import argparse import gradio as gr import torch import commons import utils import re from models import SynthesizerTrn from text.symbols import symbols from text import text_to_sequence import numpy as np import os import translators.server as tss import psutil from datetime import datetime limitation = os.getenv("SYSTEM") == "spaces" # limit text and audio length in huggingface spaces max_len = 150 languages = ['日本語', '简体中文', 'English'] characters = ['0:特别周', '1:无声铃鹿', '2:东海帝王', '3:丸善斯基', '4:富士奇迹', '5:小栗帽', '6:黄金船', '7:伏特加', '8:大和赤骥', '9:大树快车', '10:草上飞', '11:菱亚马逊', '12:目白麦昆', '13:神鹰', '14:好歌剧', '15:成田白仁', '16:鲁道夫象征', '17:气槽', '18:爱丽数码', '19:青云天空', '20:玉藻十字', '21:美妙姿势', '22:琵琶晨光', '23:重炮', '24:曼城茶座', '25:美普波旁', '26:目白雷恩', '27:菱曙', '28:雪之美人', '29:米浴', '30:艾尼斯风神', '31:爱丽速子', '32:爱慕织姬', '33:稻荷一', '34:胜利奖券', '35:空中神宫', '36:荣进闪耀', '37:真机伶', '38:川上公主', '39:黄金城市', '40:樱花进王', '41:采珠', '42:新光风', '43:东商变革', '44:超级小溪', '45:醒目飞鹰', '46:荒漠英雄', '47:东瀛佐敦', '48:中山庆典', '49:成田大进', '50:西野花', '51:春乌拉拉', '52:青竹回忆', '53:微光飞驹', '54:美丽周日', '55:待兼福来', '56:Mr.C.B', '57:名将怒涛', '58:目白多伯', '59:优秀素质', '60:帝王光环', '61:待兼诗歌剧', '62:生野狄杜斯', '63:目白善信', '64:大拓太阳神', '65:双涡轮', '66:里见光钻', '67:北部玄驹', '68:樱花千代王', '69:天狼星象征', '70:目白阿尔丹', '71:八重无敌', '72:鹤丸刚志', '73:目白光明', '74:樱花桂冠', '75:成田路', '76:也文摄辉', '77:吉兆', '78:谷野美酒', '79:第一红宝石', '80:真弓快车', '81:骏川手纲', '82:凯斯奇迹', '83:小林历奇', '84:北港火山', '85:奇锐骏', '86:秋川理事长'] def show_memory_info(hint): pid = os.getpid() p = psutil.Process(pid) info = p.memory_info() memory = info.rss / 1024.0 / 1024 print("{} 内存占用: {} MB".format(hint, memory)) def get_text(text, hps): text_norm = text_to_sequence(text, hps.data.text_cleaners) if hps.data.add_blank: text_norm = commons.intersperse(text_norm, 0) text_norm = torch.LongTensor(text_norm) return text_norm hps = utils.get_hparams_from_file("./configs/uma87.json") 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) _ = net_g.eval() _ = utils.load_checkpoint("pretrained_models/G_1153000.pth", net_g, None) def infer(text, character, language, duration, noise_scale, noise_scale_w): # check character & duraction parameter if language not in languages: return "Error: No such language", None if character not in characters: return "Error: No such character", None # check text length if limitation: text_len = len(re.sub("\[([A-Z]{2})\]", "", text)) if text_len > max_len: return "Error: Text is too long", None if text_len == 0: return "Error: Please input text!", None currentDateAndTime = datetime.now() show_memory_info(str(currentDateAndTime) + "infer调用前") if language == '日本語': pass elif language == '简体中文': text = tss.google(text, from_language='zh', to_language='ja') elif language == 'English': text = tss.google(text, from_language='en', to_language='ja') char_id = int(character.split(':')[0]) stn_tst = get_text(text, hps) with torch.no_grad(): x_tst = stn_tst.unsqueeze(0) x_tst_lengths = torch.LongTensor([stn_tst.size(0)]) sid = torch.LongTensor([char_id]) audio = net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=duration)[0][0, 0].data.cpu().float().numpy() currentDateAndTime = datetime.now() show_memory_info(str(currentDateAndTime) + "infer调用后") return (text, (22050, audio)) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--share", action="store_true", default=False, help="share gradio app") args = parser.parse_args() app = gr.Blocks() with app: gr.Markdown("# Umamusume voice synthesizer 赛马娘语音合成器\n\n" "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=Plachta.VITS-Umamusume-voice-synthesizer)\n\n" "This synthesizer is created based on [VITS](https://arxiv.org/abs/2106.06103) model, trained on voice data extracted from mobile game Umamusume Pretty Derby \n\n" "这个合成器是基于VITS文本到语音模型，在从手游《賽馬娘：Pretty Derby》解包的语音数据上训练得到。\n\n" "[introduction video / 模型介绍视频](https://www.bilibili.com/video/BV1T84y1e7p5/?vd_source=6d5c00c796eff1cbbe25f1ae722c2f9f#reply607277701)\n\n" "You may duplicate this space or [open in Colab](https://colab.research.google.com/drive/1J2Vm5dczTF99ckyNLXV0K-hQTxLwEaj5?usp=sharing) to run it privately and without any queue.\n\n" "您可以复制该空间至私人空间运行或打开[Google Colab](https://colab.research.google.com/drive/1J2Vm5dczTF99ckyNLXV0K-hQTxLwEaj5?usp=sharing)在线运行。\n\n" "If your input language is not Japanese, it will be translated to Japanese by Google translator, but accuracy is not guaranteed.\n\n" "如果您的输入语言不是日语，则会由谷歌翻译自动翻译为日语，但是准确性不能保证。\n\n" ) with gr.Row(): with gr.Column(): # We instantiate the Textbox class textbox = gr.Textbox(label="Text", placeholder="Type your sentence here (Maximum 150 words)", value = "こんにちわ！", lines=2) # select character char_dropdown = gr.Dropdown(choices=characters, value = "0:特别周", label='character') language_dropdown = gr.Dropdown(choices=languages, value = "日本語", label='language') duration_slider = gr.Slider(minimum=0.1, maximum=5, value=1, step=0.1, label='时长 Duration') noise_scale_slider = gr.Slider(minimum=0.1, maximum=5, value=0.667, step=0.001, label='噪声比例 noise_scale') noise_scale_w_slider = gr.Slider(minimum=0.1, maximum=5, value=0.8, step=0.1, label='噪声偏差 noise_scale_w') with gr.Column(): text_output = gr.Textbox(label="Output Text") audio_output = gr.Audio(label="Output Voice") btn = gr.Button("Generate!") btn.click(infer, inputs=[textbox, char_dropdown, language_dropdown, duration_slider, noise_scale_slider, noise_scale_w_slider], outputs=[text_output, audio_output]) examples = [['お疲れ様です，トレーナーさん。', '1:无声铃鹿', '日本語', 1, 0.667, 0.8], ['張り切っていこう！', '67:北部玄驹', '日本語', 1, 0.667, 0.8], ['何でこんなに慣れでんのよ，私のほが先に好きだっだのに。', '10:草上飞', '日本語', 1, 0.667, 0.8], ['授業中に出しだら，学校生活終わるですわ。', '12:目白麦昆', '日本語', 1, 0.667, 0.8], ['お帰りなさい，お兄様！', '29:米浴', '日本語', 1, 0.667, 0.8], ['私の処女をもらっでください！', '29:米浴', '日本語', 1, 0.667, 0.8]] gr.Examples( examples=examples, inputs=[textbox, char_dropdown, language_dropdown, duration_slider, noise_scale_slider,noise_scale_w_slider], outputs=[text_output, audio_output], fn=infer ) app.queue(concurrency_count=3).launch(show_api=False, share=args.share)