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 gc
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import numpy as np
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import gradio as gr
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import re
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import subprocess
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import torch
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import torchaudio
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import threading
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import os, time, math
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from einops import rearrange
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from stable_audio_3.interface.aeiou import audio_spectrogram_image
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from stable_audio_3.inference.distribution_shift import LogSNRShift, FluxDistributionShift, DistributionShift, IdentityDistributionShift
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from stable_audio_3.models.lora import has_lora
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from stable_audio_3.interface.reprompt import reprompt as _reprompt_fn, get_model as _reprompt_get_model, is_model_cached as _reprompt_is_model_cached
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stable_audio_3_model = None
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sample_size = 5324800
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sample_rate = 44100
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n_loras = 0
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_LENGTH_EXTRACT_RE = re.compile(r' Length: (\d+) seconds\.?\s*$')
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def parse_inpaint_regions(starts_csv, ends_csv):
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"""Parse matching comma-separated inpaint start/end regions."""
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def parse(value):
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values = [item.strip() for item in str(value or "").split(",") if item.strip()]
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try:
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return [float(item) for item in values]
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except ValueError as exc:
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raise gr.Error("Inpaint regions must be comma-separated numbers.") from exc
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starts = parse(starts_csv)
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ends = parse(ends_csv)
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if not starts and not ends:
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return None, None
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if not starts or not ends:
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raise gr.Error("Inpaint starts and ends must both be provided.")
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if len(starts) != len(ends):
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raise gr.Error("Inpaint starts and ends must contain the same number of regions.")
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if any(start < 0 or end <= start for start, end in zip(starts, ends)):
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raise gr.Error("Each inpaint region must have a non-negative start before its end.")
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return (
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starts[0] if len(starts) == 1 else starts,
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ends[0] if len(ends) == 1 else ends,
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)
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# when using a prompt in a filename
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def condense_prompt(prompt):
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pattern = r'[\\/:*?"<>|]'
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# Replace special characters with hyphens
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prompt = re.sub(pattern, '-', prompt)
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# set a character limit
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prompt = prompt[:150]
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# zero length prompts may lead to filenames (ie ".wav") which seem cause problems with gradio
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if len(prompt)==0:
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prompt = "_"
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return prompt
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def generate_cond(
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prompt,
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negative_prompt=None,
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seconds_total=30,
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cfg_scale=6.0,
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steps=250,
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preview_every=None,
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seed=-1,
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sampler_type="dpmpp-3m-sde",
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sigma_max=1000,
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cfg_interval_min=0.0,
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cfg_interval_max=1.0,
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cfg_rescale=0.0,
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cfg_norm_threshold=0.0,
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apg_scale=1.0,
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file_format="wav",
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file_naming="verbose",
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cut_to_seconds_total=False,
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init_audio=None,
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init_noise_level=1.0,
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mask_maskstart=None,
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mask_maskend=None,
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inpaint_audio=None,
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init_audio_type="Init audio",
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inversion_steps=100,
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inversion_gamma=0.3,
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inversion_unconditional=False,
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duration_padding_sec=6.0,
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batch_size=1,
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dist_shift=None,
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*lora_args
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):
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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gc.collect()
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print(f"Prompt: {prompt}")
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global preview_images
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preview_images = []
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if preview_every == 0:
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preview_every = None
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# Parse per-LoRA controls from trailing args
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# Each LoRA has 5 controls: strength, interval_min, interval_max, layer_filter
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lora_configs = None
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if n_loras > 0 and len(lora_args) >= n_loras * 4:
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lora_configs = []
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for i in range(n_loras):
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off = i * 4
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strength = lora_args[off]
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interval_min = lora_args[off + 1]
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interval_max = lora_args[off + 2]
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layer_filter = lora_args[off + 3]
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stable_audio_3_model.set_lora_strength(strength, lora_index=i)
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lora_configs.append({
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"lora_index": i,
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"interval": (interval_min, interval_max),
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"layer_filter": layer_filter,
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})
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input_sample_size = sample_size
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def progress_callback(callback_info):
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global preview_images
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denoised = callback_info["denoised"]
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current_step = callback_info["i"]
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t = callback_info["t"]
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sigma = callback_info["sigma"]
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# Extract scalar from tensor if needed (samplers pass tensors to avoid GPU sync)
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if isinstance(t, torch.Tensor):
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t = t[0].item() if t.dim() > 0 else t.item()
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if isinstance(sigma, torch.Tensor):
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sigma = sigma[0].item() if sigma.dim() > 0 else sigma.item()
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log_snr = math.log(((1 - sigma) / sigma) + 1e-6)
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if (current_step - 1) % preview_every == 0:
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if stable_audio_3_model.model.pretransform is not None:
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denoised = stable_audio_3_model.model.pretransform.decode(denoised)
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denoised = rearrange(denoised, "b d n -> d (b n)")
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denoised = denoised.clamp(-1, 1).mul(32767).to(torch.int16).cpu()
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audio_spectrogram = audio_spectrogram_image(denoised, sample_rate=sample_rate)
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preview_images.append((audio_spectrogram, f"Step {current_step} sigma={sigma:.3f} logSNR={log_snr:.3f}"))
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if init_audio_type == "RF-Inversion":
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inversion_params = {
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"inversion_steps": inversion_steps,
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"inversion_gamma": inversion_gamma,
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"inversion_unconditional": inversion_unconditional,
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"inversion_cfg_scale": 1.0,
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"inversion_sigma_max": 1.0
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}
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else:
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inversion_params = None
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mask_maskstart, mask_maskend = parse_inpaint_regions(
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mask_maskstart, mask_maskend
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)
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if inpaint_audio is not None and mask_maskstart is None:
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raise gr.Error("Add at least one inpaint start/end region.")
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generate_args = {
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"prompt": prompt,
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"negative_prompt": negative_prompt,
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"duration": seconds_total,
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"steps": steps,
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"cfg_scale": cfg_scale,
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"cfg_interval": (cfg_interval_min, cfg_interval_max),
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"lora_configs": lora_configs,
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"batch_size": int(batch_size),
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"sample_size": input_sample_size,
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"seed": seed,
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"sampler_type": sampler_type,
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"sigma_max": sigma_max,
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"init_audio": init_audio,
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"init_noise_level": init_noise_level,
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"callback": progress_callback if preview_every is not None else None,
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"scale_phi": cfg_rescale,
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"cfg_norm_threshold": cfg_norm_threshold,
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"apg_scale": apg_scale,
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"duration_padding_sec": duration_padding_sec,
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"dist_shift": dist_shift,
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}
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# If inpainting, send mask args
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# This will definitely change in the future
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if inpaint_audio is not None:
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generate_args.update({
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"inpaint_audio": inpaint_audio,
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"inpaint_mask_start_seconds": mask_maskstart,
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"inpaint_mask_end_seconds": mask_maskend,
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})
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audio = stable_audio_3_model.generate(**generate_args)
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# Filenaming convention
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prompt_condensed = condense_prompt(prompt)
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if file_naming=="verbose":
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basename = prompt_condensed
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if negative_prompt:
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basename += ".neg-%s" % condense_prompt(negative_prompt)
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basename += ".cfg%s" % (cfg_scale)
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if sigma_max not in [1.0, 100.0]:
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# this is a common parameter to tweak, if it's not a default value, put it in the verbose filename
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basename += ".smx%s" % sigma_max
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basename += ".%s" % seed
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elif file_naming=="prompt":
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basename = prompt_condensed
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else:
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# simple e.g. "output.wav"
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basename = "output"
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if file_format:
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filename_extension = file_format.split(" ")[0].lower()
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else:
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filename_extension = "wav"
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output_filename = "%s.%s" % (basename, filename_extension)
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output_wav = "%s.wav" % basename
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# Cut the extra silence off the end, if the user requested a smaller seconds_total
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if cut_to_seconds_total:
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audio = audio[:,:,:seconds_total*sample_rate]
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# Encode the audio to WAV format
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audio = rearrange(audio, "b d n -> d (b n)")
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audio = audio.to(torch.float32).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
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# save as wav file
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torchaudio.save(output_wav, audio, sample_rate)
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# If file_format is other than wav, convert to other file format
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cmd = ""
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if file_format == "m4a aac_he_v2 32k":
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# note: need to compile ffmpeg with --enable-libfdk_aac
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cmd = f"ffmpeg -i \"{output_wav}\" -c:a libfdk_aac -profile:a aac_he_v2 -b:a 32k -y \"{output_filename}\""
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elif file_format == "m4a aac_he_v2 64k":
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cmd = f"ffmpeg -i \"{output_wav}\" -c:a libfdk_aac -profile:a aac_he_v2 -b:a 64k -y \"{output_filename}\""
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elif file_format == "flac":
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cmd = f"ffmpeg -i \"{output_wav}\" -y \"{output_filename}\""
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elif file_format == "mp3 320k":
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cmd = f"ffmpeg -i \"{output_wav}\" -b:a 320k -y \"{output_filename}\""
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elif file_format == "mp3 128k":
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cmd = f"ffmpeg -i \"{output_wav}\" -b:a 128k -y \"{output_filename}\""
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elif file_format == "mp3 v0":
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cmd = f"ffmpeg -i \"{output_wav}\" -q:a 0 -y \"{output_filename}\""
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else: # wav
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pass
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if cmd:
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cmd += " -loglevel error" # make output less verbose in the cmd window
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subprocess.run(cmd, shell=True, check=True)
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# Let's look at a nice spectrogram too
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audio_spectrogram = audio_spectrogram_image(audio, sample_rate=sample_rate)
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# Asynchronously delete the files after returning the output file, so as to prevent clutter in the directory
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delete_files_async([output_wav, output_filename], 30)
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return (output_filename, [audio_spectrogram, *preview_images])
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# Asynchronously delete the given list of filenames after delay seconds. Sets up thread that sleeps for delay then deletes.
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def delete_files_async(filenames, delay):
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def delete_files_after_delay(filenames, delay):
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time.sleep(delay) # Wait for the specified delay
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for filename in filenames:
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if os.path.exists(filename):
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os.remove(filename) # Delete the file
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threading.Thread(target=delete_files_after_delay, args=(filenames, delay)).start()
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def create_sampling_ui(stable_audio_3_model, default_prompt=None):
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global n_loras
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diffusion_objective = stable_audio_3_model.model.diffusion_objective
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is_rf = diffusion_objective == "rectified_flow"
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is_rf_denoiser = diffusion_objective == "rf_denoiser" # includes ARC models
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# Extract default dist_shift params from model's sampling_dist_shift
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default_sampling_dist_shift = getattr(stable_audio_3_model.model, 'sampling_dist_shift', None)
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default_dist_shift_type = "LogSNR"
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default_logsnr_params = {"anchor_length": 2000, "anchor_logsnr": -6.2, "rate": 0.0, "logsnr_end": 2.0}
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default_flux_params = {"min_length": 256, "max_length": 4096, "alpha_min": 6.93, "alpha_max": 6.93}
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default_full_params = {"base_shift": 0.5, "max_shift": 1.15, "min_length": 256, "max_length": 4096}
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if isinstance(default_sampling_dist_shift, LogSNRShift):
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default_dist_shift_type = "LogSNR"
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default_logsnr_params = {
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"anchor_length": getattr(default_sampling_dist_shift, 'anchor_length', 2000),
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"anchor_logsnr": getattr(default_sampling_dist_shift, 'anchor_logsnr', -6.2),
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"rate": getattr(default_sampling_dist_shift, 'rate', 0.0),
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"logsnr_end": getattr(default_sampling_dist_shift, 'logsnr_end', 2.0),
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}
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elif isinstance(default_sampling_dist_shift, FluxDistributionShift):
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default_dist_shift_type = "Flux"
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default_flux_params = {
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"min_length": default_sampling_dist_shift.min_length,
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"max_length": default_sampling_dist_shift.max_length,
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"alpha_min": default_sampling_dist_shift.alpha_min,
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"alpha_max": default_sampling_dist_shift.alpha_max,
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}
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elif isinstance(default_sampling_dist_shift, DistributionShift):
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default_dist_shift_type = "Full"
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default_full_params = {
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"base_shift": default_sampling_dist_shift.base_shift,
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"max_shift": default_sampling_dist_shift.max_shift,
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"min_length": default_sampling_dist_shift.min_length,
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"max_length": default_sampling_dist_shift.max_length,
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}
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elif default_sampling_dist_shift is None:
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default_dist_shift_type = "None"
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has_seconds_total = True
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use_lora = has_lora(stable_audio_3_model.model)
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lora_names = getattr(stable_audio_3_model.model, 'lora_names', [])
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n_loras = len(lora_names)
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if default_prompt is None:
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default_prompt = ""
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_reprompt_model_id = "Qwen/Qwen3.5-2B"
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_reprompt_cached = _reprompt_is_model_cached(_reprompt_model_id)
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with gr.Row():
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with gr.Column(scale=6):
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prompt = gr.Textbox(show_label=False, placeholder="Prompt", value=default_prompt)
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negative_prompt = gr.Textbox(show_label=False, placeholder="Negative prompt")
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prompt_assistant_button = gr.Button(
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"Prompt Assistant" if _reprompt_cached else "Download Prompt Assistant (~4.2 GB)",
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scale=1
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)
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generate_button = gr.Button("Generate", variant='primary', scale=1)
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with gr.Row(equal_height=False):
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with gr.Column():
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with gr.Row(visible = True):
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# Timing controls
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seconds_total_slider = gr.Slider(minimum=0, maximum=sample_size//sample_rate, step=1, value=sample_size//sample_rate, label="Seconds total", visible=has_seconds_total)
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with gr.Row():
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# Steps slider
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if is_rf:
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default_steps = 50
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elif is_rf_denoiser:
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default_steps = 8
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steps_slider = gr.Slider(minimum=1, maximum=500, step=1, value=default_steps, label="Steps")
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# CFG scale
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default_cfg_scale = 1.0 if is_rf_denoiser else 7.0
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cfg_scale_slider = gr.Slider(minimum=0.0, maximum=25.0, step=0.1, value=default_cfg_scale, label="CFG scale")
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# Per-LoRA controls (dynamic based on number of loaded LoRAs)
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lora_ui_inputs = []
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if use_lora and lora_names:
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for i, lora_name in enumerate(lora_names):
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with gr.Accordion("LoRA {}: {}".format(i + 1, lora_name), open=(i == 0)):
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with gr.Row():
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strength = gr.Slider(minimum=0.0, maximum=10.0, step=0.1, value=1.0, label="strength")
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with gr.Row():
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int_min = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.0, label="Interval min")
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int_max = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=1.0, label="Interval max")
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lyr_filt = gr.Textbox(label="Layer filter", placeholder="")
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lora_ui_inputs.extend([strength, int_min, int_max, lyr_filt])
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with gr.Accordion("Sampler params", open=False):
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with gr.Row():
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# Seed
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seed_textbox = gr.Number(label="Seed (set to -1 for random seed)", value=-1, precision=0)
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cfg_interval_min_slider = gr.Slider(minimum=0.0, maximum=1, step=0.01, value=0.0, label="CFG interval min")
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cfg_interval_max_slider = gr.Slider(minimum=0.0, maximum=1, step=0.01, value=1.0, label="CFG interval max")
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with gr.Row():
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cfg_rescale_slider = gr.Slider(minimum=0.0, maximum=1, step=0.01, value=0.0, label="CFG rescale amount")
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cfg_norm_threshold = gr.Slider(minimum=0.0, maximum=100, step=0.1, value=0.0, label="CFG norm threshold")
|
||||
apg_scale_slider = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=1.0, label="APG scale", info="1.0=full APG, 0.0=vanilla CFG")
|
||||
|
||||
with gr.Row():
|
||||
# Sampler params
|
||||
if is_rf:
|
||||
sampler_types = ["euler", "rk4", "dpmpp"]
|
||||
default_sampler_type = "euler"
|
||||
sigma_max_max = 1.0
|
||||
sigma_max_default = 1.0
|
||||
elif is_rf_denoiser:
|
||||
sampler_types = ["pingpong"]
|
||||
default_sampler_type = "pingpong"
|
||||
sigma_max_max = 1.0
|
||||
sigma_max_default = 1.0
|
||||
else:
|
||||
sampler_types = ["dpmpp-2m-sde", "dpmpp-3m-sde", "dpmpp-2m", "k-heun", "k-lms", "k-dpmpp-2s-ancestral", "k-dpm-2", "k-dpm-adaptive", "k-dpm-fast", "v-ddim", "v-ddim-cfgpp"]
|
||||
default_sampler_type = "dpmpp-3m-sde"
|
||||
sigma_max_max = 1000.0
|
||||
sigma_max_default = 100.0
|
||||
|
||||
sampler_type_dropdown = gr.Dropdown(sampler_types, label="Sampler type", value=default_sampler_type)
|
||||
sigma_max_slider = gr.Slider(minimum=0.0, maximum=sigma_max_max, step=0.1, value=sigma_max_default, label="Sigma max", visible=True)
|
||||
|
||||
with gr.Row():
|
||||
duration_padding_slider = gr.Slider(minimum=0.0, maximum=30.0, step=0.5, value=6.0, label="Duration padding (sec)")
|
||||
|
||||
def build_dist_shift(shift_type, p1, p2, p3, p4):
|
||||
"""Build dist_shift from type + 4 params (meaning depends on type)."""
|
||||
if shift_type == "LogSNR":
|
||||
return LogSNRShift(anchor_length=int(p1), anchor_logsnr=p2, rate=p3, logsnr_end=p4)
|
||||
elif shift_type == "Flux":
|
||||
return FluxDistributionShift(min_length=int(p1), max_length=int(p2), alpha_min=p3, alpha_max=p4)
|
||||
elif shift_type == "Full":
|
||||
return DistributionShift(base_shift=p1, max_shift=p2, min_length=int(p3), max_length=int(p4))
|
||||
return IdentityDistributionShift() # "None" = no shift
|
||||
|
||||
dist_shift_state = gr.State(value=default_sampling_dist_shift)
|
||||
|
||||
with gr.Row(visible=is_rf or is_rf_denoiser):
|
||||
dist_shift_type_dropdown = gr.Dropdown(
|
||||
["LogSNR", "Flux", "Full", "None"],
|
||||
label="Sampling schedule shift",
|
||||
value=default_dist_shift_type,
|
||||
info="Distribution shift applied to sampling timesteps"
|
||||
)
|
||||
with gr.Row(visible=(is_rf or is_rf_denoiser) and default_dist_shift_type == "LogSNR") as logsnr_params_row:
|
||||
logsnr_anchor_length_slider = gr.Slider(minimum=100, maximum=10000, step=100, value=default_logsnr_params["anchor_length"], label="Anchor length")
|
||||
logsnr_anchor_logsnr_slider = gr.Slider(minimum=-12.0, maximum=0.0, step=0.1, value=default_logsnr_params["anchor_logsnr"], label="Anchor log-SNR")
|
||||
logsnr_rate_slider = gr.Slider(minimum=-2.0, maximum=2.0, step=0.1, value=default_logsnr_params["rate"], label="Rate")
|
||||
logsnr_end_slider = gr.Slider(minimum=-2.0, maximum=6.0, step=0.1, value=default_logsnr_params["logsnr_end"], label="log-SNR end")
|
||||
with gr.Row(visible=(is_rf or is_rf_denoiser) and default_dist_shift_type == "Flux") as flux_params_row:
|
||||
flux_min_length_slider = gr.Slider(minimum=1, maximum=10000, step=1, value=default_flux_params["min_length"], label="Min seq len")
|
||||
flux_max_length_slider = gr.Slider(minimum=1, maximum=10000, step=1, value=default_flux_params["max_length"], label="Max seq len")
|
||||
flux_alpha_min_slider = gr.Slider(minimum=0.1, maximum=20.0, step=0.1, value=default_flux_params["alpha_min"], label="Alpha min")
|
||||
flux_alpha_max_slider = gr.Slider(minimum=0.1, maximum=20.0, step=0.1, value=default_flux_params["alpha_max"], label="Alpha max")
|
||||
with gr.Row(visible=(is_rf or is_rf_denoiser) and default_dist_shift_type == "Full") as full_params_row:
|
||||
full_base_shift_slider = gr.Slider(minimum=0.0, maximum=5.0, step=0.05, value=default_full_params["base_shift"], label="Base shift")
|
||||
full_max_shift_slider = gr.Slider(minimum=0.0, maximum=5.0, step=0.05, value=default_full_params["max_shift"], label="Max shift")
|
||||
full_min_length_slider = gr.Slider(minimum=1, maximum=10000, step=1, value=default_full_params["min_length"], label="Min length")
|
||||
full_max_length_slider = gr.Slider(minimum=1, maximum=10000, step=1, value=default_full_params["max_length"], label="Max length")
|
||||
|
||||
# Per-type slider groups for wiring to state
|
||||
logsnr_sliders = [logsnr_anchor_length_slider, logsnr_anchor_logsnr_slider, logsnr_rate_slider, logsnr_end_slider]
|
||||
flux_sliders = [flux_min_length_slider, flux_max_length_slider, flux_alpha_min_slider, flux_alpha_max_slider]
|
||||
full_sliders = [full_base_shift_slider, full_max_shift_slider, full_min_length_slider, full_max_length_slider]
|
||||
all_dist_shift_inputs = [dist_shift_type_dropdown] + logsnr_sliders + flux_sliders + full_sliders
|
||||
|
||||
def update_dist_shift_state(shift_type, *params):
|
||||
"""Route the 4 relevant params to build_dist_shift based on type."""
|
||||
type_to_slice = {"LogSNR": params[0:4], "Flux": params[4:8], "Full": params[8:12]}
|
||||
p = type_to_slice.get(shift_type, (0, 0, 0, 0))
|
||||
return (
|
||||
build_dist_shift(shift_type, *p),
|
||||
gr.update(visible=((is_rf or is_rf_denoiser) and (shift_type == "LogSNR"))),
|
||||
gr.update(visible=((is_rf or is_rf_denoiser) and (shift_type == "Flux"))),
|
||||
gr.update(visible=((is_rf or is_rf_denoiser) and (shift_type == "Full"))),
|
||||
)
|
||||
|
||||
for component in all_dist_shift_inputs:
|
||||
component.change(
|
||||
update_dist_shift_state,
|
||||
inputs=all_dist_shift_inputs,
|
||||
outputs=[dist_shift_state, logsnr_params_row, flux_params_row, full_params_row],
|
||||
)
|
||||
|
||||
with gr.Accordion("Batch", open=False):
|
||||
batch_size_number = gr.Number(
|
||||
label="Batch size",
|
||||
value=1,
|
||||
minimum=1,
|
||||
precision=0,
|
||||
info="Generate multiple variations in one run.",
|
||||
)
|
||||
|
||||
with gr.Accordion("Output params", open=False):
|
||||
# Output params
|
||||
with gr.Row():
|
||||
file_format_dropdown = gr.Dropdown(["wav", "flac", "mp3 320k", "mp3 v0", "mp3 128k", "m4a aac_he_v2 64k", "m4a aac_he_v2 32k"], label="File format", value="wav")
|
||||
file_naming_dropdown = gr.Dropdown(["verbose", "prompt", "output.wav"], label="File naming", value="verbose") # ,"prompt","verbose"
|
||||
preview_every_slider = gr.Slider(minimum=0, maximum=100, step=1, value=0, label="Spec Preview Every")
|
||||
|
||||
cut_to_seconds_total_checkbox = gr.Checkbox(label="Cut to seconds total", value=True)
|
||||
autoplay_checkbox = gr.Checkbox(label="Autoplay", value=False, elem_id="autoplay")
|
||||
infinite_radio_checkbox = gr.Checkbox(label="Infinite Radio", value=False, elem_id="infinite-radio")
|
||||
automatic_download_checkbox = gr.Checkbox(label="Auto Download", value=False, elem_id="automatic-download")
|
||||
|
||||
# Default generation tab
|
||||
with gr.Accordion("Init audio", open=False):
|
||||
init_audio_input = gr.Audio(label="Init audio", waveform_options=gr.WaveformOptions(show_recording_waveform=False))
|
||||
min_noise_level = 0.01
|
||||
max_noise_level = 1.0
|
||||
default_noise_level = 0.9 # roughly halfway style transfer values
|
||||
if is_rf:
|
||||
choices = ["Init audio","RF-Inversion"]
|
||||
else:
|
||||
choices = ["Init audio"]
|
||||
|
||||
init_audio_type_radio = gr.Radio(label="Techniques", choices=choices, value=choices[0], visible=len(choices)>1)
|
||||
with gr.Column(visible=True) as interface_a:
|
||||
init_noise_level_slider = gr.Slider(minimum=min_noise_level, maximum=max_noise_level, step=0.01, value=default_noise_level, label="Init noise level")
|
||||
with gr.Column(visible=False) as interface_b:
|
||||
inversion_steps_slider = gr.Slider(minimum=1, maximum=500, step=1, value=100, label="Inversion Steps")
|
||||
inversion_gamma_slider = gr.Slider(minimum=0, maximum=1, step=0.1, value=0, label="Gamma", visible=True)
|
||||
inversion_unconditional_checkbox = gr.Checkbox(label="Unconditional", value=False)
|
||||
gr.HTML("<div style='opacity: 0.5; padding: 0px'>For reproduction, try empty prompt, cfg 1, gamma .3<br>\
|
||||
For prompt re-stylization, try cfg 1-7, gamma 0-.15, unconditional</div>")
|
||||
def init_audio_type_switch(choice):
|
||||
return (
|
||||
gr.update(visible=(choice == "Init audio")),
|
||||
gr.update(visible=(choice == "RF-Inversion"))
|
||||
)
|
||||
init_audio_type_radio.change(init_audio_type_switch, inputs=init_audio_type_radio, outputs=[interface_a, interface_b])
|
||||
|
||||
with gr.Accordion("Inpainting", open=False):
|
||||
inpaint_audio_input = gr.Audio(label="Inpaint audio", waveform_options=gr.WaveformOptions(show_recording_waveform=False))
|
||||
mask_maskstart_slider = gr.Textbox(label="Mask starts (sec)", placeholder="4 or 4, 16", info="Comma-separate values to inpaint multiple regions.")
|
||||
mask_maskend_slider = gr.Textbox(label="Mask ends (sec)", placeholder="8 or 8, 20")
|
||||
|
||||
inputs = [
|
||||
prompt,
|
||||
negative_prompt,
|
||||
seconds_total_slider,
|
||||
cfg_scale_slider,
|
||||
steps_slider,
|
||||
preview_every_slider,
|
||||
seed_textbox,
|
||||
sampler_type_dropdown,
|
||||
sigma_max_slider,
|
||||
cfg_interval_min_slider,
|
||||
cfg_interval_max_slider,
|
||||
cfg_rescale_slider,
|
||||
cfg_norm_threshold,
|
||||
apg_scale_slider,
|
||||
file_format_dropdown,
|
||||
file_naming_dropdown,
|
||||
cut_to_seconds_total_checkbox,
|
||||
init_audio_input,
|
||||
init_noise_level_slider,
|
||||
mask_maskstart_slider,
|
||||
mask_maskend_slider,
|
||||
inpaint_audio_input,
|
||||
init_audio_type_radio,
|
||||
inversion_steps_slider,
|
||||
inversion_gamma_slider,
|
||||
inversion_unconditional_checkbox,
|
||||
duration_padding_slider,
|
||||
batch_size_number,
|
||||
dist_shift_state,
|
||||
] + lora_ui_inputs
|
||||
|
||||
with gr.Column():
|
||||
audio_output = gr.Audio(label="Output audio", interactive=False,
|
||||
waveform_options=gr.WaveformOptions(show_recording_waveform=False))
|
||||
audio_spectrogram_output = gr.Gallery(label="Output spectrogram", show_label=False)
|
||||
send_to_init_button = gr.Button("Send to init audio", scale=1)
|
||||
send_to_init_button.click(fn=lambda audio: audio, inputs=[audio_output], outputs=[init_audio_input])
|
||||
|
||||
send_to_inpaint_button = gr.Button("Send to inpaint audio", scale=1)
|
||||
send_to_inpaint_button.click(fn=lambda audio: audio, inputs=[audio_output], outputs=[inpaint_audio_input])
|
||||
|
||||
generate_button.click(fn=generate_cond,
|
||||
inputs=inputs,
|
||||
outputs=[
|
||||
audio_output,
|
||||
audio_spectrogram_output
|
||||
],
|
||||
api_name="generate")
|
||||
|
||||
def _prompt_assistant_or_download(text, progress=gr.Progress(track_tqdm=True)):
|
||||
if not _reprompt_is_model_cached(_reprompt_model_id):
|
||||
_reprompt_get_model(_reprompt_model_id)
|
||||
return text, gr.update(), gr.update(value="Prompt Assistant")
|
||||
_, result, _ = _reprompt_fn(text, "Auto", "", _reprompt_model_id, 128, 1.11)
|
||||
m = _LENGTH_EXTRACT_RE.search(result)
|
||||
if m:
|
||||
max_seconds = sample_size // sample_rate
|
||||
seconds = min(int(m.group(1)), max_seconds)
|
||||
result = result[:m.start()]
|
||||
else:
|
||||
seconds = gr.update()
|
||||
return result, seconds, gr.update()
|
||||
|
||||
prompt_assistant_button.click(
|
||||
fn=_prompt_assistant_or_download,
|
||||
inputs=[prompt],
|
||||
outputs=[prompt, seconds_total_slider, prompt_assistant_button],
|
||||
concurrency_limit=1,
|
||||
)
|
||||
|
||||
def create_diffusion_cond_ui(model, gradio_title="", default_prompt=None):
|
||||
global sample_size, sample_rate, stable_audio_3_model
|
||||
|
||||
sample_size = model.model_config["sample_size"]
|
||||
sample_rate = model.model_config["sample_rate"]
|
||||
stable_audio_3_model = model
|
||||
|
||||
|
||||
js ="""function run_javascript_on_page_load(){
|
||||
const generateBtn = Array.from(document.querySelectorAll('button'))
|
||||
.find(btn => btn.innerText.trim() === 'Generate');
|
||||
function getAudioOutputPlayer () {
|
||||
return [...document.querySelectorAll('label')].find(label => label.textContent.trim() === 'Output audio')?.parentElement.querySelector('audio');
|
||||
}
|
||||
const infiniteRadio = document.querySelector('#infinite-radio input[type="checkbox"]');
|
||||
const autoplay = document.querySelector('#autoplay input[type="checkbox"]');
|
||||
const automaticDownload = document.querySelector('#automatic-download input[type="checkbox"]');
|
||||
let radioAutoStart = false;
|
||||
let listenersSetup = false;
|
||||
const setupListeners = () => {
|
||||
const audioEl = getAudioOutputPlayer();
|
||||
if (!audioEl) return;
|
||||
audioEl.addEventListener('loadedmetadata', () => {
|
||||
if(automaticDownload.checked){
|
||||
downloadAudio(audioEl);
|
||||
}
|
||||
if(autoplay.checked || radioAutoStart){
|
||||
audioEl.play();
|
||||
radioAutoStart = false;
|
||||
}
|
||||
if(infiniteRadio.checked){
|
||||
audioEl.addEventListener('timeupdate', function checkAudioEnd() {
|
||||
// Can set window.headstart (seconds) in the dev console if you want to start generating before the song is over
|
||||
let headstart = 1;
|
||||
if(window.headstart) headstart = window.headstart;
|
||||
if (audioEl.duration - audioEl.currentTime <= headstart) {
|
||||
generateBtn.click();
|
||||
radioAutoStart = true;
|
||||
audioEl.removeEventListener('timeupdate', checkAudioEnd);
|
||||
}
|
||||
});
|
||||
}
|
||||
});
|
||||
listenersSetup = true;
|
||||
};
|
||||
generateBtn.addEventListener('click', () => {
|
||||
if(listenersSetup) return;
|
||||
const interval = setInterval(() => {
|
||||
console.log("...")
|
||||
const audioEl = document.querySelector('audio');
|
||||
if (audioEl?.src && audioEl.src !== window.location.href) {
|
||||
setupListeners();
|
||||
clearInterval(interval);
|
||||
}
|
||||
}, 100);
|
||||
});
|
||||
// Respond to >> button on MacBookPro and on steering wheel during CarPlay
|
||||
if ('mediaSession' in navigator) {
|
||||
navigator.mediaSession.setActionHandler('nexttrack', () => generateBtn.click());
|
||||
navigator.mediaSession.setActionHandler('play', () => getAudioOutputPlayer()?.play());
|
||||
navigator.mediaSession.setActionHandler('pause', () => getAudioOutputPlayer()?.pause());
|
||||
}
|
||||
// Automatic Download
|
||||
function downloadAudio(audioEl) {
|
||||
const audioSrc = audioEl.src;
|
||||
const link = document.createElement('a');
|
||||
link.href = audioSrc;
|
||||
link.download = audioSrc.substring(audioSrc.lastIndexOf('/') + 1);
|
||||
document.body.appendChild(link);
|
||||
link.click();
|
||||
document.body.removeChild(link);
|
||||
}
|
||||
}
|
||||
"""
|
||||
|
||||
with gr.Blocks() as ui:
|
||||
ui._sao_js = js
|
||||
ui._sao_theme = gr.themes.Base()
|
||||
if gradio_title:
|
||||
gr.Markdown("### %s" % gradio_title)
|
||||
with gr.Tab("Generation"):
|
||||
create_sampling_ui(model, default_prompt=default_prompt)
|
||||
|
||||
# JavaScript to autoplay audio immediately after generation (if autoplay enabled)
|
||||
return ui
|
||||
Reference in New Issue
Block a user