mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-08-20 10:18:32 +08:00
1979 lines
72 KiB
Python
1979 lines
72 KiB
Python
"""
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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 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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import soundfile # 新增导入
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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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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
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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},
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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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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},
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{"__type__": "update", "visible": True if model_version == "v3" else False},
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{"__type__": "update", "value": i18n("合成语音"), "interactive": True},
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)
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with open("./weight.json") as f:
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data = f.read()
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data = json.loads(data)
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data["SoVITS"][version] = sovits_path
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with open("./weight.json", "w") as f:
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f.write(json.dumps(data))
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try:
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next(change_sovits_weights(sovits_path))
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except:
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pass
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def change_gpt_weights(gpt_path):
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if "！" in gpt_path or "!" in gpt_path:
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gpt_path = name2gpt_path[gpt_path]
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global hz, max_sec, t2s_model, config
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hz = 50
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dict_s1 = torch.load(gpt_path, map_location="cpu", weights_only=False)
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config = dict_s1["config"]
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max_sec = config["data"]["max_sec"]
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t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
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t2s_model.load_state_dict(dict_s1["weight"])
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if is_half == True:
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t2s_model = t2s_model.half()
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t2s_model = t2s_model.to(device)
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t2s_model.eval()
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# total = sum([param.nelement() for param in t2s_model.parameters()])
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# print("Number of parameter: %.2fM" % (total / 1e6))
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with open("./weight.json") as f:
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data = f.read()
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data = json.loads(data)
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data["GPT"][version] = gpt_path
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with open("./weight.json", "w") as f:
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f.write(json.dumps(data))
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change_gpt_weights(gpt_path)
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os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
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import torch
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now_dir = os.getcwd()
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def clean_hifigan_model():
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global hifigan_model
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if hifigan_model:
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hifigan_model = hifigan_model.cpu()
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hifigan_model = None
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try:
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torch.cuda.empty_cache()
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except:
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pass
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def clean_bigvgan_model():
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global bigvgan_model
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if bigvgan_model:
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bigvgan_model = bigvgan_model.cpu()
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bigvgan_model = None
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try:
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torch.cuda.empty_cache()
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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):
|
||
if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}:
|
||
formattext = text
|
||
while " " in formattext:
|
||
formattext = formattext.replace(" ", " ")
|
||
if language == "all_zh":
|
||
if re.search(r"[A-Za-z]", formattext):
|
||
formattext = re.sub(r"[a-z]", lambda x: x.group(0).upper(), formattext)
|
||
formattext = chinese.mix_text_normalize(formattext)
|
||
return get_phones_and_bert(formattext, "zh", version)
|
||
else:
|
||
phones, word2ph, norm_text = clean_text_inf(formattext, language, version)
|
||
bert = get_bert_feature(norm_text, word2ph).to(device)
|
||
elif language == "all_yue" and re.search(r"[A-Za-z]", formattext):
|
||
formattext = re.sub(r"[a-z]", lambda x: x.group(0).upper(), formattext)
|
||
formattext = chinese.mix_text_normalize(formattext)
|
||
return get_phones_and_bert(formattext, "yue", version)
|
||
else:
|
||
phones, word2ph, norm_text = clean_text_inf(formattext, language, version)
|
||
bert = torch.zeros(
|
||
(1024, len(phones)),
|
||
dtype=torch.float16 if is_half == True else torch.float32,
|
||
).to(device)
|
||
elif language in {"zh", "ja", "ko", "yue", "auto", "auto_yue"}:
|
||
textlist = []
|
||
langlist = []
|
||
if 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,
|
||
):
|
||
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)
|
||
if wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000:
|
||
gr.Warning(i18n("参考音频在3~10秒范围外，请更换！"))
|
||
raise OSError(i18n("参考音频在3~10秒范围外，请更换！"))
|
||
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:
|
||
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)
|
||
|
||
|
||
import uuid
|
||
import shutil
|
||
from pydub import AudioSegment
|
||
|
||
TEMP_FOLDER = "TEMP" # 临时文件夹路径
|
||
os.makedirs(TEMP_FOLDER, exist_ok=True)
|
||
|
||
def clean_temp_folder_on_startup():
|
||
"""启动时清理临时文件夹"""
|
||
try:
|
||
if os.path.exists(TEMP_FOLDER):
|
||
shutil.rmtree(TEMP_FOLDER)
|
||
os.makedirs(TEMP_FOLDER, exist_ok=True)
|
||
print("启动时已清理临时文件夹")
|
||
else:
|
||
os.makedirs(TEMP_FOLDER, exist_ok=True)
|
||
print("临时文件夹已创建")
|
||
except Exception as e:
|
||
print(f"启动时清理临时文件夹失败: {str(e)}")
|
||
|
||
def split_text_by_punctuation(text, period_pause=0.3, comma_pause=0.15):
|
||
"""改进的文本分割函数"""
|
||
if not text or not isinstance(text, str):
|
||
print("收到空或非字符串文本输入")
|
||
return []
|
||
|
||
segments = []
|
||
current_segment = ""
|
||
punctuation_marks = ['，', ',', '。', '.']
|
||
|
||
for char in text:
|
||
current_segment += char
|
||
if char in punctuation_marks:
|
||
# 根据标点类型设置停顿时间
|
||
pause = period_pause if char in ['。', '.'] else comma_pause
|
||
segments.append({
|
||
"text": current_segment.strip(),
|
||
"pause": pause,
|
||
"punctuation": char
|
||
})
|
||
current_segment = ""
|
||
|
||
if current_segment:
|
||
segments.append({
|
||
"text": current_segment.strip(),
|
||
"pause": comma_pause, # 默认使用非句号停顿时间
|
||
"punctuation": ""
|
||
})
|
||
|
||
print(f"分割结果: {[seg['text'] for seg in segments]}")
|
||
return segments
|
||
|
||
def generate_segment_audio(segment_data, ref_wav_path, prompt_text, prompt_language, text_language, top_k, top_p, temperature):
|
||
"""增强的音频生成函数"""
|
||
try:
|
||
if not os.path.exists(ref_wav_path):
|
||
raise FileNotFoundError(f"参考音频不存在: {ref_wav_path}")
|
||
|
||
# 生成音频
|
||
sr, audio_data = next(get_tts_wav(
|
||
ref_wav_path=ref_wav_path,
|
||
prompt_text=prompt_text,
|
||
prompt_language=prompt_language,
|
||
text=segment_data["text"],
|
||
text_language=text_language,
|
||
how_to_cut=i18n("不切"),
|
||
top_k=top_k,
|
||
top_p=top_p,
|
||
temperature=temperature,
|
||
pause_second=0 # 不在内部添加停顿
|
||
))
|
||
|
||
# 这里不再生成ID，由调用者提供
|
||
temp_path = os.path.join(TEMP_FOLDER, "temp_generate.wav")
|
||
soundfile.write(temp_path, audio_data, sr)
|
||
|
||
# 添加停顿
|
||
audio = AudioSegment.from_wav(temp_path)
|
||
pause = AudioSegment.silent(duration=int(segment_data["pause"]*1000))
|
||
final_audio = audio + pause
|
||
final_audio.export(temp_path, format="wav")
|
||
|
||
return {
|
||
"success": True,
|
||
"audio_path": temp_path,
|
||
"text": segment_data["text"],
|
||
"pause": segment_data["pause"],
|
||
"message": "生成成功"
|
||
}
|
||
except Exception as e:
|
||
print(f"生成片段失败: {str(e)}", exc_info=True)
|
||
return {
|
||
"success": False,
|
||
"audio_path": None,
|
||
"text": segment_data["text"],
|
||
"pause": segment_data["pause"],
|
||
"message": f"生成失败: {str(e)}"
|
||
}
|
||
|
||
def process_all_segments(text, ref_wav_path, prompt_text, prompt_language, text_language,
|
||
top_k, top_p, temperature, period_pause, comma_pause):
|
||
"""完整处理流程"""
|
||
# 输入验证
|
||
if not text or not isinstance(text, str):
|
||
error_msg = "输入文本无效"
|
||
print(error_msg)
|
||
return [[1, error_msg, "错误"]], None
|
||
|
||
if not os.path.exists(ref_wav_path):
|
||
error_msg = f"参考音频不存在: {ref_wav_path}"
|
||
print(error_msg)
|
||
return [[1, error_msg, "错误"]], None
|
||
|
||
# 处理分段
|
||
segments = split_text_by_punctuation(text, period_pause, comma_pause)
|
||
if not segments:
|
||
error_msg = "无法分割文本"
|
||
print(error_msg)
|
||
return [[1, error_msg, "错误"]], None
|
||
|
||
results = []
|
||
audio_files = []
|
||
|
||
# 修改这里：使用enumerate从1开始编号，而不是基于文件夹内容
|
||
for i, segment in enumerate(segments, 1):
|
||
result = generate_segment_audio(
|
||
segment, ref_wav_path, prompt_text,
|
||
prompt_language, text_language, top_k, top_p, temperature
|
||
)
|
||
|
||
# 更新结果中的segment_id
|
||
result["segment_id"] = f"{i}temp"
|
||
if result["success"] and result["audio_path"]:
|
||
# 重命名文件以匹配新的编号
|
||
new_path = os.path.join(TEMP_FOLDER, f"{result['segment_id']}.wav")
|
||
os.rename(result["audio_path"], new_path)
|
||
result["audio_path"] = new_path
|
||
audio_files.append(new_path)
|
||
|
||
results.append(result)
|
||
print(f"处理进度: {i}/{len(segments)} - {result['message']}")
|
||
|
||
# 准备显示数据
|
||
df_data = []
|
||
for i, result in enumerate(results, 1):
|
||
df_data.append([
|
||
f"{i}temp",
|
||
result["text"],
|
||
result["message"]
|
||
])
|
||
|
||
first_audio = audio_files[0] if audio_files else None
|
||
return df_data, first_audio
|
||
|
||
def regenerate_segment(segment_id, new_text, ref_wav_path, prompt_text,
|
||
prompt_language, text_language, top_k, top_p, temperature,
|
||
period_pause, comma_pause):
|
||
try:
|
||
if not segment_id or not new_text:
|
||
raise ValueError("缺少片段ID或新文本内容")
|
||
|
||
# 从文件名解析原始停顿时间
|
||
try:
|
||
pause = 0.25 if segment_id.endswith(("。", ".")) else 0.1
|
||
except:
|
||
pause = 0.1 # 默认值
|
||
|
||
is_period = segment_id.endswith(("。", "."))
|
||
pause = period_pause if is_period else comma_pause
|
||
|
||
segment_data = {
|
||
"text": new_text,
|
||
"pause": pause,
|
||
"punctuation": "。" if is_period else "，"
|
||
}
|
||
result = generate_segment_audio(
|
||
segment_data, ref_wav_path, prompt_text,
|
||
prompt_language, text_language, top_k, top_p, temperature
|
||
)
|
||
|
||
# 更新文件
|
||
if result["success"]:
|
||
old_path = os.path.join(TEMP_FOLDER, f"{segment_id}.wav")
|
||
if os.path.exists(old_path):
|
||
os.remove(old_path)
|
||
os.rename(result["audio_path"], old_path)
|
||
result["audio_path"] = old_path
|
||
|
||
return (
|
||
result["audio_path"],
|
||
segment_id,
|
||
result["message"]
|
||
)
|
||
except Exception as e:
|
||
print(f"重新生成片段失败: {str(e)}", exc_info=True)
|
||
return None, segment_id, f"重新生成失败: {str(e)}"
|
||
|
||
def merge_all_segments():
|
||
try:
|
||
# 获取并按编号排序片段
|
||
segments = sorted(
|
||
[f for f in os.listdir(TEMP_FOLDER) if f.endswith(".wav") and f != "final_output.wav"],
|
||
key=lambda x: int(x.split("temp")[0])
|
||
)
|
||
if not segments:
|
||
raise ValueError("没有找到可合并的音频片段")
|
||
|
||
combined = AudioSegment.empty()
|
||
for seg in segments:
|
||
seg_path = os.path.join(TEMP_FOLDER, seg)
|
||
audio = AudioSegment.from_wav(seg_path)
|
||
combined += audio
|
||
|
||
# 保存最终结果
|
||
output_path = os.path.join(TEMP_FOLDER, "final_output.wav")
|
||
combined.export(output_path, format="wav")
|
||
|
||
print(f"成功合并 {len(segments)} 个片段")
|
||
return output_path, "合并成功"
|
||
except Exception as e:
|
||
print(f"合并片段失败: {str(e)}", exc_info=True)
|
||
return None, f"合并失败: {str(e)}"
|
||
|
||
def clean_temp_files():
|
||
"""清理临时文件函数"""
|
||
try:
|
||
for filename in os.listdir(TEMP_FOLDER):
|
||
file_path = os.path.join(TEMP_FOLDER, filename)
|
||
try:
|
||
if os.path.isfile(file_path):
|
||
os.unlink(file_path)
|
||
except Exception as e:
|
||
print(f"删除文件 {file_path} 失败: {e}")
|
||
return "临时文件已清理"
|
||
except Exception as e:
|
||
return f"清理失败: {str(e)}"
|
||
|
||
def on_segment_select(df, evt: gr.SelectData):
|
||
"""当选择分段列表中的项目时更新显示"""
|
||
if evt.index:
|
||
selected_row = df.iloc[evt.index[0]]
|
||
audio_path = os.path.join(TEMP_FOLDER, f"{selected_row['编号']}.wav")
|
||
return (
|
||
selected_row["编号"],
|
||
selected_row["文本内容"],
|
||
audio_path if os.path.exists(audio_path) else None
|
||
)
|
||
return "1temp", "", None
|
||
|
||
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
|
||
# ======================== 合并功能实现 ========================
|
||
def merge_selected_segments(merge_range, segment_list_data, ref_wav_path, prompt_text,
|
||
prompt_language, text_language, top_k, top_p, temperature,
|
||
pause_period, pause_comma):
|
||
"""合并选中的句子并立即生成新音频"""
|
||
try:
|
||
if not merge_range:
|
||
return segment_list_data, "请输入合并范围（例如：1-3）", None
|
||
|
||
# 解析合并范围
|
||
start, end = map(int, merge_range.split('-'))
|
||
if start <= 0 or end <= 0 or start > end:
|
||
return segment_list_data, "无效的合并范围", None
|
||
|
||
# 检查范围是否有效
|
||
if end > len(segment_list_data):
|
||
return segment_list_data, f"结束编号 {end} 超过总段数 {len(segment_list_data)}", None
|
||
|
||
# === 第一步：收集要删除的文件并立即删除 ===
|
||
files_to_delete = []
|
||
for i in range(start-1, end):
|
||
file_id = segment_list_data.iloc[i, 0]
|
||
file_path = os.path.join(TEMP_FOLDER, f"{file_id}.wav")
|
||
if os.path.exists(file_path):
|
||
os.remove(file_path)
|
||
files_to_delete.append(file_id)
|
||
|
||
# === 第二步：合并文本 ===
|
||
merged_text = ""
|
||
for i in range(start-1, end):
|
||
merged_text += segment_list_data.iloc[i, 1] # 文本内容在第二列
|
||
|
||
# 确定合并后的停顿类型（取最后一个片段的标点）
|
||
last_punctuation = segment_list_data.iloc[end-1, 1][-1] if segment_list_data.iloc[end-1, 1] else ""
|
||
is_period = last_punctuation in ["。", "."]
|
||
pause = pause_period if is_period else pause_comma
|
||
|
||
# === 第三步：立即生成合并后的音频 ===
|
||
segment_data = {
|
||
"text": merged_text,
|
||
"pause": pause,
|
||
"punctuation": last_punctuation
|
||
}
|
||
# 生成音频
|
||
result = generate_segment_audio(
|
||
segment_data, ref_wav_path, prompt_text,
|
||
prompt_language, text_language, top_k, top_p, temperature
|
||
)
|
||
if not result["success"]:
|
||
return segment_list_data, result["message"], None
|
||
|
||
# === 第四步：构建新的分段列表 ===
|
||
# 创建新的合并条目 - 使用起始编号
|
||
new_id = f"{start}temp"
|
||
merged_entry = [new_id, merged_text, "已生成"]
|
||
|
||
# 移动生成的音频到正确位置
|
||
new_path = os.path.join(TEMP_FOLDER, f"{new_id}.wav")
|
||
shutil.move(result["audio_path"], new_path)
|
||
|
||
# 构建新的分段列表
|
||
new_segment_list = []
|
||
|
||
# 添加合并前的部分
|
||
if start > 1:
|
||
new_segment_list.extend(segment_list_data.iloc[:start-1].values.tolist())
|
||
|
||
# 添加合并条目
|
||
new_segment_list.append(merged_entry)
|
||
|
||
# 添加合并后的部分
|
||
if end < len(segment_list_data):
|
||
new_segment_list.extend(segment_list_data.iloc[end:].values.tolist())
|
||
|
||
# === 第五步：重新编号 ===
|
||
reindexed_list = []
|
||
new_id_counter = 1
|
||
for segment in new_segment_list:
|
||
old_id = segment[0]
|
||
# 为新列表生成连续编号
|
||
new_id = f"{new_id_counter}temp"
|
||
|
||
# 重命名文件（如果存在）
|
||
old_path = os.path.join(TEMP_FOLDER, f"{old_id}.wav")
|
||
new_path = os.path.join(TEMP_FOLDER, f"{new_id}.wav")
|
||
|
||
if os.path.exists(old_path) and old_id != new_id:
|
||
os.rename(old_path, new_path)
|
||
|
||
# 更新ID
|
||
segment[0] = new_id
|
||
reindexed_list.append(segment)
|
||
new_id_counter += 1
|
||
|
||
return reindexed_list, "合并成功", new_id
|
||
|
||
except Exception as e:
|
||
traceback.print_exc()
|
||
return segment_list_data, f"合并失败: {str(e)}", None
|
||
|
||
# ======================== 实现拆分逻辑函数 ========================
|
||
def split_selected_segment(split_id, segment_list_data, ref_wav_path, prompt_text,
|
||
prompt_language, text_language, top_k, top_p, temperature,
|
||
pause_period, pause_comma):
|
||
"""拆分选中的句子并重新生成 - 使用倒序重命名避免冲突"""
|
||
try:
|
||
if not split_id:
|
||
return segment_list_data, "请输入要拆分的句子编号", None
|
||
|
||
# 从文件名解析原始停顿时间
|
||
try:
|
||
pause = 0.25 if split_id.endswith(("。", ".")) else 0.1
|
||
except:
|
||
pause = 0.1 # 默认值
|
||
|
||
# 查找要拆分的句子
|
||
df = segment_list_data
|
||
target_row = None
|
||
target_idx = None
|
||
|
||
# 查找匹配的句子
|
||
for idx, row in enumerate(df.itertuples()):
|
||
if str(row[1]) == split_id: # 第一列是ID
|
||
target_row = row
|
||
target_idx = idx
|
||
break
|
||
|
||
if target_row is None:
|
||
return segment_list_data, f"未找到编号为 {split_id} 的句子", None
|
||
|
||
# 获取原始文本
|
||
original_text = target_row[2] # 第二列是文本
|
||
|
||
# 根据标点符号拆分文本
|
||
segments = []
|
||
current_segment = ""
|
||
punctuation_marks = ['，', ',', '。', '.', '？', '?', '！', '!', '；', ';']
|
||
|
||
for char in original_text:
|
||
current_segment += char
|
||
if char in punctuation_marks:
|
||
# 根据标点类型设置停顿时间
|
||
pause = pause_period if char in ['。', '.', '？', '?', '！', '!'] else pause_comma
|
||
segments.append({
|
||
"text": current_segment.strip(),
|
||
"pause": pause,
|
||
"punctuation": char
|
||
})
|
||
current_segment = ""
|
||
|
||
if current_segment:
|
||
segments.append({
|
||
"text": current_segment.strip(),
|
||
"pause": pause_comma, # 默认使用非句号停顿时间
|
||
"punctuation": ""
|
||
})
|
||
|
||
if len(segments) <= 1:
|
||
return segment_list_data, "句子无法拆分（没有标点符号）", None
|
||
|
||
# 计算拆分后新增的句子数量
|
||
num_new_segments = len(segments)
|
||
offset = num_new_segments - 1 # 拆分后增加的句子数
|
||
|
||
# === 第一步：删除原始音频文件 ===
|
||
original_path = os.path.join(TEMP_FOLDER, f"{split_id}.wav")
|
||
if os.path.exists(original_path):
|
||
os.remove(original_path)
|
||
|
||
# === 第二步：倒序重命名后续句子避免冲突 ===
|
||
new_segment_list = []
|
||
|
||
# 添加拆分前的句子
|
||
for i in range(target_idx):
|
||
new_segment_list.append(df.iloc[i].tolist())
|
||
|
||
# 倒序重命名从最后一个句子开始，避免冲突
|
||
total_segments = len(df)
|
||
|
||
# 从最后一个句子开始倒序遍历
|
||
for i in range(total_segments - 1, target_idx, -1):
|
||
old_id = df.iloc[i, 0]
|
||
old_num = int(old_id.replace("temp", ""))
|
||
new_id_num = old_num + offset
|
||
new_id = f"{new_id_num}temp"
|
||
|
||
# 重命名音频文件
|
||
old_path = os.path.join(TEMP_FOLDER, f"{old_id}.wav")
|
||
new_path = os.path.join(TEMP_FOLDER, f"{new_id}.wav")
|
||
if os.path.exists(old_path):
|
||
os.rename(old_path, new_path)
|
||
|
||
# 更新列表条目
|
||
new_segment_list.append([new_id, df.iloc[i, 1], df.iloc[i, 2]])
|
||
|
||
# === 第三步：添加拆分后的新句子 ===
|
||
for i, segment in enumerate(segments):
|
||
new_id = f"{target_idx+1+i}temp" # 新ID从原始位置开始
|
||
new_segment_list.append([new_id, segment["text"], "待生成"])
|
||
|
||
# === 第四步：重新排序列表 ===
|
||
# 按照ID中的数字排序
|
||
new_segment_list.sort(key=lambda x: int(x[0].replace("temp", "")))
|
||
|
||
# === 第五步：生成拆分后的新句子 ===
|
||
for i, segment in enumerate(segments):
|
||
segment_id = f"{target_idx+1+i}temp"
|
||
result = generate_segment_audio(
|
||
segment, ref_wav_path, prompt_text,
|
||
prompt_language, text_language, top_k, top_p, temperature
|
||
)
|
||
|
||
if result["success"]:
|
||
# 更新文件
|
||
new_path = os.path.join(TEMP_FOLDER, f"{segment_id}.wav")
|
||
if os.path.exists(result["audio_path"]):
|
||
os.rename(result["audio_path"], new_path)
|
||
|
||
# 更新状态
|
||
for j, item in enumerate(new_segment_list):
|
||
if item[0] == segment_id:
|
||
new_segment_list[j][2] = "已生成"
|
||
break
|
||
|
||
return new_segment_list, f"成功拆分为 {num_new_segments} 个句子", f"{target_idx+1}temp"
|
||
|
||
except Exception as e:
|
||
traceback.print_exc()
|
||
return segment_list_data, f"拆分失败: {str(e)}", None
|
||
|
||
|
||
|
||
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: 100; padding: 50;">
|
||
<{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>"""
|
||
|
||
|
||
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"))
|
||
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=14,
|
||
)
|
||
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=14,
|
||
)
|
||
refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary", scale=14)
|
||
refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
|
||
gr.Markdown(html_center(i18n("*请上传并填写参考信息"), "h3"))
|
||
with gr.Row():
|
||
inp_ref = gr.Audio(label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath", scale=13)
|
||
with gr.Column(scale=13):
|
||
ref_text_free = gr.Checkbox(
|
||
label=i18n("开启无参考文本模式。不填参考文本亦相当于开启。")
|
||
+ i18n("v3暂不支持该模式，使用了会报错。"),
|
||
value=False,
|
||
interactive=True if model_version not in v3v4set else False,
|
||
show_label=True,
|
||
scale=1,
|
||
)
|
||
gr.Markdown(
|
||
html_left(
|
||
i18n("使用无参考文本模式时建议使用微调的GPT")
|
||
+ "<br>"
|
||
+ i18n("听不清参考音频说的啥(不晓得写啥)可以开。开启后无视填写的参考文本。")
|
||
)
|
||
)
|
||
prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="", lines=5, max_lines=5, scale=1)
|
||
with gr.Column(scale=14):
|
||
prompt_language = gr.Dropdown(
|
||
label=i18n("参考音频的语种"),
|
||
choices=list(dict_language.keys()),
|
||
value=i18n("中文"),
|
||
)
|
||
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)
|
||
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,
|
||
],
|
||
[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], [])
|
||
|
||
|
||
|
||
# ======================== 插入分段合成UI开始 ========================
|
||
with gr.Tab(i18n("分段合成模式")):
|
||
with gr.Row():
|
||
with gr.Column():
|
||
segmented_text = gr.Textbox(label=i18n("需要分段的文本"), lines=10, value="")
|
||
segment_button = gr.Button(i18n("分割文本并生成所有片段"), variant="primary")
|
||
segmented_output = gr.Audio(label=i18n("当前选中片段"), interactive=False)
|
||
|
||
with gr.Row():
|
||
segment_index = gr.Textbox(label=i18n("片段编号"), interactive=False, visible=False)
|
||
new_segment_text = gr.Textbox(label=i18n("新文本内容"), lines=2, max_lines=4)
|
||
regenerate_button = gr.Button(i18n("重新生成当前片段"), variant="primary")
|
||
with gr.Row():
|
||
pause_period = gr.Slider(
|
||
minimum=0.1,
|
||
maximum=1.0,
|
||
step=0.05,
|
||
label=i18n("句号停顿时间(秒)"),
|
||
value=0.3,
|
||
interactive=True
|
||
)
|
||
pause_comma = gr.Slider(
|
||
minimum=0.1,
|
||
maximum=0.5,
|
||
step=0.05,
|
||
label=i18n("非句号停顿时间(秒)"),
|
||
value=0.15,
|
||
interactive=True
|
||
)
|
||
with gr.Row():
|
||
clean_button = gr.Button(i18n("清理临时文件"), variant="secondary", scale=1)
|
||
confirm_button = gr.Button(i18n("确认并合并所有片段"), variant="primary", scale=2)
|
||
|
||
final_output = gr.Audio(label=i18n("最终合成结果"), interactive=False)
|
||
segment_status = gr.Textbox(label=i18n("状态"), interactive=False)
|
||
# 添加合并功能
|
||
with gr.Row():
|
||
merge_range = gr.Textbox(label=i18n("合并范围（例如：1-3）"), scale=3)
|
||
merge_button = gr.Button(i18n("合并句子"), variant="primary", scale=1)
|
||
# 添加拆分功能控件
|
||
with gr.Row():
|
||
split_index = gr.Textbox(label=i18n("要拆分的句子编号"), scale=1)
|
||
split_button = gr.Button(i18n("拆分句子"), variant="primary", scale=1)
|
||
with gr.Column():
|
||
segment_list = gr.Dataframe(
|
||
headers=["编号", "文本内容", "状态"],
|
||
datatype=["str", "str", "str"],
|
||
interactive=False,
|
||
label=i18n("分段列表"),
|
||
value=[]
|
||
)
|
||
# 在分段合成UI部分添加以下代码（大约在line 3200附近）
|
||
|
||
# ======================== 插入分段合成UI结束 ========================
|
||
|
||
# ======================== 插入分段合成事件绑定开始 ========================
|
||
# 分割文本并生成所有片段
|
||
segment_button.click(
|
||
process_all_segments,
|
||
inputs=[
|
||
segmented_text,
|
||
inp_ref,
|
||
prompt_text,
|
||
prompt_language,
|
||
text_language,
|
||
top_k,
|
||
top_p,
|
||
temperature,
|
||
pause_period, # 新增句号停顿参数
|
||
pause_comma # 新增非句号停顿参数
|
||
],
|
||
outputs=[segment_list, segmented_output]
|
||
)
|
||
# 重新生成当前片段
|
||
regenerate_button.click(
|
||
regenerate_segment,
|
||
inputs=[
|
||
segment_index, # 第一个参数应该是segment_id
|
||
new_segment_text,
|
||
inp_ref,
|
||
prompt_text,
|
||
prompt_language,
|
||
text_language,
|
||
top_k,
|
||
top_p,
|
||
temperature,
|
||
pause_period,
|
||
pause_comma
|
||
],
|
||
outputs=[segmented_output, segment_index, segment_status]
|
||
)
|
||
|
||
# 合并所有片段
|
||
confirm_button.click(
|
||
merge_all_segments,
|
||
inputs=[],
|
||
outputs=[final_output, segment_status]
|
||
)
|
||
|
||
# 清理临时文件
|
||
clean_button.click(
|
||
clean_temp_files,
|
||
inputs=[],
|
||
outputs=[segment_status]
|
||
)
|
||
|
||
# 当选择分段列表中的项目时
|
||
segment_list.select(
|
||
fn=on_segment_select,
|
||
inputs=[segment_list],
|
||
outputs=[segment_index, new_segment_text, segmented_output],
|
||
)
|
||
merge_button.click(
|
||
fn=merge_selected_segments,
|
||
inputs=[
|
||
merge_range,
|
||
segment_list,
|
||
inp_ref,
|
||
prompt_text,
|
||
prompt_language,
|
||
text_language,
|
||
top_k,
|
||
top_p,
|
||
temperature,
|
||
pause_period,
|
||
pause_comma
|
||
],
|
||
outputs=[segment_list, segment_status, segment_index]
|
||
)
|
||
# ======================== 添加拆分按钮的事件绑定 ========================
|
||
split_button.click(
|
||
fn=split_selected_segment,
|
||
inputs=[
|
||
split_index,
|
||
segment_list,
|
||
inp_ref,
|
||
prompt_text,
|
||
prompt_language,
|
||
text_language,
|
||
top_k,
|
||
top_p,
|
||
temperature,
|
||
pause_period,
|
||
pause_comma
|
||
],
|
||
outputs=[segment_list, segment_status, segment_index]
|
||
)
|
||
# ======================== 插入分段合成事件绑定结束 ========================
|
||
|
||
|
||
|
||
|
||
# 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,
|
||
)
|
||
|