lym0302
4 years ago
parent
1a3c811f04
commit
82992b3ed6
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009901 昨日，这名伤者与医生全部被警方依法刑事拘留。
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009902 钱伟长想到上海来办学校是经过深思熟虑的。
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009903 她见我一进门就骂，吃饭时也骂，骂得我抬不起头。
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009904 李述德在离开之前，只说了一句柱驼杀父亲了。
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009905 这种车票和保险单捆绑出售属于重复性购买。
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009906 戴佩妮的男友西米露接唱情歌，让她非常开心。
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009907 观大势，谋大局，出大策始终是该院的办院方针。
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009908 他们骑着摩托回家，正好为农忙时的父母帮忙。
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009909 但是因为还没到退休年龄，只能掰着指头捱日子。
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009910 这几天雨水不断，人们恨不得待在家里不出门。
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009911 没想到徐赟，张海翔两人就此玩起了人间蒸发。
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009912 藤村此番发言可能是为了凸显野田的领导能力。
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009913 程长庚，生在清王朝嘉庆年间，安徽的潜山小县。
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009914 南海海域综合补给基地码头项目正在论证中。
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009915 也就是说今晚成都市民极有可能再次看到飘雪。
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009916 随着天气转热，各地的游泳场所开始人头攒动。
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009917 更让徐先生纳闷的是，房客的手机也打不通了。
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009918 遇到颠簸时，应听从乘务员的安全指令，回座位坐好。
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009919 他在后面呆惯了，怕自己一插身后的人会不满，不敢排进去。
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009920 傍晚七个小人回来了，白雪公主说，你们就是我命中的七个小矮人吧。
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009921 他本想说，教育局管这个，他们是一路的，这样一管岂不是妓女起嫖客？
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009922 一种表示商品所有权的财物证券，也称商品证券，如提货单，交货单。
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009923 会有很丰富的东西留下来，说都说不完。
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009924 这句话像从天而降，吓得四周一片寂静。
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009925 记者所在的是受害人家属所在的右区。
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009926 不管哈大爷去哪，它都一步不离地跟着。
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009927 大家抬头望去，一只老鼠正趴在吊顶上。
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009928 我决定过年就辞职，接手我爸的废品站！
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009929 最终，中国男子乒乓球队获得此奖项。
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009930 防汛抗旱两手抓，抗旱相对抓的不够。
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009931 图们江下游地区开发开放的进展如何？
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009932 这要求中国必须有一个坚强的政党领导。
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009933 再说，关于利益上的事俺俩都不好开口。
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009934 明代瓦剌，鞑靼入侵明境也是通过此地。
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009935 咪咪舔着孩子，把它身上的毛舔干净。
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009936 是否这次的国标修订被大企业绑架了？
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009937 判决后，姚某妻子胡某不服，提起上诉。
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009938 由此可以看出邯钢的经济效益来自何处。
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009939 琳达说，是瑜伽改变了她和马儿的生活。
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009940 楼下的保安告诉记者，这里不租也不卖。
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009941 习近平说，中斯两国人民传统友谊深厚。
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009942 传闻越来越多，后来连老汉儿自己都怕了。
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009943 我怒吼一声冲上去，举起砖头砸了过去。
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009944 我现在还不会，这就回去问问发明我的人。
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009945 显然，洛阳性奴案不具备上述两个前提。
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009946 另外，杰克逊有文唇线，眼线，眉毛的动作。
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009947 昨晚，华西都市报记者电话采访了尹琪。
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009948 涅拉季科未透露这些航空公司的名称。
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009949 从运行轨迹上来说，它也不可能是星星。
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009950 目前看，如果继续加息也存在两难问题。
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009951 曾宝仪在节目录制现场大爆观众糗事。
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009952 但任凭周某怎么叫，男子仍酣睡不醒。
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009953 老大爷说，小子，你挡我财路了，知道不？
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009954 没料到，闯下大头佛的阿伟还不知悔改。
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009955 卡扎菲部落式统治已遭遇部落内讧。
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009956 这个孩子的生命一半来源于另一位女士捐赠的冷冻卵子。
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009957 出现这种泥鳅内阁的局面既是野田有意为之，也实属无奈。
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009958 济青高速济南，华山，章丘，邹平，周村，淄博，临淄站。
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009959 赵凌飞的话，反映了沈阳赛区所有奥运志愿者的共同心声。
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009960 因为，我们所发出的力量必会因难度加大而减弱。
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009961 发生事故的楼梯拐角处仍可看到血迹。
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009962 想过进公安，可能身高不够，老汉儿也不让我进去。
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009963 路上关卡很多，为了方便撤离，只好轻装前进。
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009964 原来比尔盖茨就是美国微软公司联合创始人呀。
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009965 之后他们一家三口将与双方父母往峇里岛旅游。
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009966 谢谢总理，也感谢广大网友的参与，我们明年再见。
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009967 事实上是，从来没有一个欺善怕恶的人能作出过稍大一点的成就。
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009968 我会打开邮件，你可以从那里继续。
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009969 美方对近期东海局势表示关切。
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009970 据悉，奥巴马一家人对这座冬季白宫极为满意。
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009971 打扫完你会很有成就感的，试一试，你就信了。
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009972 诺曼站在滑板车上，各就各位，准备出发啦！
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009973 塔河的寒夜，气温降到了零下三十多摄氏度。
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009974 其间，连破六点六，六点五，六点四，六点三五等多个重要关口。
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009975 算命其实只是人们的一种自我安慰和自我暗示而已，我们还是要相信科学才好。
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009976 这一切都令人欢欣鼓舞，阿讷西没理由不坚持到最后。
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009977 直至公元前一万一千年，它又再次出现。
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009978 尽量少玩电脑，少看电视，少打游戏。
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009979 从五到七，前后也就是六个月的时间。
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009980 一进咖啡店，他就遇见一张熟悉的脸。
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009981 好在众弟兄看到了把她追了回来。
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009982 有一个人说，哥们儿我们跑过它才能活。
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009983 捅了她以后，模糊记得她没咋动了。
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009984 从小到大，葛启义没有收到过压岁钱。
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009985 舞台下的你会对舞台上的你说什么？
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009986 但考生普遍认为，试题的怪多过难。
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009987 我希望每个人都能够尊重我们的隐私。
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009988 漫天的红霞使劲给两人增添气氛。
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009989 晚上加完班开车回家，太累了，迷迷糊糊开着车，走一半的时候，铛一声！
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009990 该车将三人撞倒后，在大雾中逃窜。
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009991 这人一哆嗦，方向盘也把不稳了，差点撞上了高速边道护栏。
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009992 那女孩儿委屈的说，我一回头见你已经进去了我不敢进去啊！
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009993 小明摇摇头说，不是，我只是美女看多了，想换个口味而已。
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009994 接下来，红娘要求记者交费，记者表示不知表姐身份证号码。
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009995 李东蓊表示，自己当时在法庭上发表了一次独特的公诉意见。
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009996 另一男子扑了上来，手里拿着明晃晃的长刀，向他胸口直刺。
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009997 今天，快递员拿着一个快递在办公室喊，秦王是哪个，有他快递？
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009998 这场抗议活动究竟是如何发展演变的，又究竟是谁伤害了谁？
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009999 因华国锋肖鸡，墓地设计根据其属相设计。
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010000 在狱中，张明宝悔恨交加，写了一份忏悔书。
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@ -0,0 +1,64 @@
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model_path=/home/users/liangyunming/.paddlespeech/models/
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#am_model_dir=$model_path/fastspeech2_csmsc-zh/fastspeech2_nosil_baker_ckpt_0.4/ ## fastspeech2
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am_model_dir=$model_path/fastspeech2_csmsc-zh/fastspeech2_cnndecoder_csmsc_ckpt_1.0.0/ ## fastspeech2_cnn
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voc_model_dir=$model_path/hifigan_csmsc-zh/hifigan_csmsc_ckpt_0.1.1/ ## hifigan
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#voc_model_dir=$model_path/mb_melgan_csmsc-zh/mb_melgan_csmsc_ckpt_0.1.1/ ## mb_melgan
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if [[ $am_model_dir == *"fastspeech2_cnndecoder"* ]]; then
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am_support_stream=True
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else
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am_support_stream=False
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fi
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# get am file
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for file in $(ls $am_model_dir)
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do
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if [[ $file == *"yaml"* ]]; then
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am_config_file=$file
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elif [[ $file == *"pdz"* ]]; then
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am_ckpt_file=$file
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elif [[ $file == *"stat"* ]]; then
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am_stat_file=$file
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elif [[ $file == *"phone"* ]]; then
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phones_dict_file=$file
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fi
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done
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# get voc file
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for file in $(ls $voc_model_dir)
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do
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if [[ $file == *"yaml"* ]]; then
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voc_config_file=$file
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elif [[ $file == *"pdz"* ]]; then
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voc_ckpt_file=$file
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elif [[ $file == *"stat"* ]]; then
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voc_stat_file=$file
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fi
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done
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#run
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python test_online_tts.py --am fastspeech2_csmsc \
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--am_support_stream $am_support_stream \
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--am_config $am_model_dir/$am_config_file \
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--am_ckpt $am_model_dir/$am_ckpt_file \
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--am_stat $am_model_dir/$am_stat_file \
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--phones_dict $am_model_dir/$phones_dict_file \
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--voc hifigan_csmsc \
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--voc_config $voc_model_dir/$voc_config_file \
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--voc_ckpt $voc_model_dir/$voc_ckpt_file \
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--voc_stat $voc_model_dir/$voc_stat_file \
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--lang zh \
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--device cpu \
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--text ./csmsc_test.txt \
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--output_dir ./output \
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--log_file ./result.log \
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--am_streaming False \
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--am_pad 12 \
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--am_block 42 \
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--voc_streaming True \
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--voc_pad 14 \
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--voc_block 14 \
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@ -0,0 +1,650 @@
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import logging
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import math
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import threading
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import time
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from pathlib import Path
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import numpy as np
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import paddle
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import soundfile as sf
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import yaml
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from yacs.config import CfgNode
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from paddlespeech.s2t.utils.dynamic_import import dynamic_import
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from paddlespeech.t2s.exps.syn_utils import get_am_inference
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from paddlespeech.t2s.exps.syn_utils import get_frontend
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from paddlespeech.t2s.exps.syn_utils import get_sentences
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from paddlespeech.t2s.exps.syn_utils import get_voc_inference
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from paddlespeech.t2s.exps.syn_utils import model_alias
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from paddlespeech.t2s.utils import str2bool
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mel_streaming = None
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wav_streaming = None
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stream_first_time = 0.0
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voc_stream_st = 0.0
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sample_rate = 0
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def denorm(data, mean, std):
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return data * std + mean
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def get_chunks(data, block_size, pad_size, step):
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if step == "am":
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data_len = data.shape[1]
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elif step == "voc":
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data_len = data.shape[0]
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else:
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print("Please set correct type to get chunks, am or voc")
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chunks = []
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n = math.ceil(data_len / block_size)
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for i in range(n):
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start = max(0, i * block_size - pad_size)
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end = min((i + 1) * block_size + pad_size, data_len)
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if step == "am":
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chunks.append(data[:, start:end, :])
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elif step == "voc":
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chunks.append(data[start:end, :])
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else:
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print("Please set correct type to get chunks, am or voc")
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return chunks
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def get_stream_am_inference(args, am_config):
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with open(args.phones_dict, "r") as f:
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phn_id = [line.strip().split() for line in f.readlines()]
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vocab_size = len(phn_id)
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print("vocab_size:", vocab_size)
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am_name = args.am[:args.am.rindex('_')]
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am_dataset = args.am[args.am.rindex('_') + 1:]
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odim = am_config.n_mels
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am_class = dynamic_import(am_name, model_alias)
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am = am_class(idim=vocab_size, odim=odim, **am_config["model"])
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am.set_state_dict(paddle.load(args.am_ckpt)["main_params"])
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am.eval()
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am_mu, am_std = np.load(args.am_stat)
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am_mu = paddle.to_tensor(am_mu)
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am_std = paddle.to_tensor(am_std)
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return am, am_mu, am_std
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def init(args):
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global sample_rate
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# get config
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with open(args.am_config) as f:
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am_config = CfgNode(yaml.safe_load(f))
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with open(args.voc_config) as f:
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voc_config = CfgNode(yaml.safe_load(f))
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sample_rate = am_config.fs
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# frontend
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frontend = get_frontend(args)
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# acoustic model
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if args.am_support_stream:
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am, am_mu, am_std = get_stream_am_inference(args, am_config)
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am_infer_info = [am, am_mu, am_std, am_config]
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else:
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am_inference, am_name, am_dataset = get_am_inference(args, am_config)
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am_infer_info = [am_inference, am_name, am_dataset, am_config]
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# vocoder
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voc_inference = get_voc_inference(args, voc_config)
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voc_infer_info = [voc_inference, voc_config]
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return frontend, am_infer_info, voc_infer_info
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def get_phone(args, frontend, sentence, merge_sentences, get_tone_ids):
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am_name = args.am[:args.am.rindex('_')]
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tone_ids = None
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if am_name == 'speedyspeech':
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get_tone_ids = True
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if args.lang == 'zh':
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input_ids = frontend.get_input_ids(
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sentence,
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merge_sentences=merge_sentences,
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get_tone_ids=get_tone_ids)
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phone_ids = input_ids["phone_ids"]
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if get_tone_ids:
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tone_ids = input_ids["tone_ids"]
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elif args.lang == 'en':
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input_ids = frontend.get_input_ids(
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sentence, merge_sentences=merge_sentences)
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phone_ids = input_ids["phone_ids"]
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else:
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print("lang should in {'zh', 'en'}!")
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return phone_ids, tone_ids
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@paddle.no_grad()
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# 生成完整的mel
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def gen_mel(args, am_infer_info, part_phone_ids, part_tone_ids):
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# 如果是支持流式的AM模型
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if args.am_support_stream:
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am, am_mu, am_std, am_config = am_infer_info
|
||||
orig_hs, h_masks = am.encoder_infer(part_phone_ids)
|
||||
if args.am_streaming:
|
||||
am_pad = args.am_pad
|
||||
am_block = args.am_block
|
||||
hss = get_chunks(orig_hs, am_block, am_pad, "am")
|
||||
chunk_num = len(hss)
|
||||
mel_list = []
|
||||
for i, hs in enumerate(hss):
|
||||
before_outs, _ = am.decoder(hs)
|
||||
after_outs = before_outs + am.postnet(
|
||||
before_outs.transpose((0, 2, 1))).transpose((0, 2, 1))
|
||||
normalized_mel = after_outs[0]
|
||||
sub_mel = denorm(normalized_mel, am_mu, am_std)
|
||||
# clip output part of pad
|
||||
if i == 0:
|
||||
sub_mel = sub_mel[:-am_pad]
|
||||
elif i == chunk_num - 1:
|
||||
# 最后一块的右侧一定没有 pad 够
|
||||
sub_mel = sub_mel[am_pad:]
|
||||
else:
|
||||
# 倒数几块的右侧也可能没有 pad 够
|
||||
sub_mel = sub_mel[am_pad:(am_block + am_pad) -
|
||||
sub_mel.shape[0]]
|
||||
mel_list.append(sub_mel)
|
||||
mel = paddle.concat(mel_list, axis=0)
|
||||
|
||||
else:
|
||||
orig_hs, h_masks = am.encoder_infer(part_phone_ids)
|
||||
before_outs, _ = am.decoder(orig_hs)
|
||||
after_outs = before_outs + am.postnet(
|
||||
before_outs.transpose((0, 2, 1))).transpose((0, 2, 1))
|
||||
normalized_mel = after_outs[0]
|
||||
mel = denorm(normalized_mel, am_mu, am_std)
|
||||
|
||||
else:
|
||||
am_inference, am_name, am_dataset, am_config = am_infer_info
|
||||
# acoustic model
|
||||
if am_name == 'fastspeech2':
|
||||
# multi speaker
|
||||
if am_dataset in {"aishell3", "vctk"}:
|
||||
spk_id = paddle.to_tensor(args.spk_id)
|
||||
mel = am_inference(part_phone_ids, spk_id)
|
||||
else:
|
||||
mel = am_inference(part_phone_ids)
|
||||
elif am_name == 'speedyspeech':
|
||||
part_tone_ids = tone_ids[i]
|
||||
if am_dataset in {"aishell3", "vctk"}:
|
||||
spk_id = paddle.to_tensor(args.spk_id)
|
||||
mel = am_inference(part_phone_ids, part_tone_ids, spk_id)
|
||||
else:
|
||||
mel = am_inference(part_phone_ids, part_tone_ids)
|
||||
elif am_name == 'tacotron2':
|
||||
mel = am_inference(part_phone_ids)
|
||||
|
||||
return mel
|
||||
|
||||
|
||||
@paddle.no_grad()
|
||||
def stream_voc_infer(args, voc_infer_info, mel_len):
|
||||
global mel_streaming
|
||||
global stream_first_time
|
||||
global wav_streaming
|
||||
voc_inference, voc_config = voc_infer_info
|
||||
block = args.voc_block
|
||||
pad = args.voc_pad
|
||||
upsample = voc_config.n_shift
|
||||
wav_list = []
|
||||
flag = 1
|
||||
|
||||
valid_start = 0
|
||||
valid_end = min(valid_start + block, mel_len)
|
||||
actual_start = 0
|
||||
actual_end = min(valid_end + pad, mel_len)
|
||||
mel_chunk = mel_streaming[actual_start:actual_end, :]
|
||||
|
||||
while valid_end <= mel_len:
|
||||
sub_wav = voc_inference(mel_chunk)
|
||||
if flag == 1:
|
||||
stream_first_time = time.time()
|
||||
flag = 0
|
||||
|
||||
# get valid wav
|
||||
start = valid_start - actual_start
|
||||
if valid_end == mel_len:
|
||||
sub_wav = sub_wav[start * upsample:]
|
||||
wav_list.append(sub_wav)
|
||||
break
|
||||
else:
|
||||
end = start + block
|
||||
sub_wav = sub_wav[start * upsample:end * upsample]
|
||||
wav_list.append(sub_wav)
|
||||
|
||||
# generate new mel chunk
|
||||
valid_start = valid_end
|
||||
valid_end = min(valid_start + block, mel_len)
|
||||
if valid_start - pad < 0:
|
||||
actual_start = 0
|
||||
else:
|
||||
actual_start = valid_start - pad
|
||||
actual_end = min(valid_end + pad, mel_len)
|
||||
mel_chunk = mel_streaming[actual_start:actual_end, :]
|
||||
|
||||
wav = paddle.concat(wav_list, axis=0)
|
||||
wav_streaming = wav
|
||||
|
||||
|
||||
@paddle.no_grad()
|
||||
# 非流式AM / 流式AM + 非流式Voc
|
||||
def am_nostream_voc(args, am_infer_info, voc_infer_info, part_phone_ids,
|
||||
part_tone_ids):
|
||||
mel = gen_mel(args, am_infer_info, part_phone_ids, part_tone_ids)
|
||||
am_infer_time = time.time()
|
||||
voc_inference, voc_config = voc_infer_info
|
||||
wav = voc_inference(mel)
|
||||
first_response_time = time.time()
|
||||
final_response_time = first_response_time
|
||||
voc_infer_time = first_response_time
|
||||
|
||||
return am_infer_time, voc_infer_time, first_response_time, final_response_time, wav
|
||||
|
||||
|
||||
@paddle.no_grad()
|
||||
# 非流式AM + 流式Voc
|
||||
def nostream_am_stream_voc(args, am_infer_info, voc_infer_info, part_phone_ids,
|
||||
part_tone_ids):
|
||||
global mel_streaming
|
||||
global stream_first_time
|
||||
global wav_streaming
|
||||
|
||||
mel = gen_mel(args, am_infer_info, part_phone_ids, part_tone_ids)
|
||||
am_infer_time = time.time()
|
||||
|
||||
# voc streaming
|
||||
mel_streaming = mel
|
||||
mel_len = mel.shape[0]
|
||||
stream_voc_infer(args, voc_infer_info, mel_len)
|
||||
first_response_time = stream_first_time
|
||||
wav = wav_streaming
|
||||
final_response_time = time.time()
|
||||
voc_infer_time = final_response_time
|
||||
|
||||
return am_infer_time, voc_infer_time, first_response_time, final_response_time, wav
|
||||
|
||||
|
||||
@paddle.no_grad()
|
||||
# 流式AM + 流式 Voc
|
||||
def stream_am_stream_voc(args, am_infer_info, voc_infer_info, part_phone_ids,
|
||||
part_tone_ids):
|
||||
global mel_streaming
|
||||
global stream_first_time
|
||||
global wav_streaming
|
||||
global voc_stream_st
|
||||
mel_streaming = None
|
||||
flag = 1 #用来表示开启流式voc的线程
|
||||
|
||||
am, am_mu, am_std, am_config = am_infer_info
|
||||
orig_hs, h_masks = am.encoder_infer(part_phone_ids)
|
||||
mel_len = orig_hs.shape[1]
|
||||
am_block = args.am_block
|
||||
am_pad = args.am_pad
|
||||
hss = get_chunks(orig_hs, am_block, am_pad, "am")
|
||||
chunk_num = len(hss)
|
||||
|
||||
for i, hs in enumerate(hss):
|
||||
before_outs, _ = am.decoder(hs)
|
||||
after_outs = before_outs + am.postnet(
|
||||
before_outs.transpose((0, 2, 1))).transpose((0, 2, 1))
|
||||
normalized_mel = after_outs[0]
|
||||
sub_mel = denorm(normalized_mel, am_mu, am_std)
|
||||
# clip output part of pad
|
||||
if i == 0:
|
||||
sub_mel = sub_mel[:-am_pad]
|
||||
mel_streaming = sub_mel
|
||||
elif i == chunk_num - 1:
|
||||
# 最后一块的右侧一定没有 pad 够
|
||||
sub_mel = sub_mel[am_pad:]
|
||||
mel_streaming = paddle.concat([mel_streaming, sub_mel])
|
||||
am_infer_time = time.time()
|
||||
else:
|
||||
# 倒数几块的右侧也可能没有 pad 够
|
||||
sub_mel = sub_mel[am_pad:(am_block + am_pad) - sub_mel.shape[0]]
|
||||
mel_streaming = paddle.concat([mel_streaming, sub_mel])
|
||||
|
||||
if flag and mel_streaming.shape[0] > args.voc_block + args.voc_pad:
|
||||
t = threading.Thread(
|
||||
target=stream_voc_infer, args=(args, voc_infer_info, mel_len, ))
|
||||
t.start()
|
||||
voc_stream_st = time.time()
|
||||
flag = 0
|
||||
|
||||
t.join()
|
||||
final_response_time = time.time()
|
||||
voc_infer_time = final_response_time
|
||||
first_response_time = stream_first_time
|
||||
wav = wav_streaming
|
||||
|
||||
return am_infer_time, voc_infer_time, first_response_time, final_response_time, wav
|
||||
|
||||
|
||||
def try_infer(args, logger, frontend, am_infer_info, voc_infer_info):
|
||||
global sample_rate
|
||||
logger.info(
|
||||
"Before the formal test, we test a few texts to make the inference speed more stable."
|
||||
)
|
||||
if args.lang == 'zh':
|
||||
sentence = "您好，欢迎使用语音合成服务。"
|
||||
if args.lang == 'en':
|
||||
sentence = "Hello and welcome to the speech synthesis service."
|
||||
|
||||
if args.voc_streaming:
|
||||
if args.am_streaming:
|
||||
infer_func = stream_am_stream_voc
|
||||
else:
|
||||
infer_func = nostream_am_stream_voc
|
||||
else:
|
||||
infer_func = am_nostream_voc
|
||||
|
||||
merge_sentences = True
|
||||
get_tone_ids = False
|
||||
for i in range(3): # 推理3次
|
||||
st = time.time()
|
||||
phone_ids, tone_ids = get_phone(args, frontend, sentence,
|
||||
merge_sentences, get_tone_ids)
|
||||
part_phone_ids = phone_ids[0]
|
||||
if tone_ids:
|
||||
part_tone_ids = tone_ids[0]
|
||||
else:
|
||||
part_tone_ids = None
|
||||
|
||||
am_infer_time, voc_infer_time, first_response_time, final_response_time, wav = infer_func(
|
||||
args, am_infer_info, voc_infer_info, part_phone_ids, part_tone_ids)
|
||||
wav = wav.numpy()
|
||||
duration = wav.size / sample_rate
|
||||
logger.info(
|
||||
f"sentence: {sentence}; duration: {duration} s; first response time: {first_response_time - st} s; final response time: {final_response_time - st} s"
|
||||
)
|
||||
|
||||
|
||||
def evaluate(args, logger, frontend, am_infer_info, voc_infer_info):
|
||||
global sample_rate
|
||||
sentences = get_sentences(args)
|
||||
|
||||
output_dir = Path(args.output_dir)
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
get_tone_ids = False
|
||||
merge_sentences = True
|
||||
|
||||
# choose infer function
|
||||
if args.voc_streaming:
|
||||
if args.am_streaming:
|
||||
infer_func = stream_am_stream_voc
|
||||
else:
|
||||
infer_func = nostream_am_stream_voc
|
||||
else:
|
||||
infer_func = am_nostream_voc
|
||||
|
||||
final_up_duration = 0.0
|
||||
sentence_count = 0
|
||||
front_time_list = []
|
||||
am_time_list = []
|
||||
voc_time_list = []
|
||||
first_response_list = []
|
||||
final_response_list = []
|
||||
sentence_length_list = []
|
||||
duration_list = []
|
||||
|
||||
for utt_id, sentence in sentences:
|
||||
# front
|
||||
front_st = time.time()
|
||||
phone_ids, tone_ids = get_phone(args, frontend, sentence,
|
||||
merge_sentences, get_tone_ids)
|
||||
part_phone_ids = phone_ids[0]
|
||||
if tone_ids:
|
||||
part_tone_ids = tone_ids[0]
|
||||
else:
|
||||
part_tone_ids = None
|
||||
front_et = time.time()
|
||||
front_time = front_et - front_st
|
||||
|
||||
am_st = time.time()
|
||||
am_infer_time, voc_infer_time, first_response_time, final_response_time, wav = infer_func(
|
||||
args, am_infer_info, voc_infer_info, part_phone_ids, part_tone_ids)
|
||||
am_time = am_infer_time - am_st
|
||||
if args.voc_streaming and args.am_streaming:
|
||||
voc_time = voc_infer_time - voc_stream_st
|
||||
else:
|
||||
voc_time = voc_infer_time - am_infer_time
|
||||
|
||||
first_response = first_response_time - front_st
|
||||
final_response = final_response_time - front_st
|
||||
|
||||
wav = wav.numpy()
|
||||
duration = wav.size / sample_rate
|
||||
sf.write(
|
||||
str(output_dir / (utt_id + ".wav")), wav, samplerate=sample_rate)
|
||||
print(f"{utt_id} done!")
|
||||
|
||||
sentence_count += 1
|
||||
front_time_list.append(front_time)
|
||||
am_time_list.append(am_time)
|
||||
voc_time_list.append(voc_time)
|
||||
first_response_list.append(first_response)
|
||||
final_response_list.append(final_response)
|
||||
sentence_length_list.append(len(sentence))
|
||||
duration_list.append(duration)
|
||||
|
||||
logger.info(
|
||||
f"uttid: {utt_id}; sentence: '{sentence}'; front time: {front_time} s; am time: {am_time} s; voc time: {voc_time} s; \
|
||||
first response time: {first_response} s; final response time: {final_response} s; audio duration: {duration} s;"
|
||||
)
|
||||
|
||||
if final_response > duration:
|
||||
final_up_duration += 1
|
||||
|
||||
all_time_sum = sum(final_response_list)
|
||||
front_rate = sum(front_time_list) / all_time_sum
|
||||
am_rate = sum(am_time_list) / all_time_sum
|
||||
voc_rate = sum(voc_time_list) / all_time_sum
|
||||
rtf = all_time_sum / sum(duration_list)
|
||||
|
||||
logger.info(
|
||||
f"The length of test text information, test num: {sentence_count}; text num: {sum(sentence_length_list)}; min: {min(sentence_length_list)}; max: {max(sentence_length_list)}; avg: {sum(sentence_length_list)/len(sentence_length_list)}"
|
||||
)
|
||||
logger.info(
|
||||
f"duration information, min: {min(duration_list)}; max: {max(duration_list)}; avg: {sum(duration_list) / len(duration_list)}; sum: {sum(duration_list)}"
|
||||
)
|
||||
logger.info(
|
||||
f"Front time information: min: {min(front_time_list)} s; max: {max(front_time_list)} s; avg: {sum(front_time_list)/len(front_time_list)} s; ratio: {front_rate * 100}%"
|
||||
)
|
||||
logger.info(
|
||||
f"AM time information: min: {min(am_time_list)} s; max: {max(am_time_list)} s; avg: {sum(am_time_list)/len(am_time_list)} s; ratio: {am_rate * 100}%"
|
||||
)
|
||||
logger.info(
|
||||
f"Vocoder time information: min: {min(voc_time_list)} s, max: {max(voc_time_list)} s; avg: {sum(voc_time_list)/len(voc_time_list)} s; ratio: {voc_rate * 100}%"
|
||||
)
|
||||
logger.info(
|
||||
f"first response time information: min: {min(first_response_list)} s; max: {max(first_response_list)} s; avg: {sum(first_response_list)/len(first_response_list)} s"
|
||||
)
|
||||
logger.info(
|
||||
f"final response time information: min: {min(final_response_list)} s; max: {max(final_response_list)} s; avg: {sum(final_response_list)/len(final_response_list)} s"
|
||||
)
|
||||
logger.info(f"RTF is: {rtf}")
|
||||
logger.info(
|
||||
f"The number of final_response is greater than duration is {final_up_duration}, ratio: {final_up_duration / sentence_count}%"
|
||||
)
|
||||
|
||||
|
||||
def parse_args():
|
||||
# parse args and config and redirect to train_sp
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Synthesize with acoustic model & vocoder")
|
||||
# acoustic model
|
||||
parser.add_argument(
|
||||
'--am',
|
||||
type=str,
|
||||
default='fastspeech2_csmsc',
|
||||
choices=[
|
||||
'speedyspeech_csmsc', 'speedyspeech_aishell3', 'fastspeech2_csmsc',
|
||||
'fastspeech2_ljspeech', 'fastspeech2_aishell3', 'fastspeech2_vctk',
|
||||
'tacotron2_csmsc', 'tacotron2_ljspeech'
|
||||
],
|
||||
help='Choose acoustic model type of tts task.')
|
||||
parser.add_argument(
|
||||
'--am_support_stream',
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help='if am model is fastspeech2_csmsc, specify whether it supports streaming'
|
||||
)
|
||||
parser.add_argument(
|
||||
'--am_config',
|
||||
type=str,
|
||||
default=None,
|
||||
help='Config of acoustic model. Use deault config when it is None.')
|
||||
parser.add_argument(
|
||||
'--am_ckpt',
|
||||
type=str,
|
||||
default=None,
|
||||
help='Checkpoint file of acoustic model.')
|
||||
parser.add_argument(
|
||||
"--am_stat",
|
||||
type=str,
|
||||
default=None,
|
||||
help="mean and standard deviation used to normalize spectrogram when training acoustic model."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--phones_dict", type=str, default=None, help="phone vocabulary file.")
|
||||
parser.add_argument(
|
||||
"--tones_dict", type=str, default=None, help="tone vocabulary file.")
|
||||
parser.add_argument(
|
||||
"--speaker_dict", type=str, default=None, help="speaker id map file.")
|
||||
parser.add_argument(
|
||||
'--spk_id',
|
||||
type=int,
|
||||
default=0,
|
||||
help='spk id for multi speaker acoustic model')
|
||||
# vocoder
|
||||
parser.add_argument(
|
||||
'--voc',
|
||||
type=str,
|
||||
default='mb_melgan_csmsc',
|
||||
choices=[
|
||||
'pwgan_csmsc', 'pwgan_ljspeech', 'pwgan_aishell3', 'pwgan_vctk',
|
||||
'mb_melgan_csmsc', 'style_melgan_csmsc', 'hifigan_csmsc',
|
||||
'wavernn_csmsc'
|
||||
],
|
||||
help='Choose vocoder type of tts task.')
|
||||
parser.add_argument(
|
||||
'--voc_config',
|
||||
type=str,
|
||||
default=None,
|
||||
help='Config of voc. Use deault config when it is None.')
|
||||
parser.add_argument(
|
||||
'--voc_ckpt', type=str, default=None, help='Checkpoint file of voc.')
|
||||
parser.add_argument(
|
||||
"--voc_stat",
|
||||
type=str,
|
||||
default=None,
|
||||
help="mean and standard deviation used to normalize spectrogram when training voc."
|
||||
)
|
||||
# other
|
||||
parser.add_argument(
|
||||
'--lang',
|
||||
type=str,
|
||||
default='zh',
|
||||
choices=['zh', 'en'],
|
||||
help='Choose model language. zh or en')
|
||||
|
||||
parser.add_argument(
|
||||
"--device", type=str, default='cpu', help="set cpu or gpu:id")
|
||||
|
||||
parser.add_argument(
|
||||
"--text",
|
||||
type=str,
|
||||
default="./csmsc_test.txt",
|
||||
help="text to synthesize, a 'utt_id sentence' pair per line.")
|
||||
parser.add_argument("--output_dir", type=str, help="output dir.")
|
||||
parser.add_argument(
|
||||
"--log_file", type=str, default="result.log", help="log file.")
|
||||
|
||||
parser.add_argument(
|
||||
"--am_streaming",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="whether use streaming acoustic model")
|
||||
|
||||
parser.add_argument("--am_pad", type=int, default=12, help="am pad size.")
|
||||
|
||||
parser.add_argument(
|
||||
"--am_block", type=int, default=42, help="am block size.")
|
||||
|
||||
parser.add_argument(
|
||||
"--voc_streaming",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="whether use streaming vocoder model")
|
||||
|
||||
parser.add_argument("--voc_pad", type=int, default=14, help="voc pad size.")
|
||||
|
||||
parser.add_argument(
|
||||
"--voc_block", type=int, default=14, help="voc block size.")
|
||||
|
||||
args = parser.parse_args()
|
||||
return args
|
||||
|
||||
|
||||
def main():
|
||||
args = parse_args()
|
||||
paddle.set_device(args.device)
|
||||
if args.am_support_stream:
|
||||
assert (args.am == 'fastspeech2_csmsc')
|
||||
if args.am_streaming:
|
||||
assert (args.am_support_stream and args.am == 'fastspeech2_csmsc')
|
||||
if args.voc_streaming:
|
||||
assert (args.voc == 'mb_melgan_csmsc' or args.voc == 'hifigan_csmsc')
|
||||
|
||||
logger = logging.getLogger()
|
||||
fhandler = logging.FileHandler(filename=args.log_file, mode='w')
|
||||
formatter = logging.Formatter(
|
||||
'%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s'
|
||||
)
|
||||
fhandler.setFormatter(formatter)
|
||||
logger.addHandler(fhandler)
|
||||
logger.setLevel(logging.DEBUG)
|
||||
|
||||
# set basic information
|
||||
logger.info(
|
||||
f"AM: {args.am}; Vocoder: {args.voc}; device: {args.device}; am streaming: {args.am_streaming}; voc streaming: {args.voc_streaming}"
|
||||
)
|
||||
logger.info(
|
||||
f"am pad size: {args.am_pad}; am block size: {args.am_block}; voc pad size: {args.voc_pad}; voc block size: {args.voc_block};"
|
||||
)
|
||||
|
||||
# get information about model
|
||||
frontend, am_infer_info, voc_infer_info = init(args)
|
||||
logger.info(
|
||||
"************************ try infer *********************************")
|
||||
try_infer(args, logger, frontend, am_infer_info, voc_infer_info)
|
||||
logger.info(
|
||||
"************************ normal test *******************************")
|
||||
evaluate(args, logger, frontend, am_infer_info, voc_infer_info)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
Reference in new issue