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import argparse |
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import gradio as gr |
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import torch |
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import commons |
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import utils |
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from models import SynthesizerTrn |
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from text.symbols import symbols |
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from text import text_to_sequence |
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import numpy as np |
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import os |
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import translators.server as tss |
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def get_text(text, hps): |
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text_norm = text_to_sequence(text, hps.data.text_cleaners) |
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if hps.data.add_blank: |
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text_norm = commons.intersperse(text_norm, 0) |
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text_norm = torch.LongTensor(text_norm) |
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return text_norm |
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hps = utils.get_hparams_from_file("./configs/uma87.json") |
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net_g = SynthesizerTrn( |
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len(symbols), |
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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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_ = net_g.eval() |
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_ = utils.load_checkpoint("pretrained_models/G_1153000.pth", net_g, None) |
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title = "Umamusume voice synthesizer \n 赛马娘语音合成器" |
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description = """ |
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This synthesizer is created based on [VITS][paper] model, trained on voice data extracted from mobile game Umamusume Pretty Derby\n |
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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 |
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[introduction video][video] [模型介绍视频][video]\n |
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Due to some unknown reason, VITS inference on CPU results in accumulative memory leakage, resulting in Runtime error：Memory limit exceeded.\n |
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In case of space crash, you may duplicate this space to run it privately and without any queue.\n |
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由于未知原因，VITS模型在CPU上执行推理时会有逐步累积的内存泄漏，最终导致空间报错Runtime error：Memory limit exceeded，目前正在排查。\n |
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以防该空间崩溃，您可以复制该空间至私人空间运行。\n |
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If your input language is not Japanese, it will be translated to Japanese by Google translator, but accuracy is not guaranteed.\n |
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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 |
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[video]: https://www.bilibili.com/video/BV1T84y1e7p5/?vd_source=6d5c00c796eff1cbbe25f1ae722c2f9f#reply607277701 |
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[paper]: https://arxiv.org/abs/2106.06103 |
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""" |
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article = """ |
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""" |
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def infer(text, character, language, duration, noise_scale, noise_scale_w): |
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if language == '日本語': |
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pass |
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elif language == '简体中文': |
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text = tss.google(text, from_language='zh', to_language='ja') |
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elif language == 'English': |
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text = tss.google(text, from_language='en', to_language='ja') |
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char_id = int(character.split(':')[0]) |
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stn_tst = get_text(text, hps) |
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with torch.no_grad(): |
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x_tst = stn_tst.unsqueeze(0) |
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x_tst_lengths = torch.LongTensor([stn_tst.size(0)]) |
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sid = torch.LongTensor([char_id]) |
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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() |
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return (text,(22050, audio)) |
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textbox = gr.Textbox(label="Text", placeholder="Type your sentence here", lines=2) |
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char_dropdown = gr.Dropdown(['0:特别周','1:无声铃鹿','2:东海帝王','3:丸善斯基', |
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'4:富士奇迹','5:小栗帽','6:黄金船','7:伏特加', |
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'8:大和赤骥','9:大树快车','10:草上飞','11:菱亚马逊', |
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'12:目白麦昆','13:神鹰','14:好歌剧','15:成田白仁', |
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'16:鲁道夫象征','17:气槽','18:爱丽数码','19:青云天空', |
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'20:玉藻十字','21:美妙姿势','22:琵琶晨光','23:重炮', |
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'24:曼城茶座','25:美普波旁','26:目白雷恩','27:菱曙', |
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'28:雪之美人','29:米浴','30:艾尼斯风神','31:爱丽速子', |
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'32:爱慕织姬','33:稻荷一','34:胜利奖券','35:空中神宫', |
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'36:荣进闪耀','37:真机伶','38:川上公主','39:黄金城市', |
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'40:樱花进王','41:采珠','42:新光风','43:东商变革', |
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'44:超级小溪','45:醒目飞鹰','46:荒漠英雄','47:东瀛佐敦', |
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'48:中山庆典','49:成田大进','50:西野花','51:春乌拉拉', |
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'52:青竹回忆','53:微光飞驹','54:美丽周日','55:待兼福来', |
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'56:Mr.C.B','57:名将怒涛','58:目白多伯','59:优秀素质', |
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'60:帝王光环','61:待兼诗歌剧','62:生野狄杜斯','63:目白善信', |
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'64:大拓太阳神','65:双涡轮','66:里见光钻','67:北部玄驹', |
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'68:樱花千代王','69:天狼星象征','70:目白阿尔丹','71:八重无敌', |
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'72:鹤丸刚志','73:目白光明','74:樱花桂冠','75:成田路', |
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'76:也文摄辉','77:吉兆','78:谷野美酒','79:第一红宝石', |
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'80:真弓快车','81:骏川手纲','82:凯斯奇迹','83:小林历奇', |
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'84:北港火山','85:奇锐骏','86:秋川理事长']) |
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language_dropdown = gr.Dropdown(['日本語','简体中文','English']) |
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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], |
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['張り切っていこう！', '67:北部玄驹', '日本語', 1, 0.667, 0.8], |
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['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], |
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['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], |
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['お帰りなさい，お兄様！', '29:米浴','日本語', 1, 0.667, 0.8], |
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['私の処女をもらっでください！', '29:米浴','日本語', 1, 0.667, 0.8]] |
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duration_slider = gr.Slider(minimum=0.1, maximum=5, value=1, step=0.1, label='时长 Duration') |
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noise_scale_slider = gr.Slider(minimum=0.1, maximum=5, value=0.667, step=0.001, label='噪声比例 noise_scale') |
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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') |
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app = gr.Interface(fn=infer, inputs=[textbox, char_dropdown, language_dropdown, duration_slider, noise_scale_slider, noise_scale_w_slider,], outputs=["text","audio"],title=title, description=description, article=article, examples=examples) |
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if __name__=="__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--share", action="store_true", default=False, help="share gradio app") |
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args = parser.parse_args() |
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app.queue(concurrency_count=3).launch(show_api=False, share=args.share) |
