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| import json | |
| import re | |
| import numpy as np | |
| import IPython.display as ipd | |
| import torch | |
| import commons | |
| import utils | |
| from models import SynthesizerTrn | |
| from text.symbols import symbols | |
| from text import text_to_sequence | |
| import gradio as gr | |
| import time | |
| import json | |
| import datetime | |
| import os | |
| import pickle | |
| from scipy.io.wavfile import write | |
| import librosa | |
| import romajitable | |
| from mel_processing import spectrogram_torch | |
| import soundfile as sf | |
| from scipy import signal | |
| class VitsGradio: | |
| def __init__(self): | |
| self.lan = ["中文","日文","自动"] | |
| self.modelPaths = [] | |
| for root,dirs,files in os.walk("checkpoints"): | |
| for dir in dirs: | |
| self.modelPaths.append(dir) | |
| with gr.Blocks() as self.Vits: | |
| with gr.Tab("小说合成"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| self.Text = gr.File(label="Text") | |
| self.audio_path = gr.TextArea(label="音频路径",lines=1,value = 'audiobook/chapter.wav') | |
| btnbook = gr.Button("小说合成") | |
| btnbook.click(self.tts_fn, inputs=[self.Text,self.audio_path]) | |
| with gr.Tab("TTS设定"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| self.input1 = gr.Dropdown(label = "模型", choices = self.modelPaths, value = self.modelPaths[0], type = "value") | |
| self.input2 = gr.Dropdown(label="Language", choices=self.lan, value="自动", interactive=True) | |
| self.input3 = gr.Dropdown(label="Speaker", choices=list(range(1001)), value=0, interactive=True) | |
| self.input4 = gr.Slider(minimum=0, maximum=1.0, label="更改噪声比例(noise scale)，以控制情感", value=0.6) | |
| self.input5 = gr.Slider(minimum=0, maximum=1.0, label="更改噪声偏差(noise scale w)，以控制音素长短", value=0.667) | |
| self.input6 = gr.Slider(minimum=0.1, maximum=10, label="duration", value=1) | |
| statusa = gr.TextArea() | |
| btnVC = gr.Button("完成vits TTS端设定") | |
| btnVC.click(self.create_tts_fn, inputs=[self.input1, self.input2, self.input3, self.input4, self.input5, self.input6], outputs = [statusa]) | |
| def is_japanese(self,string): | |
| for ch in string: | |
| if ord(ch) > 0x3040 and ord(ch) < 0x30FF: | |
| return True | |
| return False | |
| def is_english(self,string): | |
| import re | |
| pattern = re.compile('^[A-Za-z0-9.,:;!?()_*"\' ]+$') | |
| if pattern.fullmatch(string): | |
| return True | |
| else: | |
| return False | |
| def get_text(self,text, hps, cleaned=False): | |
| if cleaned: | |
| text_norm = text_to_sequence(text, self.hps_ms.symbols, []) | |
| else: | |
| text_norm = text_to_sequence(text, self.hps_ms.symbols, self.hps_ms.data.text_cleaners) | |
| if self.hps_ms.data.add_blank: | |
| text_norm = commons.intersperse(text_norm, 0) | |
| text_norm = torch.LongTensor(text_norm) | |
| return text_norm | |
| def get_label(self,text, label): | |
| if f'[{label}]' in text: | |
| return True, text.replace(f'[{label}]', '') | |
| else: | |
| return False, text | |
| def sle(self,language,text): | |
| text = text.replace('\n','。').replace(' ',',') | |
| if language == "中文": | |
| tts_input1 = "[ZH]" + text + "[ZH]" | |
| return tts_input1 | |
| elif language == "自动": | |
| tts_input1 = f"[JA]{text}[JA]" if self.is_japanese(text) else f"[ZH]{text}[ZH]" | |
| return tts_input1 | |
| elif language == "日文": | |
| tts_input1 = "[JA]" + text + "[JA]" | |
| return tts_input1 | |
| def create_tts_fn(self,path, input2, input3, n_scale= 0.667,n_scale_w = 0.8, l_scale = 1 ): | |
| self.language = input2 | |
| self.speaker_id = int(input3) | |
| self.n_scale = n_scale | |
| self.n_scale_w = n_scale_w | |
| self.l_scale = l_scale | |
| self.dev = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| self.hps_ms = utils.get_hparams_from_file(f"checkpoints/{path}/config.json") | |
| self.n_speakers = self.hps_ms.data.n_speakers if 'n_speakers' in self.hps_ms.data.keys() else 0 | |
| self.n_symbols = len(self.hps_ms.symbols) if 'symbols' in self.hps_ms.keys() else 0 | |
| self.net_g_ms = SynthesizerTrn( | |
| self.n_symbols, | |
| self.hps_ms.data.filter_length // 2 + 1, | |
| self.hps_ms.train.segment_size // self.hps_ms.data.hop_length, | |
| n_speakers=self.n_speakers, | |
| **self.hps_ms.model).to(self.dev) | |
| _ = self.net_g_ms.eval() | |
| _ = utils.load_checkpoint(f"checkpoints/{path}/model.pth", self.net_g_ms) | |
| return 'success' | |
| def transfer(self,text): | |
| text = re.sub("<[^>]*>","",text) | |
| result_list = re.split(r'\n', text) | |
| final_list = [] | |
| for j in result_list: | |
| result_list2 = re.split(r'。|！|——|：|；|……|——|。|！', j) | |
| for i in result_list2: | |
| if self.is_english(i): | |
| i = romajitable.to_kana(i).katakana | |
| for m in range(20): | |
| i = i.replace('\n','').replace(' ','').replace('……','。').replace('…','。').replace('还','孩').replace('“','').replace('”','').replace('！','。').replace('」','').replace('「','') | |
| #Current length of single sentence: 50 | |
| if len(i)>1: | |
| if len(i) > 50: | |
| try: | |
| cur_list = re.split(r'。|！|——|，|：', i) | |
| for i in cur_list: | |
| if len(i)>1: | |
| final_list.append(i+'。') | |
| except: | |
| pass | |
| else: | |
| final_list.append(i) | |
| final_list = [x for x in final_list if x != ''] | |
| return final_list | |
| def tts_fn(self,text,audio_path): | |
| with open(text.name, "r", encoding="utf-8") as f: | |
| text = f.read() | |
| a = ['【','[','(','（','〔'] | |
| b = ['】',']',')','）','〕'] | |
| for i in a: | |
| text = text.replace(i,'<') | |
| for i in b: | |
| text = text.replace(i,'>') | |
| final_list = self.transfer(text) | |
| split_list = [] | |
| while len(final_list) > 0: | |
| split_list.append(final_list[:1000]) | |
| final_list = final_list[1000:] | |
| c0 = 0 | |
| for lists in split_list: | |
| audio_fin = [] | |
| t = datetime.timedelta(seconds=0) | |
| c = 0 | |
| f1 = open(audio_path.replace('.wav',str(c0)+".srt"),'w',encoding='utf-8') | |
| for sentence in lists: | |
| try: | |
| c +=1 | |
| with torch.no_grad(): | |
| stn_tst = self.get_text(self.sle(self.language,sentence), self.hps_ms, cleaned=False) | |
| x_tst = stn_tst.unsqueeze(0).to(self.dev) | |
| x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(self.dev) | |
| sid = torch.LongTensor([self.speaker_id]).to(self.dev) | |
| t1 = time.time() | |
| audio = self.net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=self.n_scale, noise_scale_w=self.n_scale_w, length_scale=self.l_scale)[0][ | |
| 0, 0].data.cpu().float().numpy() | |
| t2 = time.time() | |
| spending_time = "第"+str(c)+"句的推理时间为："+str(t2-t1)+"s" | |
| print(spending_time) | |
| time_start = str(t).split(".")[0] + "," + str(t.microseconds)[:3] | |
| last_time = datetime.timedelta(seconds=len(audio)/float(22050)) | |
| t+=last_time | |
| time_end = str(t).split(".")[0] + "," + str(t.microseconds)[:3] | |
| print(time_end) | |
| f1.write(str(c-1)+'\n'+time_start+' --> '+time_end+'\n'+sentence.replace('。','')+'\n\n') | |
| resampled_audio_data = signal.resample(audio, len(audio) * 2) | |
| audio_fin.append(resampled_audio_data) | |
| except: | |
| pass | |
| sf.write(audio_path.replace('.wav',str(c0)+'.wav'), np.concatenate(audio_fin), 44100, 'PCM_24') | |
| c0 += 1 | |
| file_path = audio_path.replace('.wav',str(c0)+".srt") | |
| if __name__ == '__main__': | |
| print("开始部署") | |
| grVits = VitsGradio() | |
| grVits.Vits.launch() |