From fd3a64d3924324c86fa8379ae5bfb47db04d544c Mon Sep 17 00:00:00 2001 From: Spr_Aachen <2835946988@qq.com> Date: Thu, 20 Jun 2024 19:01:24 +0800 Subject: [PATCH] Update gui.py 1. Fix the issue that inference_gui needs to reload models for each inference. 2. Simplify GUI's code and address various inefficiencies, including: enabling direct input of ref text and target text (akin to the WebUI), facilitating file selection for ref audio uploads, adding language options for CH-EN/JA-EN/Multi (with Multi as the default), standardizing variable name to enhance readability. --- GPT_SoVITS/inference_gui.py | 687 ++++++++++++++++++++++++++++++++---- 1 file changed, 613 insertions(+), 74 deletions(-) diff --git a/GPT_SoVITS/inference_gui.py b/GPT_SoVITS/inference_gui.py index f6cfdc5e..7a0e2d33 100644 --- a/GPT_SoVITS/inference_gui.py +++ b/GPT_SoVITS/inference_gui.py @@ -1,22 +1,592 @@ +''' +按中英混合识别 +按日英混合识别 +多语种启动切分识别语种 +全部按中文识别 +全部按英文识别 +全部按日文识别 +''' +import os, re, logging +import LangSegment +logging.getLogger("markdown_it").setLevel(logging.ERROR) +logging.getLogger("urllib3").setLevel(logging.ERROR) +logging.getLogger("httpcore").setLevel(logging.ERROR) +logging.getLogger("httpx").setLevel(logging.ERROR) +logging.getLogger("asyncio").setLevel(logging.ERROR) +logging.getLogger("charset_normalizer").setLevel(logging.ERROR) +logging.getLogger("torchaudio._extension").setLevel(logging.ERROR) +import pdb +import torch + +if os.path.exists("./gweight.txt"): + with open("./gweight.txt", 'r', encoding="utf-8") as file: + gweight_data = file.read() + gpt_path = os.environ.get( + "gpt_path", gweight_data) +else: + gpt_path = os.environ.get( + "gpt_path", "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt") + +if os.path.exists("./sweight.txt"): + with open("./sweight.txt", 'r', encoding="utf-8") as file: + sweight_data = file.read() + sovits_path = os.environ.get("sovits_path", sweight_data) +else: + sovits_path = os.environ.get("sovits_path", "GPT_SoVITS/pretrained_models/s2G488k.pth") +# gpt_path = os.environ.get( +# "gpt_path", "pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt" +# ) +# sovits_path = os.environ.get("sovits_path", "pretrained_models/s2G488k.pth") +cnhubert_base_path = os.environ.get( + "cnhubert_base_path", "GPT_SoVITS/pretrained_models/chinese-hubert-base" +) +bert_path = os.environ.get( + "bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large" +) +infer_ttswebui = os.environ.get("infer_ttswebui", 9872) +infer_ttswebui = int(infer_ttswebui) +is_share = os.environ.get("is_share", "False") +is_share = eval(is_share) +if "_CUDA_VISIBLE_DEVICES" in os.environ: + os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"] +is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available() +punctuation = set(['!', '?', '…', ',', '.', '-'," "]) + import sys from PyQt5.QtCore import QEvent from PyQt5.QtWidgets import QApplication, QMainWindow, QLabel, QLineEdit, QPushButton, QTextEdit from PyQt5.QtWidgets import QGridLayout, QVBoxLayout, QWidget, QFileDialog, QStatusBar, QComboBox import soundfile as sf +from transformers import AutoModelForMaskedLM, AutoTokenizer +import numpy as np +import librosa +from feature_extractor import cnhubert +cnhubert.cnhubert_base_path = cnhubert_base_path + +from module.models import SynthesizerTrn +from AR.models.t2s_lightning_module import Text2SemanticLightningModule +from text import cleaned_text_to_sequence +from text.cleaner import clean_text +from time import time as ttime +from module.mel_processing import spectrogram_torch +from my_utils import load_audio from tools.i18n.i18n import I18nAuto + i18n = I18nAuto() -from GPT_SoVITS.inference_webui import change_gpt_weights, change_sovits_weights, get_tts_wav +# os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 确保直接启动推理UI时也能够设置。 + +if torch.cuda.is_available(): + device = "cuda" +else: + device = "cpu" + +tokenizer = AutoTokenizer.from_pretrained(bert_path) +bert_model = AutoModelForMaskedLM.from_pretrained(bert_path) +if is_half == True: + bert_model = bert_model.half().to(device) +else: + bert_model = bert_model.to(device) + + +def get_bert_feature(text, word2ph): + with torch.no_grad(): + inputs = tokenizer(text, return_tensors="pt") + for i in inputs: + inputs[i] = inputs[i].to(device) + res = bert_model(**inputs, output_hidden_states=True) + res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1] + assert len(word2ph) == len(text) + phone_level_feature = [] + for i in range(len(word2ph)): + repeat_feature = res[i].repeat(word2ph[i], 1) + phone_level_feature.append(repeat_feature) + phone_level_feature = torch.cat(phone_level_feature, dim=0) + return phone_level_feature.T + + +class DictToAttrRecursive(dict): + def __init__(self, input_dict): + super().__init__(input_dict) + for key, value in input_dict.items(): + if isinstance(value, dict): + value = DictToAttrRecursive(value) + self[key] = value + setattr(self, key, value) + + def __getattr__(self, item): + try: + return self[item] + except KeyError: + raise AttributeError(f"Attribute {item} not found") + + def __setattr__(self, key, value): + if isinstance(value, dict): + value = DictToAttrRecursive(value) + super(DictToAttrRecursive, self).__setitem__(key, value) + super().__setattr__(key, value) + + def __delattr__(self, item): + try: + del self[item] + except KeyError: + raise AttributeError(f"Attribute {item} not found") + + +ssl_model = cnhubert.get_model() +if is_half == True: + ssl_model = ssl_model.half().to(device) +else: + ssl_model = ssl_model.to(device) + + +def change_sovits_weights(sovits_path): + global vq_model, hps + dict_s2 = torch.load(sovits_path, map_location="cpu") + hps = dict_s2["config"] + hps = DictToAttrRecursive(hps) + hps.model.semantic_frame_rate = "25hz" + vq_model = SynthesizerTrn( + hps.data.filter_length // 2 + 1, + hps.train.segment_size // hps.data.hop_length, + n_speakers=hps.data.n_speakers, + **hps.model + ) + if ("pretrained" not in sovits_path): + del vq_model.enc_q + if is_half == True: + vq_model = vq_model.half().to(device) + else: + vq_model = vq_model.to(device) + vq_model.eval() + print(vq_model.load_state_dict(dict_s2["weight"], strict=False)) + with open("./sweight.txt", "w", encoding="utf-8") as f: + f.write(sovits_path) + + +change_sovits_weights(sovits_path) + + +def change_gpt_weights(gpt_path): + global hz, max_sec, t2s_model, config + hz = 50 + dict_s1 = torch.load(gpt_path, map_location="cpu") + 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("./gweight.txt", "w", encoding="utf-8") as f: f.write(gpt_path) + + +change_gpt_weights(gpt_path) + + +def get_spepc(hps, filename): + audio = load_audio(filename, int(hps.data.sampling_rate)) + audio = torch.FloatTensor(audio) + audio_norm = audio + audio_norm = audio_norm.unsqueeze(0) + spec = spectrogram_torch( + audio_norm, + hps.data.filter_length, + hps.data.sampling_rate, + hps.data.hop_length, + hps.data.win_length, + center=False, + ) + return spec + + +dict_language = { + i18n("中文"): "all_zh",#全部按中文识别 + i18n("英文"): "en",#全部按英文识别#######不变 + i18n("日文"): "all_ja",#全部按日文识别 + i18n("中英混合"): "zh",#按中英混合识别####不变 + i18n("日英混合"): "ja",#按日英混合识别####不变 + i18n("多语种混合"): "auto",#多语种启动切分识别语种 +} + + +def clean_text_inf(text, language): + phones, word2ph, norm_text = clean_text(text, language) + phones = cleaned_text_to_sequence(phones) + 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 + + +def get_phones_and_bert(text,language): + if language in {"en","all_zh","all_ja"}: + language = language.replace("all_","") + if language == "en": + LangSegment.setfilters(["en"]) + formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text)) + else: + # 因无法区别中日文汉字,以用户输入为准 + formattext = text + while " " in formattext: + formattext = formattext.replace(" ", " ") + phones, word2ph, norm_text = clean_text_inf(formattext, language) + if language == "zh": + bert = get_bert_feature(norm_text, word2ph).to(device) + else: + bert = torch.zeros( + (1024, len(phones)), + dtype=torch.float16 if is_half == True else torch.float32, + ).to(device) + elif language in {"zh", "ja","auto"}: + textlist=[] + langlist=[] + LangSegment.setfilters(["zh","ja","en","ko"]) + if language == "auto": + for tmp in LangSegment.getTexts(text): + if tmp["lang"] == "ko": + langlist.append("zh") + textlist.append(tmp["text"]) + else: + langlist.append(tmp["lang"]) + textlist.append(tmp["text"]) + else: + for tmp in LangSegment.getTexts(text): + if tmp["lang"] == "en": + langlist.append(tmp["lang"]) + else: + # 因无法区别中日文汉字,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) + 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) + + return phones,bert.to(dtype),norm_text + + +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 + +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): + if prompt_text is None or len(prompt_text) == 0: + ref_free = True + 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") + text = replace_consecutive_punctuation(text) + 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 * 0.3), + dtype=np.float16 if is_half == True else np.float32, + ) + with torch.no_grad(): + wav16k, sr = librosa.load(ref_wav_path, sr=16000) + if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000): + raise OSError(i18n("参考音频在3~10秒范围外，请更换！")) + wav16k = torch.from_numpy(wav16k) + zero_wav_torch = torch.from_numpy(zero_wav) + if is_half == True: + wav16k = wav16k.half().to(device) + zero_wav_torch = zero_wav_torch.half().to(device) + else: + wav16k = wav16k.to(device) + zero_wav_torch = zero_wav_torch.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] + t1 = ttime() + + 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 = [] + if not ref_free: + phones1,bert1,norm_text1=get_phones_and_bert(prompt_text, prompt_language) + + for text in 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) + 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) + prompt = prompt_semantic.unsqueeze(0).to(device) + t2 = ttime() + with torch.no_grad(): + # pred_semantic = t2s_model.model.infer( + 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, + ) + t3 = ttime() + # print(pred_semantic.shape,idx) + pred_semantic = pred_semantic[:, -idx:].unsqueeze( + 0 + ) # .unsqueeze(0)#mq要多unsqueeze一次 + refer = get_spepc(hps, ref_wav_path) # .to(device) + if is_half == True: + refer = refer.half().to(device) + else: + refer = refer.to(device) + # audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0] + audio = ( + vq_model.decode( + pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer + ) + .detach() + .cpu() + .numpy()[0, 0] + ) ###试试重建不带上prompt部分 + max_audio=np.abs(audio).max()#简单防止16bit爆音 + if max_audio>1:audio/=max_audio + audio_opt.append(audio) + audio_opt.append(zero_wav) + t4 = ttime() + print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3)) + yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).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 = ["%s" % item for item in inp.strip(".").split(".")] + 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): + # if not re.search(r'[^\w\s]', inp[-1]): + # inp += '。' + inp = inp.strip("\n") + punds = r'[,.;?!、，。？！;：…]' + items = re.split(f'({punds})', inp) + mergeitems = ["".join(group) for group in zip(items[::2], items[1::2])] + # 在句子不存在符号或句尾无符号的时候保证文本完整 + if len(items)%2 == 1: + mergeitems.append(items[-1]) + opt = [item for item in mergeitems if not set(item).issubset(punctuation)] + 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 replace_consecutive_punctuation(text): + punctuations = ''.join(re.escape(p) for p in punctuation) + pattern = f'([{punctuations}])([{punctuations}])+' + result = re.sub(pattern, r'\1', text) + return result + + +def change_choices(): + SoVITS_names, GPT_names = get_weights_names() + return {"choices": sorted(SoVITS_names, key=custom_sort_key), "__type__": "update"}, {"choices": sorted(GPT_names, key=custom_sort_key), "__type__": "update"} + + +pretrained_sovits_name = "GPT_SoVITS/pretrained_models/s2G488k.pth" +pretrained_gpt_name = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt" +SoVITS_weight_root = "SoVITS_weights" +GPT_weight_root = "GPT_weights" +os.makedirs(SoVITS_weight_root, exist_ok=True) +os.makedirs(GPT_weight_root, exist_ok=True) + + +def get_weights_names(): + SoVITS_names = [pretrained_sovits_name] + for name in os.listdir(SoVITS_weight_root): + if name.endswith(".pth"): SoVITS_names.append("%s/%s" % (SoVITS_weight_root, name)) + GPT_names = [pretrained_gpt_name] + for name in os.listdir(GPT_weight_root): + if name.endswith(".ckpt"): GPT_names.append("%s/%s" % (GPT_weight_root, name)) + return SoVITS_names, GPT_names + + +SoVITS_names, GPT_names = get_weights_names() class GPTSoVITSGUI(QMainWindow): + gpt_path = gpt_path + sovits_path = sovits_path + def __init__(self): super().__init__() - self.init_ui() - - def init_ui(self): self.setWindowTitle('GPT-SoVITS GUI') self.setGeometry(800, 450, 950, 850) @@ -71,6 +641,7 @@ class GPTSoVITSGUI(QMainWindow): self.GPT_model_label = QLabel("选择GPT模型:") self.GPT_model_input = QLineEdit() self.GPT_model_input.setPlaceholderText("拖拽或选择文件") + self.GPT_model_input.setText(self.gpt_path) self.GPT_model_input.setReadOnly(True) self.GPT_model_button = QPushButton("选择GPT模型文件") self.GPT_model_button.clicked.connect(self.select_GPT_model) @@ -78,6 +649,7 @@ class GPTSoVITSGUI(QMainWindow): self.SoVITS_model_label = QLabel("选择SoVITS模型:") self.SoVITS_model_input = QLineEdit() self.SoVITS_model_input.setPlaceholderText("拖拽或选择文件") + self.SoVITS_model_input.setText(self.sovits_path) self.SoVITS_model_input.setReadOnly(True) self.SoVITS_model_button = QPushButton("选择SoVITS模型文件") self.SoVITS_model_button.clicked.connect(self.select_SoVITS_model) @@ -91,25 +663,25 @@ class GPTSoVITSGUI(QMainWindow): self.ref_text_label = QLabel("参考音频文本:") self.ref_text_input = QLineEdit() - self.ref_text_input.setPlaceholderText("拖拽或选择文件") - self.ref_text_input.setReadOnly(True) + self.ref_text_input.setPlaceholderText("直接输入文字或上传文本") self.ref_text_button = QPushButton("上传文本") self.ref_text_button.clicked.connect(self.upload_ref_text) - self.language_label = QLabel("参考音频语言:") - self.language_combobox = QComboBox() - self.language_combobox.addItems(["中文", "英文", "日文"]) + self.ref_language_label = QLabel("参考音频语言:") + self.ref_language_combobox = QComboBox() + self.ref_language_combobox.addItems(["中文", "英文", "日文", "中英混合", "日英混合", "多语种混合"]) + self.ref_language_combobox.setCurrentText("多语种混合") self.target_text_label = QLabel("合成目标文本:") self.target_text_input = QLineEdit() - self.target_text_input.setPlaceholderText("拖拽或选择文件") - self.target_text_input.setReadOnly(True) + self.target_text_input.setPlaceholderText("直接输入文字或上传文本") self.target_text_button = QPushButton("上传文本") self.target_text_button.clicked.connect(self.upload_target_text) - self.language_label_02 = QLabel("合成音频语言:") - self.language_combobox_02 = QComboBox() - self.language_combobox_02.addItems(["中文", "英文", "日文"]) + self.target_language_label = QLabel("合成音频语言:") + self.target_language_combobox = QComboBox() + self.target_language_combobox.addItems(["中文", "英文", "日文", "中英混合", "日英混合", "多语种混合"]) + self.target_language_combobox.setCurrentText("多语种混合") self.output_label = QLabel("输出音频路径:") self.output_input = QLineEdit() @@ -140,10 +712,8 @@ class GPTSoVITSGUI(QMainWindow): main_layout = QVBoxLayout() - input_layout = QGridLayout() - input_layout.setSpacing(10) - - self.setLayout(input_layout) + input_layout = QGridLayout(self) + input_layout.setSpacing(10) input_layout.addWidget(license_label, 0, 0, 1, 3) @@ -159,22 +729,22 @@ class GPTSoVITSGUI(QMainWindow): input_layout.addWidget(self.ref_audio_input, 6, 0, 1, 2) input_layout.addWidget(self.ref_audio_button, 6, 2) - input_layout.addWidget(self.language_label, 7, 0) - input_layout.addWidget(self.language_combobox, 8, 0, 1, 1) + input_layout.addWidget(self.ref_language_label, 7, 0) + input_layout.addWidget(self.ref_language_combobox, 8, 0, 1, 1) input_layout.addWidget(self.ref_text_label, 9, 0) input_layout.addWidget(self.ref_text_input, 10, 0, 1, 2) input_layout.addWidget(self.ref_text_button, 10, 2) - input_layout.addWidget(self.language_label_02, 11, 0) - input_layout.addWidget(self.language_combobox_02, 12, 0, 1, 1) + input_layout.addWidget(self.target_language_label, 11, 0) + input_layout.addWidget(self.target_language_combobox, 12, 0, 1, 1) input_layout.addWidget(self.target_text_label, 13, 0) input_layout.addWidget(self.target_text_input, 14, 0, 1, 2) input_layout.addWidget(self.target_text_button, 14, 2) - + input_layout.addWidget(self.output_label, 15, 0) input_layout.addWidget(self.output_input, 16, 0, 1, 2) input_layout.addWidget(self.output_button, 16, 2) - + main_layout.addLayout(input_layout) output_layout = QVBoxLayout() @@ -198,10 +768,8 @@ class GPTSoVITSGUI(QMainWindow): def dropEvent(self, event): if event.mimeData().hasUrls(): file_paths = [url.toLocalFile() for url in event.mimeData().urls()] - if len(file_paths) == 1: self.update_ref_audio(file_paths[0]) - self.update_input_paths(self.ref_audio_input, file_paths[0]) else: self.update_ref_audio(", ".join(file_paths)) @@ -211,23 +779,13 @@ class GPTSoVITSGUI(QMainWindow): widget.installEventFilter(self) def eventFilter(self, obj, event): - if event.type() == QEvent.DragEnter: + if event.type() in (QEvent.DragEnter, QEvent.Drop): mime_data = event.mimeData() if mime_data.hasUrls(): event.acceptProposedAction() - - elif event.type() == QEvent.Drop: - mime_data = event.mimeData() - if mime_data.hasUrls(): - file_paths = [url.toLocalFile() for url in mime_data.urls()] - if len(file_paths) == 1: - self.update_input_paths(obj, file_paths[0]) - else: - self.update_input_paths(obj, ", ".join(file_paths)) - event.acceptProposedAction() return super().eventFilter(obj, event) - + def select_GPT_model(self): file_path, _ = QFileDialog.getOpenFileName(self, "选择GPT模型文件", "", "GPT Files (*.ckpt)") if file_path: @@ -239,24 +797,9 @@ class GPTSoVITSGUI(QMainWindow): self.SoVITS_model_input.setText(file_path) def select_ref_audio(self): - options = QFileDialog.Options() - options |= QFileDialog.DontUseNativeDialog - options |= QFileDialog.ShowDirsOnly - - file_dialog = QFileDialog() - file_dialog.setOptions(options) - - file_dialog.setFileMode(QFileDialog.AnyFile) - file_dialog.setNameFilter("Audio Files (*.wav *.mp3)") - - if file_dialog.exec_(): - file_paths = file_dialog.selectedFiles() - - if len(file_paths) == 1: - self.update_ref_audio(file_paths[0]) - self.update_input_paths(self.ref_audio_input, file_paths[0]) - else: - self.update_ref_audio(", ".join(file_paths)) + file_path, _ = QFileDialog.getOpenFileName(self, "选择参考音频文件", "", "Audio Files (*.wav *.mp3)") + if file_path: + self.update_ref_audio(file_path) def upload_ref_text(self): file_path, _ = QFileDialog.getOpenFileName(self, "选择文本文件", "", "Text Files (*.txt)") @@ -264,7 +807,6 @@ class GPTSoVITSGUI(QMainWindow): with open(file_path, 'r', encoding='utf-8') as file: content = file.read() self.ref_text_input.setText(content) - self.update_input_paths(self.ref_text_input, file_path) def upload_target_text(self): file_path, _ = QFileDialog.getOpenFileName(self, "选择文本文件", "", "Text Files (*.txt)") @@ -272,7 +814,6 @@ class GPTSoVITSGUI(QMainWindow): with open(file_path, 'r', encoding='utf-8') as file: content = file.read() self.target_text_input.setText(content) - self.update_input_paths(self.target_text_input, file_path) def select_output_path(self): options = QFileDialog.Options() @@ -290,9 +831,6 @@ class GPTSoVITSGUI(QMainWindow): def update_ref_audio(self, file_path): self.ref_audio_input.setText(file_path) - def update_input_paths(self, input_box, file_path): - input_box.setText(file_path) - def clear_output(self): self.output_text.clear() @@ -300,23 +838,27 @@ class GPTSoVITSGUI(QMainWindow): GPT_model_path = self.GPT_model_input.text() SoVITS_model_path = self.SoVITS_model_input.text() ref_audio_path = self.ref_audio_input.text() - language_combobox = self.language_combobox.currentText() + language_combobox = self.ref_language_combobox.currentText() language_combobox = i18n(language_combobox) ref_text = self.ref_text_input.text() - language_combobox_02 = self.language_combobox_02.currentText() - language_combobox_02 = i18n(language_combobox_02) + target_language_combobox = self.target_language_combobox.currentText() + target_language_combobox = i18n(target_language_combobox) target_text = self.target_text_input.text() output_path = self.output_input.text() - change_gpt_weights(gpt_path=GPT_model_path) - change_sovits_weights(sovits_path=SoVITS_model_path) + if GPT_model_path != self.gpt_path: + change_gpt_weights(gpt_path=GPT_model_path) + self.gpt_path = GPT_model_path + if SoVITS_model_path != self.sovits_path: + change_sovits_weights(sovits_path=SoVITS_model_path) + self.sovits_path = SoVITS_model_path synthesis_result = get_tts_wav(ref_wav_path=ref_audio_path, prompt_text=ref_text, prompt_language=language_combobox, text=target_text, - text_language=language_combobox_02) - + text_language=target_language_combobox) + result_list = list(synthesis_result) if result_list: @@ -329,12 +871,9 @@ class GPTSoVITSGUI(QMainWindow): self.status_bar.showMessage("合成完成！输出路径：" + output_wav_path, 5000) self.output_text.append("处理结果：\n" + result) -def main(): + +if __name__ == '__main__': app = QApplication(sys.argv) mainWin = GPTSoVITSGUI() mainWin.show() - sys.exit(app.exec_()) - - -if __name__ == '__main__': - main() + sys.exit(app.exec_()) \ No newline at end of file