mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2026-07-22 18:30:05 +08:00
3a708d01b6
1. auto_match_weights_for_model 返回值增加 choices: 选模型名后, GPT/SoVITS 下拉框只列出该模型对应的权重(过滤掉其它角色), 并自动选中 epoch 最接近 8/15 的。之前只设 value 不改 choices, 导致下拉框 仍显示全部权重。 2. 参考信息区三列对齐: - 参考音频(Audio)包进 gr.Column, 三列都是 Column, flex 行为一致 - gr.Row(equal_height=True) 强制三列等高 - 比例 10+10+8, 语种下拉移到中间列(与参考文本同列) - 参考文本 5行->4行, 配合语种下拉加入避免中间列过高 - 右侧列精简(语种移走后只剩多参考文件, v2下采样步数/超分隐藏) 实测: 选「光头TTS新-20260611」-> GPT choices 10个(全光头TTS新), SoVITS choices 6个, value 自动选 e10/e16。
1720 lines
66 KiB
Python
1720 lines
66 KiB
Python
from __future__ import annotations
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"""
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按中英混合识别
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按日英混合识别
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多语种启动切分识别语种
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全部按中文识别
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全部按英文识别
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全部按日文识别
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"""
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import psutil
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import os
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# 确保仓库根在 sys.path, 使 from tools.xxx 可用 (兼容直接运行此脚本或经 webui.py 启动)
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_now_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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if _now_dir not in __import__('sys').path:
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__import__('sys').path.insert(0, _now_dir)
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def set_high_priority():
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"""把当前 Python 进程设为 HIGH_PRIORITY_CLASS"""
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if os.name != "nt":
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return # 仅 Windows 有效
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p = psutil.Process(os.getpid())
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try:
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p.nice(psutil.HIGH_PRIORITY_CLASS)
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print("已将进程优先级设为 High")
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except psutil.AccessDenied:
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print("权限不足，无法修改优先级（请用管理员运行）")
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set_high_priority()
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import json
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import logging
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import os
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import re
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import sys
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import traceback
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import warnings
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from pathlib import Path
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import torch
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import torchaudio
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from text.LangSegmenter import LangSegmenter
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logging.getLogger("markdown_it").setLevel(logging.ERROR)
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logging.getLogger("urllib3").setLevel(logging.ERROR)
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logging.getLogger("httpcore").setLevel(logging.ERROR)
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logging.getLogger("httpx").setLevel(logging.ERROR)
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logging.getLogger("asyncio").setLevel(logging.ERROR)
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logging.getLogger("charset_normalizer").setLevel(logging.ERROR)
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logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
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logging.getLogger("multipart.multipart").setLevel(logging.ERROR)
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warnings.simplefilter(action="ignore", category=FutureWarning)
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version = model_version = os.environ.get("version", "v2")
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from config import change_choices, get_weights_names, name2gpt_path, name2sovits_path
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SoVITS_names, GPT_names = get_weights_names()
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from config import pretrained_sovits_name
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path_sovits_v3 = pretrained_sovits_name["v3"]
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path_sovits_v4 = pretrained_sovits_name["v4"]
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is_exist_s2gv3 = os.path.exists(path_sovits_v3)
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is_exist_s2gv4 = os.path.exists(path_sovits_v4)
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if os.path.exists("./weight.json"):
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pass
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else:
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with open("./weight.json", "w", encoding="utf-8") as file:
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json.dump({"GPT": {}, "SoVITS": {}}, file)
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with open("./weight.json", "r", encoding="utf-8") as file:
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weight_data = file.read()
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weight_data = json.loads(weight_data)
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gpt_path = os.environ.get("gpt_path", weight_data.get("GPT", {}).get(version, GPT_names[-1]))
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sovits_path = os.environ.get("sovits_path", weight_data.get("SoVITS", {}).get(version, SoVITS_names[0]))
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if isinstance(gpt_path, list):
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gpt_path = gpt_path[0]
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if isinstance(sovits_path, list):
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sovits_path = sovits_path[0]
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# print(2333333)
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# print(os.environ["gpt_path"])
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# print(gpt_path)
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# print(GPT_names)
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# print(weight_data)
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# print(weight_data.get("GPT", {}))
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# print(version)###GPT version里没有s2的v2pro
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# print(weight_data.get("GPT", {}).get(version, GPT_names[-1]))
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cnhubert_base_path = os.environ.get("cnhubert_base_path", "GPT_SoVITS/pretrained_models/chinese-hubert-base")
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bert_path = os.environ.get("bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large")
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infer_ttswebui = os.environ.get("infer_ttswebui", 9872)
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infer_ttswebui = int(infer_ttswebui)
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is_share = os.environ.get("is_share", "False")
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is_share = eval(is_share)
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if "_CUDA_VISIBLE_DEVICES" in os.environ:
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os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
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is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
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# is_half=False
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punctuation = set(["!", "?", "…", ",", ".", "-", " "])
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import gradio as gr
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import librosa
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import numpy as np
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from feature_extractor import cnhubert
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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cnhubert.cnhubert_base_path = cnhubert_base_path
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import random
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from GPT_SoVITS.module.models import Generator, SynthesizerTrn, SynthesizerTrnV3
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def set_seed(seed):
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if seed == -1:
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seed = random.randint(0, 1000000)
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seed = int(seed)
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random.seed(seed)
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os.environ["PYTHONHASHSEED"] = str(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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# set_seed(42)
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from time import time as ttime
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from AR.models.t2s_lightning_module import Text2SemanticLightningModule
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from peft import LoraConfig, get_peft_model
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from text import cleaned_text_to_sequence
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from text.cleaner import clean_text
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from tools.assets import css, js, top_html
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from tools.i18n.i18n import I18nAuto, scan_language_list
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language = os.environ.get("language", "Auto")
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language = sys.argv[-1] if sys.argv[-1] in scan_language_list() else language
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i18n = I18nAuto(language=language)
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# os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 确保直接启动推理UI时也能够设置。
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if torch.cuda.is_available():
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device = "cuda"
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else:
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device = "cpu"
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def check_cuda_graph_support():
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if device != "cuda":
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return False
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try:
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major, _ = torch.cuda.get_device_capability()
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if major < 7:
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print("CUDA Graph: GPU compute capability < 7.0, disabled")
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return False
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a = torch.randn(2, 2, device="cuda")
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g = torch.cuda.CUDAGraph()
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s = torch.cuda.Stream()
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s.wait_stream(torch.cuda.current_stream())
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with torch.cuda.stream(s):
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b = a * 2
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torch.cuda.current_stream().wait_stream(s)
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out = torch.empty_like(b)
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with torch.cuda.graph(g):
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out.copy_(a * 2)
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g.replay()
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torch.cuda.synchronize()
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del a, b, out, g, s
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torch.cuda.empty_cache()
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print("CUDA Graph: support check passed, auto-enabled")
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return True
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except Exception as e:
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print(f"CUDA Graph: support check failed ({e}), disabled")
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return False
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cuda_graph_supported = check_cuda_graph_support()
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dict_language_v1 = {
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i18n("中文"): "all_zh", # 全部按中文识别
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i18n("英文"): "en", # 全部按英文识别#######不变
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i18n("日文"): "all_ja", # 全部按日文识别
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i18n("中英混合"): "zh", # 按中英混合识别####不变
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i18n("日英混合"): "ja", # 按日英混合识别####不变
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i18n("多语种混合"): "auto", # 多语种启动切分识别语种
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}
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dict_language_v2 = {
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i18n("中文"): "all_zh", # 全部按中文识别
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i18n("英文"): "en", # 全部按英文识别#######不变
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i18n("日文"): "all_ja", # 全部按日文识别
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i18n("粤语"): "all_yue", # 全部按中文识别
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i18n("韩文"): "all_ko", # 全部按韩文识别
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i18n("中英混合"): "zh", # 按中英混合识别####不变
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i18n("日英混合"): "ja", # 按日英混合识别####不变
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i18n("粤英混合"): "yue", # 按粤英混合识别####不变
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i18n("韩英混合"): "ko", # 按韩英混合识别####不变
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i18n("多语种混合"): "auto", # 多语种启动切分识别语种
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i18n("多语种混合(粤语)"): "auto_yue", # 多语种启动切分识别语种
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}
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dict_language = dict_language_v1 if version == "v1" else dict_language_v2
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tokenizer = AutoTokenizer.from_pretrained(bert_path)
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bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
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if is_half == True:
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bert_model = bert_model.half().to(device)
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else:
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bert_model = bert_model.to(device)
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def get_bert_feature(text, word2ph):
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with torch.no_grad():
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inputs = tokenizer(text, return_tensors="pt")
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for i in inputs:
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inputs[i] = inputs[i].to(device)
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res = bert_model(**inputs, output_hidden_states=True)
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res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
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assert len(word2ph) == len(text)
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phone_level_feature = []
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for i in range(len(word2ph)):
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repeat_feature = res[i].repeat(word2ph[i], 1)
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phone_level_feature.append(repeat_feature)
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phone_level_feature = torch.cat(phone_level_feature, dim=0)
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return phone_level_feature.T
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class DictToAttrRecursive(dict):
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def __init__(self, input_dict):
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super().__init__(input_dict)
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for key, value in input_dict.items():
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if isinstance(value, dict):
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value = DictToAttrRecursive(value)
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self[key] = value
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setattr(self, key, value)
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def __getattr__(self, item):
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try:
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return self[item]
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except KeyError:
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raise AttributeError(f"Attribute {item} not found")
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def __setattr__(self, key, value):
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if isinstance(value, dict):
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value = DictToAttrRecursive(value)
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super(DictToAttrRecursive, self).__setitem__(key, value)
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super().__setattr__(key, value)
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def __delattr__(self, item):
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try:
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del self[item]
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except KeyError:
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raise AttributeError(f"Attribute {item} not found")
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ssl_model = cnhubert.get_model()
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if is_half == True:
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ssl_model = ssl_model.half().to(device)
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else:
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ssl_model = ssl_model.to(device)
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###todo:put them to process_ckpt and modify my_save func (save sovits weights), gpt save weights use my_save in process_ckpt
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# symbol_version-model_version-if_lora_v3
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from process_ckpt import get_sovits_version_from_path_fast, load_sovits_new
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v3v4set = {"v3", "v4"}
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def change_sovits_weights(sovits_path, prompt_language=None, text_language=None):
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if "！" in sovits_path or "!" in sovits_path:
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sovits_path = name2sovits_path[sovits_path]
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global vq_model, hps, version, model_version, dict_language, if_lora_v3, t2s_model_cudagraph
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t2s_model_cudagraph = None
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version, model_version, if_lora_v3 = get_sovits_version_from_path_fast(sovits_path)
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print(sovits_path, version, model_version, if_lora_v3)
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is_exist = is_exist_s2gv3 if model_version == "v3" else is_exist_s2gv4
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path_sovits = path_sovits_v3 if model_version == "v3" else path_sovits_v4
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if if_lora_v3 == True and is_exist == False:
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info = path_sovits + "SoVITS %s" % model_version + i18n("底模缺失，无法加载相应 LoRA 权重")
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gr.Warning(info)
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raise FileExistsError(info)
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dict_language = dict_language_v1 if version == "v1" else dict_language_v2
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if prompt_language is not None and text_language is not None:
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if prompt_language in list(dict_language.keys()):
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prompt_text_update, prompt_language_update = (
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{"__type__": "update"},
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{"__type__": "update", "value": prompt_language},
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)
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else:
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prompt_text_update = {"__type__": "update", "value": ""}
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prompt_language_update = {"__type__": "update", "value": i18n("中文")}
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if text_language in list(dict_language.keys()):
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text_update, text_language_update = {"__type__": "update"}, {"__type__": "update", "value": text_language}
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else:
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text_update = {"__type__": "update", "value": ""}
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text_language_update = {"__type__": "update", "value": i18n("中文")}
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if model_version in v3v4set:
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visible_sample_steps = True
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visible_inp_refs = False
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else:
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visible_sample_steps = False
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visible_inp_refs = True
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yield (
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{"__type__": "update", "choices": list(dict_language.keys())},
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{"__type__": "update", "choices": list(dict_language.keys())},
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prompt_text_update,
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prompt_language_update,
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text_update,
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text_language_update,
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{
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"__type__": "update",
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"visible": visible_sample_steps,
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"value": 32 if model_version == "v3" else 8,
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"choices": [4, 8, 16, 32, 64, 128] if model_version == "v3" else [4, 8, 16, 32],
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},
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{"__type__": "update", "visible": visible_inp_refs},
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{"__type__": "update", "value": False, "interactive": True if model_version not in v3v4set else False, "visible": model_version not in v3v4set},
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{"__type__": "update", "visible": True if model_version == "v3" else False},
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{"__type__": "update", "value": i18n("模型加载中，请等待"), "interactive": False},
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)
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dict_s2 = load_sovits_new(sovits_path)
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hps = dict_s2["config"]
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hps = DictToAttrRecursive(hps)
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hps.model.semantic_frame_rate = "25hz"
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if "enc_p.text_embedding.weight" not in dict_s2["weight"]:
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hps.model.version = "v2" # v3model,v2sybomls
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elif dict_s2["weight"]["enc_p.text_embedding.weight"].shape[0] == 322:
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hps.model.version = "v1"
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else:
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hps.model.version = "v2"
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version = hps.model.version
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# print("sovits版本:",hps.model.version)
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if model_version not in v3v4set:
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if "Pro" not in model_version:
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model_version = version
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else:
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hps.model.version = model_version
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vq_model = SynthesizerTrn(
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hps.data.filter_length // 2 + 1,
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hps.train.segment_size // hps.data.hop_length,
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n_speakers=hps.data.n_speakers,
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**hps.model,
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)
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else:
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hps.model.version = model_version
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vq_model = SynthesizerTrnV3(
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hps.data.filter_length // 2 + 1,
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hps.train.segment_size // hps.data.hop_length,
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n_speakers=hps.data.n_speakers,
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**hps.model,
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)
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if "pretrained" not in sovits_path:
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try:
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del vq_model.enc_q
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except:
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pass
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if is_half == True:
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vq_model = vq_model.half().to(device)
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else:
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vq_model = vq_model.to(device)
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vq_model.eval()
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if if_lora_v3 == False:
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print("loading sovits_%s" % model_version, vq_model.load_state_dict(dict_s2["weight"], strict=False))
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else:
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path_sovits = path_sovits_v3 if model_version == "v3" else path_sovits_v4
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print(
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"loading sovits_%spretrained_G" % model_version,
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vq_model.load_state_dict(load_sovits_new(path_sovits)["weight"], strict=False),
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)
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lora_rank = dict_s2["lora_rank"]
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lora_config = LoraConfig(
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target_modules=["to_k", "to_q", "to_v", "to_out.0"],
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r=lora_rank,
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lora_alpha=lora_rank,
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init_lora_weights=True,
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)
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vq_model.cfm = get_peft_model(vq_model.cfm, lora_config)
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print("loading sovits_%s_lora%s" % (model_version, lora_rank))
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vq_model.load_state_dict(dict_s2["weight"], strict=False)
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vq_model.cfm = vq_model.cfm.merge_and_unload()
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||
# torch.save(vq_model.state_dict(),"merge_win.pth")
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vq_model.eval()
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||
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||
yield (
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{"__type__": "update", "choices": list(dict_language.keys())},
|
||
{"__type__": "update", "choices": list(dict_language.keys())},
|
||
prompt_text_update,
|
||
prompt_language_update,
|
||
text_update,
|
||
text_language_update,
|
||
{
|
||
"__type__": "update",
|
||
"visible": visible_sample_steps,
|
||
"value": 32 if model_version == "v3" else 8,
|
||
"choices": [4, 8, 16, 32, 64, 128] if model_version == "v3" else [4, 8, 16, 32],
|
||
},
|
||
{"__type__": "update", "visible": visible_inp_refs},
|
||
{"__type__": "update", "value": False, "interactive": True if model_version not in v3v4set else False, "visible": model_version not in v3v4set},
|
||
{"__type__": "update", "visible": True if model_version == "v3" else False},
|
||
{"__type__": "update", "value": i18n("合成语音"), "interactive": True},
|
||
)
|
||
with open("./weight.json") as f:
|
||
data = f.read()
|
||
data = json.loads(data)
|
||
data["SoVITS"][version] = sovits_path
|
||
with open("./weight.json", "w") as f:
|
||
f.write(json.dumps(data))
|
||
|
||
|
||
try:
|
||
next(change_sovits_weights(sovits_path))
|
||
except:
|
||
pass
|
||
|
||
|
||
t2s_model_cudagraph = None
|
||
gpt_path_global = None
|
||
|
||
|
||
def change_gpt_weights(gpt_path):
|
||
if "！" in gpt_path or "!" in gpt_path:
|
||
gpt_path = name2gpt_path[gpt_path]
|
||
global hz, max_sec, t2s_model, config, t2s_model_cudagraph, gpt_path_global
|
||
t2s_model_cudagraph = None
|
||
gpt_path_global = gpt_path
|
||
hz = 50
|
||
dict_s1 = torch.load(gpt_path, map_location="cpu", weights_only=False)
|
||
config = dict_s1["config"]
|
||
max_sec = config["data"]["max_sec"]
|
||
t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
|
||
t2s_model.load_state_dict(dict_s1["weight"])
|
||
if is_half == True:
|
||
t2s_model = t2s_model.half()
|
||
t2s_model = t2s_model.to(device)
|
||
t2s_model.eval()
|
||
# total = sum([param.nelement() for param in t2s_model.parameters()])
|
||
# print("Number of parameter: %.2fM" % (total / 1e6))
|
||
with open("./weight.json") as f:
|
||
data = f.read()
|
||
data = json.loads(data)
|
||
data["GPT"][version] = gpt_path
|
||
with open("./weight.json", "w") as f:
|
||
f.write(json.dumps(data))
|
||
|
||
|
||
change_gpt_weights(gpt_path)
|
||
os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
|
||
import torch
|
||
|
||
now_dir = os.getcwd()
|
||
|
||
|
||
def clean_hifigan_model():
|
||
global hifigan_model
|
||
if hifigan_model:
|
||
hifigan_model = hifigan_model.cpu()
|
||
hifigan_model = None
|
||
try:
|
||
torch.cuda.empty_cache()
|
||
except:
|
||
pass
|
||
|
||
|
||
def clean_bigvgan_model():
|
||
global bigvgan_model
|
||
if bigvgan_model:
|
||
bigvgan_model = bigvgan_model.cpu()
|
||
bigvgan_model = None
|
||
try:
|
||
torch.cuda.empty_cache()
|
||
except:
|
||
pass
|
||
|
||
|
||
def clean_sv_cn_model():
|
||
global sv_cn_model
|
||
if sv_cn_model:
|
||
sv_cn_model.embedding_model = sv_cn_model.embedding_model.cpu()
|
||
sv_cn_model = None
|
||
try:
|
||
torch.cuda.empty_cache()
|
||
except:
|
||
pass
|
||
|
||
|
||
def init_bigvgan():
|
||
global bigvgan_model, hifigan_model, sv_cn_model
|
||
from BigVGAN import bigvgan
|
||
|
||
bigvgan_model = bigvgan.BigVGAN.from_pretrained(
|
||
"%s/GPT_SoVITS/pretrained_models/models--nvidia--bigvgan_v2_24khz_100band_256x" % (now_dir,),
|
||
use_cuda_kernel=False,
|
||
) # if True, RuntimeError: Ninja is required to load C++ extensions
|
||
# remove weight norm in the model and set to eval mode
|
||
bigvgan_model.remove_weight_norm()
|
||
bigvgan_model = bigvgan_model.eval()
|
||
clean_hifigan_model()
|
||
clean_sv_cn_model()
|
||
if is_half == True:
|
||
bigvgan_model = bigvgan_model.half().to(device)
|
||
else:
|
||
bigvgan_model = bigvgan_model.to(device)
|
||
|
||
|
||
def init_hifigan():
|
||
global hifigan_model, bigvgan_model, sv_cn_model
|
||
hifigan_model = Generator(
|
||
initial_channel=100,
|
||
resblock="1",
|
||
resblock_kernel_sizes=[3, 7, 11],
|
||
resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
|
||
upsample_rates=[10, 6, 2, 2, 2],
|
||
upsample_initial_channel=512,
|
||
upsample_kernel_sizes=[20, 12, 4, 4, 4],
|
||
gin_channels=0,
|
||
is_bias=True,
|
||
)
|
||
hifigan_model.eval()
|
||
hifigan_model.remove_weight_norm()
|
||
state_dict_g = torch.load(
|
||
"%s/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth" % (now_dir,),
|
||
map_location="cpu",
|
||
weights_only=False,
|
||
)
|
||
print("loading vocoder", hifigan_model.load_state_dict(state_dict_g))
|
||
clean_bigvgan_model()
|
||
clean_sv_cn_model()
|
||
if is_half == True:
|
||
hifigan_model = hifigan_model.half().to(device)
|
||
else:
|
||
hifigan_model = hifigan_model.to(device)
|
||
|
||
|
||
from sv import SV
|
||
|
||
|
||
def init_sv_cn():
|
||
global hifigan_model, bigvgan_model, sv_cn_model
|
||
sv_cn_model = SV(device, is_half)
|
||
clean_bigvgan_model()
|
||
clean_hifigan_model()
|
||
|
||
|
||
bigvgan_model = hifigan_model = sv_cn_model = None
|
||
if model_version == "v3":
|
||
init_bigvgan()
|
||
if model_version == "v4":
|
||
init_hifigan()
|
||
if model_version in {"v2Pro", "v2ProPlus"}:
|
||
init_sv_cn()
|
||
|
||
resample_transform_dict = {}
|
||
|
||
|
||
def resample(audio_tensor, sr0, sr1, device):
|
||
global resample_transform_dict
|
||
key = "%s-%s-%s" % (sr0, sr1, str(device))
|
||
if key not in resample_transform_dict:
|
||
resample_transform_dict[key] = torchaudio.transforms.Resample(sr0, sr1).to(device)
|
||
return resample_transform_dict[key](audio_tensor)
|
||
|
||
|
||
def get_spepc(hps, filename, dtype, device, is_v2pro=False):
|
||
# audio = load_audio(filename, int(hps.data.sampling_rate))
|
||
|
||
# audio, sampling_rate = librosa.load(filename, sr=int(hps.data.sampling_rate))
|
||
# audio = torch.FloatTensor(audio)
|
||
|
||
sr1 = int(hps.data.sampling_rate)
|
||
audio, sr0 = torchaudio.load(filename)
|
||
if sr0 != sr1:
|
||
audio = audio.to(device)
|
||
if audio.shape[0] == 2:
|
||
audio = audio.mean(0).unsqueeze(0)
|
||
audio = resample(audio, sr0, sr1, device)
|
||
else:
|
||
audio = audio.to(device)
|
||
if audio.shape[0] == 2:
|
||
audio = audio.mean(0).unsqueeze(0)
|
||
|
||
maxx = audio.abs().max()
|
||
if maxx > 1:
|
||
audio /= min(2, maxx)
|
||
spec = spectrogram_torch(
|
||
audio,
|
||
hps.data.filter_length,
|
||
hps.data.sampling_rate,
|
||
hps.data.hop_length,
|
||
hps.data.win_length,
|
||
center=False,
|
||
)
|
||
spec = spec.to(dtype)
|
||
if is_v2pro == True:
|
||
audio = resample(audio, sr1, 16000, device).to(dtype)
|
||
return spec, audio
|
||
|
||
|
||
def clean_text_inf(text, language, version):
|
||
language = language.replace("all_", "")
|
||
phones, word2ph, norm_text = clean_text(text, language, version)
|
||
phones = cleaned_text_to_sequence(phones, version)
|
||
return phones, word2ph, norm_text
|
||
|
||
|
||
dtype = torch.float16 if is_half == True else torch.float32
|
||
|
||
|
||
def get_bert_inf(phones, word2ph, norm_text, language):
|
||
language = language.replace("all_", "")
|
||
if language == "zh":
|
||
bert = get_bert_feature(norm_text, word2ph).to(device) # .to(dtype)
|
||
else:
|
||
bert = torch.zeros(
|
||
(1024, len(phones)),
|
||
dtype=torch.float16 if is_half == True else torch.float32,
|
||
).to(device)
|
||
|
||
return bert
|
||
|
||
|
||
splits = {
|
||
"，",
|
||
"。",
|
||
"？",
|
||
"！",
|
||
",",
|
||
".",
|
||
"?",
|
||
"!",
|
||
"~",
|
||
":",
|
||
"：",
|
||
"—",
|
||
"…",
|
||
}
|
||
|
||
|
||
def get_first(text):
|
||
pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
|
||
text = re.split(pattern, text)[0].strip()
|
||
return text
|
||
|
||
|
||
from text import chinese
|
||
|
||
|
||
def get_phones_and_bert(text, language, version, final=False):
|
||
text = re.sub(r' {2,}', ' ', text)
|
||
textlist = []
|
||
langlist = []
|
||
if language == "all_zh":
|
||
for tmp in LangSegmenter.getTexts(text,"zh"):
|
||
langlist.append(tmp["lang"])
|
||
textlist.append(tmp["text"])
|
||
elif language == "all_yue":
|
||
for tmp in LangSegmenter.getTexts(text,"zh"):
|
||
if tmp["lang"] == "zh":
|
||
tmp["lang"] = "yue"
|
||
langlist.append(tmp["lang"])
|
||
textlist.append(tmp["text"])
|
||
elif language == "all_ja":
|
||
for tmp in LangSegmenter.getTexts(text,"ja"):
|
||
langlist.append(tmp["lang"])
|
||
textlist.append(tmp["text"])
|
||
elif language == "all_ko":
|
||
for tmp in LangSegmenter.getTexts(text,"ko"):
|
||
langlist.append(tmp["lang"])
|
||
textlist.append(tmp["text"])
|
||
elif language == "en":
|
||
langlist.append("en")
|
||
textlist.append(text)
|
||
elif language == "auto":
|
||
for tmp in LangSegmenter.getTexts(text):
|
||
langlist.append(tmp["lang"])
|
||
textlist.append(tmp["text"])
|
||
elif language == "auto_yue":
|
||
for tmp in LangSegmenter.getTexts(text):
|
||
if tmp["lang"] == "zh":
|
||
tmp["lang"] = "yue"
|
||
langlist.append(tmp["lang"])
|
||
textlist.append(tmp["text"])
|
||
else:
|
||
for tmp in LangSegmenter.getTexts(text):
|
||
if langlist:
|
||
if (tmp["lang"] == "en" and langlist[-1] == "en") or (tmp["lang"] != "en" and langlist[-1] != "en"):
|
||
textlist[-1] += tmp["text"]
|
||
continue
|
||
if tmp["lang"] == "en":
|
||
langlist.append(tmp["lang"])
|
||
else:
|
||
# 因无法区别中日韩文汉字,以用户输入为准
|
||
langlist.append(language)
|
||
textlist.append(tmp["text"])
|
||
print(textlist)
|
||
print(langlist)
|
||
phones_list = []
|
||
bert_list = []
|
||
norm_text_list = []
|
||
for i in range(len(textlist)):
|
||
lang = langlist[i]
|
||
phones, word2ph, norm_text = clean_text_inf(textlist[i], lang, version)
|
||
bert = get_bert_inf(phones, word2ph, norm_text, lang)
|
||
phones_list.append(phones)
|
||
norm_text_list.append(norm_text)
|
||
bert_list.append(bert)
|
||
bert = torch.cat(bert_list, dim=1)
|
||
phones = sum(phones_list, [])
|
||
norm_text = "".join(norm_text_list)
|
||
|
||
if not final and len(phones) < 6:
|
||
return get_phones_and_bert("." + text, language, version, final=True)
|
||
|
||
return phones, bert.to(dtype), norm_text
|
||
|
||
|
||
from module.mel_processing import mel_spectrogram_torch, spectrogram_torch
|
||
|
||
spec_min = -12
|
||
spec_max = 2
|
||
|
||
|
||
def norm_spec(x):
|
||
return (x - spec_min) / (spec_max - spec_min) * 2 - 1
|
||
|
||
|
||
def denorm_spec(x):
|
||
return (x + 1) / 2 * (spec_max - spec_min) + spec_min
|
||
|
||
|
||
mel_fn = lambda x: mel_spectrogram_torch(
|
||
x,
|
||
**{
|
||
"n_fft": 1024,
|
||
"win_size": 1024,
|
||
"hop_size": 256,
|
||
"num_mels": 100,
|
||
"sampling_rate": 24000,
|
||
"fmin": 0,
|
||
"fmax": None,
|
||
"center": False,
|
||
},
|
||
)
|
||
mel_fn_v4 = lambda x: mel_spectrogram_torch(
|
||
x,
|
||
**{
|
||
"n_fft": 1280,
|
||
"win_size": 1280,
|
||
"hop_size": 320,
|
||
"num_mels": 100,
|
||
"sampling_rate": 32000,
|
||
"fmin": 0,
|
||
"fmax": None,
|
||
"center": False,
|
||
},
|
||
)
|
||
|
||
|
||
def merge_short_text_in_array(texts, threshold):
|
||
if (len(texts)) < 2:
|
||
return texts
|
||
result = []
|
||
text = ""
|
||
for ele in texts:
|
||
text += ele
|
||
if len(text) >= threshold:
|
||
result.append(text)
|
||
text = ""
|
||
if len(text) > 0:
|
||
if len(result) == 0:
|
||
result.append(text)
|
||
else:
|
||
result[len(result) - 1] += text
|
||
return result
|
||
|
||
|
||
sr_model = None
|
||
|
||
|
||
def audio_sr(audio, sr):
|
||
global sr_model
|
||
if sr_model == None:
|
||
from tools.audio_sr import AP_BWE
|
||
|
||
try:
|
||
sr_model = AP_BWE(device, DictToAttrRecursive)
|
||
except FileNotFoundError:
|
||
gr.Warning(i18n("你没有下载超分模型的参数，因此不进行超分。如想超分请先参照教程把文件下载好"))
|
||
return audio.cpu().detach().numpy(), sr
|
||
return sr_model(audio, sr)
|
||
|
||
|
||
##ref_wav_path+prompt_text+prompt_language+text(单个)+text_language+top_k+top_p+temperature
|
||
# cache_tokens={}#暂未实现清理机制
|
||
cache = {}
|
||
|
||
|
||
def get_tts_wav(
|
||
ref_wav_path,
|
||
prompt_text,
|
||
prompt_language,
|
||
text,
|
||
text_language,
|
||
how_to_cut=i18n("不切"),
|
||
top_k=20,
|
||
top_p=0.6,
|
||
temperature=0.6,
|
||
ref_free=False,
|
||
speed=1,
|
||
if_freeze=False,
|
||
inp_refs=None,
|
||
sample_steps=8,
|
||
if_sr=False,
|
||
pause_second=0.3,
|
||
use_cuda_graph=False,
|
||
):
|
||
global cache
|
||
if ref_wav_path:
|
||
pass
|
||
else:
|
||
gr.Warning(i18n("请上传参考音频"))
|
||
if text:
|
||
pass
|
||
else:
|
||
gr.Warning(i18n("请填入推理文本"))
|
||
t = []
|
||
if prompt_text is None or len(prompt_text) == 0:
|
||
ref_free = True
|
||
if model_version in v3v4set:
|
||
ref_free = False # s2v3暂不支持ref_free
|
||
else:
|
||
if_sr = False
|
||
if model_version not in {"v3", "v4", "v2Pro", "v2ProPlus"}:
|
||
clean_bigvgan_model()
|
||
clean_hifigan_model()
|
||
clean_sv_cn_model()
|
||
t0 = ttime()
|
||
prompt_language = dict_language[prompt_language]
|
||
text_language = dict_language[text_language]
|
||
|
||
if not ref_free:
|
||
prompt_text = prompt_text.strip("\n")
|
||
if prompt_text[-1] not in splits:
|
||
prompt_text += "。" if prompt_language != "en" else "."
|
||
print(i18n("实际输入的参考文本:"), prompt_text)
|
||
text = text.strip("\n")
|
||
# if (text[0] not in splits and len(get_first(text)) < 4): text = "。" + text if text_language != "en" else "." + text
|
||
|
||
print(i18n("实际输入的目标文本:"), text)
|
||
zero_wav = np.zeros(
|
||
int(hps.data.sampling_rate * pause_second),
|
||
dtype=np.float16 if is_half == True else np.float32,
|
||
)
|
||
zero_wav_torch = torch.from_numpy(zero_wav)
|
||
if is_half == True:
|
||
zero_wav_torch = zero_wav_torch.half().to(device)
|
||
else:
|
||
zero_wav_torch = zero_wav_torch.to(device)
|
||
if not ref_free:
|
||
with torch.no_grad():
|
||
wav16k, sr = librosa.load(ref_wav_path, sr=16000)
|
||
ref_duration = wav16k.shape[0] / 16000
|
||
if ref_duration < 3 or ref_duration > 10:
|
||
gr.Warning(i18n(f"参考音频时长 {ref_duration:.1f} 秒，超出推荐的 3~10 秒范围，可能影响合成质量"))
|
||
wav16k = torch.from_numpy(wav16k)
|
||
if is_half == True:
|
||
wav16k = wav16k.half().to(device)
|
||
else:
|
||
wav16k = wav16k.to(device)
|
||
wav16k = torch.cat([wav16k, zero_wav_torch])
|
||
ssl_content = ssl_model.model(wav16k.unsqueeze(0))["last_hidden_state"].transpose(1, 2) # .float()
|
||
codes = vq_model.extract_latent(ssl_content)
|
||
prompt_semantic = codes[0, 0]
|
||
prompt = prompt_semantic.unsqueeze(0).to(device)
|
||
|
||
t1 = ttime()
|
||
t.append(t1 - t0)
|
||
|
||
if how_to_cut == i18n("凑四句一切"):
|
||
text = cut1(text)
|
||
elif how_to_cut == i18n("凑50字一切"):
|
||
text = cut2(text)
|
||
elif how_to_cut == i18n("按中文句号。切"):
|
||
text = cut3(text)
|
||
elif how_to_cut == i18n("按英文句号.切"):
|
||
text = cut4(text)
|
||
elif how_to_cut == i18n("按标点符号切"):
|
||
text = cut5(text)
|
||
while "\n\n" in text:
|
||
text = text.replace("\n\n", "\n")
|
||
print(i18n("实际输入的目标文本(切句后):"), text)
|
||
texts = text.split("\n")
|
||
texts = process_text(texts)
|
||
texts = merge_short_text_in_array(texts, 5)
|
||
audio_opt = []
|
||
###s2v3暂不支持ref_free
|
||
if not ref_free:
|
||
phones1, bert1, norm_text1 = get_phones_and_bert(prompt_text, prompt_language, version)
|
||
|
||
for i_text, text in enumerate(texts):
|
||
# 解决输入目标文本的空行导致报错的问题
|
||
if len(text.strip()) == 0:
|
||
continue
|
||
if text[-1] not in splits:
|
||
text += "。" if text_language != "en" else "."
|
||
print(i18n("实际输入的目标文本(每句):"), text)
|
||
phones2, bert2, norm_text2 = get_phones_and_bert(text, text_language, version)
|
||
print(i18n("前端处理后的文本(每句):"), norm_text2)
|
||
if not ref_free:
|
||
bert = torch.cat([bert1, bert2], 1)
|
||
all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
|
||
else:
|
||
bert = bert2
|
||
all_phoneme_ids = torch.LongTensor(phones2).to(device).unsqueeze(0)
|
||
|
||
bert = bert.to(device).unsqueeze(0)
|
||
all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
|
||
|
||
t2 = ttime()
|
||
# cache_key="%s-%s-%s-%s-%s-%s-%s-%s"%(ref_wav_path,prompt_text,prompt_language,text,text_language,top_k,top_p,temperature)
|
||
# print(cache.keys(),if_freeze)
|
||
if i_text in cache and if_freeze == True:
|
||
pred_semantic = cache[i_text]
|
||
else:
|
||
if use_cuda_graph and device == "cuda":
|
||
global t2s_model_cudagraph
|
||
if t2s_model_cudagraph is None:
|
||
from AR.models.t2s_model_cudagraph import CUDAGraphRunner
|
||
t2s_model_cudagraph = CUDAGraphRunner(
|
||
CUDAGraphRunner.load_decoder(gpt_path_global),
|
||
torch.device(device),
|
||
torch.float16 if is_half else torch.float32,
|
||
)
|
||
from AR.models.structs_cudagraph import T2SRequest
|
||
with torch.no_grad():
|
||
t2s_request = T2SRequest(
|
||
[all_phoneme_ids.squeeze(0)],
|
||
all_phoneme_len,
|
||
all_phoneme_ids.new_zeros((1, 0)) if ref_free else prompt,
|
||
[bert.squeeze(0)],
|
||
valid_length=1,
|
||
top_k=top_k,
|
||
top_p=top_p,
|
||
temperature=temperature,
|
||
early_stop_num=hz * max_sec,
|
||
use_cuda_graph=True,
|
||
)
|
||
t2s_result = t2s_model_cudagraph.generate(t2s_request)
|
||
if t2s_result.exception is not None:
|
||
print(t2s_result.exception)
|
||
print(t2s_result.traceback)
|
||
raise RuntimeError("CUDA Graph T2S inference failed")
|
||
pred_semantic = t2s_result.result[0].unsqueeze(0).unsqueeze(0)
|
||
cache[i_text] = pred_semantic
|
||
else:
|
||
with torch.no_grad():
|
||
pred_semantic, idx = t2s_model.model.infer_panel(
|
||
all_phoneme_ids,
|
||
all_phoneme_len,
|
||
None if ref_free else prompt,
|
||
bert,
|
||
# prompt_phone_len=ph_offset,
|
||
top_k=top_k,
|
||
top_p=top_p,
|
||
temperature=temperature,
|
||
early_stop_num=hz * max_sec,
|
||
)
|
||
pred_semantic = pred_semantic[:, -idx:].unsqueeze(0)
|
||
cache[i_text] = pred_semantic
|
||
t3 = ttime()
|
||
is_v2pro = model_version in {"v2Pro", "v2ProPlus"}
|
||
# print(23333,is_v2pro,model_version)
|
||
###v3不存在以下逻辑和inp_refs
|
||
if model_version not in v3v4set:
|
||
refers = []
|
||
if is_v2pro:
|
||
sv_emb = []
|
||
if sv_cn_model == None:
|
||
init_sv_cn()
|
||
if inp_refs:
|
||
for path in inp_refs:
|
||
try: #####这里加上提取sv的逻辑，要么一堆sv一堆refer，要么单个sv单个refer
|
||
refer, audio_tensor = get_spepc(hps, path.name, dtype, device, is_v2pro)
|
||
refers.append(refer)
|
||
if is_v2pro:
|
||
sv_emb.append(sv_cn_model.compute_embedding3(audio_tensor))
|
||
except:
|
||
traceback.print_exc()
|
||
if len(refers) == 0:
|
||
refers, audio_tensor = get_spepc(hps, ref_wav_path, dtype, device, is_v2pro)
|
||
refers = [refers]
|
||
if is_v2pro:
|
||
sv_emb = [sv_cn_model.compute_embedding3(audio_tensor)]
|
||
if is_v2pro:
|
||
audio = vq_model.decode(
|
||
pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refers, speed=speed, sv_emb=sv_emb
|
||
)[0][0]
|
||
else:
|
||
audio = vq_model.decode(
|
||
pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refers, speed=speed
|
||
)[0][0]
|
||
else:
|
||
refer, audio_tensor = get_spepc(hps, ref_wav_path, dtype, device)
|
||
phoneme_ids0 = torch.LongTensor(phones1).to(device).unsqueeze(0)
|
||
phoneme_ids1 = torch.LongTensor(phones2).to(device).unsqueeze(0)
|
||
fea_ref, ge = vq_model.decode_encp(prompt.unsqueeze(0), phoneme_ids0, refer)
|
||
ref_audio, sr = torchaudio.load(ref_wav_path)
|
||
ref_audio = ref_audio.to(device).float()
|
||
if ref_audio.shape[0] == 2:
|
||
ref_audio = ref_audio.mean(0).unsqueeze(0)
|
||
tgt_sr = 24000 if model_version == "v3" else 32000
|
||
if sr != tgt_sr:
|
||
ref_audio = resample(ref_audio, sr, tgt_sr, device)
|
||
# print("ref_audio",ref_audio.abs().mean())
|
||
mel2 = mel_fn(ref_audio) if model_version == "v3" else mel_fn_v4(ref_audio)
|
||
mel2 = norm_spec(mel2)
|
||
T_min = min(mel2.shape[2], fea_ref.shape[2])
|
||
mel2 = mel2[:, :, :T_min]
|
||
fea_ref = fea_ref[:, :, :T_min]
|
||
Tref = 468 if model_version == "v3" else 500
|
||
Tchunk = 934 if model_version == "v3" else 1000
|
||
if T_min > Tref:
|
||
mel2 = mel2[:, :, -Tref:]
|
||
fea_ref = fea_ref[:, :, -Tref:]
|
||
T_min = Tref
|
||
chunk_len = Tchunk - T_min
|
||
mel2 = mel2.to(dtype)
|
||
fea_todo, ge = vq_model.decode_encp(pred_semantic, phoneme_ids1, refer, ge, speed)
|
||
cfm_resss = []
|
||
idx = 0
|
||
while 1:
|
||
fea_todo_chunk = fea_todo[:, :, idx : idx + chunk_len]
|
||
if fea_todo_chunk.shape[-1] == 0:
|
||
break
|
||
idx += chunk_len
|
||
fea = torch.cat([fea_ref, fea_todo_chunk], 2).transpose(2, 1)
|
||
cfm_res = vq_model.cfm.inference(
|
||
fea, torch.LongTensor([fea.size(1)]).to(fea.device), mel2, sample_steps, inference_cfg_rate=0
|
||
)
|
||
cfm_res = cfm_res[:, :, mel2.shape[2] :]
|
||
mel2 = cfm_res[:, :, -T_min:]
|
||
fea_ref = fea_todo_chunk[:, :, -T_min:]
|
||
cfm_resss.append(cfm_res)
|
||
cfm_res = torch.cat(cfm_resss, 2)
|
||
cfm_res = denorm_spec(cfm_res)
|
||
if model_version == "v3":
|
||
if bigvgan_model == None:
|
||
init_bigvgan()
|
||
else: # v4
|
||
if hifigan_model == None:
|
||
init_hifigan()
|
||
vocoder_model = bigvgan_model if model_version == "v3" else hifigan_model
|
||
with torch.inference_mode():
|
||
wav_gen = vocoder_model(cfm_res)
|
||
audio = wav_gen[0][0] # .cpu().detach().numpy()
|
||
max_audio = torch.abs(audio).max() # 简单防止16bit爆音
|
||
if max_audio > 1:
|
||
audio = audio / max_audio
|
||
audio_opt.append(audio)
|
||
audio_opt.append(zero_wav_torch) # zero_wav
|
||
t4 = ttime()
|
||
t.extend([t2 - t1, t3 - t2, t4 - t3])
|
||
t1 = ttime()
|
||
print("%.3f\t%.3f\t%.3f\t%.3f" % (t[0], sum(t[1::3]), sum(t[2::3]), sum(t[3::3])))
|
||
audio_opt = torch.cat(audio_opt, 0) # np.concatenate
|
||
if model_version in {"v1", "v2", "v2Pro", "v2ProPlus"}:
|
||
opt_sr = 32000
|
||
elif model_version == "v3":
|
||
opt_sr = 24000
|
||
else:
|
||
opt_sr = 48000 # v4
|
||
if if_sr == True and opt_sr == 24000:
|
||
print(i18n("音频超分中"))
|
||
audio_opt, opt_sr = audio_sr(audio_opt.unsqueeze(0), opt_sr)
|
||
max_audio = np.abs(audio_opt).max()
|
||
if max_audio > 1:
|
||
audio_opt /= max_audio
|
||
else:
|
||
audio_opt = audio_opt.cpu().detach().numpy()
|
||
yield opt_sr, (audio_opt * 32767).astype(np.int16)
|
||
|
||
|
||
def split(todo_text):
|
||
todo_text = todo_text.replace("……", "。").replace("——", "，")
|
||
if todo_text[-1] not in splits:
|
||
todo_text += "。"
|
||
i_split_head = i_split_tail = 0
|
||
len_text = len(todo_text)
|
||
todo_texts = []
|
||
while 1:
|
||
if i_split_head >= len_text:
|
||
break # 结尾一定有标点，所以直接跳出即可，最后一段在上次已加入
|
||
if todo_text[i_split_head] in splits:
|
||
i_split_head += 1
|
||
todo_texts.append(todo_text[i_split_tail:i_split_head])
|
||
i_split_tail = i_split_head
|
||
else:
|
||
i_split_head += 1
|
||
return todo_texts
|
||
|
||
|
||
def cut1(inp):
|
||
inp = inp.strip("\n")
|
||
inps = split(inp)
|
||
split_idx = list(range(0, len(inps), 4))
|
||
split_idx[-1] = None
|
||
if len(split_idx) > 1:
|
||
opts = []
|
||
for idx in range(len(split_idx) - 1):
|
||
opts.append("".join(inps[split_idx[idx] : split_idx[idx + 1]]))
|
||
else:
|
||
opts = [inp]
|
||
opts = [item for item in opts if not set(item).issubset(punctuation)]
|
||
return "\n".join(opts)
|
||
|
||
|
||
def cut2(inp):
|
||
inp = inp.strip("\n")
|
||
inps = split(inp)
|
||
if len(inps) < 2:
|
||
return inp
|
||
opts = []
|
||
summ = 0
|
||
tmp_str = ""
|
||
for i in range(len(inps)):
|
||
summ += len(inps[i])
|
||
tmp_str += inps[i]
|
||
if summ > 50:
|
||
summ = 0
|
||
opts.append(tmp_str)
|
||
tmp_str = ""
|
||
if tmp_str != "":
|
||
opts.append(tmp_str)
|
||
# print(opts)
|
||
if len(opts) > 1 and len(opts[-1]) < 50: ##如果最后一个太短了，和前一个合一起
|
||
opts[-2] = opts[-2] + opts[-1]
|
||
opts = opts[:-1]
|
||
opts = [item for item in opts if not set(item).issubset(punctuation)]
|
||
return "\n".join(opts)
|
||
|
||
|
||
def cut3(inp):
|
||
inp = inp.strip("\n")
|
||
opts = ["%s" % item for item in inp.strip("。").split("。")]
|
||
opts = [item for item in opts if not set(item).issubset(punctuation)]
|
||
return "\n".join(opts)
|
||
|
||
|
||
def cut4(inp):
|
||
inp = inp.strip("\n")
|
||
opts = re.split(r"(?<!\d)\.(?!\d)", inp.strip("."))
|
||
opts = [item for item in opts if not set(item).issubset(punctuation)]
|
||
return "\n".join(opts)
|
||
|
||
|
||
# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
|
||
def cut5(inp):
|
||
inp = inp.strip("\n")
|
||
punds = {",", ".", ";", "?", "!", "、", "，", "。", "？", "！", ";", "：", "…"}
|
||
mergeitems = []
|
||
items = []
|
||
|
||
for i, char in enumerate(inp):
|
||
if char in punds:
|
||
if char == "." and i > 0 and i < len(inp) - 1 and inp[i - 1].isdigit() and inp[i + 1].isdigit():
|
||
items.append(char)
|
||
else:
|
||
items.append(char)
|
||
mergeitems.append("".join(items))
|
||
items = []
|
||
else:
|
||
items.append(char)
|
||
|
||
if items:
|
||
mergeitems.append("".join(items))
|
||
|
||
opt = [item for item in mergeitems if not set(item).issubset(punds)]
|
||
return "\n".join(opt)
|
||
|
||
|
||
def custom_sort_key(s):
|
||
# 使用正则表达式提取字符串中的数字部分和非数字部分
|
||
parts = re.split("(\d+)", s)
|
||
# 将数字部分转换为整数，非数字部分保持不变
|
||
parts = [int(part) if part.isdigit() else part for part in parts]
|
||
return parts
|
||
|
||
|
||
def process_text(texts):
|
||
_text = []
|
||
if all(text in [None, " ", "\n", ""] for text in texts):
|
||
raise ValueError(i18n("请输入有效文本"))
|
||
for text in texts:
|
||
if text in [None, " ", ""]:
|
||
pass
|
||
else:
|
||
_text.append(text)
|
||
return _text
|
||
|
||
|
||
def html_center(text, label="p"):
|
||
return f"""<div style="text-align: center; margin: 8px 0; padding: 4px 0;">
|
||
<{label} style="margin: 0; padding: 0;">{text}</{label}>
|
||
</div>"""
|
||
|
||
|
||
def html_left(text, label="p"):
|
||
return f"""<div style="text-align: left; margin: 0; padding: 0;">
|
||
<{label} style="margin: 0; padding: 0;">{text}</{label}>
|
||
</div>"""
|
||
|
||
|
||
# ===================== 训练角色选择 / 训练样本浏览 辅助函数 =====================
|
||
def scan_model_names() -> list[str]:
|
||
"""扫描 logs/ 目录获取所有训练角色名（含 2-name2text.txt 的子目录）。"""
|
||
from config import exp_root
|
||
import os
|
||
|
||
root = Path(exp_root)
|
||
if not root.exists():
|
||
return []
|
||
return sorted(
|
||
[
|
||
d.name
|
||
for d in root.iterdir()
|
||
if d.is_dir() and (d / "2-name2text.txt").exists()
|
||
]
|
||
)
|
||
|
||
|
||
def refresh_model_dropdown():
|
||
"""刷新模型下拉框：返回 gr.Dropdown 更新（choices=全部模型名, value=首个）。
|
||
|
||
供 app.load 和「刷新模型列表」按钮使用。
|
||
注意：必须返回 gr.Dropdown(...) 而非裸 list——裸 list 会被 Gradio 当作
|
||
dropdown 的 value（多选），导致级联的 on_model_name_change 收到 list 而非 str。
|
||
"""
|
||
names = scan_model_names()
|
||
return gr.Dropdown(choices=names, value=names[0] if names else "")
|
||
|
||
|
||
def _coerce_single(value):
|
||
"""把可能被 Gradio 包装成 list/tuple 的单值还原为 str。"""
|
||
if isinstance(value, (list, tuple)):
|
||
return str(value[0]) if value else ""
|
||
return str(value) if value is not None else ""
|
||
|
||
|
||
|
||
def _read_name2text_for_webui(logs_dir: Path) -> dict[str, dict[str, str]]:
|
||
"""读取 2-name2text.txt（WebUI 版，与 api_v2.py 的 _read_name2text 逻辑一致）。
|
||
|
||
返回 {wav_name: {"text": ..., "lang": ...}}，同时以 stem 建索引。
|
||
"""
|
||
path = logs_dir / "2-name2text.txt"
|
||
if not path.exists():
|
||
return {}
|
||
output: dict[str, dict[str, str]] = {}
|
||
for line in path.read_text(encoding="utf-8", errors="ignore").splitlines():
|
||
parts = line.split("\t")
|
||
if len(parts) < 4:
|
||
continue
|
||
wav_name = parts[0].strip()
|
||
text = parts[3].strip()
|
||
if wav_name and text:
|
||
entry = {"text": text, "lang": "zh"}
|
||
output[wav_name] = entry
|
||
output[Path(wav_name).stem] = entry
|
||
return output
|
||
|
||
|
||
def _read_emotion_map_for_webui(model_name: str) -> dict[str, str]:
|
||
"""读取 ASR 标注 .list 中的 emotion 列，返回 {audio_name|stem: emotion}。
|
||
|
||
扫描 output/asr_opt/<model_name>/ 下所有 *.list（4/5/6 列均兼容）。
|
||
无 emotion 列或文件不存在时返回空 dict。
|
||
"""
|
||
from tools.list_metadata import parse_list_line
|
||
|
||
emotion_map: dict[str, str] = {}
|
||
list_dir = Path("output") / "asr_opt" / model_name
|
||
if not list_dir.exists():
|
||
return emotion_map
|
||
for list_path in sorted(list_dir.glob("*.list")):
|
||
try:
|
||
for line in list_path.read_text(encoding="utf-8", errors="ignore").splitlines():
|
||
item = parse_list_line(line)
|
||
if item is None or not item.emotion:
|
||
continue
|
||
name = os.path.basename(item.wav_path.strip("\"'"))
|
||
if not name:
|
||
continue
|
||
emotion_map[name] = item.emotion
|
||
emotion_map[Path(name).stem] = item.emotion
|
||
except Exception:
|
||
continue
|
||
return emotion_map
|
||
|
||
|
||
def on_model_name_change(model_name: str):
|
||
"""模型名变化时：加载训练样本列表 + 自动匹配最佳 GPT/SoVITS 权重。
|
||
|
||
返回: [样本下拉框, GPT下拉框, SoVITS下拉框]
|
||
样本下拉框 value 设为首个样本, 会级联触发 on_ref_sample_change 自动引用为参考音频。
|
||
GPT/SoVITS 自动选中 epoch 最接近推荐值(8/15)的权重, 用户仍可手动改。
|
||
"""
|
||
model_name = _coerce_single(model_name)
|
||
if not model_name:
|
||
return gr.Dropdown(choices=[], value=""), gr.Dropdown(), gr.Dropdown()
|
||
from config import exp_root
|
||
|
||
# 1. 加载训练样本
|
||
logs_dir = Path(exp_root) / model_name
|
||
wav_dir = logs_dir / "5-wav32k"
|
||
samples: list[tuple[str, str]] = []
|
||
if wav_dir.exists():
|
||
name2text = _read_name2text_for_webui(logs_dir)
|
||
emotion_map = _read_emotion_map_for_webui(model_name)
|
||
for f in sorted(wav_dir.iterdir()):
|
||
if f.is_file() and f.suffix.lower() in (".wav", ".mp3", ".flac"):
|
||
text = name2text.get(f.name, {}).get("text", "") or name2text.get(f.stem, {}).get("text", "")
|
||
emotion = emotion_map.get(f.name, "") or emotion_map.get(f.stem, "")
|
||
label = f"{f.name}"
|
||
if text:
|
||
label += f" | {text[:30]}"
|
||
if emotion:
|
||
label += f" | 【{emotion}】"
|
||
samples.append((label, str(f)))
|
||
choices = [s[0] for s in samples]
|
||
value = samples[0][0] if samples else ""
|
||
# 2. 自动匹配 GPT/SoVITS 权重（epoch 最接近 8/15）
|
||
gpt_dd, sovits_dd = auto_match_weights_for_model(model_name)
|
||
return gr.Dropdown(choices=choices, value=value), gpt_dd, sovits_dd
|
||
|
||
|
||
def on_ref_sample_change(sample_label: str, model_name: str):
|
||
"""训练样本选择变化时，直接引用为参考音频 + 填充参考文本 + 情绪/括注。
|
||
|
||
切换「训练样本音频」下拉框即自动联动, 无需额外按钮。
|
||
用户手动清空参考音频/文本/情绪则视为自定义输入, 不被本函数干扰
|
||
（Gradio change 仅在 dropdown 变化时触发, 手动改文本框不会重置）。
|
||
|
||
返回: [参考音频(inp_ref), 参考文本(prompt_text), 情绪/括注(ref_emotion_text)]
|
||
"""
|
||
sample_label = _coerce_single(sample_label)
|
||
model_name = _coerce_single(model_name)
|
||
if not sample_label or not model_name:
|
||
return None, "", ""
|
||
from config import exp_root
|
||
|
||
wav_name = sample_label.split(" | ")[0]
|
||
audio_path = Path(exp_root) / model_name / "5-wav32k" / wav_name
|
||
logs_dir = Path(exp_root) / model_name
|
||
name2text = _read_name2text_for_webui(logs_dir)
|
||
emotion_map = _read_emotion_map_for_webui(model_name)
|
||
text = name2text.get(wav_name, {}).get("text", "") or name2text.get(Path(wav_name).stem, {}).get("text", "")
|
||
emotion = emotion_map.get(wav_name, "") or emotion_map.get(Path(wav_name).stem, "")
|
||
audio_out = str(audio_path) if audio_path.exists() else None
|
||
return audio_out, text, emotion
|
||
|
||
|
||
def _scan_model_weights_for_webui() -> dict[str, dict[str, list[str]]]:
|
||
"""扫描权重目录（WebUI 版），按 exp_name 分组。"""
|
||
from config import GPT_weight_root, SoVITS_weight_root
|
||
|
||
grouped: dict[str, dict[str, list[str]]] = {}
|
||
for root in GPT_weight_root:
|
||
root_path = Path(root)
|
||
if not root_path.exists():
|
||
continue
|
||
for f in root_path.iterdir():
|
||
if f.is_file() and f.suffix == ".ckpt":
|
||
name = re.split(r"-e\d+", f.stem, flags=re.IGNORECASE)[0]
|
||
grouped.setdefault(name, {"gpt": [], "sovits": []})
|
||
grouped[name]["gpt"].append(f"{root}/{f.name}")
|
||
for root in SoVITS_weight_root:
|
||
root_path = Path(root)
|
||
if not root_path.exists():
|
||
continue
|
||
for f in root_path.iterdir():
|
||
if f.is_file() and f.suffix == ".pth":
|
||
name = re.split(r"_e\d+_s\d+", f.stem, flags=re.IGNORECASE)[0]
|
||
grouped.setdefault(name, {"gpt": [], "sovits": []})
|
||
grouped[name]["sovits"].append(f"{root}/{f.name}")
|
||
return grouped
|
||
|
||
|
||
def _extract_epoch_from_weight(weight_path: str, kind: str) -> int | None:
|
||
"""从权重文件名提取 epoch 数字。
|
||
|
||
kind='gpt': '<exp>-e10.ckpt' -> 10
|
||
kind='sovits': '<exp>_e12_s180.pth' -> 12
|
||
"""
|
||
name = Path(weight_path).name
|
||
pattern = r"-e(\d+)" if kind == "gpt" else r"_e(\d+)_s\d+"
|
||
m = re.search(pattern, name, flags=re.IGNORECASE)
|
||
return int(m.group(1)) if m else None
|
||
|
||
|
||
def _pick_weight_by_epoch(weights: list[str], target_epoch: int) -> str | None:
|
||
"""从权重列表中选 epoch 最接近 target_epoch 的（平手取较小 epoch）。
|
||
|
||
匹配训练 WebUI 的推荐: GPT 目标 8 (total_epoch 默认 8), SoVITS 目标 15
|
||
(total_epoch 默认 15)。无法提取 epoch 时退化为列表第一个。
|
||
"""
|
||
if not weights:
|
||
return None
|
||
best = None
|
||
best_diff = None
|
||
for w in weights:
|
||
# kind 由文件扩展名推断
|
||
kind = "gpt" if w.endswith(".ckpt") else "sovits"
|
||
ep = _extract_epoch_from_weight(w, kind)
|
||
if ep is None:
|
||
continue
|
||
diff = abs(ep - target_epoch)
|
||
if best_diff is None or diff < best_diff or (diff == best_diff and ep < (best_ep or 0)):
|
||
best = w
|
||
best_diff = diff
|
||
best_ep = ep
|
||
return best or weights[0]
|
||
|
||
|
||
# GPT/SoVITS 自动匹配权重的目标 epoch（对应训练 WebUI 的默认 total_epoch）
|
||
_GPT_TARGET_EPOCH = 8
|
||
_SOVITS_TARGET_EPOCH = 15
|
||
|
||
|
||
def auto_match_weights_for_model(model_name: str):
|
||
"""选中模型名时自动匹配最佳 GPT/SoVITS 权重（按目标 epoch 最近匹配）。
|
||
|
||
返回 (gpt_dropdown_update, sovits_dropdown_update):
|
||
- choices 只含该模型对应的权重(过滤掉其它角色)
|
||
- value 自动选中 epoch 最接近 8/15 的权重
|
||
真正的权重切换由已绑定的 GPT_dropdown.change / SoVITS_dropdown.change
|
||
在 value 变化时自动触发。
|
||
"""
|
||
if not model_name:
|
||
return gr.Dropdown(choices=[], value=""), gr.Dropdown(choices=[], value="")
|
||
weights = _scan_model_weights_for_webui()
|
||
model_weights = weights.get(model_name, {"gpt": [], "sovits": []})
|
||
gpt_list = sorted(model_weights["gpt"])
|
||
sovits_list = sorted(model_weights["sovits"])
|
||
gpt_best = _pick_weight_by_epoch(gpt_list, _GPT_TARGET_EPOCH)
|
||
sovits_best = _pick_weight_by_epoch(sovits_list, _SOVITS_TARGET_EPOCH)
|
||
return gr.Dropdown(choices=gpt_list, value=gpt_best), gr.Dropdown(choices=sovits_list, value=sovits_best)
|
||
|
||
|
||
with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False, js=js, css=css) as app:
|
||
gr.HTML(
|
||
top_html.format(
|
||
i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.")
|
||
+ i18n("如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE.")
|
||
),
|
||
elem_classes="markdown",
|
||
)
|
||
with gr.Group():
|
||
gr.Markdown(html_center(i18n("模型切换"), "h3"))
|
||
# 模型名(实验名) 是 GPT/SoVITS 的上游选择: 选角色 -> 自动匹配权重
|
||
with gr.Row():
|
||
model_name_dropdown = gr.Dropdown(
|
||
label=i18n("模型名（训练实验名）"),
|
||
choices=[],
|
||
value="",
|
||
interactive=True,
|
||
scale=20,
|
||
)
|
||
refresh_models_btn = gr.Button(i18n("刷新模型列表"), variant="primary", scale=8)
|
||
with gr.Row():
|
||
GPT_dropdown = gr.Dropdown(
|
||
label=i18n("GPT模型列表"),
|
||
choices=sorted(GPT_names, key=custom_sort_key),
|
||
value=gpt_path,
|
||
interactive=True,
|
||
scale=10,
|
||
)
|
||
SoVITS_dropdown = gr.Dropdown(
|
||
label=i18n("SoVITS模型列表"),
|
||
choices=sorted(SoVITS_names, key=custom_sort_key),
|
||
value=sovits_path,
|
||
interactive=True,
|
||
scale=10,
|
||
)
|
||
refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary", scale=8)
|
||
refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
|
||
with gr.Group():
|
||
gr.Markdown(html_center(i18n("*请上传并填写参考信息"), "h3"))
|
||
# 训练样本下拉 + 无参考文本开关: 入口与输出聚合
|
||
# ref_text_free 与样本下拉功能局部互斥(勾选后参考文本无效), 故同区呈现
|
||
with gr.Row():
|
||
ref_sample_dropdown = gr.Dropdown(
|
||
label=i18n("训练样本音频（选中即自动引用为参考音频）"),
|
||
choices=[],
|
||
value="",
|
||
interactive=True,
|
||
scale=20,
|
||
)
|
||
ref_text_free = gr.Checkbox(
|
||
label=i18n("无参考文本模式（勾选后忽略参考文本，仅用参考音频音色）"),
|
||
value=False,
|
||
interactive=True if model_version not in v3v4set else False,
|
||
show_label=True,
|
||
scale=8,
|
||
# v3/v4 不支持该模式: 代码层直接隐藏, 避免用户误操作报错
|
||
visible=model_version not in v3v4set,
|
||
)
|
||
with gr.Row(equal_height=True):
|
||
with gr.Column(scale=10):
|
||
inp_ref = gr.Audio(label=i18n("请上传参考音频（推荐3~10秒）"), type="filepath")
|
||
with gr.Column(scale=10):
|
||
prompt_language = gr.Dropdown(
|
||
label=i18n("参考音频的语种"),
|
||
choices=list(dict_language.keys()),
|
||
value=i18n("中文"),
|
||
)
|
||
prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="", lines=4, max_lines=4, scale=1)
|
||
ref_emotion_text = gr.Textbox(
|
||
label=i18n("情绪/括注（来自训练样本，无数据留空）"),
|
||
value="",
|
||
interactive=False,
|
||
scale=1,
|
||
)
|
||
with gr.Column(scale=8):
|
||
inp_refs = (
|
||
gr.File(
|
||
label=i18n(
|
||
"可选项：通过拖拽多个文件上传多个参考音频（建议同性），平均融合他们的音色。如不填写此项，音色由左侧单个参考音频控制。如是微调模型，建议参考音频全部在微调训练集音色内，底模不用管。"
|
||
),
|
||
file_count="multiple",
|
||
)
|
||
if model_version not in v3v4set
|
||
else gr.File(
|
||
label=i18n(
|
||
"可选项：通过拖拽多个文件上传多个参考音频（建议同性），平均融合他们的音色。如不填写此项，音色由左侧单个参考音频控制。如是微调模型，建议参考音频全部在微调训练集音色内，底模不用管。"
|
||
),
|
||
file_count="multiple",
|
||
visible=False,
|
||
)
|
||
)
|
||
sample_steps = (
|
||
gr.Radio(
|
||
label=i18n("采样步数,如果觉得电,提高试试,如果觉得慢,降低试试"),
|
||
value=32 if model_version == "v3" else 8,
|
||
choices=[4, 8, 16, 32, 64, 128] if model_version == "v3" else [4, 8, 16, 32],
|
||
visible=True,
|
||
)
|
||
if model_version in v3v4set
|
||
else gr.Radio(
|
||
label=i18n("采样步数,如果觉得电,提高试试,如果觉得慢,降低试试"),
|
||
choices=[4, 8, 16, 32, 64, 128] if model_version == "v3" else [4, 8, 16, 32],
|
||
visible=False,
|
||
value=32 if model_version == "v3" else 8,
|
||
)
|
||
)
|
||
if_sr_Checkbox = gr.Checkbox(
|
||
label=i18n("v3输出如果觉得闷可以试试开超分"),
|
||
value=False,
|
||
interactive=True,
|
||
show_label=True,
|
||
visible=False if model_version != "v3" else True,
|
||
)
|
||
gr.Markdown(html_center(i18n("*请填写需要合成的目标文本和语种模式"), "h3"))
|
||
with gr.Row():
|
||
with gr.Column(scale=13):
|
||
text = gr.Textbox(label=i18n("需要合成的文本"), value="", lines=26, max_lines=26)
|
||
with gr.Column(scale=7):
|
||
text_language = gr.Dropdown(
|
||
label=i18n("需要合成的语种") + i18n(".限制范围越小判别效果越好。"),
|
||
choices=list(dict_language.keys()),
|
||
value=i18n("中文"),
|
||
scale=1,
|
||
)
|
||
how_to_cut = gr.Dropdown(
|
||
label=i18n("怎么切"),
|
||
choices=[
|
||
i18n("不切"),
|
||
i18n("凑四句一切"),
|
||
i18n("凑50字一切"),
|
||
i18n("按中文句号。切"),
|
||
i18n("按英文句号.切"),
|
||
i18n("按标点符号切"),
|
||
],
|
||
value=i18n("凑四句一切"),
|
||
interactive=True,
|
||
scale=1,
|
||
)
|
||
gr.Markdown(value=html_center(i18n("语速调整，高为更快")))
|
||
if_freeze = gr.Checkbox(
|
||
label=i18n("是否直接对上次合成结果调整语速和音色。防止随机性。"),
|
||
value=False,
|
||
interactive=True,
|
||
show_label=True,
|
||
scale=1,
|
||
)
|
||
with gr.Row():
|
||
speed = gr.Slider(
|
||
minimum=0.6, maximum=1.65, step=0.05, label=i18n("语速"), value=1, interactive=True, scale=1
|
||
)
|
||
pause_second_slider = gr.Slider(
|
||
minimum=0.1,
|
||
maximum=0.5,
|
||
step=0.01,
|
||
label=i18n("句间停顿秒数"),
|
||
value=0.3,
|
||
interactive=True,
|
||
scale=1,
|
||
)
|
||
gr.Markdown(html_center(i18n("GPT采样参数(无参考文本时不要太低。不懂就用默认)：")))
|
||
top_k = gr.Slider(
|
||
minimum=1, maximum=100, step=1, label=i18n("top_k"), value=15, interactive=True, scale=1
|
||
)
|
||
top_p = gr.Slider(
|
||
minimum=0, maximum=1, step=0.05, label=i18n("top_p"), value=1, interactive=True, scale=1
|
||
)
|
||
temperature = gr.Slider(
|
||
minimum=0, maximum=1, step=0.05, label=i18n("temperature"), value=1, interactive=True, scale=1
|
||
)
|
||
# with gr.Column():
|
||
# gr.Markdown(value=i18n("手工调整音素。当音素框不为空时使用手工音素输入推理，无视目标文本框。"))
|
||
# phoneme=gr.Textbox(label=i18n("音素框"), value="")
|
||
# get_phoneme_button = gr.Button(i18n("目标文本转音素"), variant="primary")
|
||
with gr.Row():
|
||
inference_button = gr.Button(value=i18n("合成语音"), variant="primary", size="lg", scale=25)
|
||
use_cuda_graph_checkbox = gr.Checkbox(
|
||
label="CUDA Graph " + i18n("加速"),
|
||
value=cuda_graph_supported,
|
||
interactive=True if torch.cuda.is_available() else False,
|
||
show_label=True,
|
||
scale=5,
|
||
visible=False,
|
||
)
|
||
output = gr.Audio(label=i18n("输出的语音"), scale=14)
|
||
|
||
inference_button.click(
|
||
get_tts_wav,
|
||
[
|
||
inp_ref,
|
||
prompt_text,
|
||
prompt_language,
|
||
text,
|
||
text_language,
|
||
how_to_cut,
|
||
top_k,
|
||
top_p,
|
||
temperature,
|
||
ref_text_free,
|
||
speed,
|
||
if_freeze,
|
||
inp_refs,
|
||
sample_steps,
|
||
if_sr_Checkbox,
|
||
pause_second_slider,
|
||
use_cuda_graph_checkbox,
|
||
],
|
||
[output],
|
||
)
|
||
SoVITS_dropdown.change(
|
||
change_sovits_weights,
|
||
[SoVITS_dropdown, prompt_language, text_language],
|
||
[
|
||
prompt_language,
|
||
text_language,
|
||
prompt_text,
|
||
prompt_language,
|
||
text,
|
||
text_language,
|
||
sample_steps,
|
||
inp_refs,
|
||
ref_text_free,
|
||
if_sr_Checkbox,
|
||
inference_button,
|
||
],
|
||
)
|
||
GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], [])
|
||
|
||
# ===== 新增事件绑定：训练角色选择 / 样本浏览 =====
|
||
refresh_models_btn.click(
|
||
fn=refresh_model_dropdown,
|
||
inputs=[],
|
||
outputs=[model_name_dropdown],
|
||
)
|
||
model_name_dropdown.change(
|
||
fn=on_model_name_change,
|
||
inputs=[model_name_dropdown],
|
||
outputs=[ref_sample_dropdown, GPT_dropdown, SoVITS_dropdown],
|
||
)
|
||
ref_sample_dropdown.change(
|
||
fn=on_ref_sample_change,
|
||
inputs=[ref_sample_dropdown, model_name_dropdown],
|
||
outputs=[inp_ref, prompt_text, ref_emotion_text],
|
||
)
|
||
# 页面加载时自动扫描模型列表
|
||
app.load(fn=refresh_model_dropdown, inputs=[], outputs=[model_name_dropdown])
|
||
|
||
|
||
# gr.Markdown(value=i18n("文本切分工具。太长的文本合成出来效果不一定好，所以太长建议先切。合成会根据文本的换行分开合成再拼起来。"))
|
||
# with gr.Row():
|
||
# text_inp = gr.Textbox(label=i18n("需要合成的切分前文本"), value="")
|
||
# button1 = gr.Button(i18n("凑四句一切"), variant="primary")
|
||
# button2 = gr.Button(i18n("凑50字一切"), variant="primary")
|
||
# button3 = gr.Button(i18n("按中文句号。切"), variant="primary")
|
||
# button4 = gr.Button(i18n("按英文句号.切"), variant="primary")
|
||
# button5 = gr.Button(i18n("按标点符号切"), variant="primary")
|
||
# text_opt = gr.Textbox(label=i18n("切分后文本"), value="")
|
||
# button1.click(cut1, [text_inp], [text_opt])
|
||
# button2.click(cut2, [text_inp], [text_opt])
|
||
# button3.click(cut3, [text_inp], [text_opt])
|
||
# button4.click(cut4, [text_inp], [text_opt])
|
||
# button5.click(cut5, [text_inp], [text_opt])
|
||
# gr.Markdown(html_center(i18n("后续将支持转音素、手工修改音素、语音合成分步执行。")))
|
||
|
||
if __name__ == "__main__":
|
||
app.queue().launch( # concurrency_count=511, max_size=1022
|
||
server_name="0.0.0.0",
|
||
inbrowser=True,
|
||
share=is_share,
|
||
server_port=infer_ttswebui,
|
||
# quiet=True,
|
||
)
|