From a1350e068bbae36931d0d1027ad7a48a2cc8a077 Mon Sep 17 00:00:00 2001 From: Yuan-Man <68322456+Yuan-ManX@users.noreply.github.com> Date: Mon, 29 Jan 2024 23:25:08 +0800 Subject: [PATCH] Add inference_gui.py --- GPT_SoVITS/inference_gui.py | 799 ++++++++++++++++++++++++++++++++++++ 1 file changed, 799 insertions(+) create mode 100644 GPT_SoVITS/inference_gui.py diff --git a/GPT_SoVITS/inference_gui.py b/GPT_SoVITS/inference_gui.py new file mode 100644 index 0000000..fd2dae8 --- /dev/null +++ b/GPT_SoVITS/inference_gui.py @@ -0,0 +1,799 @@ +import os,re,logging +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 + +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")) +import gradio as gr +from transformers import AutoModelForMaskedLM, AutoTokenizer +import numpy as np +import librosa,torch +from feature_extractor import cnhubert +cnhubert.cnhubert_base_path=cnhubert_base_path + +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 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() + +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" +elif torch.backends.mps.is_available(): + device = "mps" +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"):"zh", + i18n("英文"):"en", + i18n("日文"):"ja" +} + + +def splite_en_inf(sentence, language): + pattern = re.compile(r'[a-zA-Z. ]+') + textlist = [] + langlist = [] + pos = 0 + for match in pattern.finditer(sentence): + start, end = match.span() + if start > pos: + textlist.append(sentence[pos:start]) + langlist.append(language) + textlist.append(sentence[start:end]) + langlist.append("en") + pos = end + if pos < len(sentence): + textlist.append(sentence[pos:]) + langlist.append(language) + + return textlist, langlist + + +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 + + +def get_bert_inf(phones, word2ph, norm_text, 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) + + return bert + + +def nonen_clean_text_inf(text, language): + textlist, langlist = splite_en_inf(text, language) + phones_list = [] + word2ph_list = [] + norm_text_list = [] + for i in range(len(textlist)): + lang = langlist[i] + phones, word2ph, norm_text = clean_text_inf(textlist[i], lang) + phones_list.append(phones) + if lang == "en" or "ja": + pass + else: + word2ph_list.append(word2ph) + norm_text_list.append(norm_text) + print(word2ph_list) + phones = sum(phones_list, []) + word2ph = sum(word2ph_list, []) + norm_text = ' '.join(norm_text_list) + + return phones, word2ph, norm_text + + +def nonen_get_bert_inf(text, language): + textlist, langlist = splite_en_inf(text, language) + print(textlist) + print(langlist) + bert_list = [] + for i in range(len(textlist)): + text = textlist[i] + lang = langlist[i] + phones, word2ph, norm_text = clean_text_inf(text, lang) + bert = get_bert_inf(phones, word2ph, norm_text, lang) + bert_list.append(bert) + bert = torch.cat(bert_list, dim=1) + + 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_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,how_to_cut=i18n("不切")): + t0 = ttime() + prompt_text = prompt_text.strip("\n") + if(prompt_text[-1]not in splits):prompt_text+="。"if prompt_text!="en"else "." + text = text.strip("\n") + if(len(get_first(text))<4):text+="。"if text!="en"else "." + 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() + prompt_language = dict_language[prompt_language] + text_language = dict_language[text_language] + + if prompt_language == "en": + phones1, word2ph1, norm_text1 = clean_text_inf(prompt_text, prompt_language) + else: + phones1, word2ph1, norm_text1 = nonen_clean_text_inf(prompt_text, prompt_language) + 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) + text = text.replace("\n\n","\n").replace("\n\n","\n").replace("\n\n","\n") + if(text[-1]not in splits):text+="。"if text_language!="en"else "." + texts=text.split("\n") + audio_opt = [] + if prompt_language == "en": + bert1 = get_bert_inf(phones1, word2ph1, norm_text1, prompt_language) + else: + bert1 = nonen_get_bert_inf(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_language == "en": + phones2, word2ph2, norm_text2 = clean_text_inf(text, text_language) + else: + phones2, word2ph2, norm_text2 = nonen_clean_text_inf(text, text_language) + + if text_language == "en": + bert2 = get_bert_inf(phones2, word2ph2, norm_text2, text_language) + else: + bert2 = nonen_get_bert_inf(text, text_language) + + bert = torch.cat([bert1, bert2], 1) + + all_phoneme_ids = torch.LongTensor(phones1 + 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, + prompt, + bert, + # prompt_phone_len=ph_offset, + top_k=config["inference"]["top_k"], + 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 + 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] + 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] + return "\n".join(opts) + + +def cut3(inp): + inp = inp.strip("\n") + return "\n".join(["%s。" % item for item in inp.strip("。").split("。")]) +def cut4(inp): + inp = inp.strip("\n") + return "\n".join(["%s." % item for item in inp.strip(".").split(".")]) + +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 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): + def __init__(self): + super().__init__() + + self.init_ui() + + def init_ui(self): + self.setWindowTitle('GPT-SoVITS GUI') + self.setGeometry(800, 450, 950, 850) + + self.setStyleSheet(""" + QWidget { + background-color: #a3d3b1; + } + + QTabWidget::pane { + background-color: #a3d3b1; + } + + QTabWidget::tab-bar { + alignment: left; + } + + QTabBar::tab { + background: #8da4bf; + color: #ffffff; + padding: 8px; + } + + QTabBar::tab:selected { + background: #2a3f54; + } + + QLabel { + color: #000000; + } + + QPushButton { + background-color: #4CAF50; + color: white; + padding: 8px; + border: 1px solid #4CAF50; + border-radius: 4px; + } + + QPushButton:hover { + background-color: #45a049; + border: 1px solid #45a049; + box-shadow: 2px 2px 2px rgba(0, 0, 0, 0.1); + } + """) + + license_text = ( + "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. " + "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.") + license_label = QLabel(license_text) + license_label.setWordWrap(True) + + 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.setReadOnly(True) + self.GPT_model_button = QPushButton("选择GPT模型文件") + self.GPT_model_button.clicked.connect(self.select_GPT_model) + + 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.setReadOnly(True) + self.SoVITS_model_button = QPushButton("选择SoVITS模型文件") + self.SoVITS_model_button.clicked.connect(self.select_SoVITS_model) + + self.ref_audio_label = QLabel("上传参考音频:") + self.ref_audio_input = QLineEdit() + self.ref_audio_input.setPlaceholderText("拖拽或选择文件") + self.ref_audio_input.setReadOnly(True) + self.ref_audio_button = QPushButton("选择音频文件") + self.ref_audio_button.clicked.connect(self.select_ref_audio) + + 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_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.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_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.output_label = QLabel("输出音频路径:") + self.output_input = QLineEdit() + self.output_input.setPlaceholderText("拖拽或选择文件") + self.output_input.setReadOnly(True) + self.output_button = QPushButton("选择文件夹") + self.output_button.clicked.connect(self.select_output_path) + + self.output_text = QTextEdit() + self.output_text.setReadOnly(True) + + self.add_drag_drop_events([ + self.GPT_model_input, + self.SoVITS_model_input, + self.ref_audio_input, + self.ref_text_input, + self.target_text_input, + self.output_input, + ]) + + self.synthesize_button = QPushButton("合成") + self.synthesize_button.clicked.connect(self.synthesize) + + self.status_bar = QStatusBar() + + main_layout = QVBoxLayout() + + input_layout = QGridLayout() + input_layout.setSpacing(10) + + self.setLayout(input_layout) + + input_layout.addWidget(license_label, 0, 0, 1, 3) + + input_layout.addWidget(self.GPT_model_label, 1, 0) + input_layout.addWidget(self.GPT_model_input, 2, 0, 1, 2) + input_layout.addWidget(self.GPT_model_button, 2, 2) + + input_layout.addWidget(self.SoVITS_model_label, 3, 0) + input_layout.addWidget(self.SoVITS_model_input, 4, 0, 1, 2) + input_layout.addWidget(self.SoVITS_model_button, 4, 2) + + input_layout.addWidget(self.ref_audio_label, 5, 0) + 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_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_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() + output_layout.addWidget(self.output_text) + main_layout.addLayout(output_layout) + + main_layout.addWidget(self.synthesize_button) + + main_layout.addWidget(self.status_bar) + + self.central_widget = QWidget() + self.central_widget.setLayout(main_layout) + self.setCentralWidget(self.central_widget) + + def dragEnterEvent(self, event): + if event.mimeData().hasUrls(): + event.acceptProposedAction() + + 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)) + + def add_drag_drop_events(self, widgets): + for widget in widgets: + widget.setAcceptDrops(True) + widget.installEventFilter(self) + + def eventFilter(self, obj, event): + if event.type() == QEvent.DragEnter: + 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: + self.GPT_model_input.setText(file_path) + + def select_SoVITS_model(self): + file_path, _ = QFileDialog.getOpenFileName(self, "选择SoVITS模型文件", "", "SoVITS Files (*.pth)") + if file_path: + 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)) + + def upload_ref_text(self): + file_path, _ = QFileDialog.getOpenFileName(self, "选择文本文件", "", "Text Files (*.txt)") + if file_path: + 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)") + if file_path: + 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() + options |= QFileDialog.DontUseNativeDialog + options |= QFileDialog.ShowDirsOnly + + folder_dialog = QFileDialog() + folder_dialog.setOptions(options) + folder_dialog.setFileMode(QFileDialog.Directory) + + if folder_dialog.exec_(): + folder_path = folder_dialog.selectedFiles()[0] + self.output_input.setText(folder_path) + + 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 synthesize(self): + 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 = 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_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) + + 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) + + result_list = list(synthesis_result) + + if result_list: + last_sampling_rate, last_audio_data = result_list[-1] + output_wav_path = os.path.join(output_path, "output.wav") + sf.write(output_wav_path, last_audio_data, last_sampling_rate) + + result = "Audio saved to " + output_wav_path + + 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(): + app = QApplication(sys.argv) + mainWin = GPTSoVITSGUI() + mainWin.show() + sys.exit(app.exec_()) + + +if __name__ == '__main__': + main()