr,
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channels=channels,
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device=str(model.device),
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half=model.model_half,
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)
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audio = model.generate(
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prompt=prompt,
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duration=total_duration,
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steps=STEPS,
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inpaint_audio=(sr, base_audio),
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inpaint_mask_start_seconds=5.0,
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inpaint_mask_end_seconds=15.0,
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)
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maybe_save_audio(audio, sr, prompt)
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assert_audio_valid(audio, total_duration, sr)
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def test_init_audio(sa3_model, maybe_save_audio):
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model = sa3_model
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sr = model.model_config["sample_rate"]
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channels = model.model_config.get("io_channels", 2)
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prompt = "funky bass groove"
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init = sine_wave(
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DURATION_SEC,
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sr,
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channels=channels,
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device=str(model.device),
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half=model.model_half,
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)
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audio = model.generate(
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prompt=prompt,
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duration=DURATION_SEC,
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steps=STEPS,
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init_audio=(sr, init),
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init_noise_level=0.8,
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)
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maybe_save_audio(audio, sr, prompt)
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assert_audio_valid(audio, DURATION_SEC, sr)
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def test_init_audio_float32_into_half_model(sa3_model, maybe_save_audio):
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model = sa3_model
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if not model.model_half:
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pytest.skip(
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"model_half is False — dtype mismatch only occurs on half-precision models"
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)
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sr = model.model_config["sample_rate"]
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channels = model.model_config.get("io_channels", 2)
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prompt = "funky bass groove"
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# Deliberately float32, regardless of model dtype — this was the bug trigger
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init = sine_wave(
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DURATION_SEC, sr, channels=channels, device=str(model.device), half=False
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)
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assert init.dtype == torch.float32
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audio = model.generate(
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prompt=prompt,
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duration=DURATION_SEC,
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steps=STEPS,
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init_audio=(sr, init),
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init_noise_level=0.8,
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)
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maybe_save_audio(audio, sr, prompt)
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assert_audio_valid(audio, DURATION_SEC, sr)
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def test_inpaint_audio_float32_into_half_model(sa3_model, maybe_save_audio):
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model = sa3_model
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if not model.model_half:
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pytest.skip(
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"model_half is False — dtype mismatch only occurs on half-precision models"
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)
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sr = model.model_config["sample_rate"]
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channels = model.model_config.get("io_channels", 2)
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inpaint_duration = 10.0
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prompt = "big trumpet solo, jazz big band, 90bpm"
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# Deliberately float32, regardless of model dtype — this was the bug trigger
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base_audio = sine_wave(
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inpaint_duration, sr, channels=channels, device=str(model.device), half=False
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)
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assert base_audio.dtype == torch.float32
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audio = model.generate(
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prompt=prompt,
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duration=inpaint_duration,
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steps=STEPS,
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inpaint_audio=(sr, base_audio),
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inpaint_mask_start_seconds=2.0,
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inpaint_mask_end_seconds=7.0,
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)
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maybe_save_audio(audio, sr, prompt)
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assert_audio_valid(audio, inpaint_duration, sr)
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@pytest.mark.skipif(not HAS_ACCEL, reason="Batch inference requires a GPU/accelerator")
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def test_batch_inference(sa3_model, maybe_save_audio):
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model = sa3_model
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sr = model.model_config["sample_rate"]
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batch_size = 3
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prompts = ["ocean waves", "summer breeze", "city traffic"]
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neg_prompts = ["loud background noise", "bad quality", "loud background noise"]
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durations = [5, 10, 20]
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duration_padding_sec = 6 # Default, just defining here for clarity
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audio_same_durations = model.generate(
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prompt=prompts,
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negative_prompt=neg_prompts,
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duration=DURATION_SEC,
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steps=STEPS,
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batch_size=batch_size,
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)
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audio_different_durations = model.generate(
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prompt=prompts,
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negative_prompt=neg_prompts,
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duration=durations,
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steps=STEPS,
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duration_padding_sec=duration_padding_sec,
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batch_size=batch_size,
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)
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assert audio_same_durations.shape[0] == batch_size, (
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f"Expected batch dim {batch_size}, got {audio_same_durations.shape[0]}"
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)
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# Validate each item in the batch individually and optionally save
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for i, prompt in enumerate(prompts):
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maybe_save_audio(audio_same_durations[i : i + 1], sr, prompt)
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assert_audio_valid(audio_same_durations[i : i + 1], DURATION_SEC, sr)
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# Check for diversity in outputs (not identical)
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diffs = []
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for i in range(batch_size):
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for j in range(i + 1, batch_size):
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diff = torch.mean(
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torch.abs(audio_same_durations[i] - audio_same_durations[j])
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).item()
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diffs.append(diff)
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avg_diff = sum(diffs) / len(diffs)
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assert avg_diff > 0.01, f"Batch outputs are too similar (avg diff {avg_diff:.4f})"
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# Validate different durations
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for i, dur in enumerate(durations):
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# Set expected_duration to be longer since we are not truncating
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d = max(durations) + duration_padding_sec
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maybe_save_audio(
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audio_different_durations[i : i + 1], sr, f"{prompts[i]}_{dur}s"
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)
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assert_audio_valid(audio_different_durations[i : i + 1], d, sr)
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audio_shared_neg = model.generate(
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prompt=prompts,
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negative_prompt="low quality, noise",
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duration=durations,
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steps=STEPS,
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duration_padding_sec=duration_padding_sec,
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batch_size=batch_size,
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)
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d = max(durations) + duration_padding_sec
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for i in range(batch_size):
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assert_audio_valid(audio_shared_neg[i : i + 1], d, sr)
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