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新增直接打开推理页面bat命令，针对参考音频、推理参数做了持久化配置，解决每次推理都要重复操作的痛点，新增模型记忆，即每次打开推理页面，默认加载最后一次选择的模型。如果是从主页进入，则主动加载主页选择的模修复bug (#2704)

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ChasonJiang 2025-12-30 16:00:21 +08:00 committed by kun
parent 9080a967d5
commit 6e027ec111
6 changed files with 1173 additions and 330 deletions
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5
.gitignore vendored
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@ -193,3 +193,8 @@ cython_debug/
# PyPI configuration file
.pypirc
/.vs
/GPT_SoVITS/configs/tts_infer.yaml
/GPT_SoVITS/configs/infer_settings.json
/last_selected_preset.json
/last_selected_models.json

3
GPT_SoVITS/AR/models/t2s_model.py
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@ -707,12 +707,11 @@ class Text2SemanticDecoder(nn.Module):
if idx == 0:
attn_mask = F.pad(attn_mask[:, :, -1].unsqueeze(-2), (0, 1), value=False)
logits = logits[:, :-1]
else:
attn_mask = F.pad(attn_mask, (0, 1), value=False)
if idx < 11: ###至少预测出10个token不然不给停止（0.4s）
logits[:, -1] = float("-inf")
logits = logits[:, :-1]
samples = sample(
logits, y, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature

56
GPT_SoVITS/configs/tts_infer.yaml
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@ -1,56 +0,0 @@
custom:
bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
device: cuda
is_half: true
t2s_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt
version: v2
vits_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth
v1:
bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
device: cpu
is_half: false
t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt
version: v1
vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth
v2:
bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
device: cpu
is_half: false
t2s_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt
version: v2
vits_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth
v2Pro:
bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
device: cpu
is_half: false
t2s_weights_path: GPT_SoVITS/pretrained_models/s1v3.ckpt
version: v2Pro
vits_weights_path: GPT_SoVITS/pretrained_models/v2Pro/s2Gv2Pro.pth
v2ProPlus:
bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
device: cpu
is_half: false
t2s_weights_path: GPT_SoVITS/pretrained_models/s1v3.ckpt
version: v2ProPlus
vits_weights_path: GPT_SoVITS/pretrained_models/v2Pro/s2Gv2ProPlus.pth
v3:
bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
device: cpu
is_half: false
t2s_weights_path: GPT_SoVITS/pretrained_models/s1v3.ckpt
version: v3
vits_weights_path: GPT_SoVITS/pretrained_models/s2Gv3.pth
v4:
bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
device: cpu
is_half: false
t2s_weights_path: GPT_SoVITS/pretrained_models/s1v3.ckpt
version: v4
vits_weights_path: GPT_SoVITS/pretrained_models/gsv-v4-pretrained/s2Gv4.pth

981
GPT_SoVITS/inference_webui_fast.py
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425
GPT_SoVITS/persistence_tools.py Normal file
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@ -0,0 +1,425 @@
# -*- coding: utf-8 -*-
"""
GPT-SoVITS 持久化工具类
包含：模型配置、参考音频、推理参数 的持久化读写与管理
抽离自主文件，减少主文件臃肿，方便后续维护
"""
import json
import yaml
import hashlib
import os
import shutil
import random
from pathlib import Path
# ===================== 全局配置（统一管理所有持久化文件路径） =====================
# 模型持久化配置文件
LAST_SELECTED_MODELS_JSON = Path("./last_selected_models.json")
# 参考预设最后选中配置文件
LAST_SELECTED_PRESET_JSON = Path("./last_selected_preset.json")
# 参考音频持久化目录
REF_AUDIO_DIR = Path("GPT_SoVITS/ref_audios")
# 参考预设配置文件
REF_PRESETS_YAML = Path("GPT_SoVITS/configs/ref_audios_presets.yaml")
# 推理参数配置文件
INFER_SETTINGS_JSON = Path("GPT_SoVITS/configs/infer_settings.json")
# 参考音频配置常量
MAX_FILENAME_LENGTH = 40
INVALID_FILE_CHARS = set(r'\/:*?"<>|')
# 默认推理参数
DEFAULT_INFER_SETTINGS = {
"batch_size": 20,
"sample_steps": 32,
"fragment_interval": 0.2,
"speed_factor": 1.0,
"top_k": 5,
"top_p": 1.0,
"temperature": 1.0,
"repetition_penalty": 1.35,
"how_to_cut": "凑四句一切",
"super_sampling": False,
"parallel_infer": True,
"split_bucket": True,
"seed": -1,
"keep_random": True
}
# ===================== 通用工具函数（抽离重复逻辑） =====================
def sanitize_filename(name):
"""清理文件名中的非法字符，替换为下划线"""
if not name:
return "unnamed_preset"
return ''.join(c if c not in INVALID_FILE_CHARS else '_' for c in name)
def get_audio_md5(file_path, chunk_size=4096):
"""计算音频文件的MD5值（取前8位），用于区分不同音频内容"""
if not os.path.exists(file_path):
return "invalid_file"
try:
md5 = hashlib.md5()
with open(file_path, 'rb') as f:
while chunk := f.read(chunk_size):
md5.update(chunk)
return md5.hexdigest()[:8]
except Exception as e:
print(f"计算音频MD5失败：{e}")
return f"err_{random.randint(10000000, 99999999)}"
def ensure_dir_exists(dir_path):
"""确保目录存在，不存在则创建"""
if dir_path and not dir_path.exists():
dir_path.mkdir(exist_ok=True, parents=True)
# ===================== 1. 模型配置持久化（last_selected_models.json） =====================
def init_last_selected_models(gpt_default, sovits_default, current_version):
"""初始化模型配置文件，写入默认模型路径"""
ensure_dir_exists(LAST_SELECTED_MODELS_JSON.parent)
init_data = {
"gpt_model_path": gpt_default,
"sovits_model_path": sovits_default,
"version": current_version
}
with open(LAST_SELECTED_MODELS_JSON, "w", encoding="utf-8") as f:
json.dump(init_data, f, ensure_ascii=False, indent=4)
print(f"首次生成模型配置文件：{LAST_SELECTED_MODELS_JSON}")
return init_data
def read_last_selected_models():
"""读取模型配置文件中的路径"""
if not LAST_SELECTED_MODELS_JSON.exists():
return None
try:
with open(LAST_SELECTED_MODELS_JSON, "r", encoding="utf-8") as f:
data = json.load(f)
# 校验必要字段
required_fields = ["gpt_model_path", "sovits_model_path", "version"]
for field in required_fields:
if field not in data:
return None
return data
except Exception as e:
print(f"读取模型配置失败：{e}")
return None
def write_last_selected_models(gpt_path_new, sovits_path_new, current_version):
"""写入新的模型路径到配置文件"""
ensure_dir_exists(LAST_SELECTED_MODELS_JSON.parent)
try:
data = read_last_selected_models() or {}
data["gpt_model_path"] = gpt_path_new
data["sovits_model_path"] = sovits_path_new
data["version"] = current_version
with open(LAST_SELECTED_MODELS_JSON, "w", encoding="utf-8") as f:
json.dump(data, f, ensure_ascii=False, indent=4)
except Exception as e:
print(f"写入模型配置失败：{e}")
# ===================== 2. 参考音频预设持久化（last_selected_preset.json + ref_audios_presets.yaml） =====================
# 2.1 最后选中预设的读写清
def read_last_selected_preset():
"""读取最后一次选中的预设名称"""
if not LAST_SELECTED_PRESET_JSON.exists():
return None
try:
with open(LAST_SELECTED_PRESET_JSON, "r", encoding="utf-8") as f:
data = json.load(f)
return data.get("last_selected_preset")
except Exception as e:
print(f"读取最后选中预设失败：{e}")
return None
def write_last_selected_preset(preset_name):
"""写入最后一次选中的预设名称"""
ensure_dir_exists(LAST_SELECTED_PRESET_JSON.parent)
try:
data = {"last_selected_preset": preset_name.strip()}
with open(LAST_SELECTED_PRESET_JSON, "w", encoding="utf-8") as f:
json.dump(data, f, ensure_ascii=False, indent=4)
print(f"已记录最后选中的预设：{preset_name.strip()}")
except Exception as e:
print(f"写入最后选中预设失败：{e}")
def clear_last_selected_preset():
"""清空最后选中的预设记录"""
if not LAST_SELECTED_PRESET_JSON.exists():
return
try:
with open(LAST_SELECTED_PRESET_JSON, "w", encoding="utf-8") as f:
json.dump({"last_selected_preset": ""}, f, ensure_ascii=False, indent=4)
except Exception as e:
print(f"清空最后选中预设失败：{e}")
# 2.2 参考预设配置的加载/保存/删除
def load_ref_presets():
"""加载多组参考预设配置"""
ensure_dir_exists(REF_PRESETS_YAML.parent)
# 新增：配置文件不存在时，自动创建空文件
if not REF_PRESETS_YAML.exists():
with open(REF_PRESETS_YAML, "w", encoding="utf-8") as f:
yaml.dump([], f, indent=4, allow_unicode=True)
print(f"暂未检测到参考预设配置文件，已自动创建空文件：{REF_PRESETS_YAML}")
return []
try:
with open(REF_PRESETS_YAML, "r", encoding="utf-8") as f:
presets = yaml.load(f, Loader=yaml.FullLoader) or []
# 兼容旧格式转换
if isinstance(presets, dict):
presets = [{"name": "旧配置转换", "ref_audio_path": presets.get("ref_audio_path"),
"prompt_text": presets.get("prompt_text", ""), "prompt_language": presets.get("prompt_language", "中文")}]
# 补充缺失字段 + 校验音频路径
default_template = {"name": "", "ref_audio_path": None, "prompt_text": "", "prompt_language": "中文"}
for preset in presets:
for key, value in default_template.items():
preset.setdefault(key, value)
# 校验音频路径有效性
audio_path = preset["ref_audio_path"]
if audio_path and not os.path.exists(str(audio_path)):
preset["ref_audio_path"] = None
# 清理冗余音频
clean_unreferenced_audios(presets)
print(f"参考预设加载成功，共 {len(presets)} 组")
return presets
except Exception as e:
print(f"参考预设加载失败：{e}")
return []
def get_preset_by_name(preset_name, presets=None):
"""根据配置名称查询对应的配置详情"""
# 核心修复：先判断 preset_name 是否为 None，避免 AttributeError
if preset_name is None:
return {"name": "", "ref_audio_path": None, "prompt_text": "", "prompt_language": "中文"}
if not presets:
presets = load_ref_presets()
# 现在再调用 strip()，确保 preset_name 不是 None
preset_name_str = preset_name.strip()
for preset in presets:
if preset["name"].strip() == preset_name_str:
return preset
# 无匹配预设时，返回空的合法预设字典
return {"name": "", "ref_audio_path": None, "prompt_text": "", "prompt_language": "中文"}
def save_ref_preset_core(preset_name, ref_audio_path, prompt_text, prompt_language, confirm_override=False):
"""保存/覆盖参考预设核心逻辑（返回：提示信息、是否成功、预设列表）"""
ensure_dir_exists(REF_AUDIO_DIR)
presets = load_ref_presets()
preset_name = preset_name.strip()
# 前置校验
if not ref_audio_path or not os.path.exists(str(ref_audio_path)):
return "保存失败！请先上传有效的主参考音频文件。", False, [p["name"] for p in presets]
if not preset_name:
return "保存失败！配置名称不能为空。", False, [p["name"] for p in presets]
# 音频持久化处理
persistent_audio_path = get_persistent_audio_path(ref_audio_path, preset_name)
if not persistent_audio_path:
return "保存失败！音频文件持久化存储失败。", False, [p["name"] for p in presets]
# 同名检测
preset_index = -1
for idx, p in enumerate(presets):
if p["name"].strip() == preset_name:
preset_index = idx
break
if preset_index >= 0 and not confirm_override:
return f"配置「{preset_name}」已存在，如需替换请确认覆盖！", False, [p["name"] for p in presets]
# 构造新配置
new_preset = {
"name": preset_name,
"ref_audio_path": persistent_audio_path,
"prompt_text": prompt_text,
"prompt_language": prompt_language
}
# 更新配置列表
is_new_preset = preset_index < 0
if preset_index >= 0:
presets[preset_index] = new_preset
tip = "同名配置已覆盖！"
else:
presets.append(new_preset)
tip = "新配置已新增！"
# 写入配置文件
try:
with open(REF_PRESETS_YAML, "w", encoding="utf-8") as f:
yaml.dump(presets, f, indent=4, allow_unicode=True)
# 新增预设自动记录为最后选中
if is_new_preset:
write_last_selected_preset(preset_name)
preset_names = [p["name"] for p in presets]
return f"配置保存成功！{tip}", True, preset_names
except Exception as e:
return f"保存失败：{str(e)}", False, [p["name"] for p in presets]
def delete_ref_preset_core(preset_name):
"""删除参考预设核心逻辑（返回：提示信息、预设列表、默认选中预设）"""
presets = load_ref_presets()
preset_name = preset_name.strip()
if not presets:
return "暂无配置可删除！", [], None
# 获取待删除音频路径
target_audio_path = None
for p in presets:
if p["name"].strip() == preset_name:
target_audio_path = p.get("ref_audio_path")
break
# 过滤删除
presets = [p for p in presets if p["name"].strip() != preset_name]
# 写入配置文件
try:
with open(REF_PRESETS_YAML, "w", encoding="utf-8") as f:
yaml.dump(presets, f, indent=4, allow_unicode=True)
# 删除对应音频
if target_audio_path and os.path.exists(target_audio_path):
os.unlink(target_audio_path)
print(f"同步删除配置对应音频：{target_audio_path}")
# 清空最后选中记录（若删除的是最后选中的预设）
last_selected = read_last_selected_preset()
if last_selected and last_selected == preset_name:
clear_last_selected_preset()
preset_names = [p["name"] for p in presets]
new_selected = preset_names[0] if preset_names else None
tip = "配置删除成功！已同步清理对应音频文件" if preset_names else "配置删除成功！已同步清理对应音频文件，当前无剩余配置"
return tip, preset_names, new_selected
except Exception as e:
return f"删除失败：{str(e)}", [p["name"] for p in presets], preset_name
# 2.3 参考音频文件管理
def get_persistent_audio_path(src_audio_path, preset_name):
"""获取音频持久化路径，清理同配置名旧音频"""
if not src_audio_path or not os.path.exists(src_audio_path):
return None
# 清理文件名
safe_preset_name = sanitize_filename(preset_name)
safe_preset_name = safe_preset_name[:MAX_FILENAME_LENGTH]
# 提取后缀
src_suffix = Path(src_audio_path).suffix.lower()
if not src_suffix or src_suffix not in [".wav", ".mp3", ".flac", ".ogg", ".m4a"]:
src_suffix = ".wav"
# 计算MD5
audio_md5 = get_audio_md5(src_audio_path)
dst_filename = f"{safe_preset_name}_{audio_md5}{src_suffix}"
dst_path = REF_AUDIO_DIR / dst_filename
# 清理同配置名旧音频
for old_audio in REF_AUDIO_DIR.glob(f"{safe_preset_name}_*"):
if old_audio.suffix.lower() in [".wav", ".mp3", ".flac", ".ogg", ".m4a"]:
try:
old_audio.unlink()
except Exception as e:
print(f"清理旧音频失败：{e}")
# 复制新音频
try:
shutil.copy2(src_audio_path, dst_path)
return str(dst_path)
except Exception as e:
print(f"音频持久化复制失败：{e}")
return None
def clean_unreferenced_audios(presets):
"""清理未被任何预设引用的冗余音频"""
if not REF_AUDIO_DIR.exists():
return
# 收集已引用音频
referenced = set()
for preset in presets:
audio_path = preset.get("ref_audio_path")
if audio_path and os.path.exists(audio_path):
referenced.add(Path(audio_path).absolute())
# 删除未引用音频
deleted_count = 0
for audio_file in REF_AUDIO_DIR.glob("*"):
if audio_file.is_file() and audio_file.suffix.lower() in [".wav", ".mp3", ".flac", ".ogg", ".m4a"]:
if audio_file.absolute() not in referenced:
try:
audio_file.unlink()
deleted_count += 1
except Exception as e:
print(f"清理冗余音频失败：{e}")
if deleted_count > 0:
print(f"清理冗余未引用音频 {deleted_count} 个")
# ===================== 3. 推理参数持久化（infer_settings.json） =====================
def load_infer_settings():
"""加载推理参数配置"""
ensure_dir_exists(INFER_SETTINGS_JSON.parent)
if not INFER_SETTINGS_JSON.exists():
return DEFAULT_INFER_SETTINGS
try:
with open(INFER_SETTINGS_JSON, "r", encoding="utf-8") as f:
saved = json.load(f)
return {**DEFAULT_INFER_SETTINGS, **saved}
except Exception as e:
print(f"加载推理参数失败，使用默认值：{e}")
return DEFAULT_INFER_SETTINGS
def save_infer_settings_core(settings):
"""保存推理参数核心逻辑（返回：提示信息）"""
ensure_dir_exists(INFER_SETTINGS_JSON.parent)
try:
with open(INFER_SETTINGS_JSON, "w", encoding="utf-8") as f:
json.dump(settings, f, indent=4, ensure_ascii=False)
# 精简日志输出
print(f"✅ 推理配置保存成功：{INFER_SETTINGS_JSON.absolute()}")
return "推理设置保存成功！已覆盖原有配置文件。"
except Exception as e:
print(f"❌ 推理配置保存失败：{e}")
return f"推理设置保存失败：{str(e)}"
def restore_default_infer_settings_core():
"""恢复推理参数默认值核心逻辑（返回：默认参数列表）"""
ensure_dir_exists(INFER_SETTINGS_JSON.parent)
try:
with open(INFER_SETTINGS_JSON, "w", encoding="utf-8") as f:
json.dump(DEFAULT_INFER_SETTINGS, f, indent=4, ensure_ascii=False)
print(f"✅ 推理配置已恢复默认值：{INFER_SETTINGS_JSON.absolute()}")
except Exception as e:
print(f"❌ 推理配置恢复默认失败：{e}")
# 返回默认参数（按顺序对应UI组件）
return [
DEFAULT_INFER_SETTINGS["batch_size"],
DEFAULT_INFER_SETTINGS["sample_steps"],
DEFAULT_INFER_SETTINGS["fragment_interval"],
DEFAULT_INFER_SETTINGS["speed_factor"],
DEFAULT_INFER_SETTINGS["top_k"],
DEFAULT_INFER_SETTINGS["top_p"],
DEFAULT_INFER_SETTINGS["temperature"],
DEFAULT_INFER_SETTINGS["repetition_penalty"],
DEFAULT_INFER_SETTINGS["how_to_cut"],
DEFAULT_INFER_SETTINGS["super_sampling"],
DEFAULT_INFER_SETTINGS["parallel_infer"],
DEFAULT_INFER_SETTINGS["split_bucket"],
DEFAULT_INFER_SETTINGS["seed"],
DEFAULT_INFER_SETTINGS["keep_random"]
]

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gowebui_batched_infer.bat Normal file
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@echo off
:: 1. 切换命令行编码为UTF-8，解决中文显示乱码（必须放在最前面）
chcp 65001 > nul
:: 2. 获取当前bat文件所在目录并格式化
set "SCRIPT_DIR=%~dp0"
set "SCRIPT_DIR=%SCRIPT_DIR:~0,-1%"
:: 3. 切换到脚本根目录
cd /d "%SCRIPT_DIR%"
:: 4. 创建专属TEMP目录（补充主页面的核心步骤）
if not exist "TEMP" md "TEMP"
set "TEMP=%SCRIPT_DIR%\TEMP"
:: 5. 设置核心环境变量（补充推理脚本依赖的配置）
set "version=v2Pro"
:: 语言配置
set "language=zh_CN"
:: 启用半精度推理（GPU用户推荐，CPU用户改为False）
set "is_half=True"
:: 指定GPU卡号（多卡可修改，无GPU则删除此行）
set "_CUDA_VISIBLE_DEVICES=0"
:: 6. 将runtime目录加入环境变量，确保能调用内置python
set "PATH=%SCRIPT_DIR%\runtime;%PATH%"
:: 7. 直接启动并行推理脚本，传入中文语言参数
echo 正在启动GPT-SoVITS并行推理页面...
runtime\python.exe -I GPT_SoVITS/inference_webui_fast.py zh_CN
:: 8. 执行完成后暂停，便于查看报错信息
pause