voicegen / app.py
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import gradio as gr
import torch
import commons
import utils
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
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)
title = "Umamusume voice synthesizer \n 赛马娘语音合成器"
description = """
This synthesizer is created based on [VITS][paper] model, trained on voice data extracted from mobile game Umamusume Pretty Derby\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
[introduction video][video] [模型介绍视频][video]\n
Due to some unknown reason, VITS inference on CPU results in accumulative memory leakage, resulting in Runtime error:Memory limit exceeded.\n
In case of space crash, you may duplicate this space to run it privately and without any queue.\n
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
以防该空间崩溃,您可以复制该空间至私人空间运行。\n
If your input language is not Japanese, it will be translated to Japanese by Google translator, but accuracy is not guaranteed.\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
[video]: https://www.bilibili.com/video/BV1T84y1e7p5/?vd_source=6d5c00c796eff1cbbe25f1ae722c2f9f#reply607277701
[paper]: https://arxiv.org/abs/2106.06103
"""
article = """
"""
def infer(text, character, language, duration, noise_scale, noise_scale_w):
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()
return (text,(22050, audio))
# We instantiate the Textbox class
textbox = gr.Textbox(label="Text", placeholder="Type your sentence here", lines=2)
# select character
char_dropdown = gr.Dropdown(['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'])
language_dropdown = gr.Dropdown(['日本語','简体中文','English'])
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]]
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')
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)
if __name__=="__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
args = parser.parse_args()
app.queue(concurrency_count=3).launch(show_api=False, share=args.share)