# flake8: noqa: E402 import logging logging.getLogger("numba").setLevel(logging.WARNING) logging.getLogger("markdown_it").setLevel(logging.WARNING) logging.getLogger("urllib3").setLevel(logging.WARNING) logging.getLogger("matplotlib").setLevel(logging.WARNING) logging.basicConfig( level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s" ) import base64 logger = logging.getLogger(__name__) import datetime import numpy as np import torch import zipfile import shutil import sys, os import json import argparse import commons import utils from models import SynthesizerTrn from text.symbols import symbols from text import cleaned_text_to_sequence, get_bert from text.cleaner import clean_text import gradio as gr import webbrowser import re from scipy.io.wavfile import write net_g = None import soundfile BandList = { " ":["AKIMOTO_MANATSU" ,"ENDO_SAKURA" ,"ETO_MISA" ,"FUKAGAWA_MAI" ,"HARUKA_KUROMI" ,"HASHIMOTO_NANAMI" ,"HAYAKAWA_SEIRA" ,"HIGUCHI_HINA" ,"HORI_MIONA" ,"HOSHINO_MINAMI" , "ICHINOSE_MIKU" ,"IKEDA_TERESA" ,"IKOMA_RINA" ,"IKUTA_ERIKA" ,"INOUE_NAGI" ,"INOUE_SAYURI" ,"IOKI_MAO" ,"ITO_JUNNA" ,"ITO_KARIN" ,"ITO_MARIKA" ,"ITO_RIRIA" ,"IWAMOTO_RENKA" , "KAKEHASHI_SAYAKA" ,"KAKI_HARUKA" ,"KANAGAWA_SAYA" ,"KAWAGO_HINA" ,"KAWAMURA_MAHIRO" ,"KAWASAKI_SAKURA" ,"KITAGAWA_YURI" ,"KITANO_HINAKO" ,"KUBO_SHIORI" ,"MATSUMURA_SAYURI" , "MIYU_MATSUO" ,"MUKAI_HAZUKI" ,"NAKADA_KANA" ,"NAKAMOTO_HIMEKA" ,"NAKAMURA_RENO" ,"NAKANISHI_ARUNO" ,"NAO_YUMIKI" ,"NISHINO_NANASE" ,"NOUJO_AMI" ,"OGAWA_AYA" ,"OKAMOTO_HINA" , "OKUDA_IROHA" ,"OZONO_MOMOKO" ,"RIKA_SATO" ,"RUNA_HAYASHI" ,"SAGARA_IORI" ,"SAITO_ASUKA" ,"SAITO_CHIHARU" ,"SAKAGUCHI_TAMAMI" ,"SAKURAI_REIKA" ,"SASAKI_KOTOKO" ,"SATO_KAEDE" , "SATO_YUURI" ,"SEIMIYA_REI" ,"SHIBATA_YUNA" ,"SHINUCHI_MAI" ,"SHIRAISHI_MAI" ,"SUGAWARA_SATSUKI" ,"SUZUKI_AYANE" ,"TAKAYAMA_KAZUMI" ,"TAMURA_MAYU" ,"TERADA_RANZE", "TOMISATO_NAO" ,"TSUTSUI_AYAME" ,"UMEZAWA_MINAMI" ,"WADA_MAAYA" ,"WAKATSUKI_YUMI" ,"WATANABE_MIRIA" ,"YAKUBO_MIO" ,"YAMASHITA_MIZUKI" ,"YAMAZAKI_RENA" ,"YODA_YUUKI" ,"YOSHIDA_AYANO_CHRISTIE" ], } if sys.platform == "darwin" and torch.backends.mps.is_available(): device = "mps" os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" else: device = "cuda" def is_japanese(string): for ch in string: if ord(ch) > 0x3040 and ord(ch) < 0x30FF: return True return False def extrac(text): text = re.sub("<[^>]*>","",text) result_list = re.split(r'\n', text) final_list = [] for i in result_list: i = i.replace('\n','').replace(' ','') #Current length of single sentence: 20 if len(i)>1: if len(i) > 20: 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.append(i) ''' final_list = [x for x in final_list if x != ''] return final_list def get_text(text, language_str, hps): norm_text, phone, tone, word2ph = clean_text(text, language_str) phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) if hps.data.add_blank: phone = commons.intersperse(phone, 0) tone = commons.intersperse(tone, 0) language = commons.intersperse(language, 0) for i in range(len(word2ph)): word2ph[i] = word2ph[i] * 2 word2ph[0] += 1 bert = get_bert(norm_text, word2ph, language_str, device) del word2ph assert bert.shape[-1] == len(phone), phone if language_str == "ZH": bert = bert ja_bert = torch.zeros(768, len(phone)) elif language_str == "JA": ja_bert = bert bert = torch.zeros(1024, len(phone)) else: bert = torch.zeros(1024, len(phone)) ja_bert = torch.zeros(768, len(phone)) assert bert.shape[-1] == len( phone ), f"Bert seq len {bert.shape[-1]} != {len(phone)}" phone = torch.LongTensor(phone) tone = torch.LongTensor(tone) language = torch.LongTensor(language) return bert, ja_bert, phone, tone, language def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid, language): print(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid, language) global net_g bert, ja_bert, phones, tones, lang_ids = get_text(text, language, hps) with torch.no_grad(): x_tst = phones.to(device).unsqueeze(0) tones = tones.to(device).unsqueeze(0) lang_ids = lang_ids.to(device).unsqueeze(0) bert = bert.to(device).unsqueeze(0) ja_bert = ja_bert.to(device).unsqueeze(0) x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device) del phones speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device) audio = ( net_g.infer( x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, ja_bert, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, )[0][0, 0] .data.cpu() .float() .numpy() ) del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers return audio def tts_fn( text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale,LongSentence,correct_language ): max_len = 1000 if len(text) > max_len: return None if correct_language == "Auto": correct_language = "JP" if is_japanese(text) else "ZH" if not LongSentence: with torch.no_grad(): audio = infer( text, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker, language= correct_language, ) torch.cuda.empty_cache() return (hps.data.sampling_rate, audio) else: audiopath = 'voice.wav' a = ['【','[','(','（'] b = ['】',']',')','）'] for i in a: text = text.replace(i,'<') for i in b: text = text.replace(i,'>') final_list = extrac(text.replace('“','').replace('”','')) audio_fin = [] for sentence in final_list: with torch.no_grad(): audio = infer( sentence, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker, language= correct_language, ) audio_fin.append(audio) soundfile.write("tts_output.mp3", np.concatenate(audio_fin), 44100, format="mp3") return ("tts_output.mp3" ) def split_into_sentences(text): """将文本分割为句子，基于中文的标点符号""" sentences = re.split(r'(?<=[。！？…\n])', text) return [sentence.strip() for sentence in sentences if sentence] def seconds_to_ass_time(seconds): """将秒数转换为ASS时间格式""" hours = int(seconds / 3600) minutes = int((seconds % 3600) / 60) seconds = int(seconds) % 60 milliseconds = int((seconds - int(seconds)) * 1000) return "{:01d}:{:02d}:{:02d}.{:02d}".format(hours, minutes, seconds, int(milliseconds / 10)) if __name__ == "__main__": import pyzipper hf_token1 = os.environ.get('TOKEN1').encode("utf-8") with pyzipper.AESZipFile('./N/vits.zip') as zf: zf.pwd = hf_token1 zf.extractall() parser = argparse.ArgumentParser() parser.add_argument( "-m", "--model", default="./G_51000.pth", help="path of your model" ) parser.add_argument( "-c", "--config", default="./N/config.json", help="path of your config file", ) parser.add_argument( "--share", default=True, help="make link public", action="store_true" ) parser.add_argument( "-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log" ) args = parser.parse_args() if args.debug: logger.info("Enable DEBUG-LEVEL log") logging.basicConfig(level=logging.DEBUG) hps = utils.get_hparams_from_file(args.config) device = ( "cuda:0" if torch.cuda.is_available() else ( "mps" if sys.platform == "darwin" and torch.backends.mps.is_available() else "cpu" ) ) net_g = SynthesizerTrn( len(symbols), hps.data.filter_length // 2 + 1, hps.train.segment_size // hps.data.hop_length, n_speakers=hps.data.n_speakers, **hps.model, ).to(device) _ = net_g.eval() _ = utils.load_checkpoint(args.model, net_g, None, skip_optimizer=True) speaker_ids = hps.data.spk2id speakers = list(speaker_ids.keys()) languages = ["Auto","ZH", "JP"] with gr.Blocks(theme='NoCrypt/miku') as app: gr.Markdown(value=base64.b64decode( b'KiDlhbbku5bnjqnlhbfvvJrkuYPmnKjlnYI0NiBTb3ZpdHPpn7PoibLovazmjaJhaee/u+WUse+8mltodHRwczovL3Nvdml0czQubm9naXpha2E0Ni5jY10oaHR0cHM6Ly9zb3ZpdHM0Lm5vZ2l6YWthNDYuY2MpIA==' ).decode()) gr.Markdown(value= "目前效果比较差，训练中.....,传播音频请严格遵守中国法律法规!") for band in BandList: with gr.TabItem(band): for name in BandList[band]: with gr.TabItem(name): with gr.Row(): #with gr.Column(): #with gr.Row(): #gr.Markdown( #'
' #f'' #'
' #) with gr.Column(): text = gr.TextArea( label="输入纯日语或者中文(500字)", placeholder="输入纯日语或者中文(1000字)", value="純粋な日本語または中国語を入力してください。", ) btn = gr.Button("点击生成", variant="primary") audio_output = gr.Audio(label="Output Audio") LongSentence = gr.Checkbox(value=True, label="Generate LongSentence") with gr.Accordion(label="TTS设定", open=True): sdp_ratio = gr.Slider( minimum=0, maximum=1, value=0.2, step=0.01, label="SDP/DP混合比(可控制一定程度的语调变化)" ) noise_scale = gr.Slider( minimum=0.1, maximum=2, value=0.6, step=0.01, label="感情调节" ) noise_scale_w = gr.Slider( minimum=0.1, maximum=2, value=0.8, step=0.01, label="音素长度" ) length_scale = gr.Slider( minimum=0.1, maximum=2, value=1.05, step=0.01, label="生成长度(数值越大，语速越慢)" ) speaker = gr.Dropdown( choices=speakers, value=name, label="说话人(在这选择说话人将保留输入文本)" ) correct_language = gr.Dropdown( choices=languages, value=languages[0], label="Language(如果发生报错请手动指定)" ) btn.click( tts_fn, inputs=[ text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale, LongSentence, correct_language, ], outputs=[audio_output], ) app.launch()