mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-04-06 03:57:44 +08:00
800 lines
29 KiB
Python
800 lines
29 KiB
Python
import os,re,logging
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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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import pdb
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if os.path.exists("./gweight.txt"):
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with open("./gweight.txt", 'r',encoding="utf-8") as file:
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gweight_data = file.read()
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gpt_path = os.environ.get(
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"gpt_path", gweight_data)
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else:
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gpt_path = os.environ.get(
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"gpt_path", "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt")
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if os.path.exists("./sweight.txt"):
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with open("./sweight.txt", 'r',encoding="utf-8") as file:
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sweight_data = file.read()
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sovits_path = os.environ.get("sovits_path", sweight_data)
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else:
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sovits_path = os.environ.get("sovits_path", "GPT_SoVITS/pretrained_models/s2G488k.pth")
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# gpt_path = os.environ.get(
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# "gpt_path", "pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
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# )
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# sovits_path = os.environ.get("sovits_path", "pretrained_models/s2G488k.pth")
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cnhubert_base_path = os.environ.get(
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"cnhubert_base_path", "GPT_SoVITS/pretrained_models/chinese-hubert-base"
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)
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bert_path = os.environ.get(
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"bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
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)
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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"))
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import gradio as gr
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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import numpy as np
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import librosa,torch
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from feature_extractor import cnhubert
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cnhubert.cnhubert_base_path=cnhubert_base_path
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import sys
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from PyQt5.QtCore import QEvent
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from PyQt5.QtWidgets import QApplication, QMainWindow, QLabel, QLineEdit, QPushButton, QTextEdit
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from PyQt5.QtWidgets import QGridLayout, QVBoxLayout, QWidget, QFileDialog, QStatusBar, QComboBox
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import soundfile as sf
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from module.models import SynthesizerTrn
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from AR.models.t2s_lightning_module import Text2SemanticLightningModule
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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 time import time as ttime
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from module.mel_processing import spectrogram_torch
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from my_utils import load_audio
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from tools.i18n.i18n import I18nAuto
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i18n = I18nAuto()
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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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elif torch.backends.mps.is_available():
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device = "mps"
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else:
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device = "cpu"
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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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def change_sovits_weights(sovits_path):
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global vq_model,hps
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dict_s2=torch.load(sovits_path,map_location="cpu")
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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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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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if("pretrained"not in sovits_path):
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del vq_model.enc_q
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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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print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
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with open("./sweight.txt","w",encoding="utf-8")as f:f.write(sovits_path)
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change_sovits_weights(sovits_path)
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def change_gpt_weights(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")
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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("./gweight.txt","w",encoding="utf-8")as f:f.write(gpt_path)
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change_gpt_weights(gpt_path)
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def get_spepc(hps, filename):
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audio = load_audio(filename, int(hps.data.sampling_rate))
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audio = torch.FloatTensor(audio)
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audio_norm = audio
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audio_norm = audio_norm.unsqueeze(0)
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spec = spectrogram_torch(
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audio_norm,
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hps.data.filter_length,
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hps.data.sampling_rate,
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hps.data.hop_length,
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hps.data.win_length,
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center=False,
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)
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return spec
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dict_language={
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i18n("中文"):"zh",
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i18n("英文"):"en",
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i18n("日文"):"ja"
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}
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def splite_en_inf(sentence, language):
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pattern = re.compile(r'[a-zA-Z. ]+')
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textlist = []
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langlist = []
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pos = 0
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for match in pattern.finditer(sentence):
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start, end = match.span()
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if start > pos:
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textlist.append(sentence[pos:start])
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langlist.append(language)
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textlist.append(sentence[start:end])
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langlist.append("en")
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pos = end
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if pos < len(sentence):
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textlist.append(sentence[pos:])
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langlist.append(language)
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return textlist, langlist
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def clean_text_inf(text, language):
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phones, word2ph, norm_text = clean_text(text, language)
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phones = cleaned_text_to_sequence(phones)
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return phones, word2ph, norm_text
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def get_bert_inf(phones, word2ph, norm_text, language):
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if language == "zh":
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bert = get_bert_feature(norm_text, word2ph).to(device)
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else:
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bert = torch.zeros(
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(1024, len(phones)),
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dtype=torch.float16 if is_half == True else torch.float32,
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).to(device)
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return bert
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def nonen_clean_text_inf(text, language):
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textlist, langlist = splite_en_inf(text, language)
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phones_list = []
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word2ph_list = []
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norm_text_list = []
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for i in range(len(textlist)):
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lang = langlist[i]
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phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
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phones_list.append(phones)
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if lang == "en" or "ja":
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pass
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else:
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word2ph_list.append(word2ph)
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norm_text_list.append(norm_text)
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print(word2ph_list)
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phones = sum(phones_list, [])
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word2ph = sum(word2ph_list, [])
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norm_text = ' '.join(norm_text_list)
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return phones, word2ph, norm_text
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def nonen_get_bert_inf(text, language):
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textlist, langlist = splite_en_inf(text, language)
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print(textlist)
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print(langlist)
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bert_list = []
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for i in range(len(textlist)):
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text = textlist[i]
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lang = langlist[i]
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phones, word2ph, norm_text = clean_text_inf(text, lang)
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bert = get_bert_inf(phones, word2ph, norm_text, lang)
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bert_list.append(bert)
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bert = torch.cat(bert_list, dim=1)
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return bert
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splits = {"，","。","？","！",",",".","?","!","~",":","：","—","…",}
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def get_first(text):
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pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
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text = re.split(pattern, text)[0].strip()
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return text
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def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,how_to_cut=i18n("不切")):
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t0 = ttime()
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prompt_text = prompt_text.strip("\n")
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if(prompt_text[-1]not in splits):prompt_text+="。"if prompt_text!="en"else "."
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text = text.strip("\n")
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if(len(get_first(text))<4):text+="。"if text!="en"else "."
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zero_wav = np.zeros(
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int(hps.data.sampling_rate * 0.3),
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dtype=np.float16 if is_half == True else np.float32,
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)
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with torch.no_grad():
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wav16k, sr = librosa.load(ref_wav_path, sr=16000)
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if(wav16k.shape[0]>160000 or wav16k.shape[0]<48000):
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raise OSError(i18n("参考音频在3~10秒范围外，请更换！"))
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wav16k = torch.from_numpy(wav16k)
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zero_wav_torch = torch.from_numpy(zero_wav)
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if is_half == True:
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wav16k = wav16k.half().to(device)
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zero_wav_torch = zero_wav_torch.half().to(device)
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else:
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wav16k = wav16k.to(device)
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zero_wav_torch = zero_wav_torch.to(device)
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wav16k=torch.cat([wav16k,zero_wav_torch])
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ssl_content = ssl_model.model(wav16k.unsqueeze(0))[
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"last_hidden_state"
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].transpose(
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1, 2
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) # .float()
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codes = vq_model.extract_latent(ssl_content)
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prompt_semantic = codes[0, 0]
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t1 = ttime()
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prompt_language = dict_language[prompt_language]
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text_language = dict_language[text_language]
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if prompt_language == "en":
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phones1, word2ph1, norm_text1 = clean_text_inf(prompt_text, prompt_language)
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else:
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phones1, word2ph1, norm_text1 = nonen_clean_text_inf(prompt_text, prompt_language)
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if(how_to_cut==i18n("凑四句一切")):text=cut1(text)
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elif(how_to_cut==i18n("凑50字一切")):text=cut2(text)
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elif(how_to_cut==i18n("按中文句号。切")):text=cut3(text)
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elif(how_to_cut==i18n("按英文句号.切")):text=cut4(text)
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text = text.replace("\n\n","\n").replace("\n\n","\n").replace("\n\n","\n")
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if(text[-1]not in splits):text+="。"if text_language!="en"else "."
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texts=text.split("\n")
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audio_opt = []
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if prompt_language == "en":
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bert1 = get_bert_inf(phones1, word2ph1, norm_text1, prompt_language)
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else:
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bert1 = nonen_get_bert_inf(prompt_text, prompt_language)
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for text in texts:
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# 解决输入目标文本的空行导致报错的问题
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if (len(text.strip()) == 0):
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continue
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if text_language == "en":
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phones2, word2ph2, norm_text2 = clean_text_inf(text, text_language)
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else:
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phones2, word2ph2, norm_text2 = nonen_clean_text_inf(text, text_language)
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if text_language == "en":
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bert2 = get_bert_inf(phones2, word2ph2, norm_text2, text_language)
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else:
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bert2 = nonen_get_bert_inf(text, text_language)
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bert = torch.cat([bert1, bert2], 1)
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all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
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bert = bert.to(device).unsqueeze(0)
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all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
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prompt = prompt_semantic.unsqueeze(0).to(device)
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t2 = ttime()
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with torch.no_grad():
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# pred_semantic = t2s_model.model.infer(
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pred_semantic, idx = t2s_model.model.infer_panel(
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all_phoneme_ids,
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all_phoneme_len,
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prompt,
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bert,
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# prompt_phone_len=ph_offset,
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top_k=config["inference"]["top_k"],
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early_stop_num=hz * max_sec,
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)
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t3 = ttime()
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# print(pred_semantic.shape,idx)
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pred_semantic = pred_semantic[:, -idx:].unsqueeze(
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0
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) # .unsqueeze(0)#mq要多unsqueeze一次
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refer = get_spepc(hps, ref_wav_path) # .to(device)
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if is_half == True:
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refer = refer.half().to(device)
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else:
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refer = refer.to(device)
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# audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
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audio = (
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vq_model.decode(
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pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer
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)
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.detach()
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.cpu()
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.numpy()[0, 0]
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) ###试试重建不带上prompt部分
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audio_opt.append(audio)
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audio_opt.append(zero_wav)
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t4 = ttime()
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print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
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yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(
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np.int16
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)
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def split(todo_text):
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todo_text = todo_text.replace("……", "。").replace("——", "，")
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if todo_text[-1] not in splits:
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todo_text += "。"
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i_split_head = i_split_tail = 0
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len_text = len(todo_text)
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todo_texts = []
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while 1:
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if i_split_head >= len_text:
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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
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if todo_text[i_split_head] in splits:
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i_split_head += 1
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todo_texts.append(todo_text[i_split_tail:i_split_head])
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i_split_tail = i_split_head
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else:
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i_split_head += 1
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return todo_texts
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def cut1(inp):
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inp = inp.strip("\n")
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inps = split(inp)
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split_idx = list(range(0, len(inps), 4))
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split_idx[-1] = None
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if len(split_idx) > 1:
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opts = []
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for idx in range(len(split_idx) - 1):
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opts.append("".join(inps[split_idx[idx] : split_idx[idx + 1]]))
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else:
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opts = [inp]
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return "\n".join(opts)
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def cut2(inp):
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inp = inp.strip("\n")
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inps = split(inp)
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if len(inps) < 2:
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return inp
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opts = []
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summ = 0
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tmp_str = ""
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for i in range(len(inps)):
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summ += len(inps[i])
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tmp_str += inps[i]
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if summ > 50:
|
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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()
|