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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"""
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stable-audio — command-line interface for Stable Audio 3.
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Basic usage::
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stable-audio --model small-music -p "lo-fi hip hop beat, 90 BPM" --duration 30 -o beat.wav
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"""
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import argparse
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import os
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import torch
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import torchaudio
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from stable_audio_3 import StableAudioModel
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def _save_output(audio: torch.Tensor, sample_rate: int, output: str, batch_size: int):
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"""Save generated audio tensor(s) to disk."""
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base, ext = os.path.splitext(output)
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if not ext:
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ext = ".wav"
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for i in range(batch_size):
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path = f"{base}_{i}{ext}" if batch_size > 1 else f"{base}{ext}"
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torchaudio.save(path, audio[i].cpu(), sample_rate)
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print(f"Saved: {path}")
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def main():
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parser = argparse.ArgumentParser(
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prog="stable-audio",
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description="Stable Audio 3 — CLI for text-to-audio, audio-to-audio, and inpainting",
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)
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# Model
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parser.add_argument(
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"--model",
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default="medium",
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choices=[
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"medium",
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"small-music",
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"small-sfx",
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"medium-base",
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"small-music-base",
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"small-sfx-base",
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],
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help="Model to load (default: medium)",
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)
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parser.add_argument(
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"--device",
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default=None,
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help="Device: cuda / mps / cpu (auto-detected if omitted)",
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)
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parser.add_argument(
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"--no-half", action="store_true", help="Disable half-precision (fp16) on CUDA"
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)
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# Generation
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parser.add_argument(
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"-p",
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"--prompt",
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required=True,
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nargs="+",
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help="Text prompt(s). Pass multiple for per-batch prompts",
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)
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parser.add_argument(
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"--negative-prompt", nargs="+", default=None, help="Negative prompt(s)"
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)
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parser.add_argument(
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"--duration",
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type=float,
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nargs="+",
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default=[120.0],
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help="Duration in seconds (default: 120). Pass multiple for per-batch durations",
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)
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parser.add_argument(
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"--steps", type=int, default=8, help="Diffusion steps (default: 8)"
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)
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parser.add_argument(
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"--cfg-scale",
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type=float,
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default=1.0,
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help="CFG scale (default: 1.0; try 7.0 for base models)",
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)
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parser.add_argument(
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"--seed", type=int, default=-1, help="Random seed (-1 = random, default: -1)"
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)
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parser.add_argument(
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"--batch-size",
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type=int,
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default=None,
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help="Batch size (default: inferred from number of prompts, or 1)",
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)
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parser.add_argument(
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"-o",
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"--output",
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default="output.wav",
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help="Output file path (default: output.wav)",
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)
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# Audio-to-Audio
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parser.add_argument(
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"--init-audio",
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default=None,
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metavar="PATH",
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help="Source audio file for audio-to-audio generation",
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)
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parser.add_argument(
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"--init-noise-level",
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type=float,
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default=0.9,
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help="Noise level for audio-to-audio (0.0–1.0, default: 0.9)",
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)
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# Inpainting / Continuation
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parser.add_argument(
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"--inpaint-audio",
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default=None,
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metavar="PATH",
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help="Source audio file for inpainting or continuation",
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)
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parser.add_argument(
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"--inpaint-start",
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type=float,
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action="append",
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dest="inpaint_starts",
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metavar="SECONDS",
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help="Start of inpaint region in seconds. Repeat for multiple regions.",
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)
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parser.add_argument(
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"--inpaint-end",
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type=float,
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action="append",
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dest="inpaint_ends",
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metavar="SECONDS",
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help="End of inpaint region in seconds. Repeat for multiple regions.",
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)
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# Chunked decode
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decode_group = parser.add_mutually_exclusive_group()
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decode_group.add_argument(
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"--chunked-decode",
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action="store_true",
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default=None,
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help="Force chunked decoding on",
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)
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decode_group.add_argument(
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"--no-chunked-decode",
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action="store_true",
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default=None,
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help="Force chunked decoding off",
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)
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# LoRA
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parser.add_argument(
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"--lora-ckpt-path",
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action="append",
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dest="loras",
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metavar="PATH",
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help="LoRA checkpoint path. Repeat to stack multiple LoRAs.",
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)
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parser.add_argument(
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"--lora-strength",
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type=float,
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default=None,
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help="LoRA strength (applied to all LoRAs)",
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)
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parser.add_argument(
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"--lora-index",
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type=int,
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default=None,
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help="Target a specific LoRA index when setting strength",
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)
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args = parser.parse_args()
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# --- Validate inpaint args ---
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if (args.inpaint_starts is None) != (args.inpaint_ends is None):
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parser.error("--inpaint-start and --inpaint-end must both be provided together")
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if args.inpaint_starts and len(args.inpaint_starts) != len(args.inpaint_ends):
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parser.error(
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"--inpaint-start and --inpaint-end must be specified the same number of times"
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)
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if args.inpaint_starts and not args.inpaint_audio:
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parser.error("--inpaint-start/--inpaint-end require --inpaint-audio")
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if args.inpaint_audio and not args.inpaint_starts:
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parser.error("--inpaint-audio requires --inpaint-start and --inpaint-end")
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# --- Resolve batch size ---
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n_prompts = len(args.prompt)
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if args.batch_size is None:
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batch_size = n_prompts
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elif n_prompts > 1 and args.batch_size != n_prompts:
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parser.error(
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f"--batch-size {args.batch_size} does not match the number of prompts "
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f"({n_prompts}); omit --batch-size to have it inferred automatically"
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)
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else:
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batch_size = args.batch_size
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# --- Validate list-flag lengths against batch size ---
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if (
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args.negative_prompt
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and len(args.negative_prompt) > 1
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and len(args.negative_prompt) != batch_size
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):
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parser.error(
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f"Got {len(args.negative_prompt)} --negative-prompt values but batch size is {batch_size}"
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)
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if len(args.duration) > 1 and len(args.duration) != batch_size:
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parser.error(
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f"Got {len(args.duration)} --duration values but batch size is {batch_size}"
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)
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# --- Build scalar / list args ---
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prompt = args.prompt[0] if len(args.prompt) == 1 else args.prompt
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negative_prompt = None
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if args.negative_prompt:
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negative_prompt = (
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args.negative_prompt[0]
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if len(args.negative_prompt) == 1
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else args.negative_prompt
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)
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duration = args.duration[0] if len(args.duration) == 1 else args.duration
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# --- chunked_decode flag ---
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chunked_decode = None
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if args.chunked_decode:
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chunked_decode = True
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elif args.no_chunked_decode:
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chunked_decode = False
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# --- Load model ---
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print(f"Loading model '{args.model}'…")
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model = StableAudioModel.from_pretrained(
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args.model, device=args.device, model_half=not args.no_half
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)
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# --- LoRA ---
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if args.loras:
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print(f"Loading LoRA(s): {args.loras}")
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model.load_lora(args.loras)
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if args.lora_strength is not None:
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model.set_lora_strength(args.lora_strength, lora_index=args.lora_index)
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# --- Load audio inputs ---
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# torchaudio.load returns (waveform, sample_rate); model.generate expects (sample_rate, waveform)
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init_audio = None
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if args.init_audio:
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waveform, sr = torchaudio.load(args.init_audio)
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init_audio = (sr, waveform)
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inpaint_audio = None
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if args.inpaint_audio:
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waveform, sr = torchaudio.load(args.inpaint_audio)
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inpaint_audio = (sr, waveform)
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inpaint_start = None
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inpaint_end = None
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if args.inpaint_starts:
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inpaint_start = (
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args.inpaint_starts[0]
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if len(args.inpaint_starts) == 1
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else args.inpaint_starts
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)
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inpaint_end = (
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args.inpaint_ends[0] if len(args.inpaint_ends) == 1 else args.inpaint_ends
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)
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sample_size = model.model_config["sample_size"]
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# --- Generate ---
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print("Generating…")
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audio = model.generate(
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prompt=prompt,
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negative_prompt=negative_prompt,
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duration=duration,
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steps=args.steps,
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cfg_scale=args.cfg_scale,
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seed=args.seed,
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batch_size=batch_size,
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sample_size=sample_size,
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init_audio=init_audio,
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init_noise_level=args.init_noise_level,
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inpaint_audio=inpaint_audio,
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inpaint_mask_start_seconds=inpaint_start,
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inpaint_mask_end_seconds=inpaint_end,
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chunked_decode=chunked_decode,
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)
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_save_output(audio, model.model.sample_rate, args.output, batch_size)
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if __name__ == "__main__":
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main()
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