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Browse files- text/.gitignore +0 -3
- text/G2PWModel/MONOPHONIC_CHARS.txt +0 -0
- text/G2PWModel/POLYPHONIC_CHARS.txt +0 -0
- text/G2PWModel/bopomofo_to_pinyin_wo_tune_dict.json +0 -1
- text/G2PWModel/char_bopomofo_dict.json +0 -0
- text/G2PWModel/config.py +0 -19
- text/G2PWModel/record.log +0 -1005
- text/G2PWModel/version +0 -1
- text/LangSegmenter/__init__.py +0 -1
- text/LangSegmenter/langsegmenter.py +0 -175
- text/__init__.py +0 -28
- text/cantonese.py +0 -222
- text/chinese.py +0 -208
- text/chinese2.py +0 -353
- text/cleaner.py +0 -94
- text/cmudict-fast.rep +0 -0
- text/cmudict.rep +0 -0
- text/en_normalization/expend.py +0 -283
- text/engdict-hot.rep +0 -3
- text/english.py +0 -374
- text/g2pw/__init__.py +0 -1
- text/g2pw/dataset.py +0 -160
- text/g2pw/g2pw.py +0 -159
- text/g2pw/onnx_api.py +0 -293
- text/g2pw/polyphonic-fix.rep +0 -0
- text/g2pw/polyphonic.rep +0 -53
- text/g2pw/utils.py +0 -143
- text/ja_userdic/userdict.md5 +0 -1
- text/japanese.py +0 -276
- text/korean.py +0 -337
- text/opencpop-strict.txt +0 -429
- text/symbols.py +0 -399
- text/symbols2.py +0 -797
- text/tone_sandhi.py +0 -778
- text/zh_normalization/README.md +0 -16
- text/zh_normalization/__init__.py +0 -14
- text/zh_normalization/char_convert.py +0 -44
- text/zh_normalization/chronology.py +0 -139
- text/zh_normalization/constants.py +0 -62
- text/zh_normalization/num.py +0 -317
- text/zh_normalization/phonecode.py +0 -59
- text/zh_normalization/quantifier.py +0 -63
- text/zh_normalization/text_normlization.py +0 -172
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{"ㄌㄧㄥ": "ling", "ㄩㄢ": "yuan", "ㄒㄧㄥ": "xing", "ㄑㄧㄡ": "qiu", "ㄊㄧㄢ": "tian", "ㄎㄨㄚ": "kua", "ㄨ": "wu", "ㄧㄣ": "yin", "ㄧ": "yi", "ㄒㄧㄝ": "xie", "ㄔㄡ": "chou", "ㄋㄨㄛ": "nuo", "ㄉㄢ": "dan", "ㄒㄩ": "xu", "ㄒㄩㄥ": "xiong", "ㄌㄧㄡ": "liu", "ㄌㄧㄣ": "lin", "ㄒㄧㄤ": "xiang", "ㄩㄥ": "yong", "ㄒㄧㄣ": "xin", "ㄓㄣ": "zhen", "ㄉㄞ": "dai", "ㄆㄢ": "pan", "ㄖㄨ": "ru", "ㄇㄚ": "ma", "ㄑㄧㄢ": "qian", "ㄘ": "ci", "ㄓㄨㄥ": "zhong", "ㄋㄟ": "nei", "ㄔㄥ": "cheng", "ㄈㄥ": "feng", "ㄓㄨㄛ": "zhuo", "ㄈㄤ": "fang", "ㄠ": "ao", "ㄗㄨㄛ": "zuo", "ㄓㄡ": "zhou", "ㄉㄨㄥ": "dong", "ㄙㄨ": "su", "ㄑㄩㄥ": "qiong", "ㄎㄨㄤ": "kuang", "ㄨㄤ": "wang", "ㄌㄟ": "lei", "ㄋㄠ": "nao", "ㄓㄨ": "zhu", "ㄕㄨ": "shu", "ㄕㄣ": "shen", "ㄐㄧㄝ": "jie", "ㄉㄧㄝ": "die", "ㄔ": "chi", "ㄌㄨㄥ": "long", "ㄧㄥ": "ying", "ㄅㄥ": "beng", "ㄌㄢ": "lan", "ㄇㄧㄠ": "miao", "ㄌㄧ": "li", "ㄐㄧ": "ji", "ㄩ": "yu", "ㄌㄨㄛ": "luo", "ㄔㄞ": "chai", "ㄏㄨㄣ": "hun", "ㄏㄨㄟ": "hui", "ㄖㄠ": "rao", "ㄏㄢ": "han", "ㄒㄧ": "xi", "ㄊㄞ": "tai", "ㄧㄠ": "yao", "ㄐㄩㄣ": "jun", "ㄌㄩㄝ": "lve", "ㄊㄤ": "tang", "ㄓㄠ": "zhao", "ㄓㄞ": "zhai", "ㄓㄚ": "zha", "ㄦ": "er", "ㄖㄢ": "ran", "ㄑㄧ": "qi", "ㄙㄜ": "se", "ㄙ": "si", "ㄙㄚ": "sa", "ㄎㄨㄟ": "kui", "ㄆㄨ": "pu", "ㄊㄚ": "ta", "ㄉㄨ": "du", "ㄊㄨ": "tu", "ㄧㄤ": "yang", "ㄡ": "ou", "ㄇㄧㄢ": "mian", "ㄨㄣ": "wen", "ㄉㄧㄠ": "diao", "ㄇㄧㄝ": "mie", "ㄨㄚ": "wa", "ㄋㄧㄠ": "niao", "ㄧㄡ": "you", "ㄔㄜ": "che", "ㄑㄩㄢ": "quan", "ㄘㄞ": "cai", "ㄌㄧㄤ": "liang", "ㄍㄨ": "gu", "ㄇㄠ": "mao", "ㄍㄨㄚ": "gua", "ㄙㄨㄟ": "sui", "ㄇㄢ": "man", "ㄕ": "shi", "ㄎㄡ": "kou", "ㄊㄧㄥ": "ting", "ㄅㄧㄥ": "bing", "ㄏㄨㄛ": "huo", "ㄍㄨㄥ": "gong", "ㄑㄧㄣ": "qin", "ㄐㄩㄥ": "jiong", "ㄌㄨ": "lu", "ㄋㄢ": "nan", "ㄅㄧ": "bi", "ㄑㄧㄚ": "qia", "ㄆㄧ": "pi", "ㄉㄧㄢ": "dian", "ㄈㄨ": "fu", "ㄍㄜ": "ge", "ㄅㄞ": "bai", "ㄍㄢ": "gan", "ㄒㄩㄢ": "xuan", "ㄌㄤ": "lang", "ㄕㄜ": "she", "ㄏㄨㄚ": "hua", "ㄊㄡ": "tou", "ㄆㄧㄢ": "pian", "ㄉㄧ": "di", "ㄖㄨㄢ": "ruan", "ㄜ": "e", "ㄑㄧㄝ": "qie", "ㄉㄡ": "dou", "ㄖㄨㄟ": "rui", "ㄘㄨㄟ": "cui", "ㄐㄧㄢ": "jian", "ㄔㄨㄥ": "chong", "ㄉㄥ": "deng", "ㄐㄩㄝ": "jue", "ㄒㄩㄝ": "xue", "ㄒㄧㄠ": "xiao", "ㄗㄢ": "zan", "ㄓㄢ": "zhan", "ㄗㄡ": "zou", "ㄘㄡ": "cou", "ㄔㄨㄚ": "chua", "ㄈㄟ": "fei", "ㄅㄟ": "bei", "ㄔㄨ": "chu", "ㄅㄚ": "ba", "ㄎㄨㄞ": "kuai", "ㄒㄧㄚ": "xia", "ㄏㄜ": "he", "ㄅㄧㄝ": "bie", "ㄌㄩ": "lv", "ㄙㄨㄢ": "suan", "ㄏㄥ": "heng", "ㄍㄨㄟ": "gui", "ㄌㄡ": "lou", "ㄊㄧ": "ti", "ㄌㄜ": "le", "ㄙㄨㄣ": "sun", "ㄒㄧㄢ": "xian", "ㄑㄩㄝ": "que", "ㄓ": "zhi", "ㄐㄧㄚ": "jia", "ㄏㄨ": "hu", "ㄌㄚ": "la", "ㄎㄜ": "ke", "ㄞ": "ai", "ㄨㄟ": "wei", "ㄏㄨㄢ": "huan", "ㄕㄨㄚ": "shua", "ㄕㄨㄤ": "shuang", "ㄍㄞ": "gai", "ㄏㄞ": "hai", "ㄧㄢ": "yan", "ㄈㄢ": "fan", "ㄆㄤ": "pang", "ㄙㄨㄥ": "song", "ㄋㄜ": "ne", "ㄔㄣ": "chen", "ㄍㄨㄛ": "guo", "ㄣ": "en", "ㄋㄍ": "ng", "ㄆㄚ": "pa", "ㄈㄚ": "fa", "ㄆㄡ": "pou", "ㄏㄡ": "hou", "ㄑㄩ": "qu", "ㄒㄩㄣ": "xun", "ㄋㄧㄝ": "nie", "ㄏㄨㄥ": "hong", "ㄊㄨㄣ": "tun", "ㄨㄞ": "wai", "ㄕㄡ": "shou", "ㄧㄝ": "ye", "ㄐㄩ": "ju", "ㄙㄡ": "sou", "ㄌㄨㄣ": "lun", "ㄋㄧㄚ": "nia", "ㄆㄣ": "pen", "ㄈㄣ": "fen", "ㄔㄨㄣ": "chun", "ㄋㄧㄡ": "niu", "ㄖㄡ": "rou", "ㄉㄨㄛ": "duo", "ㄗㄜ": "ze", "ㄕㄥ": "sheng", "ㄎㄨ": "ku", "ㄧㄚ": "ya", "ㄓㄨㄟ": "zhui", "ㄍㄡ": "gou", "ㄅㄛ": "bo", "ㄋㄚ": "na", "ㄒㄧㄡ": "xiu", "ㄘㄨ": "cu", "ㄎㄨㄛ": "kuo", "ㄌㄠ": "lao", "ㄘㄨㄥ": "cong", "ㄉㄚ": "da", "ㄆㄛ": "po", "ㄙㄞ": "sai", "ㄌㄥ": "leng", "ㄖㄨㄥ": "rong", "ㄋㄧ": "ni", "ㄆㄠ": "pao", "ㄎㄢ": "kan", "ㄨㄥ": "weng", "ㄨㄢ": "wan", "ㄏㄠ": "hao", "ㄐㄧㄥ": "jing", "ㄊㄢ": "tan", "ㄅㄨ": "bu", "ㄗㄤ": "zang", "ㄐㄧㄡ": "jiu", "ㄇㄟ": "mei", "ㄇㄨ": "mu", "ㄉㄨㄟ": "dui", "ㄅㄤ": "bang", "ㄅㄠ": "bao", "ㄔㄤ": "chang", "ㄓㄤ": "zhang", "ㄗㄨㄥ": "zong", "ㄍㄨㄣ": "gun", "ㄌㄧㄠ": "liao", "ㄔㄢ": "chan", "ㄓㄜ": "zhe", "ㄇㄥ": "meng", "ㄑㄧㄠ": "qiao", "ㄋㄤ": "nang", "ㄩㄣ": "yun", "ㄎㄞ": "kai", "ㄍㄠ": "gao", "ㄊㄠ": "tao", "ㄕㄢ": "shan", "ㄌㄞ": "lai", "ㄅㄢ": "ban", "ㄎㄨㄥ": "kong", "ㄔㄨㄛ": "chuo", "ㄋㄨ": "nu", "ㄆㄟ": "pei", "ㄆㄥ": "peng", "ㄘㄢ": "can", "ㄙㄨㄛ": "suo", "ㄊㄨㄥ": "tong", "ㄑㄧㄤ": "qiang", "ㄙㄠ": "sao", "ㄓㄨㄢ": "zhuan", "ㄢ": "an", "ㄔㄚ": "cha", "ㄕㄚ": "sha", "ㄌㄧㄢ": "lian", "ㄇㄧ": "mi", "ㄋㄡ": "nou", "ㄘㄠ": "cao", "ㄙㄣ": "sen", "ㄋㄣ": "nen", "ㄋㄧㄢ": "nian", "ㄇㄞ": "mai", "ㄩㄝ": "yue", "ㄋㄞ": "nai", "ㄏㄨㄞ": "huai", "ㄗ": "zi", "ㄌㄨㄢ": "luan", "ㄉ��ㄥ": "ding", "ㄇㄤ": "mang", "ㄋㄧㄥ": "ning", "ㄇㄧㄥ": "ming", "ㄗㄨㄟ": "zui", "ㄎㄤ": "kang", "ㄉㄜ": "de", "ㄅㄧㄢ": "bian", "ㄐㄧㄣ": "jin", "ㄔㄨㄟ": "chui", "ㄊㄨㄟ": "tui", "ㄗㄚ": "za", "ㄘㄣ": "cen", "ㄇㄧㄣ": "min", "ㄏㄨㄤ": "huang", "ㄗㄨ": "zu", "ㄘㄨㄛ": "cuo", "ㄊㄨㄛ": "tuo", "ㄑㄩㄣ": "qun", "ㄅㄧㄣ": "bin", "ㄊㄧㄠ": "tiao", "ㄍㄤ": "gang", "ㄉㄨㄢ": "duan", "ㄅㄧㄠ": "biao", "ㄉㄠ": "dao", "ㄖㄨㄣ": "run", "ㄐㄧㄠ": "jiao", "ㄨㄛ": "wo", "ㄘㄨㄢ": "cuan", "ㄖㄣ": "ren", "ㄇㄣ": "men", "ㄓㄨㄣ": "zhun", "ㄎㄨㄣ": "kun", "ㄔㄨㄤ": "chuang", "ㄗㄠ": "zao", "ㄓㄥ": "zheng", "ㄆㄧㄣ": "pin", "ㄅㄣ": "ben", "ㄐㄧㄤ": "jiang", "ㄐㄩㄢ": "juan", "ㄘㄥ": "ceng", "ㄏㄤ": "hang", "ㄋㄧㄣ": "nin", "ㄌㄧㄝ": "lie", "ㄍㄨㄤ": "guang", "ㄙㄢ": "san", "ㄊㄜ": "te", "ㄕㄨㄣ": "shun", "ㄕㄨㄟ": "shui", "ㄔㄠ": "chao", "ㄘㄜ": "ce", "ㄍㄨㄞ": "guai", "ㄎㄥ": "keng", "ㄕㄞ": "shai", "ㄉㄣ": "den", "ㄊㄨㄢ": "tuan", "ㄆㄧㄠ": "piao", "ㄑㄧㄥ": "qing", "ㄍㄥ": "geng", "ㄔㄨㄞ": "chuai", "ㄕㄠ": "shao", "ㄍㄣ": "gen", "ㄋㄨㄢ": "nuan", "ㄖㄥ": "reng", "ㄇㄡ": "mou", "ㄆㄞ": "pai", "ㄤ": "ang", "ㄎㄚ": "ka", "ㄍㄨㄢ": "guan", "ㄕㄨㄛ": "shuo", "ㄏㄣ": "hen", "ㄔㄨㄢ": "chuan", "ㄎㄨㄢ": "kuan", "ㄏㄟ": "hei", "ㄇㄛ": "mo", "ㄗㄞ": "zai", "ㄋㄥ": "neng", "ㄕㄨㄞ": "shuai", "ㄖㄜ": "re", "ㄋㄩ": "nv", "ㄆㄧㄥ": "ping", "ㄘㄤ": "cang", "ㄋㄨㄥ": "nong", "ㄎㄠ": "kao", "ㄗㄨㄢ": "zuan", "ㄎㄣ": "ken", "ㄍㄚ": "ga", "ㄗㄣ": "zen", "ㄉㄤ": "dang", "ㄗㄥ": "zeng", "ㄉㄨㄣ": "dun", "ㄘㄚ": "ca", "ㄖㄤ": "rang", "ㄘㄨㄣ": "cun", "ㄖㄨㄛ": "ruo", "ㄊㄧㄝ": "tie", "ㄊㄥ": "teng", "ㄙㄥ": "seng", "ㄖ": "ri", "ㄗㄨㄣ": "zun", "ㄋㄧㄤ": "niang", "ㄋㄩㄝ": "nve", "ㄙㄤ": "sang", "ㄓㄨㄤ": "zhuang", "ㄕㄤ": "shang", "ㄆㄧㄝ": "pie", "ㄕㄨㄢ": "shuan", "ㄈㄡ": "fou", "ㄉㄧㄡ": "diu", "ㄇㄜ": "me", "ㄈㄛ": "fo", "ㄌㄧㄚ": "lia", "ㄎㄟ": "kei", "ㄏㄚ": "ha", "ㄚ": "a", "ㄌㄛ": "lo", "ㄧㄛ": "yo", "ㄛ": "o", "ㄏㄋㄍ": "hng", "ㄋ": "n", "ㄌㄣ": "len", "ㄉㄧㄚ": "dia", "ㄇㄧㄡ": "miu", "ㄉㄟ": "dei", "ㄏㄇ": "hm", "ㄋㄨㄣ": "nun", "ㄓㄨㄞ": "zhuai", "ㄊㄟ": "tei", "ㄗㄟ": "zei", "ㄓㄨㄚ": "zhua", "ㄖㄨㄚ": "rua", "ê": "ê", "ㄟ": "ei", "ㄍㄟ": "gei", "ㄈㄧㄠ": "fiao", "ㄕㄟ": "shei", "ㄓㄟ": "zhei", "ㄥ": "eng", "ㄘㄟ": "cei", "ㄉㄧㄣ": "din", "ㄅㄧㄤ": "biang", "ㄧㄞ": "yai"}
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manual_seed = 1313
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model_source = 'bert-base-chinese'
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window_size = 32
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num_workers = 2
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use_mask = True
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use_conditional = True
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param_conditional = {
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'bias': True,
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'char-linear': True,
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batch_size = 256
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use_pos = True
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device: cuda
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now: 2022-04-01 22:13:18.349604
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[200] train_loss=0.289519 valid_loss=0.102661 valid_pos_acc=0.924619 valid_acc=0.97596 / 0.703958 / 0.586078 best_acc=0.97596
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now: 2022-04-01 22:25:27.330080
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[400] train_loss=0.089245 valid_loss=0.0703849 valid_pos_acc=0.942315 valid_acc=0.984227 / 0.747566 / 0.616754 best_acc=0.984227
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now: 2022-04-01 22:37:16.857336
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[600] train_loss=0.0663516 valid_loss=0.0597114 valid_pos_acc=0.946489 valid_acc=0.98734 / 0.77479 / 0.638442 best_acc=0.98734
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now: 2022-04-01 22:49:06.182095
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[800] train_loss=0.0559394 valid_loss=0.0535268 valid_pos_acc=0.948245 valid_acc=0.988928 / 0.774415 / 0.643435 best_acc=0.988928
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now: 2022-04-01 23:00:55.371920
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[1000] train_loss=0.0497098 valid_loss=0.0490104 valid_pos_acc=0.954161 valid_acc=0.989486 / 0.796356 / 0.664386 best_acc=0.989486
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now: 2022-04-01 23:12:49.781716
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[1200] train_loss=0.0462926 valid_loss=0.0466889 valid_pos_acc=0.954634 valid_acc=0.989913 / 0.802885 / 0.673908 best_acc=0.989913
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now: 2022-04-01 23:24:43.685062
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[1400] train_loss=0.0433836 valid_loss=0.0451725 valid_pos_acc=0.956761 valid_acc=0.99049 / 0.805024 / 0.674369 best_acc=0.99049
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now: 2022-04-01 23:36:46.100963
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[1600] train_loss=0.0404561 valid_loss=0.0436914 valid_pos_acc=0.957201 valid_acc=0.991022 / 0.811412 / 0.679481 best_acc=0.991022
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now: 2022-04-01 23:48:48.583240
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[1800] train_loss=0.040905 valid_loss=0.0412648 valid_pos_acc=0.958418 valid_acc=0.991332 / 0.815194 / 0.681627 best_acc=0.991332
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now: 2022-04-02 00:00:42.282365
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[2000] train_loss=0.0384612 valid_loss=0.0402427 valid_pos_acc=0.959796 valid_acc=0.991534 / 0.819666 / 0.689516 best_acc=0.991534
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now: 2022-04-02 00:12:52.902834
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| 23 |
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[2200] train_loss=0.0373539 valid_loss=0.0410455 valid_pos_acc=0.961692 valid_acc=0.991425 / 0.828402 / 0.696595 best_acc=0.991534
|
| 24 |
-
now: 2022-04-02 00:25:06.851427
|
| 25 |
-
[2400] train_loss=0.0367612 valid_loss=0.039694 valid_pos_acc=0.960926 valid_acc=0.991823 / 0.830391 / 0.700222 best_acc=0.991823
|
| 26 |
-
now: 2022-04-02 00:37:24.156808
|
| 27 |
-
[2600] train_loss=0.0386493 valid_loss=0.0377683 valid_pos_acc=0.962183 valid_acc=0.992202 / 0.832219 / 0.707156 best_acc=0.992202
|
| 28 |
-
now: 2022-04-02 00:49:37.943513
|
| 29 |
-
[2800] train_loss=0.0356553 valid_loss=0.0381159 valid_pos_acc=0.962729 valid_acc=0.992061 / 0.835112 / 0.707941 best_acc=0.992202
|
| 30 |
-
now: 2022-04-02 01:01:43.672504
|
| 31 |
-
[3000] train_loss=0.0338178 valid_loss=0.0386144 valid_pos_acc=0.962419 valid_acc=0.992322 / 0.835546 / 0.710556 best_acc=0.992322
|
| 32 |
-
now: 2022-04-02 01:13:56.991606
|
| 33 |
-
[3200] train_loss=0.0335683 valid_loss=0.0381786 valid_pos_acc=0.962755 valid_acc=0.992233 / 0.838008 / 0.713975 best_acc=0.992322
|
| 34 |
-
now: 2022-04-02 01:26:13.830261
|
| 35 |
-
[3400] train_loss=0.0316981 valid_loss=0.0373759 valid_pos_acc=0.963524 valid_acc=0.992309 / 0.843253 / 0.718974 best_acc=0.992322
|
| 36 |
-
now: 2022-04-02 01:38:08.308362
|
| 37 |
-
[3600] train_loss=0.0350782 valid_loss=0.0376615 valid_pos_acc=0.96404 valid_acc=0.992259 / 0.84979 / 0.725183 best_acc=0.992322
|
| 38 |
-
now: 2022-04-02 01:49:59.416353
|
| 39 |
-
[3800] train_loss=0.0321498 valid_loss=0.0367548 valid_pos_acc=0.964441 valid_acc=0.992801 / 0.850152 / 0.722988 best_acc=0.992801
|
| 40 |
-
now: 2022-04-02 02:02:09.238893
|
| 41 |
-
[4000] train_loss=0.0331685 valid_loss=0.0369892 valid_pos_acc=0.963339 valid_acc=0.992777 / 0.859395 / 0.730708 best_acc=0.992801
|
| 42 |
-
now: 2022-04-02 02:14:27.957159
|
| 43 |
-
[4200] train_loss=0.0317164 valid_loss=0.0350153 valid_pos_acc=0.965784 valid_acc=0.992656 / 0.853549 / 0.727562 best_acc=0.992801
|
| 44 |
-
now: 2022-04-02 02:26:43.092476
|
| 45 |
-
[4400] train_loss=0.0324034 valid_loss=0.0346509 valid_pos_acc=0.965843 valid_acc=0.992981 / 0.853694 / 0.73043 best_acc=0.992981
|
| 46 |
-
now: 2022-04-02 02:39:22.465030
|
| 47 |
-
[4600] train_loss=0.0298959 valid_loss=0.0356152 valid_pos_acc=0.965606 valid_acc=0.993022 / 0.855494 / 0.728954 best_acc=0.993022
|
| 48 |
-
now: 2022-04-02 02:51:53.210107
|
| 49 |
-
[4800] train_loss=0.0310447 valid_loss=0.0355586 valid_pos_acc=0.965446 valid_acc=0.992597 / 0.851145 / 0.728595 best_acc=0.993022
|
| 50 |
-
now: 2022-04-02 03:04:21.463931
|
| 51 |
-
[5000] train_loss=0.031017 valid_loss=0.034331 valid_pos_acc=0.965695 valid_acc=0.992866 / 0.852123 / 0.728928 best_acc=0.993022
|
| 52 |
-
now: 2022-04-02 03:16:32.777183
|
| 53 |
-
[5200] train_loss=0.0312034 valid_loss=0.0349778 valid_pos_acc=0.966472 valid_acc=0.993105 / 0.855114 / 0.733248 best_acc=0.993105
|
| 54 |
-
now: 2022-04-02 03:28:51.440974
|
| 55 |
-
[5400] train_loss=0.0294329 valid_loss=0.0339991 valid_pos_acc=0.966307 valid_acc=0.993109 / 0.852872 / 0.727198 best_acc=0.993109
|
| 56 |
-
now: 2022-04-02 03:41:07.884688
|
| 57 |
-
[5600] train_loss=0.0285982 valid_loss=0.0341394 valid_pos_acc=0.966307 valid_acc=0.993183 / 0.858873 / 0.736458 best_acc=0.993183
|
| 58 |
-
now: 2022-04-02 03:53:43.422479
|
| 59 |
-
[5800] train_loss=0.0283985 valid_loss=0.0325766 valid_pos_acc=0.96683 valid_acc=0.993376 / 0.856761 / 0.738166 best_acc=0.993376
|
| 60 |
-
now: 2022-04-02 04:06:06.964628
|
| 61 |
-
[6000] train_loss=0.0302441 valid_loss=0.0344224 valid_pos_acc=0.966774 valid_acc=0.992838 / 0.85689 / 0.733677 best_acc=0.993376
|
| 62 |
-
now: 2022-04-02 04:18:20.312766
|
| 63 |
-
[6200] train_loss=0.0289215 valid_loss=0.0348225 valid_pos_acc=0.966589 valid_acc=0.993367 / 0.858202 / 0.736723 best_acc=0.993376
|
| 64 |
-
now: 2022-04-02 04:30:36.722397
|
| 65 |
-
[6400] train_loss=0.0294263 valid_loss=0.0329629 valid_pos_acc=0.966854 valid_acc=0.993081 / 0.856632 / 0.7381 best_acc=0.993376
|
| 66 |
-
now: 2022-04-02 04:42:53.493232
|
| 67 |
-
[6600] train_loss=0.0285769 valid_loss=0.0333396 valid_pos_acc=0.967153 valid_acc=0.993547 / 0.865742 / 0.743425 best_acc=0.993547
|
| 68 |
-
now: 2022-04-02 04:55:17.818463
|
| 69 |
-
[6800] train_loss=0.0265485 valid_loss=0.0330653 valid_pos_acc=0.967776 valid_acc=0.993222 / 0.865918 / 0.743298 best_acc=0.993547
|
| 70 |
-
now: 2022-04-02 05:07:36.630349
|
| 71 |
-
[7000] train_loss=0.0284473 valid_loss=0.0320964 valid_pos_acc=0.968023 valid_acc=0.99355 / 0.868261 / 0.748849 best_acc=0.99355
|
| 72 |
-
now: 2022-04-02 05:20:01.434422
|
| 73 |
-
[7200] train_loss=0.0274993 valid_loss=0.0326511 valid_pos_acc=0.9669 valid_acc=0.993816 / 0.868294 / 0.746817 best_acc=0.993816
|
| 74 |
-
now: 2022-04-02 05:32:29.662142
|
| 75 |
-
[7400] train_loss=0.02851 valid_loss=0.0308467 valid_pos_acc=0.968453 valid_acc=0.993858 / 0.863909 / 0.746068 best_acc=0.993858
|
| 76 |
-
now: 2022-04-02 05:44:43.967440
|
| 77 |
-
[7600] train_loss=0.0282732 valid_loss=0.03368 valid_pos_acc=0.967292 valid_acc=0.993014 / 0.86581 / 0.745753 best_acc=0.993858
|
| 78 |
-
now: 2022-04-02 05:56:45.436298
|
| 79 |
-
[7800] train_loss=0.0252737 valid_loss=0.0315786 valid_pos_acc=0.967611 valid_acc=0.993799 / 0.869773 / 0.749114 best_acc=0.993858
|
| 80 |
-
now: 2022-04-02 06:08:51.140922
|
| 81 |
-
[8000] train_loss=0.0280509 valid_loss=0.0328118 valid_pos_acc=0.96732 valid_acc=0.99363 / 0.86537 / 0.74611 best_acc=0.993858
|
| 82 |
-
now: 2022-04-02 06:20:43.247091
|
| 83 |
-
[8200] train_loss=0.028321 valid_loss=0.0308812 valid_pos_acc=0.968106 valid_acc=0.993758 / 0.869684 / 0.751653 best_acc=0.993858
|
| 84 |
-
now: 2022-04-02 06:32:38.603877
|
| 85 |
-
[8400] train_loss=0.0271253 valid_loss=0.0326289 valid_pos_acc=0.968232 valid_acc=0.993426 / 0.869263 / 0.748637 best_acc=0.993858
|
| 86 |
-
now: 2022-04-02 06:44:45.010090
|
| 87 |
-
[8600] train_loss=0.02778 valid_loss=0.0308819 valid_pos_acc=0.968731 valid_acc=0.993693 / 0.87573 / 0.75794 best_acc=0.993858
|
| 88 |
-
now: 2022-04-02 06:56:45.886905
|
| 89 |
-
[8800] train_loss=0.0287492 valid_loss=0.0310371 valid_pos_acc=0.968256 valid_acc=0.993563 / 0.877011 / 0.759391 best_acc=0.993858
|
| 90 |
-
now: 2022-04-02 07:08:52.584840
|
| 91 |
-
[9000] train_loss=0.0281025 valid_loss=0.0297675 valid_pos_acc=0.968566 valid_acc=0.993979 / 0.866884 / 0.750877 best_acc=0.993979
|
| 92 |
-
now: 2022-04-02 07:21:04.827592
|
| 93 |
-
[9200] train_loss=0.026893 valid_loss=0.0310813 valid_pos_acc=0.968965 valid_acc=0.993758 / 0.869433 / 0.752492 best_acc=0.993979
|
| 94 |
-
now: 2022-04-02 07:33:11.165254
|
| 95 |
-
[9400] train_loss=0.0253738 valid_loss=0.0307835 valid_pos_acc=0.969295 valid_acc=0.994046 / 0.878856 / 0.754636 best_acc=0.994046
|
| 96 |
-
now: 2022-04-02 07:45:16.521889
|
| 97 |
-
[9600] train_loss=0.0263703 valid_loss=0.0308493 valid_pos_acc=0.969039 valid_acc=0.993986 / 0.873759 / 0.753114 best_acc=0.994046
|
| 98 |
-
now: 2022-04-02 07:57:19.055032
|
| 99 |
-
[9800] train_loss=0.0258709 valid_loss=0.0304116 valid_pos_acc=0.967514 valid_acc=0.993751 / 0.87442 / 0.760402 best_acc=0.994046
|
| 100 |
-
now: 2022-04-02 08:09:21.455984
|
| 101 |
-
[10000] train_loss=0.0261966 valid_loss=0.0310479 valid_pos_acc=0.968954 valid_acc=0.993786 / 0.879653 / 0.76128 best_acc=0.994046
|
| 102 |
-
now: 2022-04-02 08:21:30.441155
|
| 103 |
-
[10200] train_loss=0.0272568 valid_loss=0.0306756 valid_pos_acc=0.969087 valid_acc=0.993777 / 0.879809 / 0.760943 best_acc=0.994046
|
| 104 |
-
now: 2022-04-02 08:33:36.839764
|
| 105 |
-
[10400] train_loss=0.027559 valid_loss=0.0308756 valid_pos_acc=0.969366 valid_acc=0.993636 / 0.874443 / 0.755992 best_acc=0.994046
|
| 106 |
-
now: 2022-04-02 08:45:39.747008
|
| 107 |
-
[10600] train_loss=0.027269 valid_loss=0.0329513 valid_pos_acc=0.96888 valid_acc=0.992499 / 0.833803 / 0.727518 best_acc=0.994046
|
| 108 |
-
now: 2022-04-02 08:57:40.273311
|
| 109 |
-
[10800] train_loss=0.0255775 valid_loss=0.0318773 valid_pos_acc=0.969314 valid_acc=0.993576 / 0.865286 / 0.745435 best_acc=0.994046
|
| 110 |
-
now: 2022-04-02 09:09:28.232166
|
| 111 |
-
[11000] train_loss=0.027821 valid_loss=0.0324836 valid_pos_acc=0.969581 valid_acc=0.993517 / 0.858519 / 0.741976 best_acc=0.994046
|
| 112 |
-
now: 2022-04-02 09:21:18.956995
|
| 113 |
-
[11200] train_loss=0.0268467 valid_loss=0.0320919 valid_pos_acc=0.968768 valid_acc=0.993515 / 0.859293 / 0.743348 best_acc=0.994046
|
| 114 |
-
now: 2022-04-02 09:33:14.899728
|
| 115 |
-
[11400] train_loss=0.0277983 valid_loss=0.0304641 valid_pos_acc=0.969013 valid_acc=0.993803 / 0.857242 / 0.744822 best_acc=0.994046
|
| 116 |
-
now: 2022-04-02 09:45:20.431378
|
| 117 |
-
[11600] train_loss=0.0278141 valid_loss=0.0303669 valid_pos_acc=0.969312 valid_acc=0.993491 / 0.861739 / 0.751563 best_acc=0.994046
|
| 118 |
-
now: 2022-04-02 09:57:29.453034
|
| 119 |
-
[11800] train_loss=0.0272102 valid_loss=0.030325 valid_pos_acc=0.969045 valid_acc=0.993871 / 0.865131 / 0.753686 best_acc=0.994046
|
| 120 |
-
now: 2022-04-02 10:09:41.097392
|
| 121 |
-
[12000] train_loss=0.0271826 valid_loss=0.0302743 valid_pos_acc=0.969701 valid_acc=0.993645 / 0.865918 / 0.753973 best_acc=0.994046
|
| 122 |
-
now: 2022-04-02 10:21:38.263361
|
| 123 |
-
[12200] train_loss=0.0266099 valid_loss=0.0288773 valid_pos_acc=0.969902 valid_acc=0.994183 / 0.874035 / 0.76018 best_acc=0.994183
|
| 124 |
-
now: 2022-04-02 10:33:44.432773
|
| 125 |
-
[12400] train_loss=0.0252403 valid_loss=0.029718 valid_pos_acc=0.969434 valid_acc=0.994127 / 0.87762 / 0.756971 best_acc=0.994183
|
| 126 |
-
now: 2022-04-02 10:45:51.265489
|
| 127 |
-
[12600] train_loss=0.0247018 valid_loss=0.0312226 valid_pos_acc=0.969588 valid_acc=0.993641 / 0.881627 / 0.759701 best_acc=0.994183
|
| 128 |
-
now: 2022-04-02 10:57:42.967866
|
| 129 |
-
[12800] train_loss=0.0269899 valid_loss=0.0291686 valid_pos_acc=0.969384 valid_acc=0.994157 / 0.883137 / 0.766173 best_acc=0.994183
|
| 130 |
-
now: 2022-04-02 11:09:44.697867
|
| 131 |
-
[13000] train_loss=0.026225 valid_loss=0.0300868 valid_pos_acc=0.969607 valid_acc=0.993756 / 0.881478 / 0.764432 best_acc=0.994183
|
| 132 |
-
now: 2022-04-02 11:21:47.303966
|
| 133 |
-
[13200] train_loss=0.0251707 valid_loss=0.0292528 valid_pos_acc=0.969937 valid_acc=0.994205 / 0.882159 / 0.764326 best_acc=0.994205
|
| 134 |
-
now: 2022-04-02 11:34:07.084506
|
| 135 |
-
[13400] train_loss=0.0256715 valid_loss=0.0294879 valid_pos_acc=0.969972 valid_acc=0.994331 / 0.879444 / 0.763116 best_acc=0.994331
|
| 136 |
-
now: 2022-04-02 11:46:17.499315
|
| 137 |
-
[13600] train_loss=0.0266713 valid_loss=0.0307474 valid_pos_acc=0.968488 valid_acc=0.994092 / 0.88213 / 0.764678 best_acc=0.994331
|
| 138 |
-
now: 2022-04-02 11:58:29.053919
|
| 139 |
-
[13800] train_loss=0.0263307 valid_loss=0.0299171 valid_pos_acc=0.969182 valid_acc=0.994146 / 0.886646 / 0.766393 best_acc=0.994331
|
| 140 |
-
now: 2022-04-02 12:10:42.628035
|
| 141 |
-
[14000] train_loss=0.0254249 valid_loss=0.0291546 valid_pos_acc=0.970087 valid_acc=0.994083 / 0.884958 / 0.766853 best_acc=0.994331
|
| 142 |
-
now: 2022-04-02 12:22:47.459592
|
| 143 |
-
[14200] train_loss=0.0271003 valid_loss=0.0289376 valid_pos_acc=0.969803 valid_acc=0.994263 / 0.882713 / 0.765841 best_acc=0.994331
|
| 144 |
-
now: 2022-04-02 12:34:51.459159
|
| 145 |
-
[14400] train_loss=0.0253207 valid_loss=0.0284943 valid_pos_acc=0.97041 valid_acc=0.994483 / 0.883065 / 0.768558 best_acc=0.994483
|
| 146 |
-
now: 2022-04-02 12:47:03.082143
|
| 147 |
-
[14600] train_loss=0.0256933 valid_loss=0.0275894 valid_pos_acc=0.970781 valid_acc=0.994426 / 0.882073 / 0.768093 best_acc=0.994483
|
| 148 |
-
now: 2022-04-02 12:59:01.736374
|
| 149 |
-
[14800] train_loss=0.025288 valid_loss=0.0290729 valid_pos_acc=0.969898 valid_acc=0.994029 / 0.884465 / 0.764771 best_acc=0.994483
|
| 150 |
-
now: 2022-04-02 13:11:07.383148
|
| 151 |
-
[15000] train_loss=0.0254068 valid_loss=0.0292592 valid_pos_acc=0.970833 valid_acc=0.994096 / 0.888942 / 0.769842 best_acc=0.994483
|
| 152 |
-
now: 2022-04-02 13:23:22.842378
|
| 153 |
-
[15200] train_loss=0.0236412 valid_loss=0.0282784 valid_pos_acc=0.970644 valid_acc=0.994229 / 0.889311 / 0.768718 best_acc=0.994483
|
| 154 |
-
now: 2022-04-02 13:35:29.034906
|
| 155 |
-
[15400] train_loss=0.0243784 valid_loss=0.0292398 valid_pos_acc=0.970883 valid_acc=0.994187 / 0.889313 / 0.771062 best_acc=0.994483
|
| 156 |
-
now: 2022-04-02 13:47:31.205294
|
| 157 |
-
[15600] train_loss=0.0240879 valid_loss=0.0298062 valid_pos_acc=0.97036 valid_acc=0.994057 / 0.885163 / 0.767167 best_acc=0.994483
|
| 158 |
-
now: 2022-04-02 13:59:37.091807
|
| 159 |
-
[15800] train_loss=0.0241428 valid_loss=0.0298697 valid_pos_acc=0.97079 valid_acc=0.994118 / 0.886573 / 0.768139 best_acc=0.994483
|
| 160 |
-
now: 2022-04-02 14:11:45.813137
|
| 161 |
-
[16000] train_loss=0.0245795 valid_loss=0.028206 valid_pos_acc=0.970714 valid_acc=0.994365 / 0.895285 / 0.778247 best_acc=0.994483
|
| 162 |
-
now: 2022-04-02 14:23:31.259816
|
| 163 |
-
[16200] train_loss=0.0259529 valid_loss=0.0295037 valid_pos_acc=0.971532 valid_acc=0.994166 / 0.892761 / 0.773792 best_acc=0.994483
|
| 164 |
-
now: 2022-04-02 14:35:29.710419
|
| 165 |
-
[16400] train_loss=0.0245774 valid_loss=0.0282059 valid_pos_acc=0.970677 valid_acc=0.994159 / 0.892281 / 0.77366 best_acc=0.994483
|
| 166 |
-
now: 2022-04-02 14:47:28.883504
|
| 167 |
-
[16600] train_loss=0.0249353 valid_loss=0.0287864 valid_pos_acc=0.970805 valid_acc=0.994255 / 0.891979 / 0.776495 best_acc=0.994483
|
| 168 |
-
now: 2022-04-02 14:59:36.374751
|
| 169 |
-
[16800] train_loss=0.0266362 valid_loss=0.0283276 valid_pos_acc=0.970768 valid_acc=0.994281 / 0.898043 / 0.780269 best_acc=0.994483
|
| 170 |
-
now: 2022-04-02 15:11:40.341586
|
| 171 |
-
[17000] train_loss=0.0248526 valid_loss=0.0279962 valid_pos_acc=0.970482 valid_acc=0.994411 / 0.897109 / 0.780885 best_acc=0.994483
|
| 172 |
-
now: 2022-04-02 15:23:39.987145
|
| 173 |
-
[17200] train_loss=0.0237728 valid_loss=0.028023 valid_pos_acc=0.971417 valid_acc=0.994322 / 0.888697 / 0.776213 best_acc=0.994483
|
| 174 |
-
now: 2022-04-02 15:35:38.801398
|
| 175 |
-
[17400] train_loss=0.0249057 valid_loss=0.027339 valid_pos_acc=0.971389 valid_acc=0.994159 / 0.881219 / 0.768915 best_acc=0.994483
|
| 176 |
-
now: 2022-04-02 15:47:39.875724
|
| 177 |
-
[17600] train_loss=0.0246854 valid_loss=0.028386 valid_pos_acc=0.970968 valid_acc=0.994448 / 0.891969 / 0.775594 best_acc=0.994483
|
| 178 |
-
now: 2022-04-02 15:59:43.932068
|
| 179 |
-
[17800] train_loss=0.0264608 valid_loss=0.0281136 valid_pos_acc=0.971109 valid_acc=0.994465 / 0.896316 / 0.78033 best_acc=0.994483
|
| 180 |
-
now: 2022-04-02 16:11:42.780407
|
| 181 |
-
[18000] train_loss=0.0226492 valid_loss=0.0282867 valid_pos_acc=0.970959 valid_acc=0.994574 / 0.898661 / 0.782303 best_acc=0.994574
|
| 182 |
-
now: 2022-04-02 16:23:45.328393
|
| 183 |
-
[18200] train_loss=0.0253564 valid_loss=0.0272226 valid_pos_acc=0.971202 valid_acc=0.994485 / 0.894385 / 0.781905 best_acc=0.994574
|
| 184 |
-
now: 2022-04-02 16:35:43.743594
|
| 185 |
-
[18400] train_loss=0.0237427 valid_loss=0.0273525 valid_pos_acc=0.971284 valid_acc=0.994598 / 0.893183 / 0.778385 best_acc=0.994598
|
| 186 |
-
now: 2022-04-02 16:47:51.962569
|
| 187 |
-
[18600] train_loss=0.0226361 valid_loss=0.0275174 valid_pos_acc=0.971801 valid_acc=0.994608 / 0.897236 / 0.783469 best_acc=0.994608
|
| 188 |
-
now: 2022-04-02 17:00:05.072496
|
| 189 |
-
[18800] train_loss=0.0247811 valid_loss=0.0276029 valid_pos_acc=0.971766 valid_acc=0.994591 / 0.898223 / 0.780288 best_acc=0.994608
|
| 190 |
-
now: 2022-04-02 17:12:14.066971
|
| 191 |
-
[19000] train_loss=0.0249346 valid_loss=0.0269959 valid_pos_acc=0.9713 valid_acc=0.994433 / 0.889386 / 0.778011 best_acc=0.994608
|
| 192 |
-
now: 2022-04-02 17:24:25.443387
|
| 193 |
-
[19200] train_loss=0.024029 valid_loss=0.0273701 valid_pos_acc=0.971777 valid_acc=0.994565 / 0.89385 / 0.781098 best_acc=0.994608
|
| 194 |
-
now: 2022-04-02 17:36:21.119407
|
| 195 |
-
[19400] train_loss=0.0221598 valid_loss=0.028189 valid_pos_acc=0.971337 valid_acc=0.99447 / 0.892931 / 0.778729 best_acc=0.994608
|
| 196 |
-
now: 2022-04-02 17:48:26.051306
|
| 197 |
-
[19600] train_loss=0.0232854 valid_loss=0.027458 valid_pos_acc=0.97138 valid_acc=0.994535 / 0.892143 / 0.778963 best_acc=0.994608
|
| 198 |
-
now: 2022-04-02 18:00:41.153532
|
| 199 |
-
[19800] train_loss=0.0246367 valid_loss=0.0277884 valid_pos_acc=0.971415 valid_acc=0.994454 / 0.892544 / 0.777699 best_acc=0.994608
|
| 200 |
-
now: 2022-04-02 18:12:44.656831
|
| 201 |
-
[20000] train_loss=0.0193271 valid_loss=0.0288193 valid_pos_acc=0.97153 valid_acc=0.9945 / 0.89201 / 0.778198 best_acc=0.994608
|
| 202 |
-
now: 2022-04-02 18:24:47.237186
|
| 203 |
-
[20200] train_loss=0.0195292 valid_loss=0.0281468 valid_pos_acc=0.972115 valid_acc=0.99463 / 0.894395 / 0.782171 best_acc=0.99463
|
| 204 |
-
now: 2022-04-02 18:37:12.174319
|
| 205 |
-
[20400] train_loss=0.0194709 valid_loss=0.0272298 valid_pos_acc=0.971836 valid_acc=0.994686 / 0.901333 / 0.789041 best_acc=0.994686
|
| 206 |
-
now: 2022-04-02 18:49:37.572502
|
| 207 |
-
[20600] train_loss=0.0191372 valid_loss=0.0279598 valid_pos_acc=0.971139 valid_acc=0.994476 / 0.898648 / 0.786924 best_acc=0.994686
|
| 208 |
-
now: 2022-04-02 19:01:57.223764
|
| 209 |
-
[20800] train_loss=0.0191731 valid_loss=0.0283044 valid_pos_acc=0.971853 valid_acc=0.994665 / 0.906199 / 0.791619 best_acc=0.994686
|
| 210 |
-
now: 2022-04-02 19:14:03.413253
|
| 211 |
-
[21000] train_loss=0.0206041 valid_loss=0.0264156 valid_pos_acc=0.972094 valid_acc=0.994773 / 0.903901 / 0.792328 best_acc=0.994773
|
| 212 |
-
now: 2022-04-02 19:26:27.975422
|
| 213 |
-
[21200] train_loss=0.016823 valid_loss=0.0271615 valid_pos_acc=0.97184 valid_acc=0.99471 / 0.904068 / 0.790123 best_acc=0.994773
|
| 214 |
-
now: 2022-04-02 19:38:24.145598
|
| 215 |
-
[21400] train_loss=0.0205676 valid_loss=0.0285307 valid_pos_acc=0.971365 valid_acc=0.994502 / 0.899852 / 0.786174 best_acc=0.994773
|
| 216 |
-
now: 2022-04-02 19:50:32.570024
|
| 217 |
-
[21600] train_loss=0.0193456 valid_loss=0.0265744 valid_pos_acc=0.971851 valid_acc=0.994726 / 0.902354 / 0.791664 best_acc=0.994773
|
| 218 |
-
now: 2022-04-02 20:02:43.793933
|
| 219 |
-
[21800] train_loss=0.0202321 valid_loss=0.0266519 valid_pos_acc=0.971712 valid_acc=0.994788 / 0.900679 / 0.791454 best_acc=0.994788
|
| 220 |
-
now: 2022-04-02 20:14:55.874949
|
| 221 |
-
[22000] train_loss=0.0209497 valid_loss=0.0274821 valid_pos_acc=0.970824 valid_acc=0.99463 / 0.890966 / 0.780236 best_acc=0.994788
|
| 222 |
-
now: 2022-04-02 20:26:46.865536
|
| 223 |
-
[22200] train_loss=0.0212923 valid_loss=0.0270931 valid_pos_acc=0.971727 valid_acc=0.994695 / 0.893237 / 0.779557 best_acc=0.994788
|
| 224 |
-
now: 2022-04-02 20:38:52.796788
|
| 225 |
-
[22400] train_loss=0.021577 valid_loss=0.027899 valid_pos_acc=0.971914 valid_acc=0.994771 / 0.90721 / 0.790755 best_acc=0.994788
|
| 226 |
-
now: 2022-04-02 20:50:45.585864
|
| 227 |
-
[22600] train_loss=0.020184 valid_loss=0.0270084 valid_pos_acc=0.971606 valid_acc=0.994723 / 0.901765 / 0.793151 best_acc=0.994788
|
| 228 |
-
now: 2022-04-02 21:02:35.179860
|
| 229 |
-
[22800] train_loss=0.0210601 valid_loss=0.0255871 valid_pos_acc=0.972658 valid_acc=0.994784 / 0.89429 / 0.783486 best_acc=0.994788
|
| 230 |
-
now: 2022-04-02 21:14:32.385194
|
| 231 |
-
[23000] train_loss=0.0184889 valid_loss=0.0267728 valid_pos_acc=0.971968 valid_acc=0.994912 / 0.898744 / 0.787857 best_acc=0.994912
|
| 232 |
-
now: 2022-04-02 21:26:30.460311
|
| 233 |
-
[23200] train_loss=0.0196876 valid_loss=0.027992 valid_pos_acc=0.971883 valid_acc=0.99448 / 0.902701 / 0.789254 best_acc=0.994912
|
| 234 |
-
now: 2022-04-02 21:38:18.919183
|
| 235 |
-
[23400] train_loss=0.0192092 valid_loss=0.027083 valid_pos_acc=0.972339 valid_acc=0.994817 / 0.898885 / 0.789367 best_acc=0.994912
|
| 236 |
-
now: 2022-04-02 21:50:24.318095
|
| 237 |
-
[23600] train_loss=0.0205622 valid_loss=0.0268123 valid_pos_acc=0.972528 valid_acc=0.994867 / 0.898603 / 0.786304 best_acc=0.994912
|
| 238 |
-
now: 2022-04-02 22:02:15.239367
|
| 239 |
-
[23800] train_loss=0.0199384 valid_loss=0.0272734 valid_pos_acc=0.972411 valid_acc=0.99471 / 0.901925 / 0.78745 best_acc=0.994912
|
| 240 |
-
now: 2022-04-02 22:14:18.216068
|
| 241 |
-
[24000] train_loss=0.0178143 valid_loss=0.0279724 valid_pos_acc=0.971799 valid_acc=0.994817 / 0.902843 / 0.788122 best_acc=0.994912
|
| 242 |
-
now: 2022-04-02 22:26:08.869937
|
| 243 |
-
[24200] train_loss=0.0204505 valid_loss=0.0271799 valid_pos_acc=0.971603 valid_acc=0.994628 / 0.901783 / 0.790354 best_acc=0.994912
|
| 244 |
-
now: 2022-04-02 22:38:05.762235
|
| 245 |
-
[24400] train_loss=0.0196403 valid_loss=0.0279875 valid_pos_acc=0.971712 valid_acc=0.994673 / 0.90053 / 0.786413 best_acc=0.994912
|
| 246 |
-
now: 2022-04-02 22:49:58.498158
|
| 247 |
-
[24600] train_loss=0.0196382 valid_loss=0.0282003 valid_pos_acc=0.971235 valid_acc=0.994626 / 0.902809 / 0.787394 best_acc=0.994912
|
| 248 |
-
now: 2022-04-02 23:02:07.775949
|
| 249 |
-
[24800] train_loss=0.0193936 valid_loss=0.0267504 valid_pos_acc=0.972562 valid_acc=0.994665 / 0.902442 / 0.787082 best_acc=0.994912
|
| 250 |
-
now: 2022-04-02 23:14:14.202685
|
| 251 |
-
[25000] train_loss=0.0193612 valid_loss=0.0258062 valid_pos_acc=0.972654 valid_acc=0.994873 / 0.905545 / 0.794271 best_acc=0.994912
|
| 252 |
-
now: 2022-04-02 23:26:09.859008
|
| 253 |
-
[25200] train_loss=0.0190576 valid_loss=0.0271653 valid_pos_acc=0.972484 valid_acc=0.994906 / 0.909854 / 0.793673 best_acc=0.994912
|
| 254 |
-
now: 2022-04-02 23:38:05.448510
|
| 255 |
-
[25400] train_loss=0.0206686 valid_loss=0.0264603 valid_pos_acc=0.972343 valid_acc=0.994934 / 0.908615 / 0.793492 best_acc=0.994934
|
| 256 |
-
now: 2022-04-02 23:50:23.423237
|
| 257 |
-
[25600] train_loss=0.019207 valid_loss=0.0264518 valid_pos_acc=0.972285 valid_acc=0.994947 / 0.909035 / 0.793275 best_acc=0.994947
|
| 258 |
-
now: 2022-04-03 00:02:25.949756
|
| 259 |
-
[25800] train_loss=0.0204011 valid_loss=0.027376 valid_pos_acc=0.972029 valid_acc=0.994463 / 0.903614 / 0.787668 best_acc=0.994947
|
| 260 |
-
now: 2022-04-03 00:14:32.470694
|
| 261 |
-
[26000] train_loss=0.0198477 valid_loss=0.0271728 valid_pos_acc=0.972293 valid_acc=0.994845 / 0.909642 / 0.792003 best_acc=0.994947
|
| 262 |
-
now: 2022-04-03 00:26:30.440395
|
| 263 |
-
[26200] train_loss=0.0182446 valid_loss=0.0269998 valid_pos_acc=0.972797 valid_acc=0.994936 / 0.90766 / 0.791791 best_acc=0.994947
|
| 264 |
-
now: 2022-04-03 00:38:11.226201
|
| 265 |
-
[26400] train_loss=0.0188574 valid_loss=0.0277104 valid_pos_acc=0.971779 valid_acc=0.994897 / 0.91142 / 0.795515 best_acc=0.994947
|
| 266 |
-
now: 2022-04-03 00:50:19.930552
|
| 267 |
-
[26600] train_loss=0.0195086 valid_loss=0.0266913 valid_pos_acc=0.972246 valid_acc=0.994884 / 0.904172 / 0.791556 best_acc=0.994947
|
| 268 |
-
now: 2022-04-03 01:02:40.190107
|
| 269 |
-
[26800] train_loss=0.0204701 valid_loss=0.0262269 valid_pos_acc=0.972677 valid_acc=0.994895 / 0.901927 / 0.792913 best_acc=0.994947
|
| 270 |
-
now: 2022-04-03 01:14:37.242724
|
| 271 |
-
[27000] train_loss=0.0227353 valid_loss=0.0265531 valid_pos_acc=0.972556 valid_acc=0.994843 / 0.907322 / 0.797663 best_acc=0.994947
|
| 272 |
-
now: 2022-04-03 01:26:32.001134
|
| 273 |
-
[27200] train_loss=0.0199549 valid_loss=0.0256493 valid_pos_acc=0.972434 valid_acc=0.994821 / 0.907325 / 0.799249 best_acc=0.994947
|
| 274 |
-
now: 2022-04-03 01:38:35.194802
|
| 275 |
-
[27400] train_loss=0.0178435 valid_loss=0.0273656 valid_pos_acc=0.972109 valid_acc=0.994851 / 0.911329 / 0.795818 best_acc=0.994947
|
| 276 |
-
now: 2022-04-03 01:50:47.426096
|
| 277 |
-
[27600] train_loss=0.0196421 valid_loss=0.0268288 valid_pos_acc=0.972094 valid_acc=0.994532 / 0.90122 / 0.788515 best_acc=0.994947
|
| 278 |
-
now: 2022-04-03 02:02:43.219077
|
| 279 |
-
[27800] train_loss=0.0218845 valid_loss=0.0267693 valid_pos_acc=0.972343 valid_acc=0.994884 / 0.904811 / 0.790976 best_acc=0.994947
|
| 280 |
-
now: 2022-04-03 02:14:42.299297
|
| 281 |
-
[28000] train_loss=0.0201817 valid_loss=0.0273861 valid_pos_acc=0.972345 valid_acc=0.994552 / 0.904774 / 0.792625 best_acc=0.994947
|
| 282 |
-
now: 2022-04-03 02:26:51.221363
|
| 283 |
-
[28200] train_loss=0.0200979 valid_loss=0.026012 valid_pos_acc=0.972452 valid_acc=0.994995 / 0.908827 / 0.797558 best_acc=0.994995
|
| 284 |
-
now: 2022-04-03 02:39:03.091258
|
| 285 |
-
[28400] train_loss=0.0201783 valid_loss=0.0256883 valid_pos_acc=0.97261 valid_acc=0.994864 / 0.907562 / 0.798298 best_acc=0.994995
|
| 286 |
-
now: 2022-04-03 02:51:04.300548
|
| 287 |
-
[28600] train_loss=0.0194111 valid_loss=0.0273932 valid_pos_acc=0.971562 valid_acc=0.994906 / 0.911251 / 0.795228 best_acc=0.994995
|
| 288 |
-
now: 2022-04-03 03:03:10.729523
|
| 289 |
-
[28800] train_loss=0.0223215 valid_loss=0.0264798 valid_pos_acc=0.972521 valid_acc=0.994823 / 0.91054 / 0.798156 best_acc=0.994995
|
| 290 |
-
now: 2022-04-03 03:15:16.733585
|
| 291 |
-
[29000] train_loss=0.0203798 valid_loss=0.026267 valid_pos_acc=0.972539 valid_acc=0.994979 / 0.910358 / 0.797495 best_acc=0.994995
|
| 292 |
-
now: 2022-04-03 03:27:15.662423
|
| 293 |
-
[29200] train_loss=0.0198116 valid_loss=0.0271517 valid_pos_acc=0.972395 valid_acc=0.994804 / 0.90796 / 0.792402 best_acc=0.994995
|
| 294 |
-
now: 2022-04-03 03:39:25.302705
|
| 295 |
-
[29400] train_loss=0.0214404 valid_loss=0.0256727 valid_pos_acc=0.973261 valid_acc=0.994984 / 0.910775 / 0.799215 best_acc=0.994995
|
| 296 |
-
now: 2022-04-03 03:51:35.147315
|
| 297 |
-
[29600] train_loss=0.0187954 valid_loss=0.0264936 valid_pos_acc=0.97286 valid_acc=0.99499 / 0.91241 / 0.795951 best_acc=0.994995
|
| 298 |
-
now: 2022-04-03 04:03:37.804465
|
| 299 |
-
[29800] train_loss=0.0205343 valid_loss=0.0262582 valid_pos_acc=0.972979 valid_acc=0.994997 / 0.910193 / 0.797294 best_acc=0.994997
|
| 300 |
-
now: 2022-04-03 04:15:40.869840
|
| 301 |
-
[30000] train_loss=0.0212577 valid_loss=0.0263549 valid_pos_acc=0.972243 valid_acc=0.994684 / 0.909313 / 0.798057 best_acc=0.994997
|
| 302 |
-
now: 2022-04-03 04:27:42.822407
|
| 303 |
-
[30200] train_loss=0.0183862 valid_loss=0.0254776 valid_pos_acc=0.972784 valid_acc=0.994877 / 0.903545 / 0.791495 best_acc=0.994997
|
| 304 |
-
now: 2022-04-03 04:40:02.431413
|
| 305 |
-
[30400] train_loss=0.0197688 valid_loss=0.0264948 valid_pos_acc=0.973224 valid_acc=0.994997 / 0.911333 / 0.793087 best_acc=0.994997
|
| 306 |
-
now: 2022-04-03 04:52:02.140132
|
| 307 |
-
[30600] train_loss=0.0203299 valid_loss=0.0260468 valid_pos_acc=0.972966 valid_acc=0.994799 / 0.902308 / 0.791264 best_acc=0.994997
|
| 308 |
-
now: 2022-04-03 05:04:02.910278
|
| 309 |
-
[30800] train_loss=0.0216859 valid_loss=0.0270245 valid_pos_acc=0.972929 valid_acc=0.994676 / 0.910333 / 0.796835 best_acc=0.994997
|
| 310 |
-
now: 2022-04-03 05:16:05.153874
|
| 311 |
-
[31000] train_loss=0.0211773 valid_loss=0.0258814 valid_pos_acc=0.972768 valid_acc=0.994916 / 0.907256 / 0.793468 best_acc=0.994997
|
| 312 |
-
now: 2022-04-03 05:28:07.493337
|
| 313 |
-
[31200] train_loss=0.0202802 valid_loss=0.0254245 valid_pos_acc=0.972955 valid_acc=0.995092 / 0.914897 / 0.805234 best_acc=0.995092
|
| 314 |
-
now: 2022-04-03 05:40:12.780431
|
| 315 |
-
[31400] train_loss=0.0194152 valid_loss=0.0259566 valid_pos_acc=0.972664 valid_acc=0.99496 / 0.919699 / 0.808312 best_acc=0.995092
|
| 316 |
-
now: 2022-04-03 05:52:16.611374
|
| 317 |
-
[31600] train_loss=0.019009 valid_loss=0.0260382 valid_pos_acc=0.97263 valid_acc=0.995129 / 0.917311 / 0.804833 best_acc=0.995129
|
| 318 |
-
now: 2022-04-03 06:04:15.437060
|
| 319 |
-
[31800] train_loss=0.0205566 valid_loss=0.0260822 valid_pos_acc=0.973148 valid_acc=0.994979 / 0.919627 / 0.808927 best_acc=0.995129
|
| 320 |
-
now: 2022-04-03 06:16:10.198962
|
| 321 |
-
[32000] train_loss=0.0192955 valid_loss=0.0259151 valid_pos_acc=0.973142 valid_acc=0.995034 / 0.9158 / 0.804333 best_acc=0.995129
|
| 322 |
-
now: 2022-04-03 06:28:05.111366
|
| 323 |
-
[32200] train_loss=0.0204335 valid_loss=0.0255095 valid_pos_acc=0.972736 valid_acc=0.995073 / 0.916156 / 0.810316 best_acc=0.995129
|
| 324 |
-
now: 2022-04-03 06:40:09.460506
|
| 325 |
-
[32400] train_loss=0.0201043 valid_loss=0.0261998 valid_pos_acc=0.972545 valid_acc=0.994929 / 0.91379 / 0.802841 best_acc=0.995129
|
| 326 |
-
now: 2022-04-03 06:52:23.127760
|
| 327 |
-
[32600] train_loss=0.0180875 valid_loss=0.0249739 valid_pos_acc=0.973335 valid_acc=0.994997 / 0.912169 / 0.802927 best_acc=0.995129
|
| 328 |
-
now: 2022-04-03 07:04:31.009479
|
| 329 |
-
[32800] train_loss=0.0198901 valid_loss=0.0254487 valid_pos_acc=0.972621 valid_acc=0.995029 / 0.920603 / 0.80979 best_acc=0.995129
|
| 330 |
-
now: 2022-04-03 07:16:32.128110
|
| 331 |
-
[33000] train_loss=0.0208962 valid_loss=0.0254032 valid_pos_acc=0.972883 valid_acc=0.994979 / 0.909972 / 0.799621 best_acc=0.995129
|
| 332 |
-
now: 2022-04-03 07:28:44.400824
|
| 333 |
-
[33200] train_loss=0.0201999 valid_loss=0.0258948 valid_pos_acc=0.972847 valid_acc=0.994801 / 0.911759 / 0.799804 best_acc=0.995129
|
| 334 |
-
now: 2022-04-03 07:40:49.361680
|
| 335 |
-
[33400] train_loss=0.0217783 valid_loss=0.0256737 valid_pos_acc=0.973255 valid_acc=0.994951 / 0.914217 / 0.800689 best_acc=0.995129
|
| 336 |
-
now: 2022-04-03 07:52:50.822397
|
| 337 |
-
[33600] train_loss=0.0198491 valid_loss=0.0264241 valid_pos_acc=0.972823 valid_acc=0.994986 / 0.912473 / 0.801322 best_acc=0.995129
|
| 338 |
-
now: 2022-04-03 08:04:54.092732
|
| 339 |
-
[33800] train_loss=0.0221377 valid_loss=0.02493 valid_pos_acc=0.972877 valid_acc=0.994938 / 0.915101 / 0.804716 best_acc=0.995129
|
| 340 |
-
now: 2022-04-03 08:16:54.243602
|
| 341 |
-
[34000] train_loss=0.0213205 valid_loss=0.025545 valid_pos_acc=0.972677 valid_acc=0.994979 / 0.915158 / 0.805311 best_acc=0.995129
|
| 342 |
-
now: 2022-04-03 08:29:03.784710
|
| 343 |
-
[34200] train_loss=0.0192532 valid_loss=0.0251619 valid_pos_acc=0.97335 valid_acc=0.995099 / 0.916618 / 0.804815 best_acc=0.995129
|
| 344 |
-
now: 2022-04-03 08:41:15.345717
|
| 345 |
-
[34400] train_loss=0.0219833 valid_loss=0.0255126 valid_pos_acc=0.97335 valid_acc=0.995068 / 0.91454 / 0.801902 best_acc=0.995129
|
| 346 |
-
now: 2022-04-03 08:53:18.026172
|
| 347 |
-
[34600] train_loss=0.02057 valid_loss=0.0257689 valid_pos_acc=0.973476 valid_acc=0.995138 / 0.923171 / 0.810884 best_acc=0.995138
|
| 348 |
-
now: 2022-04-03 09:05:29.405654
|
| 349 |
-
[34800] train_loss=0.0212472 valid_loss=0.0260386 valid_pos_acc=0.973087 valid_acc=0.995238 / 0.919353 / 0.805834 best_acc=0.995238
|
| 350 |
-
now: 2022-04-03 09:17:41.688908
|
| 351 |
-
[35000] train_loss=0.0193925 valid_loss=0.02788 valid_pos_acc=0.972441 valid_acc=0.994587 / 0.915334 / 0.800469 best_acc=0.995238
|
| 352 |
-
now: 2022-04-03 09:29:44.816243
|
| 353 |
-
[35200] train_loss=0.0190577 valid_loss=0.0251073 valid_pos_acc=0.972968 valid_acc=0.995166 / 0.916023 / 0.808521 best_acc=0.995238
|
| 354 |
-
now: 2022-04-03 09:41:43.856892
|
| 355 |
-
[35400] train_loss=0.0225248 valid_loss=0.0244108 valid_pos_acc=0.973092 valid_acc=0.994988 / 0.911331 / 0.806775 best_acc=0.995238
|
| 356 |
-
now: 2022-04-03 09:53:53.841427
|
| 357 |
-
[35600] train_loss=0.0204164 valid_loss=0.0257028 valid_pos_acc=0.972951 valid_acc=0.994999 / 0.913099 / 0.803078 best_acc=0.995238
|
| 358 |
-
now: 2022-04-03 10:05:54.756481
|
| 359 |
-
[35800] train_loss=0.0207206 valid_loss=0.0250318 valid_pos_acc=0.973441 valid_acc=0.995112 / 0.915014 / 0.809688 best_acc=0.995238
|
| 360 |
-
now: 2022-04-03 10:17:52.373071
|
| 361 |
-
[36000] train_loss=0.0210285 valid_loss=0.0264345 valid_pos_acc=0.972836 valid_acc=0.994726 / 0.912637 / 0.802366 best_acc=0.995238
|
| 362 |
-
now: 2022-04-03 10:29:48.836136
|
| 363 |
-
[36200] train_loss=0.0210124 valid_loss=0.0253509 valid_pos_acc=0.972771 valid_acc=0.994962 / 0.908097 / 0.799097 best_acc=0.995238
|
| 364 |
-
now: 2022-04-03 10:41:53.650854
|
| 365 |
-
[36400] train_loss=0.0206329 valid_loss=0.0255921 valid_pos_acc=0.973576 valid_acc=0.995097 / 0.915445 / 0.807018 best_acc=0.995238
|
| 366 |
-
now: 2022-04-03 10:54:04.782468
|
| 367 |
-
[36600] train_loss=0.0190987 valid_loss=0.025047 valid_pos_acc=0.973278 valid_acc=0.99504 / 0.911809 / 0.804772 best_acc=0.995238
|
| 368 |
-
now: 2022-04-03 11:06:14.982105
|
| 369 |
-
[36800] train_loss=0.0193329 valid_loss=0.0255344 valid_pos_acc=0.973205 valid_acc=0.994995 / 0.914842 / 0.810892 best_acc=0.995238
|
| 370 |
-
now: 2022-04-03 11:18:21.542298
|
| 371 |
-
[37000] train_loss=0.019776 valid_loss=0.0257551 valid_pos_acc=0.973228 valid_acc=0.995025 / 0.911626 / 0.801857 best_acc=0.995238
|
| 372 |
-
now: 2022-04-03 11:30:14.909051
|
| 373 |
-
[37200] train_loss=0.0203762 valid_loss=0.0253398 valid_pos_acc=0.973005 valid_acc=0.995255 / 0.91017 / 0.804603 best_acc=0.995255
|
| 374 |
-
now: 2022-04-03 11:42:31.753627
|
| 375 |
-
[37400] train_loss=0.0188329 valid_loss=0.0251868 valid_pos_acc=0.973304 valid_acc=0.995248 / 0.915374 / 0.809239 best_acc=0.995255
|
| 376 |
-
now: 2022-04-03 11:54:33.700994
|
| 377 |
-
[37600] train_loss=0.0183661 valid_loss=0.0254057 valid_pos_acc=0.973443 valid_acc=0.995225 / 0.914564 / 0.805779 best_acc=0.995255
|
| 378 |
-
now: 2022-04-03 12:06:21.313158
|
| 379 |
-
[37800] train_loss=0.0211401 valid_loss=0.0246414 valid_pos_acc=0.973185 valid_acc=0.995164 / 0.914028 / 0.805807 best_acc=0.995255
|
| 380 |
-
now: 2022-04-03 12:18:25.200242
|
| 381 |
-
[38000] train_loss=0.021069 valid_loss=0.0244758 valid_pos_acc=0.973411 valid_acc=0.995285 / 0.914013 / 0.806843 best_acc=0.995285
|
| 382 |
-
now: 2022-04-03 12:30:27.765559
|
| 383 |
-
[38200] train_loss=0.0213957 valid_loss=0.0233822 valid_pos_acc=0.973997 valid_acc=0.995231 / 0.918159 / 0.809317 best_acc=0.995285
|
| 384 |
-
now: 2022-04-03 12:42:34.527640
|
| 385 |
-
[38400] train_loss=0.01998 valid_loss=0.0246681 valid_pos_acc=0.973157 valid_acc=0.995144 / 0.911465 / 0.802431 best_acc=0.995285
|
| 386 |
-
now: 2022-04-03 12:54:35.718367
|
| 387 |
-
[38600] train_loss=0.0219669 valid_loss=0.0265307 valid_pos_acc=0.973142 valid_acc=0.994856 / 0.913512 / 0.802637 best_acc=0.995285
|
| 388 |
-
now: 2022-04-03 13:06:31.081322
|
| 389 |
-
[38800] train_loss=0.0200181 valid_loss=0.0254716 valid_pos_acc=0.972988 valid_acc=0.995194 / 0.918644 / 0.809186 best_acc=0.995285
|
| 390 |
-
now: 2022-04-03 13:18:35.624914
|
| 391 |
-
[39000] train_loss=0.0210553 valid_loss=0.0242162 valid_pos_acc=0.973558 valid_acc=0.995114 / 0.914893 / 0.807304 best_acc=0.995285
|
| 392 |
-
now: 2022-04-03 13:30:40.043785
|
| 393 |
-
[39200] train_loss=0.0183049 valid_loss=0.0254598 valid_pos_acc=0.973745 valid_acc=0.995105 / 0.921543 / 0.812539 best_acc=0.995285
|
| 394 |
-
now: 2022-04-03 13:42:42.189633
|
| 395 |
-
[39400] train_loss=0.0204597 valid_loss=0.0242165 valid_pos_acc=0.973517 valid_acc=0.995151 / 0.916498 / 0.808433 best_acc=0.995285
|
| 396 |
-
now: 2022-04-03 13:54:41.379549
|
| 397 |
-
[39600] train_loss=0.0182456 valid_loss=0.0259895 valid_pos_acc=0.97315 valid_acc=0.995231 / 0.922271 / 0.812708 best_acc=0.995285
|
| 398 |
-
now: 2022-04-03 14:06:51.554192
|
| 399 |
-
[39800] train_loss=0.0163934 valid_loss=0.0258782 valid_pos_acc=0.973615 valid_acc=0.995144 / 0.916559 / 0.809627 best_acc=0.995285
|
| 400 |
-
now: 2022-04-03 14:18:44.881390
|
| 401 |
-
[40000] train_loss=0.015392 valid_loss=0.0261389 valid_pos_acc=0.974283 valid_acc=0.995225 / 0.920042 / 0.812848 best_acc=0.995285
|
| 402 |
-
now: 2022-04-03 14:30:42.900837
|
| 403 |
-
[40200] train_loss=0.0151938 valid_loss=0.0266225 valid_pos_acc=0.973879 valid_acc=0.995105 / 0.921502 / 0.815619 best_acc=0.995285
|
| 404 |
-
now: 2022-04-03 14:42:37.709297
|
| 405 |
-
[40400] train_loss=0.0156664 valid_loss=0.0252791 valid_pos_acc=0.973784 valid_acc=0.99524 / 0.920868 / 0.819319 best_acc=0.995285
|
| 406 |
-
now: 2022-04-03 14:54:43.815772
|
| 407 |
-
[40600] train_loss=0.0156026 valid_loss=0.0251239 valid_pos_acc=0.973782 valid_acc=0.995305 / 0.916191 / 0.813418 best_acc=0.995305
|
| 408 |
-
now: 2022-04-03 15:06:48.756040
|
| 409 |
-
[40800] train_loss=0.015617 valid_loss=0.0250889 valid_pos_acc=0.973797 valid_acc=0.995248 / 0.913875 / 0.809349 best_acc=0.995305
|
| 410 |
-
now: 2022-04-03 15:18:48.093498
|
| 411 |
-
[41000] train_loss=0.0160921 valid_loss=0.0254156 valid_pos_acc=0.974016 valid_acc=0.995324 / 0.918261 / 0.811825 best_acc=0.995324
|
| 412 |
-
now: 2022-04-03 15:30:49.330639
|
| 413 |
-
[41200] train_loss=0.0153382 valid_loss=0.0253904 valid_pos_acc=0.973777 valid_acc=0.995157 / 0.915275 / 0.813835 best_acc=0.995324
|
| 414 |
-
now: 2022-04-03 15:42:50.366939
|
| 415 |
-
[41400] train_loss=0.0149767 valid_loss=0.0262346 valid_pos_acc=0.97348 valid_acc=0.995216 / 0.916934 / 0.809716 best_acc=0.995324
|
| 416 |
-
now: 2022-04-03 15:54:50.211349
|
| 417 |
-
[41600] train_loss=0.0163188 valid_loss=0.0256865 valid_pos_acc=0.974062 valid_acc=0.995279 / 0.917293 / 0.810614 best_acc=0.995324
|
| 418 |
-
now: 2022-04-03 16:06:51.678378
|
| 419 |
-
[41800] train_loss=0.0152591 valid_loss=0.0257784 valid_pos_acc=0.973934 valid_acc=0.995374 / 0.918737 / 0.814463 best_acc=0.995374
|
| 420 |
-
now: 2022-04-03 16:19:01.215393
|
| 421 |
-
[42000] train_loss=0.0153742 valid_loss=0.0256425 valid_pos_acc=0.973743 valid_acc=0.995279 / 0.921392 / 0.819395 best_acc=0.995374
|
| 422 |
-
now: 2022-04-03 16:31:02.965955
|
| 423 |
-
[42200] train_loss=0.0170421 valid_loss=0.0256818 valid_pos_acc=0.973704 valid_acc=0.995233 / 0.921882 / 0.816565 best_acc=0.995374
|
| 424 |
-
now: 2022-04-03 16:42:58.346109
|
| 425 |
-
[42400] train_loss=0.0173119 valid_loss=0.0262359 valid_pos_acc=0.973567 valid_acc=0.995142 / 0.927474 / 0.824186 best_acc=0.995374
|
| 426 |
-
now: 2022-04-03 16:54:51.654450
|
| 427 |
-
[42600] train_loss=0.0151309 valid_loss=0.0263674 valid_pos_acc=0.974088 valid_acc=0.995246 / 0.928593 / 0.819351 best_acc=0.995374
|
| 428 |
-
now: 2022-04-03 17:06:53.863013
|
| 429 |
-
[42800] train_loss=0.0146644 valid_loss=0.0256878 valid_pos_acc=0.973406 valid_acc=0.995248 / 0.926729 / 0.822369 best_acc=0.995374
|
| 430 |
-
now: 2022-04-03 17:18:52.584371
|
| 431 |
-
[43000] train_loss=0.0165593 valid_loss=0.0256607 valid_pos_acc=0.973534 valid_acc=0.995077 / 0.92122 / 0.820859 best_acc=0.995374
|
| 432 |
-
now: 2022-04-03 17:30:49.691185
|
| 433 |
-
[43200] train_loss=0.0159887 valid_loss=0.0257545 valid_pos_acc=0.973704 valid_acc=0.995084 / 0.91855 / 0.813994 best_acc=0.995374
|
| 434 |
-
now: 2022-04-03 17:42:50.184875
|
| 435 |
-
[43400] train_loss=0.0176695 valid_loss=0.0257385 valid_pos_acc=0.973474 valid_acc=0.995123 / 0.92218 / 0.818823 best_acc=0.995374
|
| 436 |
-
now: 2022-04-03 17:54:44.709886
|
| 437 |
-
[43600] train_loss=0.017015 valid_loss=0.0253947 valid_pos_acc=0.973645 valid_acc=0.995281 / 0.918643 / 0.814191 best_acc=0.995374
|
| 438 |
-
now: 2022-04-03 18:06:47.227964
|
| 439 |
-
[43800] train_loss=0.0166192 valid_loss=0.0250654 valid_pos_acc=0.973309 valid_acc=0.995097 / 0.917002 / 0.81292 best_acc=0.995374
|
| 440 |
-
now: 2022-04-03 18:18:48.239180
|
| 441 |
-
[44000] train_loss=0.0179612 valid_loss=0.0247244 valid_pos_acc=0.974474 valid_acc=0.995259 / 0.922084 / 0.818199 best_acc=0.995374
|
| 442 |
-
now: 2022-04-03 18:30:58.780112
|
| 443 |
-
[44200] train_loss=0.0170823 valid_loss=0.0253463 valid_pos_acc=0.973673 valid_acc=0.995277 / 0.920828 / 0.819238 best_acc=0.995374
|
| 444 |
-
now: 2022-04-03 18:43:02.816823
|
| 445 |
-
[44400] train_loss=0.0180965 valid_loss=0.0255119 valid_pos_acc=0.973356 valid_acc=0.994997 / 0.920482 / 0.821315 best_acc=0.995374
|
| 446 |
-
now: 2022-04-03 18:55:04.839467
|
| 447 |
-
[44600] train_loss=0.0169638 valid_loss=0.0253875 valid_pos_acc=0.973916 valid_acc=0.995218 / 0.923398 / 0.819969 best_acc=0.995374
|
| 448 |
-
now: 2022-04-03 19:07:07.807908
|
| 449 |
-
[44800] train_loss=0.0175315 valid_loss=0.0250045 valid_pos_acc=0.973931 valid_acc=0.995225 / 0.919188 / 0.816001 best_acc=0.995374
|
| 450 |
-
now: 2022-04-03 19:18:53.383956
|
| 451 |
-
[45000] train_loss=0.0159776 valid_loss=0.0247916 valid_pos_acc=0.973947 valid_acc=0.99527 / 0.922533 / 0.819024 best_acc=0.995374
|
| 452 |
-
now: 2022-04-03 19:30:53.526194
|
| 453 |
-
[45200] train_loss=0.0171182 valid_loss=0.0256464 valid_pos_acc=0.97397 valid_acc=0.995225 / 0.923638 / 0.816987 best_acc=0.995374
|
| 454 |
-
now: 2022-04-03 19:42:50.236892
|
| 455 |
-
[45400] train_loss=0.0156089 valid_loss=0.0251274 valid_pos_acc=0.97379 valid_acc=0.995353 / 0.920763 / 0.815259 best_acc=0.995374
|
| 456 |
-
now: 2022-04-03 19:54:46.588740
|
| 457 |
-
[45600] train_loss=0.0163814 valid_loss=0.0255403 valid_pos_acc=0.973895 valid_acc=0.995298 / 0.923606 / 0.816856 best_acc=0.995374
|
| 458 |
-
now: 2022-04-03 20:06:53.199451
|
| 459 |
-
[45800] train_loss=0.017835 valid_loss=0.0246602 valid_pos_acc=0.973981 valid_acc=0.995372 / 0.923306 / 0.821397 best_acc=0.995374
|
| 460 |
-
now: 2022-04-03 20:19:00.767866
|
| 461 |
-
[46000] train_loss=0.0178728 valid_loss=0.025165 valid_pos_acc=0.973771 valid_acc=0.995149 / 0.922218 / 0.817612 best_acc=0.995374
|
| 462 |
-
now: 2022-04-03 20:30:58.988089
|
| 463 |
-
[46200] train_loss=0.0168901 valid_loss=0.0256853 valid_pos_acc=0.974276 valid_acc=0.995216 / 0.923973 / 0.820527 best_acc=0.995374
|
| 464 |
-
now: 2022-04-03 20:42:51.449300
|
| 465 |
-
[46400] train_loss=0.0167886 valid_loss=0.0253529 valid_pos_acc=0.973704 valid_acc=0.995159 / 0.92335 / 0.820036 best_acc=0.995374
|
| 466 |
-
now: 2022-04-03 20:55:00.160971
|
| 467 |
-
[46600] train_loss=0.0176656 valid_loss=0.0256036 valid_pos_acc=0.973929 valid_acc=0.995366 / 0.922949 / 0.818223 best_acc=0.995374
|
| 468 |
-
now: 2022-04-03 21:07:02.579327
|
| 469 |
-
[46800] train_loss=0.0168645 valid_loss=0.0251908 valid_pos_acc=0.974203 valid_acc=0.995385 / 0.92207 / 0.81494 best_acc=0.995385
|
| 470 |
-
now: 2022-04-03 21:18:57.696871
|
| 471 |
-
[47000] train_loss=0.0172549 valid_loss=0.0256528 valid_pos_acc=0.974391 valid_acc=0.995118 / 0.918959 / 0.811756 best_acc=0.995385
|
| 472 |
-
now: 2022-04-03 21:30:53.620916
|
| 473 |
-
[47200] train_loss=0.0177735 valid_loss=0.0247787 valid_pos_acc=0.97404 valid_acc=0.995071 / 0.922916 / 0.818151 best_acc=0.995385
|
| 474 |
-
now: 2022-04-03 21:42:47.443922
|
| 475 |
-
[47400] train_loss=0.0168849 valid_loss=0.0250654 valid_pos_acc=0.973947 valid_acc=0.995368 / 0.919669 / 0.817169 best_acc=0.995385
|
| 476 |
-
now: 2022-04-03 21:54:53.531320
|
| 477 |
-
[47600] train_loss=0.0162995 valid_loss=0.0245945 valid_pos_acc=0.973951 valid_acc=0.995285 / 0.922128 / 0.819555 best_acc=0.995385
|
| 478 |
-
now: 2022-04-03 22:06:54.960049
|
| 479 |
-
[47800] train_loss=0.0166094 valid_loss=0.0254666 valid_pos_acc=0.974647 valid_acc=0.995314 / 0.925199 / 0.818337 best_acc=0.995385
|
| 480 |
-
now: 2022-04-03 22:18:44.813398
|
| 481 |
-
[48000] train_loss=0.018357 valid_loss=0.0258162 valid_pos_acc=0.974018 valid_acc=0.99527 / 0.924671 / 0.820527 best_acc=0.995385
|
| 482 |
-
now: 2022-04-03 22:30:33.755723
|
| 483 |
-
[48200] train_loss=0.0168674 valid_loss=0.025839 valid_pos_acc=0.973747 valid_acc=0.995309 / 0.921703 / 0.813788 best_acc=0.995385
|
| 484 |
-
now: 2022-04-03 22:42:23.398005
|
| 485 |
-
[48400] train_loss=0.016813 valid_loss=0.0248057 valid_pos_acc=0.973541 valid_acc=0.995385 / 0.92499 / 0.824521 best_acc=0.995385
|
| 486 |
-
now: 2022-04-03 22:54:25.099880
|
| 487 |
-
[48600] train_loss=0.016574 valid_loss=0.0255942 valid_pos_acc=0.973608 valid_acc=0.995177 / 0.925379 / 0.821092 best_acc=0.995385
|
| 488 |
-
now: 2022-04-03 23:06:29.088401
|
| 489 |
-
[48800] train_loss=0.0164469 valid_loss=0.025258 valid_pos_acc=0.973758 valid_acc=0.995409 / 0.924966 / 0.819685 best_acc=0.995409
|
| 490 |
-
now: 2022-04-03 23:18:37.642881
|
| 491 |
-
[49000] train_loss=0.0179612 valid_loss=0.0246981 valid_pos_acc=0.974266 valid_acc=0.995318 / 0.920644 / 0.816254 best_acc=0.995409
|
| 492 |
-
now: 2022-04-03 23:30:33.455618
|
| 493 |
-
[49200] train_loss=0.0163115 valid_loss=0.025103 valid_pos_acc=0.973717 valid_acc=0.995405 / 0.917957 / 0.814416 best_acc=0.995409
|
| 494 |
-
now: 2022-04-03 23:42:28.866307
|
| 495 |
-
[49400] train_loss=0.0171099 valid_loss=0.0263086 valid_pos_acc=0.973556 valid_acc=0.995151 / 0.91743 / 0.810825 best_acc=0.995409
|
| 496 |
-
now: 2022-04-03 23:54:18.959772
|
| 497 |
-
[49600] train_loss=0.0189349 valid_loss=0.0246903 valid_pos_acc=0.974151 valid_acc=0.995272 / 0.92322 / 0.818726 best_acc=0.995409
|
| 498 |
-
now: 2022-04-04 00:06:15.909786
|
| 499 |
-
[49800] train_loss=0.0167492 valid_loss=0.025506 valid_pos_acc=0.974257 valid_acc=0.995439 / 0.925636 / 0.815986 best_acc=0.995439
|
| 500 |
-
now: 2022-04-04 00:18:24.239516
|
| 501 |
-
[50000] train_loss=0.0176046 valid_loss=0.024858 valid_pos_acc=0.974309 valid_acc=0.995346 / 0.930482 / 0.820187 best_acc=0.995439
|
| 502 |
-
now: 2022-04-04 00:30:15.506831
|
| 503 |
-
[50200] train_loss=0.0163029 valid_loss=0.0252902 valid_pos_acc=0.974383 valid_acc=0.995381 / 0.923226 / 0.818356 best_acc=0.995439
|
| 504 |
-
now: 2022-04-04 00:42:18.897896
|
| 505 |
-
[50400] train_loss=0.0171214 valid_loss=0.0246193 valid_pos_acc=0.974333 valid_acc=0.995368 / 0.921255 / 0.81669 best_acc=0.995439
|
| 506 |
-
now: 2022-04-04 00:54:13.841028
|
| 507 |
-
[50600] train_loss=0.0161805 valid_loss=0.0250435 valid_pos_acc=0.974437 valid_acc=0.995186 / 0.918271 / 0.813367 best_acc=0.995439
|
| 508 |
-
now: 2022-04-04 01:06:16.083462
|
| 509 |
-
[50800] train_loss=0.0179548 valid_loss=0.0245154 valid_pos_acc=0.974691 valid_acc=0.995259 / 0.922457 / 0.81699 best_acc=0.995439
|
| 510 |
-
now: 2022-04-04 01:18:13.790064
|
| 511 |
-
[51000] train_loss=0.0164793 valid_loss=0.0248721 valid_pos_acc=0.974378 valid_acc=0.995322 / 0.924732 / 0.817262 best_acc=0.995439
|
| 512 |
-
now: 2022-04-04 01:30:17.861722
|
| 513 |
-
[51200] train_loss=0.016939 valid_loss=0.0265039 valid_pos_acc=0.974007 valid_acc=0.995044 / 0.922191 / 0.811527 best_acc=0.995439
|
| 514 |
-
now: 2022-04-04 01:42:23.079103
|
| 515 |
-
[51400] train_loss=0.015836 valid_loss=0.0262405 valid_pos_acc=0.973289 valid_acc=0.995235 / 0.922246 / 0.817143 best_acc=0.995439
|
| 516 |
-
now: 2022-04-04 01:54:20.740833
|
| 517 |
-
[51600] train_loss=0.0175937 valid_loss=0.0250272 valid_pos_acc=0.973819 valid_acc=0.995214 / 0.925015 / 0.824207 best_acc=0.995439
|
| 518 |
-
now: 2022-04-04 02:06:12.246740
|
| 519 |
-
[51800] train_loss=0.0194151 valid_loss=0.0250101 valid_pos_acc=0.973567 valid_acc=0.995253 / 0.921771 / 0.822537 best_acc=0.995439
|
| 520 |
-
now: 2022-04-04 02:18:00.425728
|
| 521 |
-
[52000] train_loss=0.0175319 valid_loss=0.0252638 valid_pos_acc=0.973914 valid_acc=0.995235 / 0.92165 / 0.81825 best_acc=0.995439
|
| 522 |
-
now: 2022-04-04 02:30:00.527309
|
| 523 |
-
[52200] train_loss=0.0177649 valid_loss=0.0251296 valid_pos_acc=0.974302 valid_acc=0.995218 / 0.916528 / 0.820442 best_acc=0.995439
|
| 524 |
-
now: 2022-04-04 02:42:15.444828
|
| 525 |
-
[52400] train_loss=0.0142718 valid_loss=0.0261507 valid_pos_acc=0.974112 valid_acc=0.995218 / 0.926747 / 0.823267 best_acc=0.995439
|
| 526 |
-
now: 2022-04-04 02:54:22.534812
|
| 527 |
-
[52600] train_loss=0.0181186 valid_loss=0.024454 valid_pos_acc=0.974678 valid_acc=0.995266 / 0.920649 / 0.821322 best_acc=0.995439
|
| 528 |
-
now: 2022-04-04 03:06:28.190695
|
| 529 |
-
[52800] train_loss=0.0186914 valid_loss=0.0248696 valid_pos_acc=0.97445 valid_acc=0.995272 / 0.925439 / 0.824017 best_acc=0.995439
|
| 530 |
-
now: 2022-04-04 03:18:37.506758
|
| 531 |
-
[53000] train_loss=0.0180586 valid_loss=0.0249947 valid_pos_acc=0.973493 valid_acc=0.995246 / 0.922071 / 0.820278 best_acc=0.995439
|
| 532 |
-
now: 2022-04-04 03:30:42.193804
|
| 533 |
-
[53200] train_loss=0.0174878 valid_loss=0.0238657 valid_pos_acc=0.974348 valid_acc=0.995357 / 0.928082 / 0.822476 best_acc=0.995439
|
| 534 |
-
now: 2022-04-04 03:42:35.505948
|
| 535 |
-
[53400] train_loss=0.0175929 valid_loss=0.0238299 valid_pos_acc=0.974042 valid_acc=0.995331 / 0.921639 / 0.819943 best_acc=0.995439
|
| 536 |
-
now: 2022-04-04 03:54:28.949782
|
| 537 |
-
[53600] train_loss=0.0177671 valid_loss=0.0252258 valid_pos_acc=0.974318 valid_acc=0.995259 / 0.923376 / 0.819476 best_acc=0.995439
|
| 538 |
-
now: 2022-04-04 04:06:31.080946
|
| 539 |
-
[53800] train_loss=0.0181123 valid_loss=0.0245157 valid_pos_acc=0.974474 valid_acc=0.995344 / 0.926992 / 0.822282 best_acc=0.995439
|
| 540 |
-
now: 2022-04-04 04:18:31.824945
|
| 541 |
-
[54000] train_loss=0.0163909 valid_loss=0.0247618 valid_pos_acc=0.973955 valid_acc=0.995322 / 0.926681 / 0.819102 best_acc=0.995439
|
| 542 |
-
now: 2022-04-04 04:30:40.751754
|
| 543 |
-
[54200] train_loss=0.0182664 valid_loss=0.0249099 valid_pos_acc=0.973999 valid_acc=0.995418 / 0.928271 / 0.824931 best_acc=0.995439
|
| 544 |
-
now: 2022-04-04 04:42:35.399533
|
| 545 |
-
[54400] train_loss=0.0186873 valid_loss=0.0251008 valid_pos_acc=0.974205 valid_acc=0.995405 / 0.932078 / 0.826293 best_acc=0.995439
|
| 546 |
-
now: 2022-04-04 04:54:44.051651
|
| 547 |
-
[54600] train_loss=0.0176127 valid_loss=0.0242345 valid_pos_acc=0.97437 valid_acc=0.995394 / 0.926177 / 0.823775 best_acc=0.995439
|
| 548 |
-
now: 2022-04-04 05:06:49.804392
|
| 549 |
-
[54800] train_loss=0.0163823 valid_loss=0.0258135 valid_pos_acc=0.974289 valid_acc=0.995075 / 0.92924 / 0.822328 best_acc=0.995439
|
| 550 |
-
now: 2022-04-04 05:18:59.599641
|
| 551 |
-
[55000] train_loss=0.0179242 valid_loss=0.024379 valid_pos_acc=0.973957 valid_acc=0.995426 / 0.928179 / 0.828975 best_acc=0.995439
|
| 552 |
-
now: 2022-04-04 05:31:03.121724
|
| 553 |
-
[55200] train_loss=0.0184118 valid_loss=0.0241673 valid_pos_acc=0.974077 valid_acc=0.995316 / 0.929769 / 0.826971 best_acc=0.995439
|
| 554 |
-
now: 2022-04-04 05:43:02.794339
|
| 555 |
-
[55400] train_loss=0.0165821 valid_loss=0.0247912 valid_pos_acc=0.974302 valid_acc=0.995285 / 0.925138 / 0.817615 best_acc=0.995439
|
| 556 |
-
now: 2022-04-04 05:55:02.273212
|
| 557 |
-
[55600] train_loss=0.0186432 valid_loss=0.0251953 valid_pos_acc=0.974374 valid_acc=0.995231 / 0.926073 / 0.822135 best_acc=0.995439
|
| 558 |
-
now: 2022-04-04 06:07:03.296464
|
| 559 |
-
[55800] train_loss=0.0171283 valid_loss=0.0251252 valid_pos_acc=0.974962 valid_acc=0.995283 / 0.92335 / 0.820246 best_acc=0.995439
|
| 560 |
-
now: 2022-04-04 06:19:11.613771
|
| 561 |
-
[56000] train_loss=0.0186047 valid_loss=0.0247604 valid_pos_acc=0.97448 valid_acc=0.995277 / 0.927931 / 0.823571 best_acc=0.995439
|
| 562 |
-
now: 2022-04-04 06:31:02.274835
|
| 563 |
-
[56200] train_loss=0.0167043 valid_loss=0.0252192 valid_pos_acc=0.974166 valid_acc=0.995279 / 0.92564 / 0.822268 best_acc=0.995439
|
| 564 |
-
now: 2022-04-04 06:43:00.961416
|
| 565 |
-
[56400] train_loss=0.0193165 valid_loss=0.0252836 valid_pos_acc=0.974016 valid_acc=0.995268 / 0.9252 / 0.817183 best_acc=0.995439
|
| 566 |
-
now: 2022-04-04 06:55:01.506312
|
| 567 |
-
[56600] train_loss=0.0185221 valid_loss=0.0242256 valid_pos_acc=0.974535 valid_acc=0.995424 / 0.925529 / 0.821226 best_acc=0.995439
|
| 568 |
-
now: 2022-04-04 07:06:52.135175
|
| 569 |
-
[56800] train_loss=0.0171885 valid_loss=0.0252071 valid_pos_acc=0.974706 valid_acc=0.995227 / 0.924077 / 0.817878 best_acc=0.995439
|
| 570 |
-
now: 2022-04-04 07:18:50.316902
|
| 571 |
-
[57000] train_loss=0.0175959 valid_loss=0.0248842 valid_pos_acc=0.974235 valid_acc=0.995218 / 0.92808 / 0.823735 best_acc=0.995439
|
| 572 |
-
now: 2022-04-04 07:30:56.648830
|
| 573 |
-
[57200] train_loss=0.0171702 valid_loss=0.0248792 valid_pos_acc=0.974953 valid_acc=0.995392 / 0.927917 / 0.824835 best_acc=0.995439
|
| 574 |
-
now: 2022-04-04 07:43:00.234541
|
| 575 |
-
[57400] train_loss=0.0164997 valid_loss=0.0247215 valid_pos_acc=0.97473 valid_acc=0.995363 / 0.927725 / 0.822496 best_acc=0.995439
|
| 576 |
-
now: 2022-04-04 07:55:01.561685
|
| 577 |
-
[57600] train_loss=0.0175738 valid_loss=0.0246078 valid_pos_acc=0.974686 valid_acc=0.995413 / 0.9303 / 0.822759 best_acc=0.995439
|
| 578 |
-
now: 2022-04-04 08:07:03.437555
|
| 579 |
-
[57800] train_loss=0.0182974 valid_loss=0.0242409 valid_pos_acc=0.974461 valid_acc=0.995472 / 0.929586 / 0.827576 best_acc=0.995472
|
| 580 |
-
now: 2022-04-04 08:19:09.447609
|
| 581 |
-
[58000] train_loss=0.0175345 valid_loss=0.0243995 valid_pos_acc=0.974287 valid_acc=0.995515 / 0.93219 / 0.826722 best_acc=0.995515
|
| 582 |
-
now: 2022-04-04 08:31:08.680812
|
| 583 |
-
[58200] train_loss=0.0183634 valid_loss=0.0238503 valid_pos_acc=0.974051 valid_acc=0.995335 / 0.922815 / 0.821793 best_acc=0.995515
|
| 584 |
-
now: 2022-04-04 08:43:10.046244
|
| 585 |
-
[58400] train_loss=0.0163139 valid_loss=0.024504 valid_pos_acc=0.975008 valid_acc=0.995541 / 0.930931 / 0.823118 best_acc=0.995541
|
| 586 |
-
now: 2022-04-04 08:55:18.433955
|
| 587 |
-
[58600] train_loss=0.017843 valid_loss=0.0241767 valid_pos_acc=0.9746 valid_acc=0.995476 / 0.930497 / 0.832621 best_acc=0.995541
|
| 588 |
-
now: 2022-04-04 09:07:27.795974
|
| 589 |
-
[58800] train_loss=0.0176362 valid_loss=0.0243888 valid_pos_acc=0.974819 valid_acc=0.995457 / 0.929875 / 0.82877 best_acc=0.995541
|
| 590 |
-
now: 2022-04-04 09:19:29.918584
|
| 591 |
-
[59000] train_loss=0.017933 valid_loss=0.0242775 valid_pos_acc=0.974493 valid_acc=0.995507 / 0.930605 / 0.8321 best_acc=0.995541
|
| 592 |
-
now: 2022-04-04 09:31:33.387747
|
| 593 |
-
[59200] train_loss=0.0166545 valid_loss=0.0248671 valid_pos_acc=0.974669 valid_acc=0.995372 / 0.934492 / 0.832004 best_acc=0.995541
|
| 594 |
-
now: 2022-04-04 09:43:38.498786
|
| 595 |
-
[59400] train_loss=0.0144546 valid_loss=0.0250983 valid_pos_acc=0.974652 valid_acc=0.995494 / 0.93082 / 0.829281 best_acc=0.995541
|
| 596 |
-
now: 2022-04-04 09:55:40.006405
|
| 597 |
-
[59600] train_loss=0.0139316 valid_loss=0.02474 valid_pos_acc=0.974144 valid_acc=0.99545 / 0.932469 / 0.831644 best_acc=0.995541
|
| 598 |
-
now: 2022-04-04 10:07:46.456322
|
| 599 |
-
[59800] train_loss=0.0134991 valid_loss=0.0268217 valid_pos_acc=0.974183 valid_acc=0.995357 / 0.927956 / 0.825977 best_acc=0.995541
|
| 600 |
-
now: 2022-04-04 10:19:47.170813
|
| 601 |
-
[60000] train_loss=0.0152101 valid_loss=0.0253673 valid_pos_acc=0.974528 valid_acc=0.995463 / 0.932345 / 0.830733 best_acc=0.995541
|
| 602 |
-
now: 2022-04-04 10:31:54.967223
|
| 603 |
-
[60200] train_loss=0.0138335 valid_loss=0.0252645 valid_pos_acc=0.974877 valid_acc=0.995359 / 0.928942 / 0.827531 best_acc=0.995541
|
| 604 |
-
now: 2022-04-04 10:43:57.300159
|
| 605 |
-
[60400] train_loss=0.0136089 valid_loss=0.0252041 valid_pos_acc=0.974778 valid_acc=0.995353 / 0.92988 / 0.824825 best_acc=0.995541
|
| 606 |
-
now: 2022-04-04 10:55:50.855788
|
| 607 |
-
[60600] train_loss=0.013999 valid_loss=0.0255136 valid_pos_acc=0.975068 valid_acc=0.995389 / 0.93112 / 0.827874 best_acc=0.995541
|
| 608 |
-
now: 2022-04-04 11:07:45.181290
|
| 609 |
-
[60800] train_loss=0.0147645 valid_loss=0.024898 valid_pos_acc=0.974433 valid_acc=0.9954 / 0.932707 / 0.830214 best_acc=0.995541
|
| 610 |
-
now: 2022-04-04 11:19:31.882106
|
| 611 |
-
[61000] train_loss=0.0144584 valid_loss=0.0256239 valid_pos_acc=0.974613 valid_acc=0.995318 / 0.931253 / 0.826158 best_acc=0.995541
|
| 612 |
-
now: 2022-04-04 11:31:20.954761
|
| 613 |
-
[61200] train_loss=0.0148378 valid_loss=0.025611 valid_pos_acc=0.974391 valid_acc=0.995316 / 0.933647 / 0.831387 best_acc=0.995541
|
| 614 |
-
now: 2022-04-04 11:43:16.513341
|
| 615 |
-
[61400] train_loss=0.0164332 valid_loss=0.0245625 valid_pos_acc=0.974274 valid_acc=0.995533 / 0.933621 / 0.832726 best_acc=0.995541
|
| 616 |
-
now: 2022-04-04 11:55:19.484783
|
| 617 |
-
[61600] train_loss=0.0128714 valid_loss=0.0261392 valid_pos_acc=0.974322 valid_acc=0.995478 / 0.929096 / 0.826284 best_acc=0.995541
|
| 618 |
-
now: 2022-04-04 12:07:11.369487
|
| 619 |
-
[61800] train_loss=0.0133303 valid_loss=0.0250031 valid_pos_acc=0.974207 valid_acc=0.995429 / 0.929365 / 0.828611 best_acc=0.995541
|
| 620 |
-
now: 2022-04-04 12:19:18.413032
|
| 621 |
-
[62000] train_loss=0.015107 valid_loss=0.0254061 valid_pos_acc=0.974804 valid_acc=0.995526 / 0.9288 / 0.82914 best_acc=0.995541
|
| 622 |
-
now: 2022-04-04 12:31:20.055266
|
| 623 |
-
[62200] train_loss=0.0138232 valid_loss=0.0257889 valid_pos_acc=0.974927 valid_acc=0.99542 / 0.931311 / 0.830115 best_acc=0.995541
|
| 624 |
-
now: 2022-04-04 12:43:13.349393
|
| 625 |
-
[62400] train_loss=0.0141178 valid_loss=0.0256085 valid_pos_acc=0.97463 valid_acc=0.995348 / 0.926439 / 0.829561 best_acc=0.995541
|
| 626 |
-
now: 2022-04-04 12:55:00.906269
|
| 627 |
-
[62600] train_loss=0.0142263 valid_loss=0.0262747 valid_pos_acc=0.974962 valid_acc=0.995402 / 0.925902 / 0.82448 best_acc=0.995541
|
| 628 |
-
now: 2022-04-04 13:06:57.609555
|
| 629 |
-
[62800] train_loss=0.0147258 valid_loss=0.0259502 valid_pos_acc=0.974404 valid_acc=0.995504 / 0.928971 / 0.826749 best_acc=0.995541
|
| 630 |
-
now: 2022-04-04 13:18:51.020957
|
| 631 |
-
[63000] train_loss=0.0158923 valid_loss=0.0249411 valid_pos_acc=0.974795 valid_acc=0.995535 / 0.924258 / 0.825275 best_acc=0.995541
|
| 632 |
-
now: 2022-04-04 13:30:41.825005
|
| 633 |
-
[63200] train_loss=0.0129023 valid_loss=0.0256269 valid_pos_acc=0.974296 valid_acc=0.99553 / 0.924678 / 0.824117 best_acc=0.995541
|
| 634 |
-
now: 2022-04-04 13:42:34.233889
|
| 635 |
-
[63400] train_loss=0.0155354 valid_loss=0.0239082 valid_pos_acc=0.974923 valid_acc=0.995526 / 0.923666 / 0.830834 best_acc=0.995541
|
| 636 |
-
now: 2022-04-04 13:54:30.059726
|
| 637 |
-
[63600] train_loss=0.0151712 valid_loss=0.0252058 valid_pos_acc=0.97448 valid_acc=0.995481 / 0.926182 / 0.827018 best_acc=0.995541
|
| 638 |
-
now: 2022-04-04 14:06:25.923276
|
| 639 |
-
[63800] train_loss=0.0154875 valid_loss=0.0249389 valid_pos_acc=0.974504 valid_acc=0.995502 / 0.92361 / 0.826288 best_acc=0.995541
|
| 640 |
-
now: 2022-04-04 14:18:29.316170
|
| 641 |
-
[64000] train_loss=0.0151137 valid_loss=0.0256968 valid_pos_acc=0.974333 valid_acc=0.995459 / 0.924967 / 0.829635 best_acc=0.995541
|
| 642 |
-
now: 2022-04-04 14:30:34.246579
|
| 643 |
-
[64200] train_loss=0.0152567 valid_loss=0.0251615 valid_pos_acc=0.974465 valid_acc=0.995544 / 0.930084 / 0.832375 best_acc=0.995544
|
| 644 |
-
now: 2022-04-04 14:42:51.084382
|
| 645 |
-
[64400] train_loss=0.0145794 valid_loss=0.0253407 valid_pos_acc=0.975203 valid_acc=0.995587 / 0.929932 / 0.82946 best_acc=0.995587
|
| 646 |
-
now: 2022-04-04 14:54:58.723482
|
| 647 |
-
[64600] train_loss=0.0145396 valid_loss=0.0245939 valid_pos_acc=0.974552 valid_acc=0.995468 / 0.926718 / 0.83152 best_acc=0.995587
|
| 648 |
-
now: 2022-04-04 15:06:51.962741
|
| 649 |
-
[64800] train_loss=0.0141351 valid_loss=0.0257409 valid_pos_acc=0.975149 valid_acc=0.995465 / 0.92718 / 0.826698 best_acc=0.995587
|
| 650 |
-
now: 2022-04-04 15:18:55.351985
|
| 651 |
-
[65000] train_loss=0.0150484 valid_loss=0.0243046 valid_pos_acc=0.974875 valid_acc=0.995609 / 0.928307 / 0.831687 best_acc=0.995609
|
| 652 |
-
now: 2022-04-04 15:31:02.499770
|
| 653 |
-
[65200] train_loss=0.0137273 valid_loss=0.0247366 valid_pos_acc=0.974598 valid_acc=0.995478 / 0.927726 / 0.828495 best_acc=0.995609
|
| 654 |
-
now: 2022-04-04 15:43:12.293967
|
| 655 |
-
[65400] train_loss=0.0146922 valid_loss=0.0248483 valid_pos_acc=0.97481 valid_acc=0.995433 / 0.926791 / 0.822958 best_acc=0.995609
|
| 656 |
-
now: 2022-04-04 15:55:16.151208
|
| 657 |
-
[65600] train_loss=0.0163436 valid_loss=0.0252635 valid_pos_acc=0.974797 valid_acc=0.995318 / 0.927537 / 0.824153 best_acc=0.995609
|
| 658 |
-
now: 2022-04-04 16:07:11.212065
|
| 659 |
-
[65800] train_loss=0.0148312 valid_loss=0.0246498 valid_pos_acc=0.975016 valid_acc=0.995429 / 0.927171 / 0.82399 best_acc=0.995609
|
| 660 |
-
now: 2022-04-04 16:19:08.685980
|
| 661 |
-
[66000] train_loss=0.015976 valid_loss=0.0249127 valid_pos_acc=0.974988 valid_acc=0.995481 / 0.928756 / 0.828428 best_acc=0.995609
|
| 662 |
-
now: 2022-04-04 16:31:02.374897
|
| 663 |
-
[66200] train_loss=0.014783 valid_loss=0.0250196 valid_pos_acc=0.974611 valid_acc=0.995452 / 0.925158 / 0.827485 best_acc=0.995609
|
| 664 |
-
now: 2022-04-04 16:42:47.960220
|
| 665 |
-
[66400] train_loss=0.0157549 valid_loss=0.0247606 valid_pos_acc=0.974945 valid_acc=0.995422 / 0.927323 / 0.829169 best_acc=0.995609
|
| 666 |
-
now: 2022-04-04 16:54:48.551560
|
| 667 |
-
[66600] train_loss=0.0150551 valid_loss=0.0252349 valid_pos_acc=0.975318 valid_acc=0.995544 / 0.929646 / 0.829167 best_acc=0.995609
|
| 668 |
-
now: 2022-04-04 17:06:51.181160
|
| 669 |
-
[66800] train_loss=0.014767 valid_loss=0.0253356 valid_pos_acc=0.9747 valid_acc=0.995528 / 0.930906 / 0.830688 best_acc=0.995609
|
| 670 |
-
now: 2022-04-04 17:18:56.196395
|
| 671 |
-
[67000] train_loss=0.0143435 valid_loss=0.0247641 valid_pos_acc=0.974734 valid_acc=0.99553 / 0.930212 / 0.832562 best_acc=0.995609
|
| 672 |
-
now: 2022-04-04 17:30:49.412781
|
| 673 |
-
[67200] train_loss=0.0143362 valid_loss=0.0262739 valid_pos_acc=0.974728 valid_acc=0.995355 / 0.92964 / 0.824912 best_acc=0.995609
|
| 674 |
-
now: 2022-04-04 17:42:55.107881
|
| 675 |
-
[67400] train_loss=0.0162339 valid_loss=0.0246594 valid_pos_acc=0.974643 valid_acc=0.995465 / 0.927617 / 0.827191 best_acc=0.995609
|
| 676 |
-
now: 2022-04-04 17:54:54.064581
|
| 677 |
-
[67600] train_loss=0.0160092 valid_loss=0.0252428 valid_pos_acc=0.974259 valid_acc=0.995446 / 0.930018 / 0.833761 best_acc=0.995609
|
| 678 |
-
now: 2022-04-04 18:06:53.764486
|
| 679 |
-
[67800] train_loss=0.0152992 valid_loss=0.0253698 valid_pos_acc=0.97448 valid_acc=0.995392 / 0.926869 / 0.829631 best_acc=0.995609
|
| 680 |
-
now: 2022-04-04 18:18:51.081973
|
| 681 |
-
[68000] train_loss=0.0147154 valid_loss=0.0257934 valid_pos_acc=0.974166 valid_acc=0.995533 / 0.932229 / 0.833046 best_acc=0.995609
|
| 682 |
-
now: 2022-04-04 18:30:58.562494
|
| 683 |
-
[68200] train_loss=0.0133411 valid_loss=0.0261108 valid_pos_acc=0.974467 valid_acc=0.995472 / 0.928011 / 0.826824 best_acc=0.995609
|
| 684 |
-
now: 2022-04-04 18:42:51.828880
|
| 685 |
-
[68400] train_loss=0.0153007 valid_loss=0.0262793 valid_pos_acc=0.974007 valid_acc=0.995255 / 0.92542 / 0.825899 best_acc=0.995609
|
| 686 |
-
now: 2022-04-04 18:54:48.421492
|
| 687 |
-
[68600] train_loss=0.0156991 valid_loss=0.0250134 valid_pos_acc=0.974808 valid_acc=0.99552 / 0.925519 / 0.827053 best_acc=0.995609
|
| 688 |
-
now: 2022-04-04 19:06:46.853231
|
| 689 |
-
[68800] train_loss=0.0148552 valid_loss=0.0246115 valid_pos_acc=0.974407 valid_acc=0.995626 / 0.92897 / 0.829723 best_acc=0.995626
|
| 690 |
-
now: 2022-04-04 19:18:49.907609
|
| 691 |
-
[69000] train_loss=0.0148742 valid_loss=0.0240215 valid_pos_acc=0.974463 valid_acc=0.995648 / 0.932707 / 0.837616 best_acc=0.995648
|
| 692 |
-
now: 2022-04-04 19:30:58.657495
|
| 693 |
-
[69200] train_loss=0.0154729 valid_loss=0.0243906 valid_pos_acc=0.974762 valid_acc=0.995609 / 0.932612 / 0.832795 best_acc=0.995648
|
| 694 |
-
now: 2022-04-04 19:43:05.888719
|
| 695 |
-
[69400] train_loss=0.016396 valid_loss=0.0258633 valid_pos_acc=0.974834 valid_acc=0.995557 / 0.930491 / 0.82893 best_acc=0.995648
|
| 696 |
-
now: 2022-04-04 19:55:14.202470
|
| 697 |
-
[69600] train_loss=0.0151303 valid_loss=0.0253337 valid_pos_acc=0.975164 valid_acc=0.995507 / 0.926279 / 0.828464 best_acc=0.995648
|
| 698 |
-
now: 2022-04-04 20:07:14.789065
|
| 699 |
-
[69800] train_loss=0.0160168 valid_loss=0.0261905 valid_pos_acc=0.974936 valid_acc=0.995244 / 0.919724 / 0.822949 best_acc=0.995648
|
| 700 |
-
now: 2022-04-04 20:19:07.585227
|
| 701 |
-
[70000] train_loss=0.0145597 valid_loss=0.0257852 valid_pos_acc=0.974459 valid_acc=0.995411 / 0.922878 / 0.822821 best_acc=0.995648
|
| 702 |
-
now: 2022-04-04 20:31:00.634042
|
| 703 |
-
[70200] train_loss=0.0158036 valid_loss=0.0254432 valid_pos_acc=0.974986 valid_acc=0.995442 / 0.927001 / 0.827555 best_acc=0.995648
|
| 704 |
-
now: 2022-04-04 20:42:51.615293
|
| 705 |
-
[70400] train_loss=0.0152405 valid_loss=0.0246695 valid_pos_acc=0.974684 valid_acc=0.995517 / 0.926662 / 0.828915 best_acc=0.995648
|
| 706 |
-
now: 2022-04-04 20:54:54.958272
|
| 707 |
-
[70600] train_loss=0.0142744 valid_loss=0.0256758 valid_pos_acc=0.974656 valid_acc=0.995446 / 0.924052 / 0.827786 best_acc=0.995648
|
| 708 |
-
now: 2022-04-04 21:06:51.313734
|
| 709 |
-
[70800] train_loss=0.0156632 valid_loss=0.0254473 valid_pos_acc=0.97496 valid_acc=0.995385 / 0.92814 / 0.832267 best_acc=0.995648
|
| 710 |
-
now: 2022-04-04 21:18:43.761819
|
| 711 |
-
[71000] train_loss=0.0164862 valid_loss=0.0255363 valid_pos_acc=0.974172 valid_acc=0.995342 / 0.92511 / 0.826733 best_acc=0.995648
|
| 712 |
-
now: 2022-04-04 21:30:44.417684
|
| 713 |
-
[71200] train_loss=0.0138593 valid_loss=0.0255767 valid_pos_acc=0.974632 valid_acc=0.995394 / 0.930841 / 0.830682 best_acc=0.995648
|
| 714 |
-
now: 2022-04-04 21:42:42.850833
|
| 715 |
-
[71400] train_loss=0.0157498 valid_loss=0.025416 valid_pos_acc=0.975127 valid_acc=0.995617 / 0.928939 / 0.826405 best_acc=0.995648
|
| 716 |
-
now: 2022-04-04 21:54:46.086563
|
| 717 |
-
[71600] train_loss=0.0152817 valid_loss=0.0257021 valid_pos_acc=0.974856 valid_acc=0.995578 / 0.927289 / 0.824619 best_acc=0.995648
|
| 718 |
-
now: 2022-04-04 22:06:47.189552
|
| 719 |
-
[71800] train_loss=0.0149438 valid_loss=0.0264182 valid_pos_acc=0.974916 valid_acc=0.995507 / 0.931164 / 0.830416 best_acc=0.995648
|
| 720 |
-
now: 2022-04-04 22:18:39.870279
|
| 721 |
-
[72000] train_loss=0.0163576 valid_loss=0.0259418 valid_pos_acc=0.974795 valid_acc=0.995374 / 0.924427 / 0.824195 best_acc=0.995648
|
| 722 |
-
now: 2022-04-04 22:30:33.184078
|
| 723 |
-
[72200] train_loss=0.016734 valid_loss=0.0250246 valid_pos_acc=0.974524 valid_acc=0.995552 / 0.927199 / 0.829767 best_acc=0.995648
|
| 724 |
-
now: 2022-04-04 22:42:36.180178
|
| 725 |
-
[72400] train_loss=0.0154278 valid_loss=0.025262 valid_pos_acc=0.974578 valid_acc=0.9955 / 0.930839 / 0.835087 best_acc=0.995648
|
| 726 |
-
now: 2022-04-04 22:54:42.093287
|
| 727 |
-
[72600] train_loss=0.0154066 valid_loss=0.0257742 valid_pos_acc=0.973793 valid_acc=0.995394 / 0.933008 / 0.828687 best_acc=0.995648
|
| 728 |
-
now: 2022-04-04 23:06:54.703488
|
| 729 |
-
[72800] train_loss=0.0164941 valid_loss=0.0249958 valid_pos_acc=0.974504 valid_acc=0.995418 / 0.931091 / 0.83236 best_acc=0.995648
|
| 730 |
-
now: 2022-04-04 23:19:01.661367
|
| 731 |
-
[73000] train_loss=0.016436 valid_loss=0.0265112 valid_pos_acc=0.974443 valid_acc=0.995385 / 0.927996 / 0.829395 best_acc=0.995648
|
| 732 |
-
now: 2022-04-04 23:31:00.152399
|
| 733 |
-
[73200] train_loss=0.0162149 valid_loss=0.0255855 valid_pos_acc=0.974825 valid_acc=0.995448 / 0.928238 / 0.829202 best_acc=0.995648
|
| 734 |
-
now: 2022-04-04 23:43:00.392230
|
| 735 |
-
[73400] train_loss=0.0144402 valid_loss=0.0252108 valid_pos_acc=0.9747 valid_acc=0.995587 / 0.929875 / 0.834653 best_acc=0.995648
|
| 736 |
-
now: 2022-04-04 23:55:08.297531
|
| 737 |
-
[73600] train_loss=0.0156009 valid_loss=0.0243919 valid_pos_acc=0.974626 valid_acc=0.995526 / 0.930281 / 0.835324 best_acc=0.995648
|
| 738 |
-
now: 2022-04-05 00:07:21.557640
|
| 739 |
-
[73800] train_loss=0.0162462 valid_loss=0.0250821 valid_pos_acc=0.975079 valid_acc=0.995567 / 0.927734 / 0.826903 best_acc=0.995648
|
| 740 |
-
now: 2022-04-05 00:19:20.964981
|
| 741 |
-
[74000] train_loss=0.0149793 valid_loss=0.0246167 valid_pos_acc=0.974808 valid_acc=0.995598 / 0.930624 / 0.833348 best_acc=0.995648
|
| 742 |
-
now: 2022-04-05 00:31:27.707459
|
| 743 |
-
[74200] train_loss=0.016266 valid_loss=0.0240239 valid_pos_acc=0.975188 valid_acc=0.995574 / 0.931921 / 0.831013 best_acc=0.995648
|
| 744 |
-
now: 2022-04-05 00:43:16.494193
|
| 745 |
-
[74400] train_loss=0.0133132 valid_loss=0.0248758 valid_pos_acc=0.97506 valid_acc=0.99553 / 0.935143 / 0.835638 best_acc=0.995648
|
| 746 |
-
now: 2022-04-05 00:55:14.868213
|
| 747 |
-
[74600] train_loss=0.0161027 valid_loss=0.0249716 valid_pos_acc=0.97488 valid_acc=0.9955 / 0.933607 / 0.832621 best_acc=0.995648
|
| 748 |
-
now: 2022-04-05 01:07:25.462651
|
| 749 |
-
[74800] train_loss=0.014376 valid_loss=0.0250494 valid_pos_acc=0.975396 valid_acc=0.995504 / 0.933943 / 0.833992 best_acc=0.995648
|
| 750 |
-
now: 2022-04-05 01:19:25.187529
|
| 751 |
-
[75000] train_loss=0.0161117 valid_loss=0.0241017 valid_pos_acc=0.974871 valid_acc=0.995626 / 0.930057 / 0.833235 best_acc=0.995648
|
| 752 |
-
now: 2022-04-05 01:31:27.005032
|
| 753 |
-
[75200] train_loss=0.0158166 valid_loss=0.0236026 valid_pos_acc=0.974979 valid_acc=0.995587 / 0.929956 / 0.831575 best_acc=0.995648
|
| 754 |
-
now: 2022-04-05 01:43:35.408468
|
| 755 |
-
[75400] train_loss=0.0149878 valid_loss=0.0256847 valid_pos_acc=0.974949 valid_acc=0.995667 / 0.932654 / 0.832065 best_acc=0.995667
|
| 756 |
-
now: 2022-04-05 01:55:54.653418
|
| 757 |
-
[75600] train_loss=0.0155141 valid_loss=0.024818 valid_pos_acc=0.974934 valid_acc=0.995609 / 0.933709 / 0.831186 best_acc=0.995667
|
| 758 |
-
now: 2022-04-05 02:07:43.823418
|
| 759 |
-
[75800] train_loss=0.0144699 valid_loss=0.0258603 valid_pos_acc=0.975235 valid_acc=0.995617 / 0.933296 / 0.832935 best_acc=0.995667
|
| 760 |
-
now: 2022-04-05 02:19:44.987772
|
| 761 |
-
[76000] train_loss=0.0152171 valid_loss=0.0252886 valid_pos_acc=0.97524 valid_acc=0.995693 / 0.932408 / 0.835173 best_acc=0.995693
|
| 762 |
-
now: 2022-04-05 02:31:42.885767
|
| 763 |
-
[76200] train_loss=0.0155771 valid_loss=0.0255284 valid_pos_acc=0.97537 valid_acc=0.995643 / 0.935199 / 0.834838 best_acc=0.995693
|
| 764 |
-
now: 2022-04-05 02:43:44.509209
|
| 765 |
-
[76400] train_loss=0.0153983 valid_loss=0.0248721 valid_pos_acc=0.975023 valid_acc=0.995574 / 0.93275 / 0.835416 best_acc=0.995693
|
| 766 |
-
now: 2022-04-05 02:55:49.966478
|
| 767 |
-
[76600] train_loss=0.0154212 valid_loss=0.0257234 valid_pos_acc=0.975149 valid_acc=0.995465 / 0.930268 / 0.829088 best_acc=0.995693
|
| 768 |
-
now: 2022-04-05 03:08:00.620954
|
| 769 |
-
[76800] train_loss=0.0151146 valid_loss=0.0246848 valid_pos_acc=0.975071 valid_acc=0.995398 / 0.930615 / 0.832294 best_acc=0.995693
|
| 770 |
-
now: 2022-04-05 03:20:08.341056
|
| 771 |
-
[77000] train_loss=0.0160863 valid_loss=0.0271371 valid_pos_acc=0.974689 valid_acc=0.995244 / 0.93075 / 0.823407 best_acc=0.995693
|
| 772 |
-
now: 2022-04-05 03:32:17.172790
|
| 773 |
-
[77200] train_loss=0.0157593 valid_loss=0.0253599 valid_pos_acc=0.974697 valid_acc=0.995528 / 0.929039 / 0.8295 best_acc=0.995693
|
| 774 |
-
now: 2022-04-05 03:44:21.046580
|
| 775 |
-
[77400] train_loss=0.0161056 valid_loss=0.0245615 valid_pos_acc=0.974919 valid_acc=0.995472 / 0.93189 / 0.832852 best_acc=0.995693
|
| 776 |
-
now: 2022-04-05 03:56:20.405982
|
| 777 |
-
[77600] train_loss=0.0149618 valid_loss=0.0246419 valid_pos_acc=0.975073 valid_acc=0.995513 / 0.932703 / 0.831197 best_acc=0.995693
|
| 778 |
-
now: 2022-04-05 04:08:28.594227
|
| 779 |
-
[77800] train_loss=0.016911 valid_loss=0.0236979 valid_pos_acc=0.974723 valid_acc=0.995596 / 0.931261 / 0.832728 best_acc=0.995693
|
| 780 |
-
now: 2022-04-05 04:20:31.662452
|
| 781 |
-
[78000] train_loss=0.0165653 valid_loss=0.0239289 valid_pos_acc=0.974678 valid_acc=0.995502 / 0.929082 / 0.827912 best_acc=0.995693
|
| 782 |
-
now: 2022-04-05 04:32:28.019168
|
| 783 |
-
[78200] train_loss=0.0167179 valid_loss=0.0249402 valid_pos_acc=0.9747 valid_acc=0.995502 / 0.923062 / 0.82385 best_acc=0.995693
|
| 784 |
-
now: 2022-04-05 04:44:26.471615
|
| 785 |
-
[78400] train_loss=0.0167333 valid_loss=0.0240457 valid_pos_acc=0.9746 valid_acc=0.995606 / 0.92763 / 0.83109 best_acc=0.995693
|
| 786 |
-
now: 2022-04-05 04:56:24.357334
|
| 787 |
-
[78600] train_loss=0.0145129 valid_loss=0.0249626 valid_pos_acc=0.974871 valid_acc=0.995366 / 0.927649 / 0.828631 best_acc=0.995693
|
| 788 |
-
now: 2022-04-05 05:08:26.198865
|
| 789 |
-
[78800] train_loss=0.0172761 valid_loss=0.0238513 valid_pos_acc=0.975407 valid_acc=0.995611 / 0.925864 / 0.831329 best_acc=0.995693
|
| 790 |
-
now: 2022-04-05 05:20:20.936271
|
| 791 |
-
[79000] train_loss=0.0153685 valid_loss=0.0233658 valid_pos_acc=0.97557 valid_acc=0.995645 / 0.931677 / 0.832522 best_acc=0.995693
|
| 792 |
-
now: 2022-04-05 05:32:17.131240
|
| 793 |
-
[79200] train_loss=0.0127379 valid_loss=0.0258231 valid_pos_acc=0.974604 valid_acc=0.995533 / 0.933338 / 0.832997 best_acc=0.995693
|
| 794 |
-
now: 2022-04-05 05:44:21.126943
|
| 795 |
-
[79400] train_loss=0.0127164 valid_loss=0.025304 valid_pos_acc=0.975057 valid_acc=0.995698 / 0.930098 / 0.832254 best_acc=0.995698
|
| 796 |
-
now: 2022-04-05 05:56:18.071160
|
| 797 |
-
[79600] train_loss=0.0110389 valid_loss=0.0258263 valid_pos_acc=0.975101 valid_acc=0.995643 / 0.930643 / 0.835766 best_acc=0.995698
|
| 798 |
-
now: 2022-04-05 06:08:12.503631
|
| 799 |
-
[79800] train_loss=0.0132959 valid_loss=0.0263015 valid_pos_acc=0.974487 valid_acc=0.995446 / 0.932765 / 0.83365 best_acc=0.995698
|
| 800 |
-
now: 2022-04-05 06:20:20.191496
|
| 801 |
-
[80000] train_loss=0.0138857 valid_loss=0.025657 valid_pos_acc=0.974903 valid_acc=0.995635 / 0.931425 / 0.834477 best_acc=0.995698
|
| 802 |
-
now: 2022-04-05 06:32:30.331123
|
| 803 |
-
[80200] train_loss=0.0129796 valid_loss=0.0252719 valid_pos_acc=0.974886 valid_acc=0.995539 / 0.930875 / 0.836388 best_acc=0.995698
|
| 804 |
-
now: 2022-04-05 06:44:36.817366
|
| 805 |
-
[80400] train_loss=0.0126967 valid_loss=0.0255622 valid_pos_acc=0.97511 valid_acc=0.995624 / 0.930096 / 0.832861 best_acc=0.995698
|
| 806 |
-
now: 2022-04-05 06:56:45.374554
|
| 807 |
-
[80600] train_loss=0.0142307 valid_loss=0.0258526 valid_pos_acc=0.97511 valid_acc=0.995489 / 0.932336 / 0.832256 best_acc=0.995698
|
| 808 |
-
now: 2022-04-05 07:08:47.240323
|
| 809 |
-
[80800] train_loss=0.0138569 valid_loss=0.0268916 valid_pos_acc=0.974849 valid_acc=0.995609 / 0.936109 / 0.834517 best_acc=0.995698
|
| 810 |
-
now: 2022-04-05 07:21:07.181733
|
| 811 |
-
[81000] train_loss=0.0126373 valid_loss=0.0253882 valid_pos_acc=0.975253 valid_acc=0.995587 / 0.932359 / 0.834985 best_acc=0.995698
|
| 812 |
-
now: 2022-04-05 07:33:38.912179
|
| 813 |
-
[81200] train_loss=0.0130838 valid_loss=0.0268405 valid_pos_acc=0.974689 valid_acc=0.995491 / 0.933348 / 0.837004 best_acc=0.995698
|
| 814 |
-
now: 2022-04-05 07:45:58.486868
|
| 815 |
-
[81400] train_loss=0.0134847 valid_loss=0.0259947 valid_pos_acc=0.974574 valid_acc=0.99542 / 0.934234 / 0.837761 best_acc=0.995698
|
| 816 |
-
now: 2022-04-05 07:58:01.699833
|
| 817 |
-
[81600] train_loss=0.0127687 valid_loss=0.0267179 valid_pos_acc=0.975088 valid_acc=0.995461 / 0.932898 / 0.833357 best_acc=0.995698
|
| 818 |
-
now: 2022-04-05 08:10:22.110075
|
| 819 |
-
[81800] train_loss=0.0125759 valid_loss=0.026263 valid_pos_acc=0.974645 valid_acc=0.995409 / 0.934211 / 0.83588 best_acc=0.995698
|
| 820 |
-
now: 2022-04-05 08:22:30.965145
|
| 821 |
-
[82000] train_loss=0.0138023 valid_loss=0.0262454 valid_pos_acc=0.97488 valid_acc=0.995481 / 0.933091 / 0.835412 best_acc=0.995698
|
| 822 |
-
now: 2022-04-05 08:34:55.047735
|
| 823 |
-
[82200] train_loss=0.0127197 valid_loss=0.0260373 valid_pos_acc=0.975346 valid_acc=0.995517 / 0.934354 / 0.834983 best_acc=0.995698
|
| 824 |
-
now: 2022-04-05 08:47:21.849999
|
| 825 |
-
[82400] train_loss=0.0133346 valid_loss=0.0259693 valid_pos_acc=0.975274 valid_acc=0.995563 / 0.932429 / 0.834782 best_acc=0.995698
|
| 826 |
-
now: 2022-04-05 08:59:46.077640
|
| 827 |
-
[82600] train_loss=0.0119814 valid_loss=0.0268453 valid_pos_acc=0.97447 valid_acc=0.995485 / 0.935298 / 0.838585 best_acc=0.995698
|
| 828 |
-
now: 2022-04-05 09:12:10.429291
|
| 829 |
-
[82800] train_loss=0.0138231 valid_loss=0.0256561 valid_pos_acc=0.975485 valid_acc=0.995604 / 0.932017 / 0.837154 best_acc=0.995698
|
| 830 |
-
now: 2022-04-05 09:24:25.503501
|
| 831 |
-
[83000] train_loss=0.0138415 valid_loss=0.0260283 valid_pos_acc=0.975131 valid_acc=0.99547 / 0.93434 / 0.835245 best_acc=0.995698
|
| 832 |
-
now: 2022-04-05 09:36:38.417154
|
| 833 |
-
[83200] train_loss=0.0124775 valid_loss=0.0260958 valid_pos_acc=0.975194 valid_acc=0.995672 / 0.935893 / 0.837375 best_acc=0.995698
|
| 834 |
-
now: 2022-04-05 09:48:54.047384
|
| 835 |
-
[83400] train_loss=0.0132698 valid_loss=0.0259098 valid_pos_acc=0.974975 valid_acc=0.995465 / 0.934838 / 0.8402 best_acc=0.995698
|
| 836 |
-
now: 2022-04-05 10:00:55.953481
|
| 837 |
-
[83600] train_loss=0.0134172 valid_loss=0.0258395 valid_pos_acc=0.975396 valid_acc=0.995544 / 0.934034 / 0.836583 best_acc=0.995698
|
| 838 |
-
now: 2022-04-05 10:12:56.731723
|
| 839 |
-
[83800] train_loss=0.0129522 valid_loss=0.0251187 valid_pos_acc=0.975463 valid_acc=0.995596 / 0.933734 / 0.833624 best_acc=0.995698
|
| 840 |
-
now: 2022-04-05 10:24:54.745844
|
| 841 |
-
[84000] train_loss=0.0141191 valid_loss=0.0254002 valid_pos_acc=0.975463 valid_acc=0.995585 / 0.935384 / 0.836613 best_acc=0.995698
|
| 842 |
-
now: 2022-04-05 10:37:00.190609
|
| 843 |
-
[84200] train_loss=0.013092 valid_loss=0.0266141 valid_pos_acc=0.97488 valid_acc=0.995517 / 0.934064 / 0.836598 best_acc=0.995698
|
| 844 |
-
now: 2022-04-05 10:49:11.600372
|
| 845 |
-
[84400] train_loss=0.0146677 valid_loss=0.025732 valid_pos_acc=0.975146 valid_acc=0.995476 / 0.934118 / 0.834405 best_acc=0.995698
|
| 846 |
-
now: 2022-04-05 11:01:26.620881
|
| 847 |
-
[84600] train_loss=0.0147797 valid_loss=0.0257641 valid_pos_acc=0.974908 valid_acc=0.9955 / 0.931502 / 0.830953 best_acc=0.995698
|
| 848 |
-
now: 2022-04-05 11:13:35.462462
|
| 849 |
-
[84800] train_loss=0.0130389 valid_loss=0.0259705 valid_pos_acc=0.97511 valid_acc=0.995654 / 0.933228 / 0.838112 best_acc=0.995698
|
| 850 |
-
now: 2022-04-05 11:25:41.335912
|
| 851 |
-
[85000] train_loss=0.0130362 valid_loss=0.0270572 valid_pos_acc=0.975012 valid_acc=0.995524 / 0.935052 / 0.83898 best_acc=0.995698
|
| 852 |
-
now: 2022-04-05 11:37:45.634992
|
| 853 |
-
[85200] train_loss=0.0136579 valid_loss=0.0259226 valid_pos_acc=0.975407 valid_acc=0.995561 / 0.932682 / 0.833727 best_acc=0.995698
|
| 854 |
-
now: 2022-04-05 11:49:49.322682
|
| 855 |
-
[85400] train_loss=0.0138471 valid_loss=0.0278054 valid_pos_acc=0.974313 valid_acc=0.995253 / 0.926638 / 0.826328 best_acc=0.995698
|
| 856 |
-
now: 2022-04-05 12:02:06.577902
|
| 857 |
-
[85600] train_loss=0.0141456 valid_loss=0.0270836 valid_pos_acc=0.97522 valid_acc=0.995439 / 0.931749 / 0.83259 best_acc=0.995698
|
| 858 |
-
now: 2022-04-05 12:14:09.865872
|
| 859 |
-
[85800] train_loss=0.0136233 valid_loss=0.0261673 valid_pos_acc=0.975724 valid_acc=0.995485 / 0.932979 / 0.832328 best_acc=0.995698
|
| 860 |
-
now: 2022-04-05 12:26:24.557886
|
| 861 |
-
[86000] train_loss=0.0129815 valid_loss=0.026333 valid_pos_acc=0.975672 valid_acc=0.995426 / 0.933565 / 0.836186 best_acc=0.995698
|
| 862 |
-
now: 2022-04-05 12:38:22.054353
|
| 863 |
-
[86200] train_loss=0.0138994 valid_loss=0.025931 valid_pos_acc=0.975602 valid_acc=0.995533 / 0.927751 / 0.828184 best_acc=0.995698
|
| 864 |
-
now: 2022-04-05 12:50:25.067251
|
| 865 |
-
[86400] train_loss=0.0136555 valid_loss=0.0258883 valid_pos_acc=0.975031 valid_acc=0.995409 / 0.927127 / 0.829497 best_acc=0.995698
|
| 866 |
-
now: 2022-04-05 13:02:21.412803
|
| 867 |
-
[86600] train_loss=0.0142481 valid_loss=0.0267677 valid_pos_acc=0.975535 valid_acc=0.995535 / 0.929604 / 0.830529 best_acc=0.995698
|
| 868 |
-
now: 2022-04-05 13:14:29.577497
|
| 869 |
-
[86800] train_loss=0.0142199 valid_loss=0.026345 valid_pos_acc=0.974986 valid_acc=0.995331 / 0.93041 / 0.828971 best_acc=0.995698
|
| 870 |
-
now: 2022-04-05 13:26:39.137708
|
| 871 |
-
[87000] train_loss=0.0132453 valid_loss=0.0277596 valid_pos_acc=0.975698 valid_acc=0.995459 / 0.929227 / 0.825919 best_acc=0.995698
|
| 872 |
-
now: 2022-04-05 13:38:41.105816
|
| 873 |
-
[87200] train_loss=0.0140319 valid_loss=0.0262992 valid_pos_acc=0.975387 valid_acc=0.995372 / 0.925482 / 0.828088 best_acc=0.995698
|
| 874 |
-
now: 2022-04-05 13:50:47.452684
|
| 875 |
-
[87400] train_loss=0.0143099 valid_loss=0.0264765 valid_pos_acc=0.974565 valid_acc=0.995402 / 0.930457 / 0.829678 best_acc=0.995698
|
| 876 |
-
now: 2022-04-05 14:02:54.386306
|
| 877 |
-
[87600] train_loss=0.0139416 valid_loss=0.0260449 valid_pos_acc=0.975598 valid_acc=0.99555 / 0.930947 / 0.830807 best_acc=0.995698
|
| 878 |
-
now: 2022-04-05 14:14:49.214068
|
| 879 |
-
[87800] train_loss=0.013045 valid_loss=0.0260407 valid_pos_acc=0.974786 valid_acc=0.995637 / 0.934269 / 0.839114 best_acc=0.995698
|
| 880 |
-
now: 2022-04-05 14:26:42.833491
|
| 881 |
-
[88000] train_loss=0.0141046 valid_loss=0.0263996 valid_pos_acc=0.975235 valid_acc=0.995537 / 0.933095 / 0.831651 best_acc=0.995698
|
| 882 |
-
now: 2022-04-05 14:38:40.768985
|
| 883 |
-
[88200] train_loss=0.0146935 valid_loss=0.0263447 valid_pos_acc=0.975728 valid_acc=0.995494 / 0.932254 / 0.829169 best_acc=0.995698
|
| 884 |
-
now: 2022-04-05 14:50:39.132010
|
| 885 |
-
[88400] train_loss=0.013988 valid_loss=0.0252771 valid_pos_acc=0.975452 valid_acc=0.995494 / 0.928645 / 0.829601 best_acc=0.995698
|
| 886 |
-
now: 2022-04-05 15:02:36.068409
|
| 887 |
-
[88600] train_loss=0.0131047 valid_loss=0.0262127 valid_pos_acc=0.975194 valid_acc=0.995539 / 0.933965 / 0.833988 best_acc=0.995698
|
| 888 |
-
now: 2022-04-05 15:14:39.273565
|
| 889 |
-
[88800] train_loss=0.0134752 valid_loss=0.0262747 valid_pos_acc=0.975752 valid_acc=0.995544 / 0.93222 / 0.832838 best_acc=0.995698
|
| 890 |
-
now: 2022-04-05 15:26:47.762768
|
| 891 |
-
[89000] train_loss=0.0140879 valid_loss=0.026511 valid_pos_acc=0.975494 valid_acc=0.995559 / 0.930583 / 0.832717 best_acc=0.995698
|
| 892 |
-
now: 2022-04-05 15:38:34.298601
|
| 893 |
-
[89200] train_loss=0.0145324 valid_loss=0.0263811 valid_pos_acc=0.975348 valid_acc=0.995639 / 0.933692 / 0.831782 best_acc=0.995698
|
| 894 |
-
now: 2022-04-05 15:50:34.391187
|
| 895 |
-
[89400] train_loss=0.0143614 valid_loss=0.0266067 valid_pos_acc=0.975589 valid_acc=0.99558 / 0.93295 / 0.832933 best_acc=0.995698
|
| 896 |
-
now: 2022-04-05 16:02:28.811246
|
| 897 |
-
[89600] train_loss=0.0139432 valid_loss=0.025967 valid_pos_acc=0.975483 valid_acc=0.995678 / 0.930272 / 0.833364 best_acc=0.995698
|
| 898 |
-
now: 2022-04-05 16:14:16.963671
|
| 899 |
-
[89800] train_loss=0.015888 valid_loss=0.025742 valid_pos_acc=0.975517 valid_acc=0.995591 / 0.930457 / 0.833334 best_acc=0.995698
|
| 900 |
-
now: 2022-04-05 16:26:24.078615
|
| 901 |
-
[90000] train_loss=0.0144244 valid_loss=0.0256335 valid_pos_acc=0.975693 valid_acc=0.995632 / 0.93239 / 0.830571 best_acc=0.995698
|
| 902 |
-
now: 2022-04-05 16:38:39.760929
|
| 903 |
-
[90200] train_loss=0.0143907 valid_loss=0.0264985 valid_pos_acc=0.975496 valid_acc=0.995324 / 0.930264 / 0.822809 best_acc=0.995698
|
| 904 |
-
now: 2022-04-05 16:50:47.722632
|
| 905 |
-
[90400] train_loss=0.0137474 valid_loss=0.0254164 valid_pos_acc=0.975394 valid_acc=0.995574 / 0.93623 / 0.834253 best_acc=0.995698
|
| 906 |
-
now: 2022-04-05 17:02:52.333896
|
| 907 |
-
[90600] train_loss=0.0142556 valid_loss=0.0260749 valid_pos_acc=0.975272 valid_acc=0.995496 / 0.932219 / 0.832236 best_acc=0.995698
|
| 908 |
-
now: 2022-04-05 17:14:37.154862
|
| 909 |
-
[90800] train_loss=0.0138562 valid_loss=0.0259641 valid_pos_acc=0.975235 valid_acc=0.99537 / 0.92701 / 0.827433 best_acc=0.995698
|
| 910 |
-
now: 2022-04-05 17:26:38.096136
|
| 911 |
-
[91000] train_loss=0.0128578 valid_loss=0.0257195 valid_pos_acc=0.975418 valid_acc=0.995511 / 0.927246 / 0.829352 best_acc=0.995698
|
| 912 |
-
now: 2022-04-05 17:38:33.834741
|
| 913 |
-
[91200] train_loss=0.0139655 valid_loss=0.0263024 valid_pos_acc=0.975672 valid_acc=0.995513 / 0.931341 / 0.827522 best_acc=0.995698
|
| 914 |
-
now: 2022-04-05 17:50:27.641391
|
| 915 |
-
[91400] train_loss=0.0143926 valid_loss=0.0255845 valid_pos_acc=0.975272 valid_acc=0.995307 / 0.931624 / 0.830926 best_acc=0.995698
|
| 916 |
-
now: 2022-04-05 18:02:38.786522
|
| 917 |
-
[91600] train_loss=0.0145323 valid_loss=0.0253511 valid_pos_acc=0.975374 valid_acc=0.995478 / 0.93136 / 0.832302 best_acc=0.995698
|
| 918 |
-
now: 2022-04-05 18:14:33.259268
|
| 919 |
-
[91800] train_loss=0.0145714 valid_loss=0.0256718 valid_pos_acc=0.975031 valid_acc=0.995468 / 0.929101 / 0.830769 best_acc=0.995698
|
| 920 |
-
now: 2022-04-05 18:26:31.723280
|
| 921 |
-
[92000] train_loss=0.0150823 valid_loss=0.025818 valid_pos_acc=0.975392 valid_acc=0.995515 / 0.933814 / 0.836218 best_acc=0.995698
|
| 922 |
-
now: 2022-04-05 18:38:37.282828
|
| 923 |
-
[92200] train_loss=0.0144193 valid_loss=0.0249235 valid_pos_acc=0.975411 valid_acc=0.995576 / 0.930254 / 0.831995 best_acc=0.995698
|
| 924 |
-
now: 2022-04-05 18:50:39.861549
|
| 925 |
-
[92400] train_loss=0.0145149 valid_loss=0.0255686 valid_pos_acc=0.97557 valid_acc=0.995656 / 0.93331 / 0.837843 best_acc=0.995698
|
| 926 |
-
now: 2022-04-05 19:02:40.114394
|
| 927 |
-
[92600] train_loss=0.0136907 valid_loss=0.0251938 valid_pos_acc=0.975494 valid_acc=0.995682 / 0.934727 / 0.838754 best_acc=0.995698
|
| 928 |
-
now: 2022-04-05 19:14:38.262458
|
| 929 |
-
[92800] train_loss=0.0145302 valid_loss=0.0252417 valid_pos_acc=0.97527 valid_acc=0.995661 / 0.932038 / 0.837297 best_acc=0.995698
|
| 930 |
-
now: 2022-04-05 19:26:38.439473
|
| 931 |
-
[93000] train_loss=0.0156955 valid_loss=0.0263089 valid_pos_acc=0.975235 valid_acc=0.995504 / 0.933166 / 0.828832 best_acc=0.995698
|
| 932 |
-
now: 2022-04-05 19:38:54.778835
|
| 933 |
-
[93200] train_loss=0.0159822 valid_loss=0.0254963 valid_pos_acc=0.975159 valid_acc=0.995413 / 0.93672 / 0.840871 best_acc=0.995698
|
| 934 |
-
now: 2022-04-05 19:50:52.991181
|
| 935 |
-
[93400] train_loss=0.0137888 valid_loss=0.0254194 valid_pos_acc=0.975212 valid_acc=0.995617 / 0.933914 / 0.832067 best_acc=0.995698
|
| 936 |
-
now: 2022-04-05 20:03:09.308012
|
| 937 |
-
[93600] train_loss=0.0159022 valid_loss=0.0244989 valid_pos_acc=0.97522 valid_acc=0.995565 / 0.933225 / 0.836939 best_acc=0.995698
|
| 938 |
-
now: 2022-04-05 20:15:19.273222
|
| 939 |
-
[93800] train_loss=0.0142521 valid_loss=0.0248734 valid_pos_acc=0.975294 valid_acc=0.995643 / 0.93422 / 0.840297 best_acc=0.995698
|
| 940 |
-
now: 2022-04-05 20:27:27.195510
|
| 941 |
-
[94000] train_loss=0.0150861 valid_loss=0.0239483 valid_pos_acc=0.975533 valid_acc=0.995619 / 0.936434 / 0.84042 best_acc=0.995698
|
| 942 |
-
now: 2022-04-05 20:39:32.119195
|
| 943 |
-
[94200] train_loss=0.0152762 valid_loss=0.0248546 valid_pos_acc=0.975739 valid_acc=0.99565 / 0.932887 / 0.835955 best_acc=0.995698
|
| 944 |
-
now: 2022-04-05 20:51:35.782856
|
| 945 |
-
[94400] train_loss=0.0141213 valid_loss=0.0250847 valid_pos_acc=0.974971 valid_acc=0.995682 / 0.935789 / 0.840368 best_acc=0.995698
|
| 946 |
-
now: 2022-04-05 21:03:36.489728
|
| 947 |
-
[94600] train_loss=0.0144385 valid_loss=0.0249789 valid_pos_acc=0.975439 valid_acc=0.995544 / 0.938386 / 0.840405 best_acc=0.995698
|
| 948 |
-
now: 2022-04-05 21:15:30.678169
|
| 949 |
-
[94800] train_loss=0.013553 valid_loss=0.0256229 valid_pos_acc=0.975455 valid_acc=0.995611 / 0.93238 / 0.837687 best_acc=0.995698
|
| 950 |
-
now: 2022-04-05 21:27:32.536531
|
| 951 |
-
[95000] train_loss=0.0158608 valid_loss=0.0256513 valid_pos_acc=0.97537 valid_acc=0.995528 / 0.935879 / 0.844516 best_acc=0.995698
|
| 952 |
-
now: 2022-04-05 21:39:32.357321
|
| 953 |
-
[95200] train_loss=0.0151035 valid_loss=0.0254666 valid_pos_acc=0.97575 valid_acc=0.995669 / 0.934501 / 0.837649 best_acc=0.995698
|
| 954 |
-
now: 2022-04-05 21:51:43.227092
|
| 955 |
-
[95400] train_loss=0.0151553 valid_loss=0.0249784 valid_pos_acc=0.975474 valid_acc=0.995526 / 0.934746 / 0.835569 best_acc=0.995698
|
| 956 |
-
now: 2022-04-05 22:03:48.247576
|
| 957 |
-
[95600] train_loss=0.0155748 valid_loss=0.0260542 valid_pos_acc=0.975218 valid_acc=0.995552 / 0.933312 / 0.835163 best_acc=0.995698
|
| 958 |
-
now: 2022-04-05 22:15:38.086255
|
| 959 |
-
[95800] train_loss=0.0146772 valid_loss=0.0247799 valid_pos_acc=0.975637 valid_acc=0.995626 / 0.930346 / 0.833518 best_acc=0.995698
|
| 960 |
-
now: 2022-04-05 22:27:43.689507
|
| 961 |
-
[96000] train_loss=0.01602 valid_loss=0.0267345 valid_pos_acc=0.975233 valid_acc=0.995433 / 0.931918 / 0.829208 best_acc=0.995698
|
| 962 |
-
now: 2022-04-05 22:39:43.113855
|
| 963 |
-
[96200] train_loss=0.0151231 valid_loss=0.0255382 valid_pos_acc=0.9755 valid_acc=0.995478 / 0.926487 / 0.826398 best_acc=0.995698
|
| 964 |
-
now: 2022-04-05 22:51:31.072458
|
| 965 |
-
[96400] train_loss=0.0149376 valid_loss=0.0258116 valid_pos_acc=0.97491 valid_acc=0.995498 / 0.928117 / 0.831135 best_acc=0.995698
|
| 966 |
-
now: 2022-04-05 23:03:31.843003
|
| 967 |
-
[96600] train_loss=0.0144005 valid_loss=0.0254002 valid_pos_acc=0.975784 valid_acc=0.99558 / 0.930447 / 0.829763 best_acc=0.995698
|
| 968 |
-
now: 2022-04-05 23:15:34.625356
|
| 969 |
-
[96800] train_loss=0.0146142 valid_loss=0.0268592 valid_pos_acc=0.974348 valid_acc=0.995389 / 0.930559 / 0.830846 best_acc=0.995698
|
| 970 |
-
now: 2022-04-05 23:27:30.008875
|
| 971 |
-
[97000] train_loss=0.0142077 valid_loss=0.025247 valid_pos_acc=0.975439 valid_acc=0.995654 / 0.92956 / 0.835155 best_acc=0.995698
|
| 972 |
-
now: 2022-04-05 23:39:35.358671
|
| 973 |
-
[97200] train_loss=0.0136525 valid_loss=0.025149 valid_pos_acc=0.975429 valid_acc=0.995604 / 0.932177 / 0.838884 best_acc=0.995698
|
| 974 |
-
now: 2022-04-05 23:51:42.518252
|
| 975 |
-
[97400] train_loss=0.0142976 valid_loss=0.0252632 valid_pos_acc=0.975637 valid_acc=0.99558 / 0.928696 / 0.831515 best_acc=0.995698
|
| 976 |
-
now: 2022-04-06 00:03:39.610613
|
| 977 |
-
[97600] train_loss=0.01445 valid_loss=0.0247523 valid_pos_acc=0.975652 valid_acc=0.995626 / 0.928967 / 0.837404 best_acc=0.995698
|
| 978 |
-
now: 2022-04-06 00:15:39.948279
|
| 979 |
-
[97800] train_loss=0.0138557 valid_loss=0.0250991 valid_pos_acc=0.975672 valid_acc=0.99553 / 0.928176 / 0.832755 best_acc=0.995698
|
| 980 |
-
now: 2022-04-06 00:27:35.432314
|
| 981 |
-
[98000] train_loss=0.015877 valid_loss=0.0247035 valid_pos_acc=0.975576 valid_acc=0.995548 / 0.928882 / 0.835904 best_acc=0.995698
|
| 982 |
-
now: 2022-04-06 00:39:42.196877
|
| 983 |
-
[98200] train_loss=0.0141082 valid_loss=0.0244627 valid_pos_acc=0.975843 valid_acc=0.995676 / 0.928219 / 0.833121 best_acc=0.995698
|
| 984 |
-
now: 2022-04-06 00:51:45.295429
|
| 985 |
-
[98400] train_loss=0.0146779 valid_loss=0.0248106 valid_pos_acc=0.975962 valid_acc=0.995632 / 0.927196 / 0.83131 best_acc=0.995698
|
| 986 |
-
now: 2022-04-06 01:03:48.072286
|
| 987 |
-
[98600] train_loss=0.0150793 valid_loss=0.024931 valid_pos_acc=0.975741 valid_acc=0.995587 / 0.927742 / 0.832345 best_acc=0.995698
|
| 988 |
-
now: 2022-04-06 01:15:49.874914
|
| 989 |
-
[98800] train_loss=0.0138506 valid_loss=0.0256198 valid_pos_acc=0.976136 valid_acc=0.995611 / 0.929698 / 0.830346 best_acc=0.995698
|
| 990 |
-
now: 2022-04-06 01:27:46.508890
|
| 991 |
-
[99000] train_loss=0.0118288 valid_loss=0.026124 valid_pos_acc=0.976116 valid_acc=0.995596 / 0.930481 / 0.833991 best_acc=0.995698
|
| 992 |
-
now: 2022-04-06 01:39:52.754135
|
| 993 |
-
[99200] train_loss=0.0115159 valid_loss=0.0254321 valid_pos_acc=0.975875 valid_acc=0.995689 / 0.93085 / 0.835924 best_acc=0.995698
|
| 994 |
-
now: 2022-04-06 01:51:59.498058
|
| 995 |
-
[99400] train_loss=0.0118415 valid_loss=0.0261764 valid_pos_acc=0.976184 valid_acc=0.995604 / 0.930402 / 0.832654 best_acc=0.995698
|
| 996 |
-
now: 2022-04-06 02:04:00.516174
|
| 997 |
-
[99600] train_loss=0.0111765 valid_loss=0.0253784 valid_pos_acc=0.976212 valid_acc=0.995789 / 0.930556 / 0.834549 best_acc=0.995789
|
| 998 |
-
now: 2022-04-06 02:16:10.608263
|
| 999 |
-
[99800] train_loss=0.0122449 valid_loss=0.0257746 valid_pos_acc=0.976166 valid_acc=0.995726 / 0.931839 / 0.838174 best_acc=0.995789
|
| 1000 |
-
now: 2022-04-06 02:28:12.819393
|
| 1001 |
-
[100000] train_loss=0.0122801 valid_loss=0.0260771 valid_pos_acc=0.975372 valid_acc=0.99555 / 0.929529 / 0.838038 best_acc=0.995789
|
| 1002 |
-
now: 2022-04-06 02:40:11.627022
|
| 1003 |
-
testing ...
|
| 1004 |
-
reloading best accuracy model ...
|
| 1005 |
-
valid_best_acc=0.995789 test_loss=0.0266495 test_pos_acc=0.975942 test_acc=0.995605 / 0.918997 / 0.806213
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text/G2PWModel/version
DELETED
|
@@ -1 +0,0 @@
|
|
| 1 |
-
v2.0
|
|
|
|
|
|
text/LangSegmenter/__init__.py
DELETED
|
@@ -1 +0,0 @@
|
|
| 1 |
-
from .langsegmenter import LangSegmenter
|
|
|
|
|
|
text/LangSegmenter/langsegmenter.py
DELETED
|
@@ -1,175 +0,0 @@
|
|
| 1 |
-
import logging
|
| 2 |
-
import re
|
| 3 |
-
|
| 4 |
-
# jieba静音
|
| 5 |
-
import jieba
|
| 6 |
-
jieba.setLogLevel(logging.CRITICAL)
|
| 7 |
-
|
| 8 |
-
# 更改fast_langdetect大模型位置
|
| 9 |
-
from pathlib import Path
|
| 10 |
-
import fast_langdetect
|
| 11 |
-
fast_langdetect.infer._default_detector = fast_langdetect.infer.LangDetector(fast_langdetect.infer.LangDetectConfig(cache_dir=Path(__file__).parent.parent.parent / "pretrained_models" / "fast_langdetect"))
|
| 12 |
-
|
| 13 |
-
|
| 14 |
-
from split_lang import LangSplitter
|
| 15 |
-
|
| 16 |
-
|
| 17 |
-
def full_en(text):
|
| 18 |
-
pattern = r'^(?=.*[A-Za-z])[A-Za-z0-9\s\u0020-\u007E\u2000-\u206F\u3000-\u303F\uFF00-\uFFEF]+$'
|
| 19 |
-
return bool(re.match(pattern, text))
|
| 20 |
-
|
| 21 |
-
|
| 22 |
-
def full_cjk(text):
|
| 23 |
-
# 来自wiki
|
| 24 |
-
cjk_ranges = [
|
| 25 |
-
(0x4E00, 0x9FFF), # CJK Unified Ideographs
|
| 26 |
-
(0x3400, 0x4DB5), # CJK Extension A
|
| 27 |
-
(0x20000, 0x2A6DD), # CJK Extension B
|
| 28 |
-
(0x2A700, 0x2B73F), # CJK Extension C
|
| 29 |
-
(0x2B740, 0x2B81F), # CJK Extension D
|
| 30 |
-
(0x2B820, 0x2CEAF), # CJK Extension E
|
| 31 |
-
(0x2CEB0, 0x2EBEF), # CJK Extension F
|
| 32 |
-
(0x30000, 0x3134A), # CJK Extension G
|
| 33 |
-
(0x31350, 0x323AF), # CJK Extension H
|
| 34 |
-
(0x2EBF0, 0x2EE5D), # CJK Extension H
|
| 35 |
-
]
|
| 36 |
-
|
| 37 |
-
pattern = r'[0-9、-〜。！？.!?… /]+$'
|
| 38 |
-
|
| 39 |
-
cjk_text = ""
|
| 40 |
-
for char in text:
|
| 41 |
-
code_point = ord(char)
|
| 42 |
-
in_cjk = any(start <= code_point <= end for start, end in cjk_ranges)
|
| 43 |
-
if in_cjk or re.match(pattern, char):
|
| 44 |
-
cjk_text += char
|
| 45 |
-
return cjk_text
|
| 46 |
-
|
| 47 |
-
|
| 48 |
-
def split_jako(tag_lang,item):
|
| 49 |
-
if tag_lang == "ja":
|
| 50 |
-
pattern = r"([\u3041-\u3096\u3099\u309A\u30A1-\u30FA\u30FC]+(?:[0-9、-〜。！？.!?… ]+[\u3041-\u3096\u3099\u309A\u30A1-\u30FA\u30FC]*)*)"
|
| 51 |
-
else:
|
| 52 |
-
pattern = r"([\u1100-\u11FF\u3130-\u318F\uAC00-\uD7AF]+(?:[0-9、-〜。！？.!?… ]+[\u1100-\u11FF\u3130-\u318F\uAC00-\uD7AF]*)*)"
|
| 53 |
-
|
| 54 |
-
lang_list: list[dict] = []
|
| 55 |
-
tag = 0
|
| 56 |
-
for match in re.finditer(pattern, item['text']):
|
| 57 |
-
if match.start() > tag:
|
| 58 |
-
lang_list.append({'lang':item['lang'],'text':item['text'][tag:match.start()]})
|
| 59 |
-
|
| 60 |
-
tag = match.end()
|
| 61 |
-
lang_list.append({'lang':tag_lang,'text':item['text'][match.start():match.end()]})
|
| 62 |
-
|
| 63 |
-
if tag < len(item['text']):
|
| 64 |
-
lang_list.append({'lang':item['lang'],'text':item['text'][tag:len(item['text'])]})
|
| 65 |
-
|
| 66 |
-
return lang_list
|
| 67 |
-
|
| 68 |
-
|
| 69 |
-
def merge_lang(lang_list, item):
|
| 70 |
-
if lang_list and item['lang'] == lang_list[-1]['lang']:
|
| 71 |
-
lang_list[-1]['text'] += item['text']
|
| 72 |
-
else:
|
| 73 |
-
lang_list.append(item)
|
| 74 |
-
return lang_list
|
| 75 |
-
|
| 76 |
-
|
| 77 |
-
class LangSegmenter():
|
| 78 |
-
# 默认过滤器, 基于gsv目前四种语言
|
| 79 |
-
DEFAULT_LANG_MAP = {
|
| 80 |
-
"zh": "zh",
|
| 81 |
-
"yue": "zh", # 粤语
|
| 82 |
-
"wuu": "zh", # 吴语
|
| 83 |
-
"zh-cn": "zh",
|
| 84 |
-
"zh-tw": "x", # 繁体设置为x
|
| 85 |
-
"ko": "ko",
|
| 86 |
-
"ja": "ja",
|
| 87 |
-
"en": "en",
|
| 88 |
-
}
|
| 89 |
-
|
| 90 |
-
|
| 91 |
-
def getTexts(text):
|
| 92 |
-
lang_splitter = LangSplitter(lang_map=LangSegmenter.DEFAULT_LANG_MAP)
|
| 93 |
-
substr = lang_splitter.split_by_lang(text=text)
|
| 94 |
-
|
| 95 |
-
lang_list: list[dict] = []
|
| 96 |
-
|
| 97 |
-
for _, item in enumerate(substr):
|
| 98 |
-
dict_item = {'lang':item.lang,'text':item.text}
|
| 99 |
-
|
| 100 |
-
# 处理短英文被识别为其他语言的问题
|
| 101 |
-
if full_en(dict_item['text']):
|
| 102 |
-
dict_item['lang'] = 'en'
|
| 103 |
-
lang_list = merge_lang(lang_list,dict_item)
|
| 104 |
-
continue
|
| 105 |
-
|
| 106 |
-
# 处理非日语夹日文的问题(不包含CJK)
|
| 107 |
-
ja_list: list[dict] = []
|
| 108 |
-
if dict_item['lang'] != 'ja':
|
| 109 |
-
ja_list = split_jako('ja',dict_item)
|
| 110 |
-
|
| 111 |
-
if not ja_list:
|
| 112 |
-
ja_list.append(dict_item)
|
| 113 |
-
|
| 114 |
-
# 处理非韩语夹韩语的问题(不包含CJK)
|
| 115 |
-
ko_list: list[dict] = []
|
| 116 |
-
temp_list: list[dict] = []
|
| 117 |
-
for _, ko_item in enumerate(ja_list):
|
| 118 |
-
if ko_item["lang"] != 'ko':
|
| 119 |
-
ko_list = split_jako('ko',ko_item)
|
| 120 |
-
|
| 121 |
-
if ko_list:
|
| 122 |
-
temp_list.extend(ko_list)
|
| 123 |
-
else:
|
| 124 |
-
temp_list.append(ko_item)
|
| 125 |
-
|
| 126 |
-
# 未存在非日韩文夹日韩文
|
| 127 |
-
if len(temp_list) == 1:
|
| 128 |
-
# 未知语言检查是否为CJK
|
| 129 |
-
if dict_item['lang'] == 'x':
|
| 130 |
-
cjk_text = full_cjk(dict_item['text'])
|
| 131 |
-
if cjk_text:
|
| 132 |
-
dict_item = {'lang':'zh','text':cjk_text}
|
| 133 |
-
lang_list = merge_lang(lang_list,dict_item)
|
| 134 |
-
else:
|
| 135 |
-
lang_list = merge_lang(lang_list,dict_item)
|
| 136 |
-
continue
|
| 137 |
-
else:
|
| 138 |
-
lang_list = merge_lang(lang_list,dict_item)
|
| 139 |
-
continue
|
| 140 |
-
|
| 141 |
-
# 存在非日韩文夹日韩文
|
| 142 |
-
for _, temp_item in enumerate(temp_list):
|
| 143 |
-
# 未知语言检查是否为CJK
|
| 144 |
-
if temp_item['lang'] == 'x':
|
| 145 |
-
cjk_text = full_cjk(dict_item['text'])
|
| 146 |
-
if cjk_text:
|
| 147 |
-
dict_item = {'lang':'zh','text':cjk_text}
|
| 148 |
-
lang_list = merge_lang(lang_list,dict_item)
|
| 149 |
-
else:
|
| 150 |
-
lang_list = merge_lang(lang_list,dict_item)
|
| 151 |
-
else:
|
| 152 |
-
lang_list = merge_lang(lang_list,temp_item)
|
| 153 |
-
|
| 154 |
-
temp_list = lang_list
|
| 155 |
-
lang_list = []
|
| 156 |
-
for _, temp_item in enumerate(temp_list):
|
| 157 |
-
if temp_item['lang'] == 'x':
|
| 158 |
-
if lang_list:
|
| 159 |
-
temp_item['lang'] = lang_list[-1]['lang']
|
| 160 |
-
elif len(temp_list) > 1:
|
| 161 |
-
temp_item['lang'] = temp_list[1]['lang']
|
| 162 |
-
else:
|
| 163 |
-
temp_item['lang'] = 'zh'
|
| 164 |
-
|
| 165 |
-
lang_list = merge_lang(lang_list,temp_item)
|
| 166 |
-
|
| 167 |
-
return lang_list
|
| 168 |
-
|
| 169 |
-
|
| 170 |
-
if __name__ == "__main__":
|
| 171 |
-
text = "MyGO?,你也喜欢まいご吗？"
|
| 172 |
-
print(LangSegmenter.getTexts(text))
|
| 173 |
-
|
| 174 |
-
text = "ねえ、知ってる？最近、僕は天文学を勉強してるんだ。君の瞳が星空みたいにキラキラしてるからさ。"
|
| 175 |
-
print(LangSegmenter.getTexts(text))
|
|
|
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text/__init__.py
DELETED
|
@@ -1,28 +0,0 @@
|
|
| 1 |
-
import os
|
| 2 |
-
# if os.environ.get("version","v1")=="v1":
|
| 3 |
-
# from text.symbols import symbols
|
| 4 |
-
# else:
|
| 5 |
-
# from text.symbols2 import symbols
|
| 6 |
-
|
| 7 |
-
from text import symbols as symbols_v1
|
| 8 |
-
from text import symbols2 as symbols_v2
|
| 9 |
-
|
| 10 |
-
_symbol_to_id_v1 = {s: i for i, s in enumerate(symbols_v1.symbols)}
|
| 11 |
-
_symbol_to_id_v2 = {s: i for i, s in enumerate(symbols_v2.symbols)}
|
| 12 |
-
|
| 13 |
-
|
| 14 |
-
def cleaned_text_to_sequence(cleaned_text, version=None):
|
| 15 |
-
"""Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
|
| 16 |
-
Args:
|
| 17 |
-
text: string to convert to a sequence
|
| 18 |
-
Returns:
|
| 19 |
-
List of integers corresponding to the symbols in the text
|
| 20 |
-
"""
|
| 21 |
-
if version is None:
|
| 22 |
-
version = os.environ.get("version", "v2")
|
| 23 |
-
if version == "v1":
|
| 24 |
-
phones = [_symbol_to_id_v1[symbol] for symbol in cleaned_text]
|
| 25 |
-
else:
|
| 26 |
-
phones = [_symbol_to_id_v2[symbol] for symbol in cleaned_text]
|
| 27 |
-
|
| 28 |
-
return phones
|
|
|
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|
text/cantonese.py
DELETED
|
@@ -1,222 +0,0 @@
|
|
| 1 |
-
# reference: https://huggingface.co/spaces/Naozumi0512/Bert-VITS2-Cantonese-Yue/blob/main/text/chinese.py
|
| 2 |
-
|
| 3 |
-
import re
|
| 4 |
-
import cn2an
|
| 5 |
-
import ToJyutping
|
| 6 |
-
|
| 7 |
-
from text.symbols import punctuation
|
| 8 |
-
from text.zh_normalization.text_normlization import TextNormalizer
|
| 9 |
-
|
| 10 |
-
normalizer = lambda x: cn2an.transform(x, "an2cn")
|
| 11 |
-
|
| 12 |
-
INITIALS = [
|
| 13 |
-
"aa",
|
| 14 |
-
"aai",
|
| 15 |
-
"aak",
|
| 16 |
-
"aap",
|
| 17 |
-
"aat",
|
| 18 |
-
"aau",
|
| 19 |
-
"ai",
|
| 20 |
-
"au",
|
| 21 |
-
"ap",
|
| 22 |
-
"at",
|
| 23 |
-
"ak",
|
| 24 |
-
"a",
|
| 25 |
-
"p",
|
| 26 |
-
"b",
|
| 27 |
-
"e",
|
| 28 |
-
"ts",
|
| 29 |
-
"t",
|
| 30 |
-
"dz",
|
| 31 |
-
"d",
|
| 32 |
-
"kw",
|
| 33 |
-
"k",
|
| 34 |
-
"gw",
|
| 35 |
-
"g",
|
| 36 |
-
"f",
|
| 37 |
-
"h",
|
| 38 |
-
"l",
|
| 39 |
-
"m",
|
| 40 |
-
"ng",
|
| 41 |
-
"n",
|
| 42 |
-
"s",
|
| 43 |
-
"y",
|
| 44 |
-
"w",
|
| 45 |
-
"c",
|
| 46 |
-
"z",
|
| 47 |
-
"j",
|
| 48 |
-
"ong",
|
| 49 |
-
"on",
|
| 50 |
-
"ou",
|
| 51 |
-
"oi",
|
| 52 |
-
"ok",
|
| 53 |
-
"o",
|
| 54 |
-
"uk",
|
| 55 |
-
"ung",
|
| 56 |
-
]
|
| 57 |
-
INITIALS += ["sp", "spl", "spn", "sil"]
|
| 58 |
-
|
| 59 |
-
|
| 60 |
-
rep_map = {
|
| 61 |
-
"：": ",",
|
| 62 |
-
"；": ",",
|
| 63 |
-
"，": ",",
|
| 64 |
-
"。": ".",
|
| 65 |
-
"！": "!",
|
| 66 |
-
"？": "?",
|
| 67 |
-
"\n": ".",
|
| 68 |
-
"·": ",",
|
| 69 |
-
"、": ",",
|
| 70 |
-
"...": "…",
|
| 71 |
-
"$": ".",
|
| 72 |
-
"“": "'",
|
| 73 |
-
"”": "'",
|
| 74 |
-
'"': "'",
|
| 75 |
-
"‘": "'",
|
| 76 |
-
"’": "'",
|
| 77 |
-
"（": "'",
|
| 78 |
-
"）": "'",
|
| 79 |
-
"(": "'",
|
| 80 |
-
")": "'",
|
| 81 |
-
"《": "'",
|
| 82 |
-
"》": "'",
|
| 83 |
-
"【": "'",
|
| 84 |
-
"】": "'",
|
| 85 |
-
"[": "'",
|
| 86 |
-
"]": "'",
|
| 87 |
-
"—": "-",
|
| 88 |
-
"～": "-",
|
| 89 |
-
"~": "-",
|
| 90 |
-
"「": "'",
|
| 91 |
-
"」": "'",
|
| 92 |
-
}
|
| 93 |
-
|
| 94 |
-
|
| 95 |
-
def replace_punctuation(text):
|
| 96 |
-
# text = text.replace("嗯", "恩").replace("呣", "母")
|
| 97 |
-
pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
|
| 98 |
-
|
| 99 |
-
replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
|
| 100 |
-
|
| 101 |
-
replaced_text = re.sub(r"[^\u4e00-\u9fa5" + "".join(punctuation) + r"]+", "", replaced_text)
|
| 102 |
-
|
| 103 |
-
return replaced_text
|
| 104 |
-
|
| 105 |
-
|
| 106 |
-
def text_normalize(text):
|
| 107 |
-
tx = TextNormalizer()
|
| 108 |
-
sentences = tx.normalize(text)
|
| 109 |
-
dest_text = ""
|
| 110 |
-
for sentence in sentences:
|
| 111 |
-
dest_text += replace_punctuation(sentence)
|
| 112 |
-
return dest_text
|
| 113 |
-
|
| 114 |
-
|
| 115 |
-
punctuation_set = set(punctuation)
|
| 116 |
-
|
| 117 |
-
|
| 118 |
-
def jyuping_to_initials_finals_tones(jyuping_syllables):
|
| 119 |
-
initials_finals = []
|
| 120 |
-
tones = []
|
| 121 |
-
word2ph = []
|
| 122 |
-
|
| 123 |
-
for syllable in jyuping_syllables:
|
| 124 |
-
if syllable in punctuation:
|
| 125 |
-
initials_finals.append(syllable)
|
| 126 |
-
tones.append(0)
|
| 127 |
-
word2ph.append(1) # Add 1 for punctuation
|
| 128 |
-
elif syllable == "_":
|
| 129 |
-
initials_finals.append(syllable)
|
| 130 |
-
tones.append(0)
|
| 131 |
-
word2ph.append(1) # Add 1 for underscore
|
| 132 |
-
else:
|
| 133 |
-
try:
|
| 134 |
-
tone = int(syllable[-1])
|
| 135 |
-
syllable_without_tone = syllable[:-1]
|
| 136 |
-
except ValueError:
|
| 137 |
-
tone = 0
|
| 138 |
-
syllable_without_tone = syllable
|
| 139 |
-
|
| 140 |
-
for initial in INITIALS:
|
| 141 |
-
if syllable_without_tone.startswith(initial):
|
| 142 |
-
if syllable_without_tone.startswith("nga"):
|
| 143 |
-
initials_finals.extend(
|
| 144 |
-
[
|
| 145 |
-
syllable_without_tone[:2],
|
| 146 |
-
syllable_without_tone[2:] or syllable_without_tone[-1],
|
| 147 |
-
]
|
| 148 |
-
)
|
| 149 |
-
# tones.extend([tone, tone])
|
| 150 |
-
tones.extend([-1, tone])
|
| 151 |
-
word2ph.append(2)
|
| 152 |
-
else:
|
| 153 |
-
final = syllable_without_tone[len(initial) :] or initial[-1]
|
| 154 |
-
initials_finals.extend([initial, final])
|
| 155 |
-
# tones.extend([tone, tone])
|
| 156 |
-
tones.extend([-1, tone])
|
| 157 |
-
word2ph.append(2)
|
| 158 |
-
break
|
| 159 |
-
assert len(initials_finals) == len(tones)
|
| 160 |
-
|
| 161 |
-
###魔改为辅音+带音调的元音
|
| 162 |
-
phones = []
|
| 163 |
-
for a, b in zip(initials_finals, tones):
|
| 164 |
-
if b not in [-1, 0]: ###防止粤语和普通话重合开头加Y，如果是标点，不加。
|
| 165 |
-
todo = "%s%s" % (a, b)
|
| 166 |
-
else:
|
| 167 |
-
todo = a
|
| 168 |
-
if todo not in punctuation_set:
|
| 169 |
-
todo = "Y%s" % todo
|
| 170 |
-
phones.append(todo)
|
| 171 |
-
|
| 172 |
-
# return initials_finals, tones, word2ph
|
| 173 |
-
return phones, word2ph
|
| 174 |
-
|
| 175 |
-
|
| 176 |
-
def get_jyutping(text):
|
| 177 |
-
jyutping_array = []
|
| 178 |
-
punct_pattern = re.compile(r"^[{}]+$".format(re.escape("".join(punctuation))))
|
| 179 |
-
|
| 180 |
-
syllables = ToJyutping.get_jyutping_list(text)
|
| 181 |
-
|
| 182 |
-
for word, syllable in syllables:
|
| 183 |
-
if punct_pattern.match(word):
|
| 184 |
-
puncts = re.split(r"([{}])".format(re.escape("".join(punctuation))), word)
|
| 185 |
-
for punct in puncts:
|
| 186 |
-
if len(punct) > 0:
|
| 187 |
-
jyutping_array.append(punct)
|
| 188 |
-
else:
|
| 189 |
-
# match multple jyutping eg: liu4 ge3, or single jyutping eg: liu4
|
| 190 |
-
if not re.search(r"^([a-z]+[1-6]+[ ]?)+$", syllable):
|
| 191 |
-
raise ValueError(f"Failed to convert {word} to jyutping: {syllable}")
|
| 192 |
-
jyutping_array.append(syllable)
|
| 193 |
-
|
| 194 |
-
return jyutping_array
|
| 195 |
-
|
| 196 |
-
|
| 197 |
-
def get_bert_feature(text, word2ph):
|
| 198 |
-
from text import chinese_bert
|
| 199 |
-
|
| 200 |
-
return chinese_bert.get_bert_feature(text, word2ph)
|
| 201 |
-
|
| 202 |
-
|
| 203 |
-
def g2p(text):
|
| 204 |
-
# word2ph = []
|
| 205 |
-
jyuping = get_jyutping(text)
|
| 206 |
-
# print(jyuping)
|
| 207 |
-
# phones, tones, word2ph = jyuping_to_initials_finals_tones(jyuping)
|
| 208 |
-
phones, word2ph = jyuping_to_initials_finals_tones(jyuping)
|
| 209 |
-
# phones = ["_"] + phones + ["_"]
|
| 210 |
-
# tones = [0] + tones + [0]
|
| 211 |
-
# word2ph = [1] + word2ph + [1]
|
| 212 |
-
return phones, word2ph
|
| 213 |
-
|
| 214 |
-
|
| 215 |
-
if __name__ == "__main__":
|
| 216 |
-
# text = "啊！但是《原神》是由,米哈\游自主， [研发]的一款全.新开放世界.冒险游戏"
|
| 217 |
-
text = "佢個鋤頭太短啦。"
|
| 218 |
-
text = text_normalize(text)
|
| 219 |
-
# phones, tones, word2ph = g2p(text)
|
| 220 |
-
phones, word2ph = g2p(text)
|
| 221 |
-
# print(phones, tones, word2ph)
|
| 222 |
-
print(phones, word2ph)
|
|
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text/chinese.py
DELETED
|
@@ -1,208 +0,0 @@
|
|
| 1 |
-
import os
|
| 2 |
-
import re
|
| 3 |
-
|
| 4 |
-
import cn2an
|
| 5 |
-
from pypinyin import lazy_pinyin, Style
|
| 6 |
-
|
| 7 |
-
from text.symbols import punctuation
|
| 8 |
-
from text.tone_sandhi import ToneSandhi
|
| 9 |
-
from text.zh_normalization.text_normlization import TextNormalizer
|
| 10 |
-
|
| 11 |
-
normalizer = lambda x: cn2an.transform(x, "an2cn")
|
| 12 |
-
|
| 13 |
-
current_file_path = os.path.dirname(__file__)
|
| 14 |
-
pinyin_to_symbol_map = {
|
| 15 |
-
line.split("\t")[0]: line.strip().split("\t")[1]
|
| 16 |
-
for line in open(os.path.join(current_file_path, "opencpop-strict.txt")).readlines()
|
| 17 |
-
}
|
| 18 |
-
|
| 19 |
-
import jieba_fast
|
| 20 |
-
import logging
|
| 21 |
-
|
| 22 |
-
jieba_fast.setLogLevel(logging.CRITICAL)
|
| 23 |
-
import jieba_fast.posseg as psg
|
| 24 |
-
|
| 25 |
-
|
| 26 |
-
rep_map = {
|
| 27 |
-
"：": ",",
|
| 28 |
-
"；": ",",
|
| 29 |
-
"，": ",",
|
| 30 |
-
"。": ".",
|
| 31 |
-
"！": "!",
|
| 32 |
-
"？": "?",
|
| 33 |
-
"\n": ".",
|
| 34 |
-
"·": ",",
|
| 35 |
-
"、": ",",
|
| 36 |
-
"...": "…",
|
| 37 |
-
"$": ".",
|
| 38 |
-
"/": ",",
|
| 39 |
-
"—": "-",
|
| 40 |
-
"~": "…",
|
| 41 |
-
"～": "…",
|
| 42 |
-
}
|
| 43 |
-
|
| 44 |
-
tone_modifier = ToneSandhi()
|
| 45 |
-
|
| 46 |
-
|
| 47 |
-
def replace_punctuation(text):
|
| 48 |
-
text = text.replace("嗯", "恩").replace("呣", "母")
|
| 49 |
-
pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
|
| 50 |
-
|
| 51 |
-
replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
|
| 52 |
-
|
| 53 |
-
replaced_text = re.sub(r"[^\u4e00-\u9fa5" + "".join(punctuation) + r"]+", "", replaced_text)
|
| 54 |
-
|
| 55 |
-
return replaced_text
|
| 56 |
-
|
| 57 |
-
|
| 58 |
-
def replace_punctuation_with_en(text):
|
| 59 |
-
text = text.replace("嗯", "恩").replace("呣", "母")
|
| 60 |
-
pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
|
| 61 |
-
|
| 62 |
-
replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
|
| 63 |
-
|
| 64 |
-
replaced_text = re.sub(r"[^\u4e00-\u9fa5A-Za-z" + "".join(punctuation) + r"]+", "", replaced_text)
|
| 65 |
-
|
| 66 |
-
return replaced_text
|
| 67 |
-
|
| 68 |
-
|
| 69 |
-
def replace_consecutive_punctuation(text):
|
| 70 |
-
punctuations = "".join(re.escape(p) for p in punctuation)
|
| 71 |
-
pattern = f"([{punctuations}])([{punctuations}])+"
|
| 72 |
-
result = re.sub(pattern, r"\1", text)
|
| 73 |
-
return result
|
| 74 |
-
|
| 75 |
-
|
| 76 |
-
def g2p(text):
|
| 77 |
-
pattern = r"(?<=[{0}])\s*".format("".join(punctuation))
|
| 78 |
-
sentences = [i for i in re.split(pattern, text) if i.strip() != ""]
|
| 79 |
-
phones, word2ph = _g2p(sentences)
|
| 80 |
-
return phones, word2ph
|
| 81 |
-
|
| 82 |
-
|
| 83 |
-
def _get_initials_finals(word):
|
| 84 |
-
initials = []
|
| 85 |
-
finals = []
|
| 86 |
-
orig_initials = lazy_pinyin(word, neutral_tone_with_five=True, style=Style.INITIALS)
|
| 87 |
-
orig_finals = lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
|
| 88 |
-
for c, v in zip(orig_initials, orig_finals):
|
| 89 |
-
initials.append(c)
|
| 90 |
-
finals.append(v)
|
| 91 |
-
return initials, finals
|
| 92 |
-
|
| 93 |
-
|
| 94 |
-
def _g2p(segments):
|
| 95 |
-
phones_list = []
|
| 96 |
-
word2ph = []
|
| 97 |
-
for seg in segments:
|
| 98 |
-
pinyins = []
|
| 99 |
-
# Replace all English words in the sentence
|
| 100 |
-
seg = re.sub("[a-zA-Z]+", "", seg)
|
| 101 |
-
seg_cut = psg.lcut(seg)
|
| 102 |
-
initials = []
|
| 103 |
-
finals = []
|
| 104 |
-
seg_cut = tone_modifier.pre_merge_for_modify(seg_cut)
|
| 105 |
-
for word, pos in seg_cut:
|
| 106 |
-
if pos == "eng":
|
| 107 |
-
continue
|
| 108 |
-
sub_initials, sub_finals = _get_initials_finals(word)
|
| 109 |
-
sub_finals = tone_modifier.modified_tone(word, pos, sub_finals)
|
| 110 |
-
initials.append(sub_initials)
|
| 111 |
-
finals.append(sub_finals)
|
| 112 |
-
|
| 113 |
-
# assert len(sub_initials) == len(sub_finals) == len(word)
|
| 114 |
-
initials = sum(initials, [])
|
| 115 |
-
finals = sum(finals, [])
|
| 116 |
-
#
|
| 117 |
-
for c, v in zip(initials, finals):
|
| 118 |
-
raw_pinyin = c + v
|
| 119 |
-
# NOTE: post process for pypinyin outputs
|
| 120 |
-
# we discriminate i, ii and iii
|
| 121 |
-
if c == v:
|
| 122 |
-
assert c in punctuation
|
| 123 |
-
phone = [c]
|
| 124 |
-
word2ph.append(1)
|
| 125 |
-
else:
|
| 126 |
-
v_without_tone = v[:-1]
|
| 127 |
-
tone = v[-1]
|
| 128 |
-
|
| 129 |
-
pinyin = c + v_without_tone
|
| 130 |
-
assert tone in "12345"
|
| 131 |
-
|
| 132 |
-
if c:
|
| 133 |
-
# 多音节
|
| 134 |
-
v_rep_map = {
|
| 135 |
-
"uei": "ui",
|
| 136 |
-
"iou": "iu",
|
| 137 |
-
"uen": "un",
|
| 138 |
-
}
|
| 139 |
-
if v_without_tone in v_rep_map.keys():
|
| 140 |
-
pinyin = c + v_rep_map[v_without_tone]
|
| 141 |
-
else:
|
| 142 |
-
# 单音节
|
| 143 |
-
pinyin_rep_map = {
|
| 144 |
-
"ing": "ying",
|
| 145 |
-
"i": "yi",
|
| 146 |
-
"in": "yin",
|
| 147 |
-
"u": "wu",
|
| 148 |
-
}
|
| 149 |
-
if pinyin in pinyin_rep_map.keys():
|
| 150 |
-
pinyin = pinyin_rep_map[pinyin]
|
| 151 |
-
else:
|
| 152 |
-
single_rep_map = {
|
| 153 |
-
"v": "yu",
|
| 154 |
-
"e": "e",
|
| 155 |
-
"i": "y",
|
| 156 |
-
"u": "w",
|
| 157 |
-
}
|
| 158 |
-
if pinyin[0] in single_rep_map.keys():
|
| 159 |
-
pinyin = single_rep_map[pinyin[0]] + pinyin[1:]
|
| 160 |
-
|
| 161 |
-
assert pinyin in pinyin_to_symbol_map.keys(), (pinyin, seg, raw_pinyin)
|
| 162 |
-
new_c, new_v = pinyin_to_symbol_map[pinyin].split(" ")
|
| 163 |
-
new_v = new_v + tone
|
| 164 |
-
phone = [new_c, new_v]
|
| 165 |
-
word2ph.append(len(phone))
|
| 166 |
-
|
| 167 |
-
phones_list += phone
|
| 168 |
-
return phones_list, word2ph
|
| 169 |
-
|
| 170 |
-
|
| 171 |
-
def text_normalize(text):
|
| 172 |
-
# https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/zh_normalization
|
| 173 |
-
tx = TextNormalizer()
|
| 174 |
-
sentences = tx.normalize(text)
|
| 175 |
-
dest_text = ""
|
| 176 |
-
for sentence in sentences:
|
| 177 |
-
dest_text += replace_punctuation(sentence)
|
| 178 |
-
|
| 179 |
-
# 避免重复标点引起的参考泄露
|
| 180 |
-
dest_text = replace_consecutive_punctuation(dest_text)
|
| 181 |
-
return dest_text
|
| 182 |
-
|
| 183 |
-
|
| 184 |
-
# 不排除英文的文本格式化
|
| 185 |
-
def mix_text_normalize(text):
|
| 186 |
-
# https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/zh_normalization
|
| 187 |
-
tx = TextNormalizer()
|
| 188 |
-
sentences = tx.normalize(text)
|
| 189 |
-
dest_text = ""
|
| 190 |
-
for sentence in sentences:
|
| 191 |
-
dest_text += replace_punctuation_with_en(sentence)
|
| 192 |
-
|
| 193 |
-
# 避免重复标点引起的参考泄露
|
| 194 |
-
dest_text = replace_consecutive_punctuation(dest_text)
|
| 195 |
-
return dest_text
|
| 196 |
-
|
| 197 |
-
|
| 198 |
-
if __name__ == "__main__":
|
| 199 |
-
text = "啊——但是《原神》是由,米哈\游自主，研发的一款全.新开放世界.冒险游戏"
|
| 200 |
-
text = "呣呣呣～就是…大人的鼹鼠党吧？"
|
| 201 |
-
text = "你好"
|
| 202 |
-
text = text_normalize(text)
|
| 203 |
-
print(g2p(text))
|
| 204 |
-
|
| 205 |
-
|
| 206 |
-
# # 示例用法
|
| 207 |
-
# text = "这是一个示例文本：,你好！这是一个测试..."
|
| 208 |
-
# print(g2p_paddle(text)) # 输出: 这是一个示例文本你好这是一个测试
|
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|
text/chinese2.py
DELETED
|
@@ -1,353 +0,0 @@
|
|
| 1 |
-
import os
|
| 2 |
-
import re
|
| 3 |
-
|
| 4 |
-
import cn2an
|
| 5 |
-
from pypinyin import lazy_pinyin, Style
|
| 6 |
-
from pypinyin.contrib.tone_convert import to_finals_tone3, to_initials
|
| 7 |
-
|
| 8 |
-
from text.symbols import punctuation
|
| 9 |
-
from text.tone_sandhi import ToneSandhi
|
| 10 |
-
from text.zh_normalization.text_normlization import TextNormalizer
|
| 11 |
-
|
| 12 |
-
normalizer = lambda x: cn2an.transform(x, "an2cn")
|
| 13 |
-
|
| 14 |
-
current_file_path = os.path.dirname(__file__)
|
| 15 |
-
pinyin_to_symbol_map = {
|
| 16 |
-
line.split("\t")[0]: line.strip().split("\t")[1]
|
| 17 |
-
for line in open(os.path.join(current_file_path, "opencpop-strict.txt")).readlines()
|
| 18 |
-
}
|
| 19 |
-
|
| 20 |
-
import jieba_fast
|
| 21 |
-
import logging
|
| 22 |
-
|
| 23 |
-
jieba_fast.setLogLevel(logging.CRITICAL)
|
| 24 |
-
import jieba_fast.posseg as psg
|
| 25 |
-
|
| 26 |
-
# is_g2pw_str = os.environ.get("is_g2pw", "True")##默认开启
|
| 27 |
-
# is_g2pw = False#True if is_g2pw_str.lower() == 'true' else False
|
| 28 |
-
is_g2pw = True # True if is_g2pw_str.lower() == 'true' else False
|
| 29 |
-
if is_g2pw:
|
| 30 |
-
# print("当前使用g2pw进行拼音推理")
|
| 31 |
-
from text.g2pw import G2PWPinyin, correct_pronunciation
|
| 32 |
-
|
| 33 |
-
parent_directory = os.path.dirname(current_file_path)
|
| 34 |
-
g2pw = G2PWPinyin(
|
| 35 |
-
model_dir="text/G2PWModel",
|
| 36 |
-
model_source=os.environ.get("bert_path", "pretrained_models/chinese-roberta-wwm-ext-large"),
|
| 37 |
-
v_to_u=False,
|
| 38 |
-
neutral_tone_with_five=True,
|
| 39 |
-
)
|
| 40 |
-
|
| 41 |
-
rep_map = {
|
| 42 |
-
"：": ",",
|
| 43 |
-
"；": ",",
|
| 44 |
-
"，": ",",
|
| 45 |
-
"。": ".",
|
| 46 |
-
"！": "!",
|
| 47 |
-
"？": "?",
|
| 48 |
-
"\n": ".",
|
| 49 |
-
"·": ",",
|
| 50 |
-
"、": ",",
|
| 51 |
-
"...": "…",
|
| 52 |
-
"$": ".",
|
| 53 |
-
"/": ",",
|
| 54 |
-
"—": "-",
|
| 55 |
-
"~": "…",
|
| 56 |
-
"～": "…",
|
| 57 |
-
}
|
| 58 |
-
|
| 59 |
-
tone_modifier = ToneSandhi()
|
| 60 |
-
|
| 61 |
-
|
| 62 |
-
def replace_punctuation(text):
|
| 63 |
-
text = text.replace("嗯", "恩").replace("呣", "母")
|
| 64 |
-
pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
|
| 65 |
-
|
| 66 |
-
replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
|
| 67 |
-
|
| 68 |
-
replaced_text = re.sub(r"[^\u4e00-\u9fa5" + "".join(punctuation) + r"]+", "", replaced_text)
|
| 69 |
-
|
| 70 |
-
return replaced_text
|
| 71 |
-
|
| 72 |
-
|
| 73 |
-
def g2p(text):
|
| 74 |
-
pattern = r"(?<=[{0}])\s*".format("".join(punctuation))
|
| 75 |
-
sentences = [i for i in re.split(pattern, text) if i.strip() != ""]
|
| 76 |
-
phones, word2ph = _g2p(sentences)
|
| 77 |
-
return phones, word2ph
|
| 78 |
-
|
| 79 |
-
|
| 80 |
-
def _get_initials_finals(word):
|
| 81 |
-
initials = []
|
| 82 |
-
finals = []
|
| 83 |
-
|
| 84 |
-
orig_initials = lazy_pinyin(word, neutral_tone_with_five=True, style=Style.INITIALS)
|
| 85 |
-
orig_finals = lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
|
| 86 |
-
|
| 87 |
-
for c, v in zip(orig_initials, orig_finals):
|
| 88 |
-
initials.append(c)
|
| 89 |
-
finals.append(v)
|
| 90 |
-
return initials, finals
|
| 91 |
-
|
| 92 |
-
|
| 93 |
-
must_erhua = {"小院儿", "胡同儿", "范儿", "老汉儿", "撒欢儿", "寻老礼儿", "妥妥儿", "媳妇儿"}
|
| 94 |
-
not_erhua = {
|
| 95 |
-
"虐儿",
|
| 96 |
-
"为儿",
|
| 97 |
-
"护儿",
|
| 98 |
-
"瞒儿",
|
| 99 |
-
"救儿",
|
| 100 |
-
"替儿",
|
| 101 |
-
"有儿",
|
| 102 |
-
"一儿",
|
| 103 |
-
"我儿",
|
| 104 |
-
"俺儿",
|
| 105 |
-
"妻儿",
|
| 106 |
-
"拐儿",
|
| 107 |
-
"聋儿",
|
| 108 |
-
"乞儿",
|
| 109 |
-
"患儿",
|
| 110 |
-
"幼儿",
|
| 111 |
-
"孤儿",
|
| 112 |
-
"婴儿",
|
| 113 |
-
"婴幼儿",
|
| 114 |
-
"连体儿",
|
| 115 |
-
"脑瘫儿",
|
| 116 |
-
"流浪儿",
|
| 117 |
-
"体弱儿",
|
| 118 |
-
"混血儿",
|
| 119 |
-
"蜜雪儿",
|
| 120 |
-
"舫儿",
|
| 121 |
-
"祖儿",
|
| 122 |
-
"美儿",
|
| 123 |
-
"应采儿",
|
| 124 |
-
"可儿",
|
| 125 |
-
"侄儿",
|
| 126 |
-
"孙儿",
|
| 127 |
-
"侄孙儿",
|
| 128 |
-
"女儿",
|
| 129 |
-
"男儿",
|
| 130 |
-
"红孩儿",
|
| 131 |
-
"花儿",
|
| 132 |
-
"虫儿",
|
| 133 |
-
"马儿",
|
| 134 |
-
"鸟儿",
|
| 135 |
-
"猪儿",
|
| 136 |
-
"猫儿",
|
| 137 |
-
"狗儿",
|
| 138 |
-
"少儿",
|
| 139 |
-
}
|
| 140 |
-
|
| 141 |
-
|
| 142 |
-
def _merge_erhua(initials: list[str], finals: list[str], word: str, pos: str) -> list[list[str]]:
|
| 143 |
-
"""
|
| 144 |
-
Do erhub.
|
| 145 |
-
"""
|
| 146 |
-
# fix er1
|
| 147 |
-
for i, phn in enumerate(finals):
|
| 148 |
-
if i == len(finals) - 1 and word[i] == "儿" and phn == "er1":
|
| 149 |
-
finals[i] = "er2"
|
| 150 |
-
|
| 151 |
-
# 发音
|
| 152 |
-
if word not in must_erhua and (word in not_erhua or pos in {"a", "j", "nr"}):
|
| 153 |
-
return initials, finals
|
| 154 |
-
|
| 155 |
-
# "……" 等情况直接返回
|
| 156 |
-
if len(finals) != len(word):
|
| 157 |
-
return initials, finals
|
| 158 |
-
|
| 159 |
-
assert len(finals) == len(word)
|
| 160 |
-
|
| 161 |
-
# 与前一个字发同音
|
| 162 |
-
new_initials = []
|
| 163 |
-
new_finals = []
|
| 164 |
-
for i, phn in enumerate(finals):
|
| 165 |
-
if (
|
| 166 |
-
i == len(finals) - 1
|
| 167 |
-
and word[i] == "儿"
|
| 168 |
-
and phn in {"er2", "er5"}
|
| 169 |
-
and word[-2:] not in not_erhua
|
| 170 |
-
and new_finals
|
| 171 |
-
):
|
| 172 |
-
phn = "er" + new_finals[-1][-1]
|
| 173 |
-
|
| 174 |
-
new_initials.append(initials[i])
|
| 175 |
-
new_finals.append(phn)
|
| 176 |
-
|
| 177 |
-
return new_initials, new_finals
|
| 178 |
-
|
| 179 |
-
|
| 180 |
-
def _g2p(segments):
|
| 181 |
-
phones_list = []
|
| 182 |
-
word2ph = []
|
| 183 |
-
for seg in segments:
|
| 184 |
-
pinyins = []
|
| 185 |
-
# Replace all English words in the sentence
|
| 186 |
-
seg = re.sub("[a-zA-Z]+", "", seg)
|
| 187 |
-
seg_cut = psg.lcut(seg)
|
| 188 |
-
seg_cut = tone_modifier.pre_merge_for_modify(seg_cut)
|
| 189 |
-
initials = []
|
| 190 |
-
finals = []
|
| 191 |
-
|
| 192 |
-
if not is_g2pw:
|
| 193 |
-
for word, pos in seg_cut:
|
| 194 |
-
if pos == "eng":
|
| 195 |
-
continue
|
| 196 |
-
sub_initials, sub_finals = _get_initials_finals(word)
|
| 197 |
-
sub_finals = tone_modifier.modified_tone(word, pos, sub_finals)
|
| 198 |
-
# 儿化
|
| 199 |
-
sub_initials, sub_finals = _merge_erhua(sub_initials, sub_finals, word, pos)
|
| 200 |
-
initials.append(sub_initials)
|
| 201 |
-
finals.append(sub_finals)
|
| 202 |
-
# assert len(sub_initials) == len(sub_finals) == len(word)
|
| 203 |
-
initials = sum(initials, [])
|
| 204 |
-
finals = sum(finals, [])
|
| 205 |
-
print("pypinyin结果", initials, finals)
|
| 206 |
-
else:
|
| 207 |
-
# g2pw采用整句推理
|
| 208 |
-
pinyins = g2pw.lazy_pinyin(seg, neutral_tone_with_five=True, style=Style.TONE3)
|
| 209 |
-
|
| 210 |
-
pre_word_length = 0
|
| 211 |
-
for word, pos in seg_cut:
|
| 212 |
-
sub_initials = []
|
| 213 |
-
sub_finals = []
|
| 214 |
-
now_word_length = pre_word_length + len(word)
|
| 215 |
-
|
| 216 |
-
if pos == "eng":
|
| 217 |
-
pre_word_length = now_word_length
|
| 218 |
-
continue
|
| 219 |
-
|
| 220 |
-
word_pinyins = pinyins[pre_word_length:now_word_length]
|
| 221 |
-
|
| 222 |
-
# 多音字消歧
|
| 223 |
-
word_pinyins = correct_pronunciation(word, word_pinyins)
|
| 224 |
-
|
| 225 |
-
for pinyin in word_pinyins:
|
| 226 |
-
if pinyin[0].isalpha():
|
| 227 |
-
sub_initials.append(to_initials(pinyin))
|
| 228 |
-
sub_finals.append(to_finals_tone3(pinyin, neutral_tone_with_five=True))
|
| 229 |
-
else:
|
| 230 |
-
sub_initials.append(pinyin)
|
| 231 |
-
sub_finals.append(pinyin)
|
| 232 |
-
|
| 233 |
-
pre_word_length = now_word_length
|
| 234 |
-
sub_finals = tone_modifier.modified_tone(word, pos, sub_finals)
|
| 235 |
-
# 儿化
|
| 236 |
-
sub_initials, sub_finals = _merge_erhua(sub_initials, sub_finals, word, pos)
|
| 237 |
-
initials.append(sub_initials)
|
| 238 |
-
finals.append(sub_finals)
|
| 239 |
-
|
| 240 |
-
initials = sum(initials, [])
|
| 241 |
-
finals = sum(finals, [])
|
| 242 |
-
# print("g2pw结果",initials,finals)
|
| 243 |
-
|
| 244 |
-
for c, v in zip(initials, finals):
|
| 245 |
-
raw_pinyin = c + v
|
| 246 |
-
# NOTE: post process for pypinyin outputs
|
| 247 |
-
# we discriminate i, ii and iii
|
| 248 |
-
if c == v:
|
| 249 |
-
assert c in punctuation
|
| 250 |
-
phone = [c]
|
| 251 |
-
word2ph.append(1)
|
| 252 |
-
else:
|
| 253 |
-
v_without_tone = v[:-1]
|
| 254 |
-
tone = v[-1]
|
| 255 |
-
|
| 256 |
-
pinyin = c + v_without_tone
|
| 257 |
-
assert tone in "12345"
|
| 258 |
-
|
| 259 |
-
if c:
|
| 260 |
-
# 多音节
|
| 261 |
-
v_rep_map = {
|
| 262 |
-
"uei": "ui",
|
| 263 |
-
"iou": "iu",
|
| 264 |
-
"uen": "un",
|
| 265 |
-
}
|
| 266 |
-
if v_without_tone in v_rep_map.keys():
|
| 267 |
-
pinyin = c + v_rep_map[v_without_tone]
|
| 268 |
-
else:
|
| 269 |
-
# 单音节
|
| 270 |
-
pinyin_rep_map = {
|
| 271 |
-
"ing": "ying",
|
| 272 |
-
"i": "yi",
|
| 273 |
-
"in": "yin",
|
| 274 |
-
"u": "wu",
|
| 275 |
-
}
|
| 276 |
-
if pinyin in pinyin_rep_map.keys():
|
| 277 |
-
pinyin = pinyin_rep_map[pinyin]
|
| 278 |
-
else:
|
| 279 |
-
single_rep_map = {
|
| 280 |
-
"v": "yu",
|
| 281 |
-
"e": "e",
|
| 282 |
-
"i": "y",
|
| 283 |
-
"u": "w",
|
| 284 |
-
}
|
| 285 |
-
if pinyin[0] in single_rep_map.keys():
|
| 286 |
-
pinyin = single_rep_map[pinyin[0]] + pinyin[1:]
|
| 287 |
-
|
| 288 |
-
assert pinyin in pinyin_to_symbol_map.keys(), (pinyin, seg, raw_pinyin)
|
| 289 |
-
new_c, new_v = pinyin_to_symbol_map[pinyin].split(" ")
|
| 290 |
-
new_v = new_v + tone
|
| 291 |
-
phone = [new_c, new_v]
|
| 292 |
-
word2ph.append(len(phone))
|
| 293 |
-
|
| 294 |
-
phones_list += phone
|
| 295 |
-
return phones_list, word2ph
|
| 296 |
-
|
| 297 |
-
|
| 298 |
-
def replace_punctuation_with_en(text):
|
| 299 |
-
text = text.replace("嗯", "恩").replace("呣", "母")
|
| 300 |
-
pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
|
| 301 |
-
|
| 302 |
-
replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
|
| 303 |
-
|
| 304 |
-
replaced_text = re.sub(r"[^\u4e00-\u9fa5A-Za-z" + "".join(punctuation) + r"]+", "", replaced_text)
|
| 305 |
-
|
| 306 |
-
return replaced_text
|
| 307 |
-
|
| 308 |
-
|
| 309 |
-
def replace_consecutive_punctuation(text):
|
| 310 |
-
punctuations = "".join(re.escape(p) for p in punctuation)
|
| 311 |
-
pattern = f"([{punctuations}])([{punctuations}])+"
|
| 312 |
-
result = re.sub(pattern, r"\1", text)
|
| 313 |
-
return result
|
| 314 |
-
|
| 315 |
-
|
| 316 |
-
def text_normalize(text):
|
| 317 |
-
# https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/zh_normalization
|
| 318 |
-
tx = TextNormalizer()
|
| 319 |
-
sentences = tx.normalize(text)
|
| 320 |
-
dest_text = ""
|
| 321 |
-
for sentence in sentences:
|
| 322 |
-
dest_text += replace_punctuation(sentence)
|
| 323 |
-
|
| 324 |
-
# 避免重复标点引起的参考泄露
|
| 325 |
-
dest_text = replace_consecutive_punctuation(dest_text)
|
| 326 |
-
return dest_text
|
| 327 |
-
|
| 328 |
-
|
| 329 |
-
# 不排除英文的文本格式化
|
| 330 |
-
def mix_text_normalize(text):
|
| 331 |
-
# https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/zh_normalization
|
| 332 |
-
tx = TextNormalizer()
|
| 333 |
-
sentences = tx.normalize(text)
|
| 334 |
-
dest_text = ""
|
| 335 |
-
for sentence in sentences:
|
| 336 |
-
dest_text += replace_punctuation_with_en(sentence)
|
| 337 |
-
|
| 338 |
-
# 避免重复标点引起的参考泄露
|
| 339 |
-
dest_text = replace_consecutive_punctuation(dest_text)
|
| 340 |
-
return dest_text
|
| 341 |
-
|
| 342 |
-
|
| 343 |
-
if __name__ == "__main__":
|
| 344 |
-
text = "啊——但是《原神》是由,米哈\游自主，研发的一款全.新开放世界.冒险游戏"
|
| 345 |
-
text = "呣呣呣～就是…大人的鼹鼠党吧？"
|
| 346 |
-
text = "你好"
|
| 347 |
-
text = text_normalize(text)
|
| 348 |
-
print(g2p(text))
|
| 349 |
-
|
| 350 |
-
|
| 351 |
-
# # 示例用法
|
| 352 |
-
# text = "这是一个示例文本：,你好！这是一个测试..."
|
| 353 |
-
# print(g2p_paddle(text)) # 输出: 这是一个示例文本你好这是一个测试
|
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text/cleaner.py
DELETED
|
@@ -1,94 +0,0 @@
|
|
| 1 |
-
from text import cleaned_text_to_sequence
|
| 2 |
-
import os
|
| 3 |
-
# if os.environ.get("version","v1")=="v1":
|
| 4 |
-
# from text import chinese
|
| 5 |
-
# from text.symbols import symbols
|
| 6 |
-
# else:
|
| 7 |
-
# from text import chinese2 as chinese
|
| 8 |
-
# from text.symbols2 import symbols
|
| 9 |
-
|
| 10 |
-
from text import symbols as symbols_v1
|
| 11 |
-
from text import symbols2 as symbols_v2
|
| 12 |
-
|
| 13 |
-
special = [
|
| 14 |
-
# ("%", "zh", "SP"),
|
| 15 |
-
("￥", "zh", "SP2"),
|
| 16 |
-
("^", "zh", "SP3"),
|
| 17 |
-
# ('@', 'zh', "SP4")#不搞鬼畜了，和第二版保持一致吧
|
| 18 |
-
]
|
| 19 |
-
|
| 20 |
-
|
| 21 |
-
def clean_text(text, language, version=None):
|
| 22 |
-
if version is None:
|
| 23 |
-
version = os.environ.get("version", "v2")
|
| 24 |
-
if version == "v1":
|
| 25 |
-
symbols = symbols_v1.symbols
|
| 26 |
-
language_module_map = {"zh": "chinese", "ja": "japanese", "en": "english"}
|
| 27 |
-
else:
|
| 28 |
-
symbols = symbols_v2.symbols
|
| 29 |
-
language_module_map = {"zh": "chinese2", "ja": "japanese", "en": "english", "ko": "korean", "yue": "cantonese"}
|
| 30 |
-
|
| 31 |
-
if language not in language_module_map:
|
| 32 |
-
language = "en"
|
| 33 |
-
text = " "
|
| 34 |
-
for special_s, special_l, target_symbol in special:
|
| 35 |
-
if special_s in text and language == special_l:
|
| 36 |
-
return clean_special(text, language, special_s, target_symbol, version)
|
| 37 |
-
language_module = __import__("text." + language_module_map[language], fromlist=[language_module_map[language]])
|
| 38 |
-
if hasattr(language_module, "text_normalize"):
|
| 39 |
-
norm_text = language_module.text_normalize(text)
|
| 40 |
-
else:
|
| 41 |
-
norm_text = text
|
| 42 |
-
if language == "zh" or language == "yue": ##########
|
| 43 |
-
phones, word2ph = language_module.g2p(norm_text)
|
| 44 |
-
assert len(phones) == sum(word2ph)
|
| 45 |
-
assert len(norm_text) == len(word2ph)
|
| 46 |
-
elif language == "en":
|
| 47 |
-
phones = language_module.g2p(norm_text)
|
| 48 |
-
if len(phones) < 4:
|
| 49 |
-
phones = [","] + phones
|
| 50 |
-
word2ph = None
|
| 51 |
-
else:
|
| 52 |
-
phones = language_module.g2p(norm_text)
|
| 53 |
-
word2ph = None
|
| 54 |
-
phones = ["UNK" if ph not in symbols else ph for ph in phones]
|
| 55 |
-
return phones, word2ph, norm_text
|
| 56 |
-
|
| 57 |
-
|
| 58 |
-
def clean_special(text, language, special_s, target_symbol, version=None):
|
| 59 |
-
if version is None:
|
| 60 |
-
version = os.environ.get("version", "v2")
|
| 61 |
-
if version == "v1":
|
| 62 |
-
symbols = symbols_v1.symbols
|
| 63 |
-
language_module_map = {"zh": "chinese", "ja": "japanese", "en": "english"}
|
| 64 |
-
else:
|
| 65 |
-
symbols = symbols_v2.symbols
|
| 66 |
-
language_module_map = {"zh": "chinese2", "ja": "japanese", "en": "english", "ko": "korean", "yue": "cantonese"}
|
| 67 |
-
|
| 68 |
-
"""
|
| 69 |
-
特殊静音段sp符号处理
|
| 70 |
-
"""
|
| 71 |
-
text = text.replace(special_s, ",")
|
| 72 |
-
language_module = __import__("text." + language_module_map[language], fromlist=[language_module_map[language]])
|
| 73 |
-
norm_text = language_module.text_normalize(text)
|
| 74 |
-
phones = language_module.g2p(norm_text)
|
| 75 |
-
new_ph = []
|
| 76 |
-
for ph in phones[0]:
|
| 77 |
-
assert ph in symbols
|
| 78 |
-
if ph == ",":
|
| 79 |
-
new_ph.append(target_symbol)
|
| 80 |
-
else:
|
| 81 |
-
new_ph.append(ph)
|
| 82 |
-
return new_ph, phones[1], norm_text
|
| 83 |
-
|
| 84 |
-
|
| 85 |
-
def text_to_sequence(text, language, version=None):
|
| 86 |
-
version = os.environ.get("version", version)
|
| 87 |
-
if version is None:
|
| 88 |
-
version = "v2"
|
| 89 |
-
phones = clean_text(text)
|
| 90 |
-
return cleaned_text_to_sequence(phones, version)
|
| 91 |
-
|
| 92 |
-
|
| 93 |
-
if __name__ == "__main__":
|
| 94 |
-
print(clean_text("你好%啊啊啊额、还是到付红四方。", "zh"))
|
|
|
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|
text/cmudict-fast.rep
DELETED
|
The diff for this file is too large to render.
See raw diff
|
|
|
text/cmudict.rep
DELETED
|
The diff for this file is too large to render.
See raw diff
|
|
|
text/en_normalization/expend.py
DELETED
|
@@ -1,283 +0,0 @@
|
|
| 1 |
-
# by https://github.com/Cosmo-klara
|
| 2 |
-
|
| 3 |
-
from __future__ import print_function
|
| 4 |
-
|
| 5 |
-
import re
|
| 6 |
-
import inflect
|
| 7 |
-
import unicodedata
|
| 8 |
-
|
| 9 |
-
# 后缀计量单位替换表
|
| 10 |
-
measurement_map = {
|
| 11 |
-
"m": ["meter", "meters"],
|
| 12 |
-
"km": ["kilometer", "kilometers"],
|
| 13 |
-
"km/h": ["kilometer per hour", "kilometers per hour"],
|
| 14 |
-
"ft": ["feet", "feet"],
|
| 15 |
-
"L": ["liter", "liters"],
|
| 16 |
-
"tbsp": ["tablespoon", "tablespoons"],
|
| 17 |
-
"tsp": ["teaspoon", "teaspoons"],
|
| 18 |
-
"h": ["hour", "hours"],
|
| 19 |
-
"min": ["minute", "minutes"],
|
| 20 |
-
"s": ["second", "seconds"],
|
| 21 |
-
"°C": ["degree celsius", "degrees celsius"],
|
| 22 |
-
"°F": ["degree fahrenheit", "degrees fahrenheit"],
|
| 23 |
-
}
|
| 24 |
-
|
| 25 |
-
|
| 26 |
-
# 识别 12,000 类型
|
| 27 |
-
_inflect = inflect.engine()
|
| 28 |
-
|
| 29 |
-
# 转化数字序数词
|
| 30 |
-
_ordinal_number_re = re.compile(r"\b([0-9]+)\. ")
|
| 31 |
-
|
| 32 |
-
# 我听说好像对于数字正则识别其实用 \d 会好一点
|
| 33 |
-
|
| 34 |
-
_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])")
|
| 35 |
-
|
| 36 |
-
# 时间识别
|
| 37 |
-
_time_re = re.compile(r"\b([01]?[0-9]|2[0-3]):([0-5][0-9])\b")
|
| 38 |
-
|
| 39 |
-
# 后缀计量单位识别
|
| 40 |
-
_measurement_re = re.compile(r"\b([0-9]+(\.[0-9]+)?(m|km|km/h|ft|L|tbsp|tsp|h|min|s|°C|°F))\b")
|
| 41 |
-
|
| 42 |
-
# 前后 £ 识别 ( 写了识别两边某一边的，但是不知道为什么失败了┭┮﹏┭┮ )
|
| 43 |
-
_pounds_re_start = re.compile(r"£([0-9\.\,]*[0-9]+)")
|
| 44 |
-
_pounds_re_end = re.compile(r"([0-9\.\,]*[0-9]+)£")
|
| 45 |
-
|
| 46 |
-
# 前后 $ 识别
|
| 47 |
-
_dollars_re_start = re.compile(r"\$([0-9\.\,]*[0-9]+)")
|
| 48 |
-
_dollars_re_end = re.compile(r"([(0-9\.\,]*[0-9]+)\$")
|
| 49 |
-
|
| 50 |
-
# 小数的识别
|
| 51 |
-
_decimal_number_re = re.compile(r"([0-9]+\.\s*[0-9]+)")
|
| 52 |
-
|
| 53 |
-
# 分数识别 (形式 "3/4" )
|
| 54 |
-
_fraction_re = re.compile(r"([0-9]+/[0-9]+)")
|
| 55 |
-
|
| 56 |
-
# 序数词识别
|
| 57 |
-
_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)")
|
| 58 |
-
|
| 59 |
-
# 数字处理
|
| 60 |
-
_number_re = re.compile(r"[0-9]+")
|
| 61 |
-
|
| 62 |
-
|
| 63 |
-
def _convert_ordinal(m):
|
| 64 |
-
"""
|
| 65 |
-
标准化序数词, 例如: 1. 2. 3. 4. 5. 6.
|
| 66 |
-
Examples:
|
| 67 |
-
input: "1. "
|
| 68 |
-
output: "1st"
|
| 69 |
-
然后在后面的 _expand_ordinal, 将其转化为 first 这类的
|
| 70 |
-
"""
|
| 71 |
-
ordinal = _inflect.ordinal(m.group(1))
|
| 72 |
-
return ordinal + ", "
|
| 73 |
-
|
| 74 |
-
|
| 75 |
-
def _remove_commas(m):
|
| 76 |
-
return m.group(1).replace(",", "")
|
| 77 |
-
|
| 78 |
-
|
| 79 |
-
def _expand_time(m):
|
| 80 |
-
"""
|
| 81 |
-
将 24 小时制的时间转换为 12 小时制的时间表示方式。
|
| 82 |
-
|
| 83 |
-
Examples:
|
| 84 |
-
input: "13:00 / 4:00 / 13:30"
|
| 85 |
-
output: "one o'clock p.m. / four o'clock am. / one thirty p.m."
|
| 86 |
-
"""
|
| 87 |
-
hours, minutes = map(int, m.group(1, 2))
|
| 88 |
-
period = "a.m." if hours < 12 else "p.m."
|
| 89 |
-
if hours > 12:
|
| 90 |
-
hours -= 12
|
| 91 |
-
|
| 92 |
-
hour_word = _inflect.number_to_words(hours)
|
| 93 |
-
minute_word = _inflect.number_to_words(minutes) if minutes != 0 else ""
|
| 94 |
-
|
| 95 |
-
if minutes == 0:
|
| 96 |
-
return f"{hour_word} o'clock {period}"
|
| 97 |
-
else:
|
| 98 |
-
return f"{hour_word} {minute_word} {period}"
|
| 99 |
-
|
| 100 |
-
|
| 101 |
-
def _expand_measurement(m):
|
| 102 |
-
"""
|
| 103 |
-
处理一些常见的测量单位后缀, 目前支持: m, km, km/h, ft, L, tbsp, tsp, h, min, s, °C, °F
|
| 104 |
-
如果要拓展的话修改: _measurement_re 和 measurement_map
|
| 105 |
-
"""
|
| 106 |
-
sign = m.group(3)
|
| 107 |
-
ptr = 1
|
| 108 |
-
# 想不到怎么方便的取数字，又懒得改正则，诶，1.2 反正也是复数读法，干脆直接去掉 "."
|
| 109 |
-
num = int(m.group(1).replace(sign, "").replace(".", ""))
|
| 110 |
-
decimal_part = m.group(2)
|
| 111 |
-
# 上面判断的漏洞，比如 0.1 的情况，在这里排除了
|
| 112 |
-
if decimal_part == None and num == 1:
|
| 113 |
-
ptr = 0
|
| 114 |
-
return m.group(1).replace(sign, " " + measurement_map[sign][ptr])
|
| 115 |
-
|
| 116 |
-
|
| 117 |
-
def _expand_pounds(m):
|
| 118 |
-
"""
|
| 119 |
-
没找到特别规范的说明，和美元的处理一样，其实可以把两个合并在一起
|
| 120 |
-
"""
|
| 121 |
-
match = m.group(1)
|
| 122 |
-
parts = match.split(".")
|
| 123 |
-
if len(parts) > 2:
|
| 124 |
-
return match + " pounds" # Unexpected format
|
| 125 |
-
pounds = int(parts[0]) if parts[0] else 0
|
| 126 |
-
pence = int(parts[1].ljust(2, "0")) if len(parts) > 1 and parts[1] else 0
|
| 127 |
-
if pounds and pence:
|
| 128 |
-
pound_unit = "pound" if pounds == 1 else "pounds"
|
| 129 |
-
penny_unit = "penny" if pence == 1 else "pence"
|
| 130 |
-
return "%s %s and %s %s" % (pounds, pound_unit, pence, penny_unit)
|
| 131 |
-
elif pounds:
|
| 132 |
-
pound_unit = "pound" if pounds == 1 else "pounds"
|
| 133 |
-
return "%s %s" % (pounds, pound_unit)
|
| 134 |
-
elif pence:
|
| 135 |
-
penny_unit = "penny" if pence == 1 else "pence"
|
| 136 |
-
return "%s %s" % (pence, penny_unit)
|
| 137 |
-
else:
|
| 138 |
-
return "zero pounds"
|
| 139 |
-
|
| 140 |
-
|
| 141 |
-
def _expand_dollars(m):
|
| 142 |
-
"""
|
| 143 |
-
change: 美分是 100 的限值, 应该要做补零的吧
|
| 144 |
-
Example:
|
| 145 |
-
input: "32.3$ / $6.24"
|
| 146 |
-
output: "thirty-two dollars and thirty cents" / "six dollars and twenty-four cents"
|
| 147 |
-
"""
|
| 148 |
-
match = m.group(1)
|
| 149 |
-
parts = match.split(".")
|
| 150 |
-
if len(parts) > 2:
|
| 151 |
-
return match + " dollars" # Unexpected format
|
| 152 |
-
dollars = int(parts[0]) if parts[0] else 0
|
| 153 |
-
cents = int(parts[1].ljust(2, "0")) if len(parts) > 1 and parts[1] else 0
|
| 154 |
-
if dollars and cents:
|
| 155 |
-
dollar_unit = "dollar" if dollars == 1 else "dollars"
|
| 156 |
-
cent_unit = "cent" if cents == 1 else "cents"
|
| 157 |
-
return "%s %s and %s %s" % (dollars, dollar_unit, cents, cent_unit)
|
| 158 |
-
elif dollars:
|
| 159 |
-
dollar_unit = "dollar" if dollars == 1 else "dollars"
|
| 160 |
-
return "%s %s" % (dollars, dollar_unit)
|
| 161 |
-
elif cents:
|
| 162 |
-
cent_unit = "cent" if cents == 1 else "cents"
|
| 163 |
-
return "%s %s" % (cents, cent_unit)
|
| 164 |
-
else:
|
| 165 |
-
return "zero dollars"
|
| 166 |
-
|
| 167 |
-
|
| 168 |
-
# 小数的处理
|
| 169 |
-
def _expand_decimal_number(m):
|
| 170 |
-
"""
|
| 171 |
-
Example:
|
| 172 |
-
input: "13.234"
|
| 173 |
-
output: "thirteen point two three four"
|
| 174 |
-
"""
|
| 175 |
-
match = m.group(1)
|
| 176 |
-
parts = match.split(".")
|
| 177 |
-
words = []
|
| 178 |
-
# 遍历字符串中的每个字符
|
| 179 |
-
for char in parts[1]:
|
| 180 |
-
if char == ".":
|
| 181 |
-
words.append("point")
|
| 182 |
-
else:
|
| 183 |
-
words.append(char)
|
| 184 |
-
return parts[0] + " point " + " ".join(words)
|
| 185 |
-
|
| 186 |
-
|
| 187 |
-
# 分数的处理
|
| 188 |
-
def _expend_fraction(m):
|
| 189 |
-
"""
|
| 190 |
-
规则1: 分子使用基数词读法, 分母用序数词读法.
|
| 191 |
-
规则2: 如果分子大于 1, 在读分母的时候使用序数词复数读法.
|
| 192 |
-
规则3: 当分母为2的时候, 分母读做 half, 并且当分子大于 1 的时候, half 也要用复数读法, 读为 halves.
|
| 193 |
-
Examples:
|
| 194 |
-
|
| 195 |
-
| Written | Said |
|
| 196 |
-
|:---:|:---:|
|
| 197 |
-
| 1/3 | one third |
|
| 198 |
-
| 3/4 | three fourths |
|
| 199 |
-
| 5/6 | five sixths |
|
| 200 |
-
| 1/2 | one half |
|
| 201 |
-
| 3/2 | three halves |
|
| 202 |
-
"""
|
| 203 |
-
match = m.group(0)
|
| 204 |
-
numerator, denominator = map(int, match.split("/"))
|
| 205 |
-
|
| 206 |
-
numerator_part = _inflect.number_to_words(numerator)
|
| 207 |
-
if denominator == 2:
|
| 208 |
-
if numerator == 1:
|
| 209 |
-
denominator_part = "half"
|
| 210 |
-
else:
|
| 211 |
-
denominator_part = "halves"
|
| 212 |
-
elif denominator == 1:
|
| 213 |
-
return f"{numerator_part}"
|
| 214 |
-
else:
|
| 215 |
-
denominator_part = _inflect.ordinal(_inflect.number_to_words(denominator))
|
| 216 |
-
if numerator > 1:
|
| 217 |
-
denominator_part += "s"
|
| 218 |
-
|
| 219 |
-
return f"{numerator_part} {denominator_part}"
|
| 220 |
-
|
| 221 |
-
|
| 222 |
-
def _expand_ordinal(m):
|
| 223 |
-
return _inflect.number_to_words(m.group(0))
|
| 224 |
-
|
| 225 |
-
|
| 226 |
-
def _expand_number(m):
|
| 227 |
-
num = int(m.group(0))
|
| 228 |
-
if num > 1000 and num < 3000:
|
| 229 |
-
if num == 2000:
|
| 230 |
-
return "two thousand"
|
| 231 |
-
elif num > 2000 and num < 2010:
|
| 232 |
-
return "two thousand " + _inflect.number_to_words(num % 100)
|
| 233 |
-
elif num % 100 == 0:
|
| 234 |
-
return _inflect.number_to_words(num // 100) + " hundred"
|
| 235 |
-
else:
|
| 236 |
-
return _inflect.number_to_words(num, andword="", zero="oh", group=2).replace(", ", " ")
|
| 237 |
-
else:
|
| 238 |
-
return _inflect.number_to_words(num, andword="")
|
| 239 |
-
|
| 240 |
-
|
| 241 |
-
def normalize(text):
|
| 242 |
-
"""
|
| 243 |
-
!!! 所有的处理都需要正确的输入 !!!
|
| 244 |
-
可以添加新的处理，只需要添加正则表达式和对应的处理函数即可
|
| 245 |
-
"""
|
| 246 |
-
|
| 247 |
-
text = re.sub(_ordinal_number_re, _convert_ordinal, text)
|
| 248 |
-
text = re.sub(r"(?<!\d)-|-(?!\d)", " minus ", text)
|
| 249 |
-
text = re.sub(_comma_number_re, _remove_commas, text)
|
| 250 |
-
text = re.sub(_time_re, _expand_time, text)
|
| 251 |
-
text = re.sub(_measurement_re, _expand_measurement, text)
|
| 252 |
-
text = re.sub(_pounds_re_start, _expand_pounds, text)
|
| 253 |
-
text = re.sub(_pounds_re_end, _expand_pounds, text)
|
| 254 |
-
text = re.sub(_dollars_re_start, _expand_dollars, text)
|
| 255 |
-
text = re.sub(_dollars_re_end, _expand_dollars, text)
|
| 256 |
-
text = re.sub(_decimal_number_re, _expand_decimal_number, text)
|
| 257 |
-
text = re.sub(_fraction_re, _expend_fraction, text)
|
| 258 |
-
text = re.sub(_ordinal_re, _expand_ordinal, text)
|
| 259 |
-
text = re.sub(_number_re, _expand_number, text)
|
| 260 |
-
|
| 261 |
-
text = "".join(
|
| 262 |
-
char for char in unicodedata.normalize("NFD", text) if unicodedata.category(char) != "Mn"
|
| 263 |
-
) # Strip accents
|
| 264 |
-
|
| 265 |
-
text = re.sub("%", " percent", text)
|
| 266 |
-
text = re.sub("[^ A-Za-z'.,?!\-]", "", text)
|
| 267 |
-
text = re.sub(r"(?i)i\.e\.", "that is", text)
|
| 268 |
-
text = re.sub(r"(?i)e\.g\.", "for example", text)
|
| 269 |
-
# 增加纯大写单词拆分
|
| 270 |
-
text = re.sub(r"(?<!^)(?<![\s])([A-Z])", r" \1", text)
|
| 271 |
-
return text
|
| 272 |
-
|
| 273 |
-
|
| 274 |
-
if __name__ == "__main__":
|
| 275 |
-
# 我觉得其实可以把切分结果展示出来（只读，或者修改不影响传给TTS的实际text）
|
| 276 |
-
# 然后让用户确认后再输入给 TTS，可以让用户检查自己有没有不标准的输入
|
| 277 |
-
print(normalize("1. test ordinal number 1st"))
|
| 278 |
-
print(normalize("32.3$, $6.24, 1.1£, £7.14."))
|
| 279 |
-
print(normalize("3/23, 1/2, 3/2, 1/3, 6/1"))
|
| 280 |
-
print(normalize("1st, 22nd"))
|
| 281 |
-
print(normalize("a test 20h, 1.2s, 1L, 0.1km"))
|
| 282 |
-
print(normalize("a test of time 4:00, 13:00, 13:30"))
|
| 283 |
-
print(normalize("a test of temperature 4°F, 23°C, -19°C"))
|
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|
text/engdict-hot.rep
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
CHATGPT CH AE1 T JH IY1 P IY1 T IY1
|
| 2 |
-
JSON JH EY1 S AH0 N
|
| 3 |
-
CONDA K AA1 N D AH0
|
|
|
|
|
|
|
|
|
|
|
|
text/english.py
DELETED
|
@@ -1,374 +0,0 @@
|
|
| 1 |
-
import pickle
|
| 2 |
-
import os
|
| 3 |
-
import re
|
| 4 |
-
import wordsegment
|
| 5 |
-
from g2p_en import G2p
|
| 6 |
-
|
| 7 |
-
from text.symbols import punctuation
|
| 8 |
-
|
| 9 |
-
from text.symbols2 import symbols
|
| 10 |
-
|
| 11 |
-
from builtins import str as unicode
|
| 12 |
-
from text.en_normalization.expend import normalize
|
| 13 |
-
from nltk.tokenize import TweetTokenizer
|
| 14 |
-
|
| 15 |
-
word_tokenize = TweetTokenizer().tokenize
|
| 16 |
-
from nltk import pos_tag
|
| 17 |
-
|
| 18 |
-
current_file_path = os.path.dirname(__file__)
|
| 19 |
-
CMU_DICT_PATH = os.path.join(current_file_path, "cmudict.rep")
|
| 20 |
-
CMU_DICT_FAST_PATH = os.path.join(current_file_path, "cmudict-fast.rep")
|
| 21 |
-
CMU_DICT_HOT_PATH = os.path.join(current_file_path, "engdict-hot.rep")
|
| 22 |
-
CACHE_PATH = os.path.join(current_file_path, "engdict_cache.pickle")
|
| 23 |
-
NAMECACHE_PATH = os.path.join(current_file_path, "namedict_cache.pickle")
|
| 24 |
-
|
| 25 |
-
|
| 26 |
-
# 适配中文及 g2p_en 标点
|
| 27 |
-
rep_map = {
|
| 28 |
-
"[;:：，；]": ",",
|
| 29 |
-
'["’]': "'",
|
| 30 |
-
"。": ".",
|
| 31 |
-
"！": "!",
|
| 32 |
-
"？": "?",
|
| 33 |
-
}
|
| 34 |
-
|
| 35 |
-
|
| 36 |
-
arpa = {
|
| 37 |
-
"AH0",
|
| 38 |
-
"S",
|
| 39 |
-
"AH1",
|
| 40 |
-
"EY2",
|
| 41 |
-
"AE2",
|
| 42 |
-
"EH0",
|
| 43 |
-
"OW2",
|
| 44 |
-
"UH0",
|
| 45 |
-
"NG",
|
| 46 |
-
"B",
|
| 47 |
-
"G",
|
| 48 |
-
"AY0",
|
| 49 |
-
"M",
|
| 50 |
-
"AA0",
|
| 51 |
-
"F",
|
| 52 |
-
"AO0",
|
| 53 |
-
"ER2",
|
| 54 |
-
"UH1",
|
| 55 |
-
"IY1",
|
| 56 |
-
"AH2",
|
| 57 |
-
"DH",
|
| 58 |
-
"IY0",
|
| 59 |
-
"EY1",
|
| 60 |
-
"IH0",
|
| 61 |
-
"K",
|
| 62 |
-
"N",
|
| 63 |
-
"W",
|
| 64 |
-
"IY2",
|
| 65 |
-
"T",
|
| 66 |
-
"AA1",
|
| 67 |
-
"ER1",
|
| 68 |
-
"EH2",
|
| 69 |
-
"OY0",
|
| 70 |
-
"UH2",
|
| 71 |
-
"UW1",
|
| 72 |
-
"Z",
|
| 73 |
-
"AW2",
|
| 74 |
-
"AW1",
|
| 75 |
-
"V",
|
| 76 |
-
"UW2",
|
| 77 |
-
"AA2",
|
| 78 |
-
"ER",
|
| 79 |
-
"AW0",
|
| 80 |
-
"UW0",
|
| 81 |
-
"R",
|
| 82 |
-
"OW1",
|
| 83 |
-
"EH1",
|
| 84 |
-
"ZH",
|
| 85 |
-
"AE0",
|
| 86 |
-
"IH2",
|
| 87 |
-
"IH",
|
| 88 |
-
"Y",
|
| 89 |
-
"JH",
|
| 90 |
-
"P",
|
| 91 |
-
"AY1",
|
| 92 |
-
"EY0",
|
| 93 |
-
"OY2",
|
| 94 |
-
"TH",
|
| 95 |
-
"HH",
|
| 96 |
-
"D",
|
| 97 |
-
"ER0",
|
| 98 |
-
"CH",
|
| 99 |
-
"AO1",
|
| 100 |
-
"AE1",
|
| 101 |
-
"AO2",
|
| 102 |
-
"OY1",
|
| 103 |
-
"AY2",
|
| 104 |
-
"IH1",
|
| 105 |
-
"OW0",
|
| 106 |
-
"L",
|
| 107 |
-
"SH",
|
| 108 |
-
}
|
| 109 |
-
|
| 110 |
-
|
| 111 |
-
def replace_phs(phs):
|
| 112 |
-
rep_map = {"'": "-"}
|
| 113 |
-
phs_new = []
|
| 114 |
-
for ph in phs:
|
| 115 |
-
if ph in symbols:
|
| 116 |
-
phs_new.append(ph)
|
| 117 |
-
elif ph in rep_map.keys():
|
| 118 |
-
phs_new.append(rep_map[ph])
|
| 119 |
-
else:
|
| 120 |
-
print("ph not in symbols: ", ph)
|
| 121 |
-
return phs_new
|
| 122 |
-
|
| 123 |
-
|
| 124 |
-
def replace_consecutive_punctuation(text):
|
| 125 |
-
punctuations = "".join(re.escape(p) for p in punctuation)
|
| 126 |
-
pattern = f"([{punctuations}\s])([{punctuations}])+"
|
| 127 |
-
result = re.sub(pattern, r"\1", text)
|
| 128 |
-
return result
|
| 129 |
-
|
| 130 |
-
|
| 131 |
-
def read_dict():
|
| 132 |
-
g2p_dict = {}
|
| 133 |
-
start_line = 49
|
| 134 |
-
with open(CMU_DICT_PATH) as f:
|
| 135 |
-
line = f.readline()
|
| 136 |
-
line_index = 1
|
| 137 |
-
while line:
|
| 138 |
-
if line_index >= start_line:
|
| 139 |
-
line = line.strip()
|
| 140 |
-
word_split = line.split(" ")
|
| 141 |
-
word = word_split[0].lower()
|
| 142 |
-
|
| 143 |
-
syllable_split = word_split[1].split(" - ")
|
| 144 |
-
g2p_dict[word] = []
|
| 145 |
-
for syllable in syllable_split:
|
| 146 |
-
phone_split = syllable.split(" ")
|
| 147 |
-
g2p_dict[word].append(phone_split)
|
| 148 |
-
|
| 149 |
-
line_index = line_index + 1
|
| 150 |
-
line = f.readline()
|
| 151 |
-
|
| 152 |
-
return g2p_dict
|
| 153 |
-
|
| 154 |
-
|
| 155 |
-
def read_dict_new():
|
| 156 |
-
g2p_dict = {}
|
| 157 |
-
with open(CMU_DICT_PATH) as f:
|
| 158 |
-
line = f.readline()
|
| 159 |
-
line_index = 1
|
| 160 |
-
while line:
|
| 161 |
-
if line_index >= 57:
|
| 162 |
-
line = line.strip()
|
| 163 |
-
word_split = line.split(" ")
|
| 164 |
-
word = word_split[0].lower()
|
| 165 |
-
g2p_dict[word] = [word_split[1].split(" ")]
|
| 166 |
-
|
| 167 |
-
line_index = line_index + 1
|
| 168 |
-
line = f.readline()
|
| 169 |
-
|
| 170 |
-
with open(CMU_DICT_FAST_PATH) as f:
|
| 171 |
-
line = f.readline()
|
| 172 |
-
line_index = 1
|
| 173 |
-
while line:
|
| 174 |
-
if line_index >= 0:
|
| 175 |
-
line = line.strip()
|
| 176 |
-
word_split = line.split(" ")
|
| 177 |
-
word = word_split[0].lower()
|
| 178 |
-
if word not in g2p_dict:
|
| 179 |
-
g2p_dict[word] = [word_split[1:]]
|
| 180 |
-
|
| 181 |
-
line_index = line_index + 1
|
| 182 |
-
line = f.readline()
|
| 183 |
-
|
| 184 |
-
return g2p_dict
|
| 185 |
-
|
| 186 |
-
|
| 187 |
-
def hot_reload_hot(g2p_dict):
|
| 188 |
-
with open(CMU_DICT_HOT_PATH) as f:
|
| 189 |
-
line = f.readline()
|
| 190 |
-
line_index = 1
|
| 191 |
-
while line:
|
| 192 |
-
if line_index >= 0:
|
| 193 |
-
line = line.strip()
|
| 194 |
-
word_split = line.split(" ")
|
| 195 |
-
word = word_split[0].lower()
|
| 196 |
-
# 自定义发音词直接覆盖字典
|
| 197 |
-
g2p_dict[word] = [word_split[1:]]
|
| 198 |
-
|
| 199 |
-
line_index = line_index + 1
|
| 200 |
-
line = f.readline()
|
| 201 |
-
|
| 202 |
-
return g2p_dict
|
| 203 |
-
|
| 204 |
-
|
| 205 |
-
def cache_dict(g2p_dict, file_path):
|
| 206 |
-
with open(file_path, "wb") as pickle_file:
|
| 207 |
-
pickle.dump(g2p_dict, pickle_file)
|
| 208 |
-
|
| 209 |
-
|
| 210 |
-
def get_dict():
|
| 211 |
-
if os.path.exists(CACHE_PATH):
|
| 212 |
-
with open(CACHE_PATH, "rb") as pickle_file:
|
| 213 |
-
g2p_dict = pickle.load(pickle_file)
|
| 214 |
-
else:
|
| 215 |
-
g2p_dict = read_dict_new()
|
| 216 |
-
cache_dict(g2p_dict, CACHE_PATH)
|
| 217 |
-
|
| 218 |
-
g2p_dict = hot_reload_hot(g2p_dict)
|
| 219 |
-
|
| 220 |
-
return g2p_dict
|
| 221 |
-
|
| 222 |
-
|
| 223 |
-
def get_namedict():
|
| 224 |
-
if os.path.exists(NAMECACHE_PATH):
|
| 225 |
-
with open(NAMECACHE_PATH, "rb") as pickle_file:
|
| 226 |
-
name_dict = pickle.load(pickle_file)
|
| 227 |
-
else:
|
| 228 |
-
name_dict = {}
|
| 229 |
-
|
| 230 |
-
return name_dict
|
| 231 |
-
|
| 232 |
-
|
| 233 |
-
def text_normalize(text):
|
| 234 |
-
# todo: eng text normalize
|
| 235 |
-
|
| 236 |
-
# 效果相同，和 chinese.py 保持一致
|
| 237 |
-
pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
|
| 238 |
-
text = pattern.sub(lambda x: rep_map[x.group()], text)
|
| 239 |
-
|
| 240 |
-
text = unicode(text)
|
| 241 |
-
text = normalize(text)
|
| 242 |
-
|
| 243 |
-
# 避免重复标点引起的参考泄露
|
| 244 |
-
text = replace_consecutive_punctuation(text)
|
| 245 |
-
return text
|
| 246 |
-
|
| 247 |
-
|
| 248 |
-
class en_G2p(G2p):
|
| 249 |
-
def __init__(self):
|
| 250 |
-
super().__init__()
|
| 251 |
-
# 分词初始化
|
| 252 |
-
wordsegment.load()
|
| 253 |
-
|
| 254 |
-
# 扩展过时字典, 添加姓名字典
|
| 255 |
-
self.cmu = get_dict()
|
| 256 |
-
self.namedict = get_namedict()
|
| 257 |
-
|
| 258 |
-
# 剔除读音错误的几个缩写
|
| 259 |
-
for word in ["AE", "AI", "AR", "IOS", "HUD", "OS"]:
|
| 260 |
-
del self.cmu[word.lower()]
|
| 261 |
-
|
| 262 |
-
# 修正多音字
|
| 263 |
-
self.homograph2features["read"] = (["R", "IY1", "D"], ["R", "EH1", "D"], "VBP")
|
| 264 |
-
self.homograph2features["complex"] = (
|
| 265 |
-
["K", "AH0", "M", "P", "L", "EH1", "K", "S"],
|
| 266 |
-
["K", "AA1", "M", "P", "L", "EH0", "K", "S"],
|
| 267 |
-
"JJ",
|
| 268 |
-
)
|
| 269 |
-
|
| 270 |
-
def __call__(self, text):
|
| 271 |
-
# tokenization
|
| 272 |
-
words = word_tokenize(text)
|
| 273 |
-
tokens = pos_tag(words) # tuples of (word, tag)
|
| 274 |
-
|
| 275 |
-
# steps
|
| 276 |
-
prons = []
|
| 277 |
-
for o_word, pos in tokens:
|
| 278 |
-
# 还原 g2p_en 小写操作逻辑
|
| 279 |
-
word = o_word.lower()
|
| 280 |
-
|
| 281 |
-
if re.search("[a-z]", word) is None:
|
| 282 |
-
pron = [word]
|
| 283 |
-
# 先把单字母推出去
|
| 284 |
-
elif len(word) == 1:
|
| 285 |
-
# 单读 A 发音修正, 这里需要原格式 o_word 判断大写
|
| 286 |
-
if o_word == "A":
|
| 287 |
-
pron = ["EY1"]
|
| 288 |
-
else:
|
| 289 |
-
pron = self.cmu[word][0]
|
| 290 |
-
# g2p_en 原版多音字处理
|
| 291 |
-
elif word in self.homograph2features: # Check homograph
|
| 292 |
-
pron1, pron2, pos1 = self.homograph2features[word]
|
| 293 |
-
if pos.startswith(pos1):
|
| 294 |
-
pron = pron1
|
| 295 |
-
# pos1比pos长仅出现在read
|
| 296 |
-
elif len(pos) < len(pos1) and pos == pos1[: len(pos)]:
|
| 297 |
-
pron = pron1
|
| 298 |
-
else:
|
| 299 |
-
pron = pron2
|
| 300 |
-
else:
|
| 301 |
-
# 递归查找预测
|
| 302 |
-
pron = self.qryword(o_word)
|
| 303 |
-
|
| 304 |
-
prons.extend(pron)
|
| 305 |
-
prons.extend([" "])
|
| 306 |
-
|
| 307 |
-
return prons[:-1]
|
| 308 |
-
|
| 309 |
-
def qryword(self, o_word):
|
| 310 |
-
word = o_word.lower()
|
| 311 |
-
|
| 312 |
-
# 查字典, 单字母除外
|
| 313 |
-
if len(word) > 1 and word in self.cmu: # lookup CMU dict
|
| 314 |
-
return self.cmu[word][0]
|
| 315 |
-
|
| 316 |
-
# 单词仅首字母大写时查找姓名字典
|
| 317 |
-
if o_word.istitle() and word in self.namedict:
|
| 318 |
-
return self.namedict[word][0]
|
| 319 |
-
|
| 320 |
-
# oov 长度小于等于 3 直接读字母
|
| 321 |
-
if len(word) <= 3:
|
| 322 |
-
phones = []
|
| 323 |
-
for w in word:
|
| 324 |
-
# 单读 A 发音修正, 此处不存在大写的情况
|
| 325 |
-
if w == "a":
|
| 326 |
-
phones.extend(["EY1"])
|
| 327 |
-
elif not w.isalpha():
|
| 328 |
-
phones.extend([w])
|
| 329 |
-
else:
|
| 330 |
-
phones.extend(self.cmu[w][0])
|
| 331 |
-
return phones
|
| 332 |
-
|
| 333 |
-
# 尝试分离所有格
|
| 334 |
-
if re.match(r"^([a-z]+)('s)$", word):
|
| 335 |
-
phones = self.qryword(word[:-2])[:]
|
| 336 |
-
# P T K F TH HH 无声辅音结尾 's 发 ['S']
|
| 337 |
-
if phones[-1] in ["P", "T", "K", "F", "TH", "HH"]:
|
| 338 |
-
phones.extend(["S"])
|
| 339 |
-
# S Z SH ZH CH JH 擦声结尾 's 发 ['IH1', 'Z'] 或 ['AH0', 'Z']
|
| 340 |
-
elif phones[-1] in ["S", "Z", "SH", "ZH", "CH", "JH"]:
|
| 341 |
-
phones.extend(["AH0", "Z"])
|
| 342 |
-
# B D G DH V M N NG L R W Y 有声辅音结尾 's 发 ['Z']
|
| 343 |
-
# AH0 AH1 AH2 EY0 EY1 EY2 AE0 AE1 AE2 EH0 EH1 EH2 OW0 OW1 OW2 UH0 UH1 UH2 IY0 IY1 IY2 AA0 AA1 AA2 AO0 AO1 AO2
|
| 344 |
-
# ER ER0 ER1 ER2 UW0 UW1 UW2 AY0 AY1 AY2 AW0 AW1 AW2 OY0 OY1 OY2 IH IH0 IH1 IH2 元音结尾 's 发 ['Z']
|
| 345 |
-
else:
|
| 346 |
-
phones.extend(["Z"])
|
| 347 |
-
return phones
|
| 348 |
-
|
| 349 |
-
# 尝试进行分词，应对复合词
|
| 350 |
-
comps = wordsegment.segment(word.lower())
|
| 351 |
-
|
| 352 |
-
# 无法分词的送回去预测
|
| 353 |
-
if len(comps) == 1:
|
| 354 |
-
return self.predict(word)
|
| 355 |
-
|
| 356 |
-
# 可以分词的递归处理
|
| 357 |
-
return [phone for comp in comps for phone in self.qryword(comp)]
|
| 358 |
-
|
| 359 |
-
|
| 360 |
-
_g2p = en_G2p()
|
| 361 |
-
|
| 362 |
-
|
| 363 |
-
def g2p(text):
|
| 364 |
-
# g2p_en 整段推理，剔除不存在的arpa返回
|
| 365 |
-
phone_list = _g2p(text)
|
| 366 |
-
phones = [ph if ph != "<unk>" else "UNK" for ph in phone_list if ph not in [" ", "<pad>", "UW", "</s>", "<s>"]]
|
| 367 |
-
|
| 368 |
-
return replace_phs(phones)
|
| 369 |
-
|
| 370 |
-
|
| 371 |
-
if __name__ == "__main__":
|
| 372 |
-
print(g2p("hello"))
|
| 373 |
-
print(g2p(text_normalize("e.g. I used openai's AI tool to draw a picture.")))
|
| 374 |
-
print(g2p(text_normalize("In this; paper, we propose 1 DSPGAN, a GAN-based universal vocoder.")))
|
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text/g2pw/__init__.py
DELETED
|
@@ -1 +0,0 @@
|
|
| 1 |
-
from text.g2pw.g2pw import *
|
|
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|
text/g2pw/dataset.py
DELETED
|
@@ -1,160 +0,0 @@
|
|
| 1 |
-
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
| 2 |
-
#
|
| 3 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 4 |
-
# you may not use this file except in compliance with the License.
|
| 5 |
-
# You may obtain a copy of the License at
|
| 6 |
-
#
|
| 7 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
| 8 |
-
#
|
| 9 |
-
# Unless required by applicable law or agreed to in writing, software
|
| 10 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 11 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 12 |
-
# See the License for the specific language governing permissions and
|
| 13 |
-
# limitations under the License.
|
| 14 |
-
"""
|
| 15 |
-
Credits
|
| 16 |
-
This code is modified from https://github.com/GitYCC/g2pW
|
| 17 |
-
"""
|
| 18 |
-
|
| 19 |
-
from typing import Dict
|
| 20 |
-
from typing import List
|
| 21 |
-
from typing import Tuple
|
| 22 |
-
|
| 23 |
-
import numpy as np
|
| 24 |
-
|
| 25 |
-
from .utils import tokenize_and_map
|
| 26 |
-
|
| 27 |
-
ANCHOR_CHAR = "▁"
|
| 28 |
-
|
| 29 |
-
|
| 30 |
-
def prepare_onnx_input(
|
| 31 |
-
tokenizer,
|
| 32 |
-
labels: List[str],
|
| 33 |
-
char2phonemes: Dict[str, List[int]],
|
| 34 |
-
chars: List[str],
|
| 35 |
-
texts: List[str],
|
| 36 |
-
query_ids: List[int],
|
| 37 |
-
use_mask: bool = False,
|
| 38 |
-
window_size: int = None,
|
| 39 |
-
max_len: int = 512,
|
| 40 |
-
) -> Dict[str, np.array]:
|
| 41 |
-
if window_size is not None:
|
| 42 |
-
truncated_texts, truncated_query_ids = _truncate_texts(
|
| 43 |
-
window_size=window_size, texts=texts, query_ids=query_ids
|
| 44 |
-
)
|
| 45 |
-
input_ids = []
|
| 46 |
-
token_type_ids = []
|
| 47 |
-
attention_masks = []
|
| 48 |
-
phoneme_masks = []
|
| 49 |
-
char_ids = []
|
| 50 |
-
position_ids = []
|
| 51 |
-
|
| 52 |
-
for idx in range(len(texts)):
|
| 53 |
-
text = (truncated_texts if window_size else texts)[idx].lower()
|
| 54 |
-
query_id = (truncated_query_ids if window_size else query_ids)[idx]
|
| 55 |
-
|
| 56 |
-
try:
|
| 57 |
-
tokens, text2token, token2text = tokenize_and_map(tokenizer=tokenizer, text=text)
|
| 58 |
-
except Exception:
|
| 59 |
-
print(f'warning: text "{text}" is invalid')
|
| 60 |
-
return {}
|
| 61 |
-
|
| 62 |
-
text, query_id, tokens, text2token, token2text = _truncate(
|
| 63 |
-
max_len=max_len, text=text, query_id=query_id, tokens=tokens, text2token=text2token, token2text=token2text
|
| 64 |
-
)
|
| 65 |
-
|
| 66 |
-
processed_tokens = ["[CLS]"] + tokens + ["[SEP]"]
|
| 67 |
-
|
| 68 |
-
input_id = list(np.array(tokenizer.convert_tokens_to_ids(processed_tokens)))
|
| 69 |
-
token_type_id = list(np.zeros((len(processed_tokens),), dtype=int))
|
| 70 |
-
attention_mask = list(np.ones((len(processed_tokens),), dtype=int))
|
| 71 |
-
|
| 72 |
-
query_char = text[query_id]
|
| 73 |
-
phoneme_mask = (
|
| 74 |
-
[1 if i in char2phonemes[query_char] else 0 for i in range(len(labels))] if use_mask else [1] * len(labels)
|
| 75 |
-
)
|
| 76 |
-
char_id = chars.index(query_char)
|
| 77 |
-
position_id = text2token[query_id] + 1 # [CLS] token locate at first place
|
| 78 |
-
|
| 79 |
-
input_ids.append(input_id)
|
| 80 |
-
token_type_ids.append(token_type_id)
|
| 81 |
-
attention_masks.append(attention_mask)
|
| 82 |
-
phoneme_masks.append(phoneme_mask)
|
| 83 |
-
char_ids.append(char_id)
|
| 84 |
-
position_ids.append(position_id)
|
| 85 |
-
|
| 86 |
-
outputs = {
|
| 87 |
-
"input_ids": np.array(input_ids).astype(np.int64),
|
| 88 |
-
"token_type_ids": np.array(token_type_ids).astype(np.int64),
|
| 89 |
-
"attention_masks": np.array(attention_masks).astype(np.int64),
|
| 90 |
-
"phoneme_masks": np.array(phoneme_masks).astype(np.float32),
|
| 91 |
-
"char_ids": np.array(char_ids).astype(np.int64),
|
| 92 |
-
"position_ids": np.array(position_ids).astype(np.int64),
|
| 93 |
-
}
|
| 94 |
-
return outputs
|
| 95 |
-
|
| 96 |
-
|
| 97 |
-
def _truncate_texts(window_size: int, texts: List[str], query_ids: List[int]) -> Tuple[List[str], List[int]]:
|
| 98 |
-
truncated_texts = []
|
| 99 |
-
truncated_query_ids = []
|
| 100 |
-
for text, query_id in zip(texts, query_ids):
|
| 101 |
-
start = max(0, query_id - window_size // 2)
|
| 102 |
-
end = min(len(text), query_id + window_size // 2)
|
| 103 |
-
truncated_text = text[start:end]
|
| 104 |
-
truncated_texts.append(truncated_text)
|
| 105 |
-
|
| 106 |
-
truncated_query_id = query_id - start
|
| 107 |
-
truncated_query_ids.append(truncated_query_id)
|
| 108 |
-
return truncated_texts, truncated_query_ids
|
| 109 |
-
|
| 110 |
-
|
| 111 |
-
def _truncate(
|
| 112 |
-
max_len: int, text: str, query_id: int, tokens: List[str], text2token: List[int], token2text: List[Tuple[int]]
|
| 113 |
-
):
|
| 114 |
-
truncate_len = max_len - 2
|
| 115 |
-
if len(tokens) <= truncate_len:
|
| 116 |
-
return (text, query_id, tokens, text2token, token2text)
|
| 117 |
-
|
| 118 |
-
token_position = text2token[query_id]
|
| 119 |
-
|
| 120 |
-
token_start = token_position - truncate_len // 2
|
| 121 |
-
token_end = token_start + truncate_len
|
| 122 |
-
font_exceed_dist = -token_start
|
| 123 |
-
back_exceed_dist = token_end - len(tokens)
|
| 124 |
-
if font_exceed_dist > 0:
|
| 125 |
-
token_start += font_exceed_dist
|
| 126 |
-
token_end += font_exceed_dist
|
| 127 |
-
elif back_exceed_dist > 0:
|
| 128 |
-
token_start -= back_exceed_dist
|
| 129 |
-
token_end -= back_exceed_dist
|
| 130 |
-
|
| 131 |
-
start = token2text[token_start][0]
|
| 132 |
-
end = token2text[token_end - 1][1]
|
| 133 |
-
|
| 134 |
-
return (
|
| 135 |
-
text[start:end],
|
| 136 |
-
query_id - start,
|
| 137 |
-
tokens[token_start:token_end],
|
| 138 |
-
[i - token_start if i is not None else None for i in text2token[start:end]],
|
| 139 |
-
[(s - start, e - start) for s, e in token2text[token_start:token_end]],
|
| 140 |
-
)
|
| 141 |
-
|
| 142 |
-
|
| 143 |
-
def get_phoneme_labels(polyphonic_chars: List[List[str]]) -> Tuple[List[str], Dict[str, List[int]]]:
|
| 144 |
-
labels = sorted(list(set([phoneme for char, phoneme in polyphonic_chars])))
|
| 145 |
-
char2phonemes = {}
|
| 146 |
-
for char, phoneme in polyphonic_chars:
|
| 147 |
-
if char not in char2phonemes:
|
| 148 |
-
char2phonemes[char] = []
|
| 149 |
-
char2phonemes[char].append(labels.index(phoneme))
|
| 150 |
-
return labels, char2phonemes
|
| 151 |
-
|
| 152 |
-
|
| 153 |
-
def get_char_phoneme_labels(polyphonic_chars: List[List[str]]) -> Tuple[List[str], Dict[str, List[int]]]:
|
| 154 |
-
labels = sorted(list(set([f"{char} {phoneme}" for char, phoneme in polyphonic_chars])))
|
| 155 |
-
char2phonemes = {}
|
| 156 |
-
for char, phoneme in polyphonic_chars:
|
| 157 |
-
if char not in char2phonemes:
|
| 158 |
-
char2phonemes[char] = []
|
| 159 |
-
char2phonemes[char].append(labels.index(f"{char} {phoneme}"))
|
| 160 |
-
return labels, char2phonemes
|
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|
text/g2pw/g2pw.py
DELETED
|
@@ -1,159 +0,0 @@
|
|
| 1 |
-
# This code is modified from https://github.com/mozillazg/pypinyin-g2pW
|
| 2 |
-
|
| 3 |
-
import pickle
|
| 4 |
-
import os
|
| 5 |
-
|
| 6 |
-
from pypinyin.constants import RE_HANS
|
| 7 |
-
from pypinyin.core import Pinyin, Style
|
| 8 |
-
from pypinyin.seg.simpleseg import simple_seg
|
| 9 |
-
from pypinyin.converter import UltimateConverter
|
| 10 |
-
from pypinyin.contrib.tone_convert import to_tone
|
| 11 |
-
from .onnx_api import G2PWOnnxConverter
|
| 12 |
-
|
| 13 |
-
current_file_path = os.path.dirname(__file__)
|
| 14 |
-
CACHE_PATH = os.path.join(current_file_path, "polyphonic.pickle")
|
| 15 |
-
PP_DICT_PATH = os.path.join(current_file_path, "polyphonic.rep")
|
| 16 |
-
PP_FIX_DICT_PATH = os.path.join(current_file_path, "polyphonic-fix.rep")
|
| 17 |
-
|
| 18 |
-
|
| 19 |
-
class G2PWPinyin(Pinyin):
|
| 20 |
-
def __init__(
|
| 21 |
-
self,
|
| 22 |
-
model_dir="G2PWModel/",
|
| 23 |
-
model_source=None,
|
| 24 |
-
enable_non_tradional_chinese=True,
|
| 25 |
-
v_to_u=False,
|
| 26 |
-
neutral_tone_with_five=False,
|
| 27 |
-
tone_sandhi=False,
|
| 28 |
-
**kwargs,
|
| 29 |
-
):
|
| 30 |
-
self._g2pw = G2PWOnnxConverter(
|
| 31 |
-
model_dir=model_dir,
|
| 32 |
-
style="pinyin",
|
| 33 |
-
model_source=model_source,
|
| 34 |
-
enable_non_tradional_chinese=enable_non_tradional_chinese,
|
| 35 |
-
)
|
| 36 |
-
self._converter = Converter(
|
| 37 |
-
self._g2pw,
|
| 38 |
-
v_to_u=v_to_u,
|
| 39 |
-
neutral_tone_with_five=neutral_tone_with_five,
|
| 40 |
-
tone_sandhi=tone_sandhi,
|
| 41 |
-
)
|
| 42 |
-
|
| 43 |
-
def get_seg(self, **kwargs):
|
| 44 |
-
return simple_seg
|
| 45 |
-
|
| 46 |
-
|
| 47 |
-
class Converter(UltimateConverter):
|
| 48 |
-
def __init__(self, g2pw_instance, v_to_u=False, neutral_tone_with_five=False, tone_sandhi=False, **kwargs):
|
| 49 |
-
super(Converter, self).__init__(
|
| 50 |
-
v_to_u=v_to_u, neutral_tone_with_five=neutral_tone_with_five, tone_sandhi=tone_sandhi, **kwargs
|
| 51 |
-
)
|
| 52 |
-
|
| 53 |
-
self._g2pw = g2pw_instance
|
| 54 |
-
|
| 55 |
-
def convert(self, words, style, heteronym, errors, strict, **kwargs):
|
| 56 |
-
pys = []
|
| 57 |
-
if RE_HANS.match(words):
|
| 58 |
-
pys = self._to_pinyin(words, style=style, heteronym=heteronym, errors=errors, strict=strict)
|
| 59 |
-
post_data = self.post_pinyin(words, heteronym, pys)
|
| 60 |
-
if post_data is not None:
|
| 61 |
-
pys = post_data
|
| 62 |
-
|
| 63 |
-
pys = self.convert_styles(pys, words, style, heteronym, errors, strict)
|
| 64 |
-
|
| 65 |
-
else:
|
| 66 |
-
py = self.handle_nopinyin(words, style=style, errors=errors, heteronym=heteronym, strict=strict)
|
| 67 |
-
if py:
|
| 68 |
-
pys.extend(py)
|
| 69 |
-
|
| 70 |
-
return _remove_dup_and_empty(pys)
|
| 71 |
-
|
| 72 |
-
def _to_pinyin(self, han, style, heteronym, errors, strict, **kwargs):
|
| 73 |
-
pinyins = []
|
| 74 |
-
|
| 75 |
-
g2pw_pinyin = self._g2pw(han)
|
| 76 |
-
|
| 77 |
-
if not g2pw_pinyin: # g2pw 不支持的汉字改为使用 pypinyin 原有逻辑
|
| 78 |
-
return super(Converter, self).convert(han, Style.TONE, heteronym, errors, strict, **kwargs)
|
| 79 |
-
|
| 80 |
-
for i, item in enumerate(g2pw_pinyin[0]):
|
| 81 |
-
if item is None: # g2pw 不支持的汉字改为使用 pypinyin 原有逻辑
|
| 82 |
-
py = super(Converter, self).convert(han[i], Style.TONE, heteronym, errors, strict, **kwargs)
|
| 83 |
-
pinyins.extend(py)
|
| 84 |
-
else:
|
| 85 |
-
pinyins.append([to_tone(item)])
|
| 86 |
-
|
| 87 |
-
return pinyins
|
| 88 |
-
|
| 89 |
-
|
| 90 |
-
def _remove_dup_items(lst, remove_empty=False):
|
| 91 |
-
new_lst = []
|
| 92 |
-
for item in lst:
|
| 93 |
-
if remove_empty and not item:
|
| 94 |
-
continue
|
| 95 |
-
if item not in new_lst:
|
| 96 |
-
new_lst.append(item)
|
| 97 |
-
return new_lst
|
| 98 |
-
|
| 99 |
-
|
| 100 |
-
def _remove_dup_and_empty(lst_list):
|
| 101 |
-
new_lst_list = []
|
| 102 |
-
for lst in lst_list:
|
| 103 |
-
lst = _remove_dup_items(lst, remove_empty=True)
|
| 104 |
-
if lst:
|
| 105 |
-
new_lst_list.append(lst)
|
| 106 |
-
else:
|
| 107 |
-
new_lst_list.append([""])
|
| 108 |
-
|
| 109 |
-
return new_lst_list
|
| 110 |
-
|
| 111 |
-
|
| 112 |
-
def cache_dict(polyphonic_dict, file_path):
|
| 113 |
-
with open(file_path, "wb") as pickle_file:
|
| 114 |
-
pickle.dump(polyphonic_dict, pickle_file)
|
| 115 |
-
|
| 116 |
-
|
| 117 |
-
def get_dict():
|
| 118 |
-
if os.path.exists(CACHE_PATH):
|
| 119 |
-
with open(CACHE_PATH, "rb") as pickle_file:
|
| 120 |
-
polyphonic_dict = pickle.load(pickle_file)
|
| 121 |
-
else:
|
| 122 |
-
polyphonic_dict = read_dict()
|
| 123 |
-
cache_dict(polyphonic_dict, CACHE_PATH)
|
| 124 |
-
|
| 125 |
-
return polyphonic_dict
|
| 126 |
-
|
| 127 |
-
|
| 128 |
-
def read_dict():
|
| 129 |
-
polyphonic_dict = {}
|
| 130 |
-
with open(PP_DICT_PATH, encoding="utf-8") as f:
|
| 131 |
-
line = f.readline()
|
| 132 |
-
while line:
|
| 133 |
-
key, value_str = line.split(":")
|
| 134 |
-
value = eval(value_str.strip())
|
| 135 |
-
polyphonic_dict[key.strip()] = value
|
| 136 |
-
line = f.readline()
|
| 137 |
-
with open(PP_FIX_DICT_PATH, encoding="utf-8") as f:
|
| 138 |
-
line = f.readline()
|
| 139 |
-
while line:
|
| 140 |
-
key, value_str = line.split(":")
|
| 141 |
-
value = eval(value_str.strip())
|
| 142 |
-
polyphonic_dict[key.strip()] = value
|
| 143 |
-
line = f.readline()
|
| 144 |
-
return polyphonic_dict
|
| 145 |
-
|
| 146 |
-
|
| 147 |
-
def correct_pronunciation(word, word_pinyins):
|
| 148 |
-
new_pinyins = pp_dict.get(word, "")
|
| 149 |
-
if new_pinyins == "":
|
| 150 |
-
for idx, w in enumerate(word):
|
| 151 |
-
w_pinyin = pp_dict.get(w, "")
|
| 152 |
-
if w_pinyin != "":
|
| 153 |
-
word_pinyins[idx] = w_pinyin[0]
|
| 154 |
-
return word_pinyins
|
| 155 |
-
else:
|
| 156 |
-
return new_pinyins
|
| 157 |
-
|
| 158 |
-
|
| 159 |
-
pp_dict = get_dict()
|
|
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|
|
text/g2pw/onnx_api.py
DELETED
|
@@ -1,293 +0,0 @@
|
|
| 1 |
-
# This code is modified from https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/g2pw
|
| 2 |
-
# This code is modified from https://github.com/GitYCC/g2pW
|
| 3 |
-
def load_nvrtc():
|
| 4 |
-
import torch,sys,os,ctypes
|
| 5 |
-
from pathlib import Path
|
| 6 |
-
|
| 7 |
-
if not torch.cuda.is_available():
|
| 8 |
-
print("[INFO] CUDA is not available, skipping nvrtc setup.")
|
| 9 |
-
return
|
| 10 |
-
|
| 11 |
-
if sys.platform == "win32":
|
| 12 |
-
torch_lib_dir = Path(torch.__file__).parent / "lib"
|
| 13 |
-
if torch_lib_dir.exists():
|
| 14 |
-
os.add_dll_directory(str(torch_lib_dir))
|
| 15 |
-
print(f"[INFO] Added DLL directory: {torch_lib_dir}")
|
| 16 |
-
matching_files = sorted(torch_lib_dir.glob("nvrtc*.dll"))
|
| 17 |
-
if not matching_files:
|
| 18 |
-
print(f"[ERROR] No nvrtc*.dll found in {torch_lib_dir}")
|
| 19 |
-
return
|
| 20 |
-
for dll_path in matching_files:
|
| 21 |
-
dll_name = os.path.basename(dll_path)
|
| 22 |
-
try:
|
| 23 |
-
ctypes.CDLL(dll_name)
|
| 24 |
-
print(f"[INFO] Loaded: {dll_name}")
|
| 25 |
-
except OSError as e:
|
| 26 |
-
print(f"[WARNING] Failed to load {dll_name}: {e}")
|
| 27 |
-
else:
|
| 28 |
-
print(f"[WARNING] Torch lib directory not found: {torch_lib_dir}")
|
| 29 |
-
|
| 30 |
-
elif sys.platform == "linux":
|
| 31 |
-
site_packages = Path(torch.__file__).resolve().parents[1]
|
| 32 |
-
nvrtc_dir = site_packages / "nvidia" / "cuda_nvrtc" / "lib"
|
| 33 |
-
|
| 34 |
-
if not nvrtc_dir.exists():
|
| 35 |
-
print(f"[ERROR] nvrtc dir not found: {nvrtc_dir}")
|
| 36 |
-
return
|
| 37 |
-
|
| 38 |
-
matching_files = sorted(nvrtc_dir.glob("libnvrtc*.so*"))
|
| 39 |
-
if not matching_files:
|
| 40 |
-
print(f"[ERROR] No libnvrtc*.so* found in {nvrtc_dir}")
|
| 41 |
-
return
|
| 42 |
-
|
| 43 |
-
for so_path in matching_files:
|
| 44 |
-
try:
|
| 45 |
-
ctypes.CDLL(so_path, mode=ctypes.RTLD_GLOBAL) # type: ignore
|
| 46 |
-
print(f"[INFO] Loaded: {so_path}")
|
| 47 |
-
except OSError as e:
|
| 48 |
-
print(f"[WARNING] Failed to load {so_path}: {e}")
|
| 49 |
-
load_nvrtc()
|
| 50 |
-
import warnings
|
| 51 |
-
|
| 52 |
-
warnings.filterwarnings("ignore")
|
| 53 |
-
import json
|
| 54 |
-
import os
|
| 55 |
-
import zipfile, requests
|
| 56 |
-
from typing import Any
|
| 57 |
-
from typing import Dict
|
| 58 |
-
from typing import List
|
| 59 |
-
from typing import Tuple
|
| 60 |
-
|
| 61 |
-
import numpy as np
|
| 62 |
-
import onnxruntime
|
| 63 |
-
|
| 64 |
-
onnxruntime.set_default_logger_severity(3)
|
| 65 |
-
try:
|
| 66 |
-
onnxruntime.preload_dlls()
|
| 67 |
-
except:
|
| 68 |
-
traceback.print_exc()
|
| 69 |
-
from opencc import OpenCC
|
| 70 |
-
from transformers import AutoTokenizer
|
| 71 |
-
from pypinyin import pinyin
|
| 72 |
-
from pypinyin import Style
|
| 73 |
-
|
| 74 |
-
from .dataset import get_char_phoneme_labels
|
| 75 |
-
from .dataset import get_phoneme_labels
|
| 76 |
-
from .dataset import prepare_onnx_input
|
| 77 |
-
from .utils import load_config
|
| 78 |
-
from ..zh_normalization.char_convert import tranditional_to_simplified
|
| 79 |
-
|
| 80 |
-
model_version = '1.1'
|
| 81 |
-
|
| 82 |
-
|
| 83 |
-
def predict(session, onnx_input: Dict[str, Any],
|
| 84 |
-
labels: List[str]) -> Tuple[List[str], List[float]]:
|
| 85 |
-
all_preds = []
|
| 86 |
-
all_confidences = []
|
| 87 |
-
probs = session.run([], {
|
| 88 |
-
"input_ids": onnx_input['input_ids'],
|
| 89 |
-
"token_type_ids": onnx_input['token_type_ids'],
|
| 90 |
-
"attention_mask": onnx_input['attention_masks'],
|
| 91 |
-
"phoneme_mask": onnx_input['phoneme_masks'],
|
| 92 |
-
"char_ids": onnx_input['char_ids'],
|
| 93 |
-
"position_ids": onnx_input['position_ids']
|
| 94 |
-
})[0]
|
| 95 |
-
|
| 96 |
-
preds = np.argmax(probs, axis=1).tolist()
|
| 97 |
-
max_probs = []
|
| 98 |
-
for index, arr in zip(preds, probs.tolist()):
|
| 99 |
-
max_probs.append(arr[index])
|
| 100 |
-
all_preds += [labels[pred] for pred in preds]
|
| 101 |
-
all_confidences += max_probs
|
| 102 |
-
|
| 103 |
-
return all_preds, all_confidences
|
| 104 |
-
|
| 105 |
-
|
| 106 |
-
def download_and_decompress(model_dir: str = 'G2PWModel/'):
|
| 107 |
-
if not os.path.exists(model_dir):
|
| 108 |
-
parent_directory = os.path.dirname(model_dir)
|
| 109 |
-
zip_dir = os.path.join(parent_directory, "G2PWModel_1.1.zip")
|
| 110 |
-
extract_dir = os.path.join(parent_directory, "G2PWModel_1.1")
|
| 111 |
-
extract_dir_new = os.path.join(parent_directory, "G2PWModel")
|
| 112 |
-
print("Downloading g2pw model...")
|
| 113 |
-
modelscope_url = "https://www.modelscope.cn/models/kamiorinn/g2pw/resolve/master/G2PWModel_1.1.zip"
|
| 114 |
-
with requests.get(modelscope_url, stream=True) as r:
|
| 115 |
-
r.raise_for_status()
|
| 116 |
-
with open(zip_dir, 'wb') as f:
|
| 117 |
-
for chunk in r.iter_content(chunk_size=8192):
|
| 118 |
-
if chunk:
|
| 119 |
-
f.write(chunk)
|
| 120 |
-
|
| 121 |
-
print("Extracting g2pw model...")
|
| 122 |
-
with zipfile.ZipFile(zip_dir, "r") as zip_ref:
|
| 123 |
-
zip_ref.extractall(parent_directory)
|
| 124 |
-
|
| 125 |
-
os.rename(extract_dir, extract_dir_new)
|
| 126 |
-
|
| 127 |
-
return model_dir
|
| 128 |
-
|
| 129 |
-
|
| 130 |
-
class G2PWOnnxConverter:
|
| 131 |
-
def __init__(self,
|
| 132 |
-
model_dir: str = 'G2PWModel/',
|
| 133 |
-
style: str = 'bopomofo',
|
| 134 |
-
model_source: str = None,
|
| 135 |
-
enable_non_tradional_chinese: bool = False):
|
| 136 |
-
uncompress_path = download_and_decompress(model_dir)
|
| 137 |
-
|
| 138 |
-
sess_options = onnxruntime.SessionOptions()
|
| 139 |
-
sess_options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
|
| 140 |
-
sess_options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL
|
| 141 |
-
sess_options.intra_op_num_threads = 2
|
| 142 |
-
# self.session_g2pW = onnxruntime.InferenceSession(os.path.join(uncompress_path, 'g2pW.onnx'), sess_options=sess_options, providers=['CPUExecutionProvider'])
|
| 143 |
-
self.session_g2pW = onnxruntime.InferenceSession(os.path.join(uncompress_path, 'g2pW.onnx'), sess_options=sess_options, providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
|
| 144 |
-
|
| 145 |
-
self.config = load_config(
|
| 146 |
-
config_path=os.path.join(uncompress_path, 'config.py'),
|
| 147 |
-
use_default=True)
|
| 148 |
-
|
| 149 |
-
self.model_source = model_source if model_source else self.config.model_source
|
| 150 |
-
self.enable_opencc = enable_non_tradional_chinese
|
| 151 |
-
|
| 152 |
-
self.tokenizer = AutoTokenizer.from_pretrained(self.model_source)
|
| 153 |
-
|
| 154 |
-
polyphonic_chars_path = os.path.join(uncompress_path,
|
| 155 |
-
'POLYPHONIC_CHARS.txt')
|
| 156 |
-
monophonic_chars_path = os.path.join(uncompress_path,
|
| 157 |
-
'MONOPHONIC_CHARS.txt')
|
| 158 |
-
self.polyphonic_chars = [
|
| 159 |
-
line.split('\t')
|
| 160 |
-
for line in open(polyphonic_chars_path, encoding='utf-8').read()
|
| 161 |
-
.strip().split('\n')
|
| 162 |
-
]
|
| 163 |
-
self.non_polyphonic = {
|
| 164 |
-
'一', '不', '和', '咋', '嗲', '剖', '差', '攢', '倒', '難', '奔', '勁', '拗',
|
| 165 |
-
'肖', '瘙', '誒', '泊', '听', '噢'
|
| 166 |
-
}
|
| 167 |
-
self.non_monophonic = {'似', '攢'}
|
| 168 |
-
self.monophonic_chars = [
|
| 169 |
-
line.split('\t')
|
| 170 |
-
for line in open(monophonic_chars_path, encoding='utf-8').read()
|
| 171 |
-
.strip().split('\n')
|
| 172 |
-
]
|
| 173 |
-
self.labels, self.char2phonemes = get_char_phoneme_labels(
|
| 174 |
-
polyphonic_chars=self.polyphonic_chars
|
| 175 |
-
) if self.config.use_char_phoneme else get_phoneme_labels(
|
| 176 |
-
polyphonic_chars=self.polyphonic_chars)
|
| 177 |
-
|
| 178 |
-
self.chars = sorted(list(self.char2phonemes.keys()))
|
| 179 |
-
|
| 180 |
-
self.polyphonic_chars_new = set(self.chars)
|
| 181 |
-
for char in self.non_polyphonic:
|
| 182 |
-
if char in self.polyphonic_chars_new:
|
| 183 |
-
self.polyphonic_chars_new.remove(char)
|
| 184 |
-
|
| 185 |
-
self.monophonic_chars_dict = {
|
| 186 |
-
char: phoneme
|
| 187 |
-
for char, phoneme in self.monophonic_chars
|
| 188 |
-
}
|
| 189 |
-
for char in self.non_monophonic:
|
| 190 |
-
if char in self.monophonic_chars_dict:
|
| 191 |
-
self.monophonic_chars_dict.pop(char)
|
| 192 |
-
|
| 193 |
-
self.pos_tags = [
|
| 194 |
-
'UNK', 'A', 'C', 'D', 'I', 'N', 'P', 'T', 'V', 'DE', 'SHI'
|
| 195 |
-
]
|
| 196 |
-
|
| 197 |
-
with open(
|
| 198 |
-
os.path.join(uncompress_path,
|
| 199 |
-
'bopomofo_to_pinyin_wo_tune_dict.json'),
|
| 200 |
-
'r',
|
| 201 |
-
encoding='utf-8') as fr:
|
| 202 |
-
self.bopomofo_convert_dict = json.load(fr)
|
| 203 |
-
self.style_convert_func = {
|
| 204 |
-
'bopomofo': lambda x: x,
|
| 205 |
-
'pinyin': self._convert_bopomofo_to_pinyin,
|
| 206 |
-
}[style]
|
| 207 |
-
|
| 208 |
-
with open(
|
| 209 |
-
os.path.join(uncompress_path, 'char_bopomofo_dict.json'),
|
| 210 |
-
'r',
|
| 211 |
-
encoding='utf-8') as fr:
|
| 212 |
-
self.char_bopomofo_dict = json.load(fr)
|
| 213 |
-
|
| 214 |
-
if self.enable_opencc:
|
| 215 |
-
self.cc = OpenCC('s2tw')
|
| 216 |
-
|
| 217 |
-
def _convert_bopomofo_to_pinyin(self, bopomofo: str) -> str:
|
| 218 |
-
tone = bopomofo[-1]
|
| 219 |
-
assert tone in '12345'
|
| 220 |
-
component = self.bopomofo_convert_dict.get(bopomofo[:-1])
|
| 221 |
-
if component:
|
| 222 |
-
return component + tone
|
| 223 |
-
else:
|
| 224 |
-
print(f'Warning: "{bopomofo}" cannot convert to pinyin')
|
| 225 |
-
return None
|
| 226 |
-
|
| 227 |
-
def __call__(self, sentences: List[str]) -> List[List[str]]:
|
| 228 |
-
if isinstance(sentences, str):
|
| 229 |
-
sentences = [sentences]
|
| 230 |
-
|
| 231 |
-
if self.enable_opencc:
|
| 232 |
-
translated_sentences = []
|
| 233 |
-
for sent in sentences:
|
| 234 |
-
translated_sent = self.cc.convert(sent)
|
| 235 |
-
assert len(translated_sent) == len(sent)
|
| 236 |
-
translated_sentences.append(translated_sent)
|
| 237 |
-
sentences = translated_sentences
|
| 238 |
-
|
| 239 |
-
texts, query_ids, sent_ids, partial_results = self._prepare_data(
|
| 240 |
-
sentences=sentences)
|
| 241 |
-
if len(texts) == 0:
|
| 242 |
-
# sentences no polyphonic words
|
| 243 |
-
return partial_results
|
| 244 |
-
|
| 245 |
-
onnx_input = prepare_onnx_input(
|
| 246 |
-
tokenizer=self.tokenizer,
|
| 247 |
-
labels=self.labels,
|
| 248 |
-
char2phonemes=self.char2phonemes,
|
| 249 |
-
chars=self.chars,
|
| 250 |
-
texts=texts,
|
| 251 |
-
query_ids=query_ids,
|
| 252 |
-
use_mask=self.config.use_mask,
|
| 253 |
-
window_size=None)
|
| 254 |
-
|
| 255 |
-
preds, confidences = predict(
|
| 256 |
-
session=self.session_g2pW,
|
| 257 |
-
onnx_input=onnx_input,
|
| 258 |
-
labels=self.labels)
|
| 259 |
-
if self.config.use_char_phoneme:
|
| 260 |
-
preds = [pred.split(' ')[1] for pred in preds]
|
| 261 |
-
|
| 262 |
-
results = partial_results
|
| 263 |
-
for sent_id, query_id, pred in zip(sent_ids, query_ids, preds):
|
| 264 |
-
results[sent_id][query_id] = self.style_convert_func(pred)
|
| 265 |
-
|
| 266 |
-
return results
|
| 267 |
-
|
| 268 |
-
def _prepare_data(
|
| 269 |
-
self, sentences: List[str]
|
| 270 |
-
) -> Tuple[List[str], List[int], List[int], List[List[str]]]:
|
| 271 |
-
texts, query_ids, sent_ids, partial_results = [], [], [], []
|
| 272 |
-
for sent_id, sent in enumerate(sentences):
|
| 273 |
-
# pypinyin works well for Simplified Chinese than Traditional Chinese
|
| 274 |
-
sent_s = tranditional_to_simplified(sent)
|
| 275 |
-
pypinyin_result = pinyin(
|
| 276 |
-
sent_s, neutral_tone_with_five=True, style=Style.TONE3)
|
| 277 |
-
partial_result = [None] * len(sent)
|
| 278 |
-
for i, char in enumerate(sent):
|
| 279 |
-
if char in self.polyphonic_chars_new:
|
| 280 |
-
texts.append(sent)
|
| 281 |
-
query_ids.append(i)
|
| 282 |
-
sent_ids.append(sent_id)
|
| 283 |
-
elif char in self.monophonic_chars_dict:
|
| 284 |
-
partial_result[i] = self.style_convert_func(
|
| 285 |
-
self.monophonic_chars_dict[char])
|
| 286 |
-
elif char in self.char_bopomofo_dict:
|
| 287 |
-
partial_result[i] = pypinyin_result[i][0]
|
| 288 |
-
# partial_result[i] = self.style_convert_func(self.char_bopomofo_dict[char][0])
|
| 289 |
-
else:
|
| 290 |
-
partial_result[i] = pypinyin_result[i][0]
|
| 291 |
-
|
| 292 |
-
partial_results.append(partial_result)
|
| 293 |
-
return texts, query_ids, sent_ids, partial_results
|
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|
text/g2pw/polyphonic-fix.rep
DELETED
|
The diff for this file is too large to render.
See raw diff
|
|
|
text/g2pw/polyphonic.rep
DELETED
|
@@ -1,53 +0,0 @@
|
|
| 1 |
-
湖泊: ['hu2','po1']
|
| 2 |
-
地壳: ['di4','qiao4']
|
| 3 |
-
柏树: ['bai3','shu4']
|
| 4 |
-
曝光: ['bao4','guang1']
|
| 5 |
-
弹力: ['tan2','li4']
|
| 6 |
-
字帖: ['zi4','tie4']
|
| 7 |
-
口吃: ['kou3','chi1']
|
| 8 |
-
包扎: ['bao1','za1']
|
| 9 |
-
哪吒: ['ne2','zha1']
|
| 10 |
-
说服: ['shuo1','fu2']
|
| 11 |
-
识字: ['shi2','zi4']
|
| 12 |
-
骨头: ['gu3','tou5']
|
| 13 |
-
对称: ['dui4','chen4']
|
| 14 |
-
口供: ['kou3','gong4']
|
| 15 |
-
抹布: ['ma1','bu4']
|
| 16 |
-
露背: ['lu4','bei4']
|
| 17 |
-
圈养: ['juan4', 'yang3']
|
| 18 |
-
眼眶: ['yan3', 'kuang4']
|
| 19 |
-
品行: ['pin3','xing2']
|
| 20 |
-
颤抖: ['chan4','dou3']
|
| 21 |
-
差不多: ['cha4','bu5','duo1']
|
| 22 |
-
鸭绿江: ['ya1','lu4','jiang1']
|
| 23 |
-
撒切尔: ['sa4','qie4','er3']
|
| 24 |
-
比比皆是: ['bi3','bi3','jie1','shi4']
|
| 25 |
-
身无长物: ['shen1','wu2','chang2','wu4']
|
| 26 |
-
手里: ['shou2','li3']
|
| 27 |
-
关卡: ['guan1','qia3']
|
| 28 |
-
怀揣: ['huai2','chuai1']
|
| 29 |
-
挑剔: ['tiao1','ti4']
|
| 30 |
-
供称: ['gong4','cheng1']
|
| 31 |
-
作坊: ['zuo1', 'fang5']
|
| 32 |
-
中医: ['zhong1','yi1']
|
| 33 |
-
嚷嚷: ['rang1','rang5']
|
| 34 |
-
商厦: ['shang1','sha4']
|
| 35 |
-
大厦: ['da4','sha4']
|
| 36 |
-
刹车: ['sha1','che1']
|
| 37 |
-
嘚瑟: ['de4','se5']
|
| 38 |
-
朝鲜: ['chao2','xian3']
|
| 39 |
-
阿房宫: ['e1','pang2','gong1']
|
| 40 |
-
阿胶: ['e1','jiao1']
|
| 41 |
-
咖喱: ['ga1','li5']
|
| 42 |
-
时分: ['shi2','fen1']
|
| 43 |
-
蚌埠: ['beng4','bu4']
|
| 44 |
-
驯服: ['xun4','fu2']
|
| 45 |
-
幸免于难: ['xing4','mian3','yu2','nan4']
|
| 46 |
-
恶行: ['e4','xing2']
|
| 47 |
-
唉: ['ai4']
|
| 48 |
-
扎实: ['zha1','shi2']
|
| 49 |
-
干将: ['gan4','jiang4']
|
| 50 |
-
陈威行: ['chen2', 'wei1', 'hang2']
|
| 51 |
-
郭晟: ['guo1', 'sheng4']
|
| 52 |
-
中标: ['zhong4', 'biao1']
|
| 53 |
-
抗住: ['kang2', 'zhu4']
|
|
|
|
|
|
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|
text/g2pw/utils.py
DELETED
|
@@ -1,143 +0,0 @@
|
|
| 1 |
-
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
| 2 |
-
#
|
| 3 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 4 |
-
# you may not use this file except in compliance with the License.
|
| 5 |
-
# You may obtain a copy of the License at
|
| 6 |
-
#
|
| 7 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
| 8 |
-
#
|
| 9 |
-
# Unless required by applicable law or agreed to in writing, software
|
| 10 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 11 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 12 |
-
# See the License for the specific language governing permissions and
|
| 13 |
-
# limitations under the License.
|
| 14 |
-
"""
|
| 15 |
-
Credits
|
| 16 |
-
This code is modified from https://github.com/GitYCC/g2pW
|
| 17 |
-
"""
|
| 18 |
-
|
| 19 |
-
import os
|
| 20 |
-
import re
|
| 21 |
-
|
| 22 |
-
|
| 23 |
-
def wordize_and_map(text: str):
|
| 24 |
-
words = []
|
| 25 |
-
index_map_from_text_to_word = []
|
| 26 |
-
index_map_from_word_to_text = []
|
| 27 |
-
while len(text) > 0:
|
| 28 |
-
match_space = re.match(r"^ +", text)
|
| 29 |
-
if match_space:
|
| 30 |
-
space_str = match_space.group(0)
|
| 31 |
-
index_map_from_text_to_word += [None] * len(space_str)
|
| 32 |
-
text = text[len(space_str) :]
|
| 33 |
-
continue
|
| 34 |
-
|
| 35 |
-
match_en = re.match(r"^[a-zA-Z0-9]+", text)
|
| 36 |
-
if match_en:
|
| 37 |
-
en_word = match_en.group(0)
|
| 38 |
-
|
| 39 |
-
word_start_pos = len(index_map_from_text_to_word)
|
| 40 |
-
word_end_pos = word_start_pos + len(en_word)
|
| 41 |
-
index_map_from_word_to_text.append((word_start_pos, word_end_pos))
|
| 42 |
-
|
| 43 |
-
index_map_from_text_to_word += [len(words)] * len(en_word)
|
| 44 |
-
|
| 45 |
-
words.append(en_word)
|
| 46 |
-
text = text[len(en_word) :]
|
| 47 |
-
else:
|
| 48 |
-
word_start_pos = len(index_map_from_text_to_word)
|
| 49 |
-
word_end_pos = word_start_pos + 1
|
| 50 |
-
index_map_from_word_to_text.append((word_start_pos, word_end_pos))
|
| 51 |
-
|
| 52 |
-
index_map_from_text_to_word += [len(words)]
|
| 53 |
-
|
| 54 |
-
words.append(text[0])
|
| 55 |
-
text = text[1:]
|
| 56 |
-
return words, index_map_from_text_to_word, index_map_from_word_to_text
|
| 57 |
-
|
| 58 |
-
|
| 59 |
-
def tokenize_and_map(tokenizer, text: str):
|
| 60 |
-
words, text2word, word2text = wordize_and_map(text=text)
|
| 61 |
-
|
| 62 |
-
tokens = []
|
| 63 |
-
index_map_from_token_to_text = []
|
| 64 |
-
for word, (word_start, word_end) in zip(words, word2text):
|
| 65 |
-
word_tokens = tokenizer.tokenize(word)
|
| 66 |
-
|
| 67 |
-
if len(word_tokens) == 0 or word_tokens == ["[UNK]"]:
|
| 68 |
-
index_map_from_token_to_text.append((word_start, word_end))
|
| 69 |
-
tokens.append("[UNK]")
|
| 70 |
-
else:
|
| 71 |
-
current_word_start = word_start
|
| 72 |
-
for word_token in word_tokens:
|
| 73 |
-
word_token_len = len(re.sub(r"^##", "", word_token))
|
| 74 |
-
index_map_from_token_to_text.append((current_word_start, current_word_start + word_token_len))
|
| 75 |
-
current_word_start = current_word_start + word_token_len
|
| 76 |
-
tokens.append(word_token)
|
| 77 |
-
|
| 78 |
-
index_map_from_text_to_token = text2word
|
| 79 |
-
for i, (token_start, token_end) in enumerate(index_map_from_token_to_text):
|
| 80 |
-
for token_pos in range(token_start, token_end):
|
| 81 |
-
index_map_from_text_to_token[token_pos] = i
|
| 82 |
-
|
| 83 |
-
return tokens, index_map_from_text_to_token, index_map_from_token_to_text
|
| 84 |
-
|
| 85 |
-
|
| 86 |
-
def _load_config(config_path: os.PathLike):
|
| 87 |
-
import importlib.util
|
| 88 |
-
|
| 89 |
-
spec = importlib.util.spec_from_file_location("__init__", config_path)
|
| 90 |
-
config = importlib.util.module_from_spec(spec)
|
| 91 |
-
spec.loader.exec_module(config)
|
| 92 |
-
return config
|
| 93 |
-
|
| 94 |
-
|
| 95 |
-
default_config_dict = {
|
| 96 |
-
"manual_seed": 1313,
|
| 97 |
-
"model_source": "bert-base-chinese",
|
| 98 |
-
"window_size": 32,
|
| 99 |
-
"num_workers": 2,
|
| 100 |
-
"use_mask": True,
|
| 101 |
-
"use_char_phoneme": False,
|
| 102 |
-
"use_conditional": True,
|
| 103 |
-
"param_conditional": {
|
| 104 |
-
"affect_location": "softmax",
|
| 105 |
-
"bias": True,
|
| 106 |
-
"char-linear": True,
|
| 107 |
-
"pos-linear": False,
|
| 108 |
-
"char+pos-second": True,
|
| 109 |
-
"char+pos-second_lowrank": False,
|
| 110 |
-
"lowrank_size": 0,
|
| 111 |
-
"char+pos-second_fm": False,
|
| 112 |
-
"fm_size": 0,
|
| 113 |
-
"fix_mode": None,
|
| 114 |
-
"count_json": "train.count.json",
|
| 115 |
-
},
|
| 116 |
-
"lr": 5e-5,
|
| 117 |
-
"val_interval": 200,
|
| 118 |
-
"num_iter": 10000,
|
| 119 |
-
"use_focal": False,
|
| 120 |
-
"param_focal": {"alpha": 0.0, "gamma": 0.7},
|
| 121 |
-
"use_pos": True,
|
| 122 |
-
"param_pos ": {
|
| 123 |
-
"weight": 0.1,
|
| 124 |
-
"pos_joint_training": True,
|
| 125 |
-
"train_pos_path": "train.pos",
|
| 126 |
-
"valid_pos_path": "dev.pos",
|
| 127 |
-
"test_pos_path": "test.pos",
|
| 128 |
-
},
|
| 129 |
-
}
|
| 130 |
-
|
| 131 |
-
|
| 132 |
-
def load_config(config_path: os.PathLike, use_default: bool = False):
|
| 133 |
-
config = _load_config(config_path)
|
| 134 |
-
if use_default:
|
| 135 |
-
for attr, val in default_config_dict.items():
|
| 136 |
-
if not hasattr(config, attr):
|
| 137 |
-
setattr(config, attr, val)
|
| 138 |
-
elif isinstance(val, dict):
|
| 139 |
-
d = getattr(config, attr)
|
| 140 |
-
for dict_k, dict_v in val.items():
|
| 141 |
-
if dict_k not in d:
|
| 142 |
-
d[dict_k] = dict_v
|
| 143 |
-
return config
|
|
|
|
|
|
|
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|
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|
|
|
text/ja_userdic/userdict.md5
DELETED
|
@@ -1 +0,0 @@
|
|
| 1 |
-
d36bd5ffba62f195d22bf4f1a41cd08f
|
|
|
|
|
|
text/japanese.py
DELETED
|
@@ -1,276 +0,0 @@
|
|
| 1 |
-
# modified from https://github.com/CjangCjengh/vits/blob/main/text/japanese.py
|
| 2 |
-
import re
|
| 3 |
-
import os
|
| 4 |
-
import hashlib
|
| 5 |
-
|
| 6 |
-
try:
|
| 7 |
-
import pyopenjtalk
|
| 8 |
-
|
| 9 |
-
current_file_path = os.path.dirname(__file__)
|
| 10 |
-
|
| 11 |
-
# 防止win下无法读取模型
|
| 12 |
-
if os.name == "nt":
|
| 13 |
-
python_dir = os.getcwd()
|
| 14 |
-
OPEN_JTALK_DICT_DIR = pyopenjtalk.OPEN_JTALK_DICT_DIR.decode("utf-8")
|
| 15 |
-
if not (re.match(r"^[A-Za-z0-9_/\\:.\-]*$", OPEN_JTALK_DICT_DIR)):
|
| 16 |
-
if OPEN_JTALK_DICT_DIR[: len(python_dir)].upper() == python_dir.upper():
|
| 17 |
-
OPEN_JTALK_DICT_DIR = os.path.join(os.path.relpath(OPEN_JTALK_DICT_DIR, python_dir))
|
| 18 |
-
else:
|
| 19 |
-
import shutil
|
| 20 |
-
|
| 21 |
-
if not os.path.exists("TEMP"):
|
| 22 |
-
os.mkdir("TEMP")
|
| 23 |
-
if not os.path.exists(os.path.join("TEMP", "ja")):
|
| 24 |
-
os.mkdir(os.path.join("TEMP", "ja"))
|
| 25 |
-
if os.path.exists(os.path.join("TEMP", "ja", "open_jtalk_dic")):
|
| 26 |
-
shutil.rmtree(os.path.join("TEMP", "ja", "open_jtalk_dic"))
|
| 27 |
-
shutil.copytree(
|
| 28 |
-
pyopenjtalk.OPEN_JTALK_DICT_DIR.decode("utf-8"),
|
| 29 |
-
os.path.join("TEMP", "ja", "open_jtalk_dic"),
|
| 30 |
-
)
|
| 31 |
-
OPEN_JTALK_DICT_DIR = os.path.join("TEMP", "ja", "open_jtalk_dic")
|
| 32 |
-
pyopenjtalk.OPEN_JTALK_DICT_DIR = OPEN_JTALK_DICT_DIR.encode("utf-8")
|
| 33 |
-
|
| 34 |
-
if not (re.match(r"^[A-Za-z0-9_/\\:.\-]*$", current_file_path)):
|
| 35 |
-
if current_file_path[: len(python_dir)].upper() == python_dir.upper():
|
| 36 |
-
current_file_path = os.path.join(os.path.relpath(current_file_path, python_dir))
|
| 37 |
-
else:
|
| 38 |
-
if not os.path.exists("TEMP"):
|
| 39 |
-
os.mkdir("TEMP")
|
| 40 |
-
if not os.path.exists(os.path.join("TEMP", "ja")):
|
| 41 |
-
os.mkdir(os.path.join("TEMP", "ja"))
|
| 42 |
-
if not os.path.exists(os.path.join("TEMP", "ja", "ja_userdic")):
|
| 43 |
-
os.mkdir(os.path.join("TEMP", "ja", "ja_userdic"))
|
| 44 |
-
shutil.copyfile(
|
| 45 |
-
os.path.join(current_file_path, "ja_userdic", "userdict.csv"),
|
| 46 |
-
os.path.join("TEMP", "ja", "ja_userdic", "userdict.csv"),
|
| 47 |
-
)
|
| 48 |
-
current_file_path = os.path.join("TEMP", "ja")
|
| 49 |
-
|
| 50 |
-
def get_hash(fp: str) -> str:
|
| 51 |
-
hash_md5 = hashlib.md5()
|
| 52 |
-
with open(fp, "rb") as f:
|
| 53 |
-
for chunk in iter(lambda: f.read(4096), b""):
|
| 54 |
-
hash_md5.update(chunk)
|
| 55 |
-
return hash_md5.hexdigest()
|
| 56 |
-
|
| 57 |
-
USERDIC_CSV_PATH = os.path.join(current_file_path, "ja_userdic", "userdict.csv")
|
| 58 |
-
USERDIC_BIN_PATH = os.path.join(current_file_path, "ja_userdic", "user.dict")
|
| 59 |
-
USERDIC_HASH_PATH = os.path.join(current_file_path, "ja_userdic", "userdict.md5")
|
| 60 |
-
# 如果没有用户词典，就生成一个；如果有，就检查md5，如果不一样，就重新生成
|
| 61 |
-
if os.path.exists(USERDIC_CSV_PATH):
|
| 62 |
-
if (
|
| 63 |
-
not os.path.exists(USERDIC_BIN_PATH)
|
| 64 |
-
or get_hash(USERDIC_CSV_PATH) != open(USERDIC_HASH_PATH, "r", encoding="utf-8").read()
|
| 65 |
-
):
|
| 66 |
-
pyopenjtalk.mecab_dict_index(USERDIC_CSV_PATH, USERDIC_BIN_PATH)
|
| 67 |
-
with open(USERDIC_HASH_PATH, "w", encoding="utf-8") as f:
|
| 68 |
-
f.write(get_hash(USERDIC_CSV_PATH))
|
| 69 |
-
|
| 70 |
-
if os.path.exists(USERDIC_BIN_PATH):
|
| 71 |
-
pyopenjtalk.update_global_jtalk_with_user_dict(USERDIC_BIN_PATH)
|
| 72 |
-
except Exception:
|
| 73 |
-
# print(e)
|
| 74 |
-
import pyopenjtalk
|
| 75 |
-
|
| 76 |
-
# failed to load user dictionary, ignore.
|
| 77 |
-
pass
|
| 78 |
-
|
| 79 |
-
|
| 80 |
-
from text.symbols import punctuation
|
| 81 |
-
|
| 82 |
-
# Regular expression matching Japanese without punctuation marks:
|
| 83 |
-
_japanese_characters = re.compile(
|
| 84 |
-
r"[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]"
|
| 85 |
-
)
|
| 86 |
-
|
| 87 |
-
# Regular expression matching non-Japanese characters or punctuation marks:
|
| 88 |
-
_japanese_marks = re.compile(
|
| 89 |
-
r"[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]"
|
| 90 |
-
)
|
| 91 |
-
|
| 92 |
-
# List of (symbol, Japanese) pairs for marks:
|
| 93 |
-
_symbols_to_japanese = [(re.compile("%s" % x[0]), x[1]) for x in [("％", "パーセント")]]
|
| 94 |
-
|
| 95 |
-
|
| 96 |
-
# List of (consonant, sokuon) pairs:
|
| 97 |
-
_real_sokuon = [
|
| 98 |
-
(re.compile("%s" % x[0]), x[1])
|
| 99 |
-
for x in [
|
| 100 |
-
(r"Q([↑↓]*[kg])", r"k#\1"),
|
| 101 |
-
(r"Q([↑↓]*[tdjʧ])", r"t#\1"),
|
| 102 |
-
(r"Q([↑↓]*[sʃ])", r"s\1"),
|
| 103 |
-
(r"Q([↑↓]*[pb])", r"p#\1"),
|
| 104 |
-
]
|
| 105 |
-
]
|
| 106 |
-
|
| 107 |
-
# List of (consonant, hatsuon) pairs:
|
| 108 |
-
_real_hatsuon = [
|
| 109 |
-
(re.compile("%s" % x[0]), x[1])
|
| 110 |
-
for x in [
|
| 111 |
-
(r"N([↑↓]*[pbm])", r"m\1"),
|
| 112 |
-
(r"N([↑↓]*[ʧʥj])", r"n^\1"),
|
| 113 |
-
(r"N([↑↓]*[tdn])", r"n\1"),
|
| 114 |
-
(r"N([↑↓]*[kg])", r"ŋ\1"),
|
| 115 |
-
]
|
| 116 |
-
]
|
| 117 |
-
|
| 118 |
-
|
| 119 |
-
def post_replace_ph(ph):
|
| 120 |
-
rep_map = {
|
| 121 |
-
"：": ",",
|
| 122 |
-
"；": ",",
|
| 123 |
-
"，": ",",
|
| 124 |
-
"。": ".",
|
| 125 |
-
"！": "!",
|
| 126 |
-
"？": "?",
|
| 127 |
-
"\n": ".",
|
| 128 |
-
"·": ",",
|
| 129 |
-
"、": ",",
|
| 130 |
-
"...": "…",
|
| 131 |
-
}
|
| 132 |
-
|
| 133 |
-
if ph in rep_map.keys():
|
| 134 |
-
ph = rep_map[ph]
|
| 135 |
-
return ph
|
| 136 |
-
|
| 137 |
-
|
| 138 |
-
def replace_consecutive_punctuation(text):
|
| 139 |
-
punctuations = "".join(re.escape(p) for p in punctuation)
|
| 140 |
-
pattern = f"([{punctuations}])([{punctuations}])+"
|
| 141 |
-
result = re.sub(pattern, r"\1", text)
|
| 142 |
-
return result
|
| 143 |
-
|
| 144 |
-
|
| 145 |
-
def symbols_to_japanese(text):
|
| 146 |
-
for regex, replacement in _symbols_to_japanese:
|
| 147 |
-
text = re.sub(regex, replacement, text)
|
| 148 |
-
return text
|
| 149 |
-
|
| 150 |
-
|
| 151 |
-
def preprocess_jap(text, with_prosody=False):
|
| 152 |
-
"""Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html"""
|
| 153 |
-
text = symbols_to_japanese(text)
|
| 154 |
-
# English words to lower case, should have no influence on japanese words.
|
| 155 |
-
text = text.lower()
|
| 156 |
-
sentences = re.split(_japanese_marks, text)
|
| 157 |
-
marks = re.findall(_japanese_marks, text)
|
| 158 |
-
text = []
|
| 159 |
-
for i, sentence in enumerate(sentences):
|
| 160 |
-
if re.match(_japanese_characters, sentence):
|
| 161 |
-
if with_prosody:
|
| 162 |
-
text += pyopenjtalk_g2p_prosody(sentence)[1:-1]
|
| 163 |
-
else:
|
| 164 |
-
p = pyopenjtalk.g2p(sentence)
|
| 165 |
-
text += p.split(" ")
|
| 166 |
-
|
| 167 |
-
if i < len(marks):
|
| 168 |
-
if marks[i] == " ": # 防止意外的UNK
|
| 169 |
-
continue
|
| 170 |
-
text += [marks[i].replace(" ", "")]
|
| 171 |
-
return text
|
| 172 |
-
|
| 173 |
-
|
| 174 |
-
def text_normalize(text):
|
| 175 |
-
# todo: jap text normalize
|
| 176 |
-
|
| 177 |
-
# 避免重复标点引起的参考泄露
|
| 178 |
-
text = replace_consecutive_punctuation(text)
|
| 179 |
-
return text
|
| 180 |
-
|
| 181 |
-
|
| 182 |
-
# Copied from espnet https://github.com/espnet/espnet/blob/master/espnet2/text/phoneme_tokenizer.py
|
| 183 |
-
def pyopenjtalk_g2p_prosody(text, drop_unvoiced_vowels=True):
|
| 184 |
-
"""Extract phoneme + prosoody symbol sequence from input full-context labels.
|
| 185 |
-
|
| 186 |
-
The algorithm is based on `Prosodic features control by symbols as input of
|
| 187 |
-
sequence-to-sequence acoustic modeling for neural TTS`_ with some r9y9's tweaks.
|
| 188 |
-
|
| 189 |
-
Args:
|
| 190 |
-
text (str): Input text.
|
| 191 |
-
drop_unvoiced_vowels (bool): whether to drop unvoiced vowels.
|
| 192 |
-
|
| 193 |
-
Returns:
|
| 194 |
-
List[str]: List of phoneme + prosody symbols.
|
| 195 |
-
|
| 196 |
-
Examples:
|
| 197 |
-
>>> from espnet2.text.phoneme_tokenizer import pyopenjtalk_g2p_prosody
|
| 198 |
-
>>> pyopenjtalk_g2p_prosody("こんにちは。")
|
| 199 |
-
['^', 'k', 'o', '[', 'N', 'n', 'i', 'ch', 'i', 'w', 'a', '$']
|
| 200 |
-
|
| 201 |
-
.. _`Prosodic features control by symbols as input of sequence-to-sequence acoustic
|
| 202 |
-
modeling for neural TTS`: https://doi.org/10.1587/transinf.2020EDP7104
|
| 203 |
-
|
| 204 |
-
"""
|
| 205 |
-
labels = pyopenjtalk.make_label(pyopenjtalk.run_frontend(text))
|
| 206 |
-
N = len(labels)
|
| 207 |
-
|
| 208 |
-
phones = []
|
| 209 |
-
for n in range(N):
|
| 210 |
-
lab_curr = labels[n]
|
| 211 |
-
|
| 212 |
-
# current phoneme
|
| 213 |
-
p3 = re.search(r"\-(.*?)\+", lab_curr).group(1)
|
| 214 |
-
# deal unvoiced vowels as normal vowels
|
| 215 |
-
if drop_unvoiced_vowels and p3 in "AEIOU":
|
| 216 |
-
p3 = p3.lower()
|
| 217 |
-
|
| 218 |
-
# deal with sil at the beginning and the end of text
|
| 219 |
-
if p3 == "sil":
|
| 220 |
-
assert n == 0 or n == N - 1
|
| 221 |
-
if n == 0:
|
| 222 |
-
phones.append("^")
|
| 223 |
-
elif n == N - 1:
|
| 224 |
-
# check question form or not
|
| 225 |
-
e3 = _numeric_feature_by_regex(r"!(\d+)_", lab_curr)
|
| 226 |
-
if e3 == 0:
|
| 227 |
-
phones.append("$")
|
| 228 |
-
elif e3 == 1:
|
| 229 |
-
phones.append("?")
|
| 230 |
-
continue
|
| 231 |
-
elif p3 == "pau":
|
| 232 |
-
phones.append("_")
|
| 233 |
-
continue
|
| 234 |
-
else:
|
| 235 |
-
phones.append(p3)
|
| 236 |
-
|
| 237 |
-
# accent type and position info (forward or backward)
|
| 238 |
-
a1 = _numeric_feature_by_regex(r"/A:([0-9\-]+)\+", lab_curr)
|
| 239 |
-
a2 = _numeric_feature_by_regex(r"\+(\d+)\+", lab_curr)
|
| 240 |
-
a3 = _numeric_feature_by_regex(r"\+(\d+)/", lab_curr)
|
| 241 |
-
|
| 242 |
-
# number of mora in accent phrase
|
| 243 |
-
f1 = _numeric_feature_by_regex(r"/F:(\d+)_", lab_curr)
|
| 244 |
-
|
| 245 |
-
a2_next = _numeric_feature_by_regex(r"\+(\d+)\+", labels[n + 1])
|
| 246 |
-
# accent phrase border
|
| 247 |
-
if a3 == 1 and a2_next == 1 and p3 in "aeiouAEIOUNcl":
|
| 248 |
-
phones.append("#")
|
| 249 |
-
# pitch falling
|
| 250 |
-
elif a1 == 0 and a2_next == a2 + 1 and a2 != f1:
|
| 251 |
-
phones.append("]")
|
| 252 |
-
# pitch rising
|
| 253 |
-
elif a2 == 1 and a2_next == 2:
|
| 254 |
-
phones.append("[")
|
| 255 |
-
|
| 256 |
-
return phones
|
| 257 |
-
|
| 258 |
-
|
| 259 |
-
# Copied from espnet https://github.com/espnet/espnet/blob/master/espnet2/text/phoneme_tokenizer.py
|
| 260 |
-
def _numeric_feature_by_regex(regex, s):
|
| 261 |
-
match = re.search(regex, s)
|
| 262 |
-
if match is None:
|
| 263 |
-
return -50
|
| 264 |
-
return int(match.group(1))
|
| 265 |
-
|
| 266 |
-
|
| 267 |
-
def g2p(norm_text, with_prosody=True):
|
| 268 |
-
phones = preprocess_jap(norm_text, with_prosody)
|
| 269 |
-
phones = [post_replace_ph(i) for i in phones]
|
| 270 |
-
# todo: implement tones and word2ph
|
| 271 |
-
return phones
|
| 272 |
-
|
| 273 |
-
|
| 274 |
-
if __name__ == "__main__":
|
| 275 |
-
phones = g2p("Hello.こんにちは！今日もNiCe天気ですね！tokyotowerに行きましょう！")
|
| 276 |
-
print(phones)
|
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|
text/korean.py
DELETED
|
@@ -1,337 +0,0 @@
|
|
| 1 |
-
# reference: https://github.com/ORI-Muchim/MB-iSTFT-VITS-Korean/blob/main/text/korean.py
|
| 2 |
-
|
| 3 |
-
import re
|
| 4 |
-
from jamo import h2j, j2hcj
|
| 5 |
-
import ko_pron
|
| 6 |
-
from g2pk2 import G2p
|
| 7 |
-
|
| 8 |
-
import importlib
|
| 9 |
-
import os
|
| 10 |
-
|
| 11 |
-
# 防止win下无法读取模型
|
| 12 |
-
if os.name == "nt":
|
| 13 |
-
|
| 14 |
-
class win_G2p(G2p):
|
| 15 |
-
def check_mecab(self):
|
| 16 |
-
super().check_mecab()
|
| 17 |
-
spam_spec = importlib.util.find_spec("eunjeon")
|
| 18 |
-
non_found = spam_spec is None
|
| 19 |
-
if non_found:
|
| 20 |
-
print("you have to install eunjeon. install it...")
|
| 21 |
-
else:
|
| 22 |
-
installpath = spam_spec.submodule_search_locations[0]
|
| 23 |
-
if not (re.match(r"^[A-Za-z0-9_/\\:.\-]*$", installpath)):
|
| 24 |
-
import sys
|
| 25 |
-
from eunjeon import Mecab as _Mecab
|
| 26 |
-
|
| 27 |
-
class Mecab(_Mecab):
|
| 28 |
-
def get_dicpath(installpath):
|
| 29 |
-
if not (re.match(r"^[A-Za-z0-9_/\\:.\-]*$", installpath)):
|
| 30 |
-
import shutil
|
| 31 |
-
|
| 32 |
-
python_dir = os.getcwd()
|
| 33 |
-
if installpath[: len(python_dir)].upper() == python_dir.upper():
|
| 34 |
-
dicpath = os.path.join(os.path.relpath(installpath, python_dir), "data", "mecabrc")
|
| 35 |
-
else:
|
| 36 |
-
if not os.path.exists("TEMP"):
|
| 37 |
-
os.mkdir("TEMP")
|
| 38 |
-
if not os.path.exists(os.path.join("TEMP", "ko")):
|
| 39 |
-
os.mkdir(os.path.join("TEMP", "ko"))
|
| 40 |
-
if os.path.exists(os.path.join("TEMP", "ko", "ko_dict")):
|
| 41 |
-
shutil.rmtree(os.path.join("TEMP", "ko", "ko_dict"))
|
| 42 |
-
|
| 43 |
-
shutil.copytree(
|
| 44 |
-
os.path.join(installpath, "data"), os.path.join("TEMP", "ko", "ko_dict")
|
| 45 |
-
)
|
| 46 |
-
dicpath = os.path.join("TEMP", "ko", "ko_dict", "mecabrc")
|
| 47 |
-
else:
|
| 48 |
-
dicpath = os.path.abspath(os.path.join(installpath, "data/mecabrc"))
|
| 49 |
-
return dicpath
|
| 50 |
-
|
| 51 |
-
def __init__(self, dicpath=get_dicpath(installpath)):
|
| 52 |
-
super().__init__(dicpath=dicpath)
|
| 53 |
-
|
| 54 |
-
sys.modules["eunjeon"].Mecab = Mecab
|
| 55 |
-
|
| 56 |
-
G2p = win_G2p
|
| 57 |
-
|
| 58 |
-
|
| 59 |
-
from text.symbols2 import symbols
|
| 60 |
-
|
| 61 |
-
# This is a list of Korean classifiers preceded by pure Korean numerals.
|
| 62 |
-
_korean_classifiers = (
|
| 63 |
-
"군데 권 개 그루 닢 대 두 마리 모 모금 뭇 발 발짝 방 번 벌 보루 살 수 술 시 쌈 움큼 정 짝 채 척 첩 축 켤레 톨 통"
|
| 64 |
-
)
|
| 65 |
-
|
| 66 |
-
# List of (hangul, hangul divided) pairs:
|
| 67 |
-
_hangul_divided = [
|
| 68 |
-
(re.compile("%s" % x[0]), x[1])
|
| 69 |
-
for x in [
|
| 70 |
-
# ('ㄳ', 'ㄱㅅ'), # g2pk2, A Syllable-ending Rule
|
| 71 |
-
# ('ㄵ', 'ㄴㅈ'),
|
| 72 |
-
# ('ㄶ', 'ㄴㅎ'),
|
| 73 |
-
# ('ㄺ', 'ㄹㄱ'),
|
| 74 |
-
# ('ㄻ', 'ㄹㅁ'),
|
| 75 |
-
# ('ㄼ', 'ㄹㅂ'),
|
| 76 |
-
# ('ㄽ', 'ㄹㅅ'),
|
| 77 |
-
# ('ㄾ', 'ㄹㅌ'),
|
| 78 |
-
# ('ㄿ', 'ㄹㅍ'),
|
| 79 |
-
# ('ㅀ', 'ㄹㅎ'),
|
| 80 |
-
# ('ㅄ', 'ㅂㅅ'),
|
| 81 |
-
("ㅘ", "ㅗㅏ"),
|
| 82 |
-
("ㅙ", "ㅗㅐ"),
|
| 83 |
-
("ㅚ", "ㅗㅣ"),
|
| 84 |
-
("ㅝ", "ㅜㅓ"),
|
| 85 |
-
("ㅞ", "ㅜㅔ"),
|
| 86 |
-
("ㅟ", "ㅜㅣ"),
|
| 87 |
-
("ㅢ", "ㅡㅣ"),
|
| 88 |
-
("ㅑ", "ㅣㅏ"),
|
| 89 |
-
("ㅒ", "ㅣㅐ"),
|
| 90 |
-
("ㅕ", "ㅣㅓ"),
|
| 91 |
-
("ㅖ", "ㅣㅔ"),
|
| 92 |
-
("ㅛ", "ㅣㅗ"),
|
| 93 |
-
("ㅠ", "ㅣㅜ"),
|
| 94 |
-
]
|
| 95 |
-
]
|
| 96 |
-
|
| 97 |
-
# List of (Latin alphabet, hangul) pairs:
|
| 98 |
-
_latin_to_hangul = [
|
| 99 |
-
(re.compile("%s" % x[0], re.IGNORECASE), x[1])
|
| 100 |
-
for x in [
|
| 101 |
-
("a", "에이"),
|
| 102 |
-
("b", "비"),
|
| 103 |
-
("c", "시"),
|
| 104 |
-
("d", "디"),
|
| 105 |
-
("e", "이"),
|
| 106 |
-
("f", "에프"),
|
| 107 |
-
("g", "지"),
|
| 108 |
-
("h", "에이치"),
|
| 109 |
-
("i", "아이"),
|
| 110 |
-
("j", "제이"),
|
| 111 |
-
("k", "케이"),
|
| 112 |
-
("l", "엘"),
|
| 113 |
-
("m", "엠"),
|
| 114 |
-
("n", "엔"),
|
| 115 |
-
("o", "오"),
|
| 116 |
-
("p", "피"),
|
| 117 |
-
("q", "큐"),
|
| 118 |
-
("r", "아르"),
|
| 119 |
-
("s", "에스"),
|
| 120 |
-
("t", "티"),
|
| 121 |
-
("u", "유"),
|
| 122 |
-
("v", "브이"),
|
| 123 |
-
("w", "더블유"),
|
| 124 |
-
("x", "엑스"),
|
| 125 |
-
("y", "와이"),
|
| 126 |
-
("z", "제트"),
|
| 127 |
-
]
|
| 128 |
-
]
|
| 129 |
-
|
| 130 |
-
# List of (ipa, lazy ipa) pairs:
|
| 131 |
-
_ipa_to_lazy_ipa = [
|
| 132 |
-
(re.compile("%s" % x[0], re.IGNORECASE), x[1])
|
| 133 |
-
for x in [
|
| 134 |
-
("t͡ɕ", "ʧ"),
|
| 135 |
-
("d͡ʑ", "ʥ"),
|
| 136 |
-
("ɲ", "n^"),
|
| 137 |
-
("ɕ", "ʃ"),
|
| 138 |
-
("ʷ", "w"),
|
| 139 |
-
("ɭ", "l`"),
|
| 140 |
-
("ʎ", "ɾ"),
|
| 141 |
-
("ɣ", "ŋ"),
|
| 142 |
-
("ɰ", "ɯ"),
|
| 143 |
-
("ʝ", "j"),
|
| 144 |
-
("ʌ", "ə"),
|
| 145 |
-
("ɡ", "g"),
|
| 146 |
-
("\u031a", "#"),
|
| 147 |
-
("\u0348", "="),
|
| 148 |
-
("\u031e", ""),
|
| 149 |
-
("\u0320", ""),
|
| 150 |
-
("\u0339", ""),
|
| 151 |
-
]
|
| 152 |
-
]
|
| 153 |
-
|
| 154 |
-
|
| 155 |
-
def fix_g2pk2_error(text):
|
| 156 |
-
new_text = ""
|
| 157 |
-
i = 0
|
| 158 |
-
while i < len(text) - 4:
|
| 159 |
-
if (text[i : i + 3] == "ㅇㅡㄹ" or text[i : i + 3] == "ㄹㅡㄹ") and text[i + 3] == " " and text[i + 4] == "ㄹ":
|
| 160 |
-
new_text += text[i : i + 3] + " " + "ㄴ"
|
| 161 |
-
i += 5
|
| 162 |
-
else:
|
| 163 |
-
new_text += text[i]
|
| 164 |
-
i += 1
|
| 165 |
-
|
| 166 |
-
new_text += text[i:]
|
| 167 |
-
return new_text
|
| 168 |
-
|
| 169 |
-
|
| 170 |
-
def latin_to_hangul(text):
|
| 171 |
-
for regex, replacement in _latin_to_hangul:
|
| 172 |
-
text = re.sub(regex, replacement, text)
|
| 173 |
-
return text
|
| 174 |
-
|
| 175 |
-
|
| 176 |
-
def divide_hangul(text):
|
| 177 |
-
text = j2hcj(h2j(text))
|
| 178 |
-
for regex, replacement in _hangul_divided:
|
| 179 |
-
text = re.sub(regex, replacement, text)
|
| 180 |
-
return text
|
| 181 |
-
|
| 182 |
-
|
| 183 |
-
def hangul_number(num, sino=True):
|
| 184 |
-
"""Reference https://github.com/Kyubyong/g2pK"""
|
| 185 |
-
num = re.sub(",", "", num)
|
| 186 |
-
|
| 187 |
-
if num == "0":
|
| 188 |
-
return "영"
|
| 189 |
-
if not sino and num == "20":
|
| 190 |
-
return "스무"
|
| 191 |
-
|
| 192 |
-
digits = "123456789"
|
| 193 |
-
names = "일이삼사오육칠팔구"
|
| 194 |
-
digit2name = {d: n for d, n in zip(digits, names)}
|
| 195 |
-
|
| 196 |
-
modifiers = "한 두 세 네 다섯 여섯 일곱 여덟 아홉"
|
| 197 |
-
decimals = "열 스물 서른 마흔 쉰 예순 일흔 여든 아흔"
|
| 198 |
-
digit2mod = {d: mod for d, mod in zip(digits, modifiers.split())}
|
| 199 |
-
digit2dec = {d: dec for d, dec in zip(digits, decimals.split())}
|
| 200 |
-
|
| 201 |
-
spelledout = []
|
| 202 |
-
for i, digit in enumerate(num):
|
| 203 |
-
i = len(num) - i - 1
|
| 204 |
-
if sino:
|
| 205 |
-
if i == 0:
|
| 206 |
-
name = digit2name.get(digit, "")
|
| 207 |
-
elif i == 1:
|
| 208 |
-
name = digit2name.get(digit, "") + "십"
|
| 209 |
-
name = name.replace("일십", "십")
|
| 210 |
-
else:
|
| 211 |
-
if i == 0:
|
| 212 |
-
name = digit2mod.get(digit, "")
|
| 213 |
-
elif i == 1:
|
| 214 |
-
name = digit2dec.get(digit, "")
|
| 215 |
-
if digit == "0":
|
| 216 |
-
if i % 4 == 0:
|
| 217 |
-
last_three = spelledout[-min(3, len(spelledout)) :]
|
| 218 |
-
if "".join(last_three) == "":
|
| 219 |
-
spelledout.append("")
|
| 220 |
-
continue
|
| 221 |
-
else:
|
| 222 |
-
spelledout.append("")
|
| 223 |
-
continue
|
| 224 |
-
if i == 2:
|
| 225 |
-
name = digit2name.get(digit, "") + "백"
|
| 226 |
-
name = name.replace("일백", "백")
|
| 227 |
-
elif i == 3:
|
| 228 |
-
name = digit2name.get(digit, "") + "천"
|
| 229 |
-
name = name.replace("일천", "천")
|
| 230 |
-
elif i == 4:
|
| 231 |
-
name = digit2name.get(digit, "") + "만"
|
| 232 |
-
name = name.replace("일만", "만")
|
| 233 |
-
elif i == 5:
|
| 234 |
-
name = digit2name.get(digit, "") + "십"
|
| 235 |
-
name = name.replace("일십", "십")
|
| 236 |
-
elif i == 6:
|
| 237 |
-
name = digit2name.get(digit, "") + "백"
|
| 238 |
-
name = name.replace("일백", "백")
|
| 239 |
-
elif i == 7:
|
| 240 |
-
name = digit2name.get(digit, "") + "천"
|
| 241 |
-
name = name.replace("일천", "천")
|
| 242 |
-
elif i == 8:
|
| 243 |
-
name = digit2name.get(digit, "") + "억"
|
| 244 |
-
elif i == 9:
|
| 245 |
-
name = digit2name.get(digit, "") + "십"
|
| 246 |
-
elif i == 10:
|
| 247 |
-
name = digit2name.get(digit, "") + "백"
|
| 248 |
-
elif i == 11:
|
| 249 |
-
name = digit2name.get(digit, "") + "천"
|
| 250 |
-
elif i == 12:
|
| 251 |
-
name = digit2name.get(digit, "") + "조"
|
| 252 |
-
elif i == 13:
|
| 253 |
-
name = digit2name.get(digit, "") + "십"
|
| 254 |
-
elif i == 14:
|
| 255 |
-
name = digit2name.get(digit, "") + "백"
|
| 256 |
-
elif i == 15:
|
| 257 |
-
name = digit2name.get(digit, "") + "천"
|
| 258 |
-
spelledout.append(name)
|
| 259 |
-
return "".join(elem for elem in spelledout)
|
| 260 |
-
|
| 261 |
-
|
| 262 |
-
def number_to_hangul(text):
|
| 263 |
-
"""Reference https://github.com/Kyubyong/g2pK"""
|
| 264 |
-
tokens = set(re.findall(r"(\d[\d,]*)([\uac00-\ud71f]+)", text))
|
| 265 |
-
for token in tokens:
|
| 266 |
-
num, classifier = token
|
| 267 |
-
if classifier[:2] in _korean_classifiers or classifier[0] in _korean_classifiers:
|
| 268 |
-
spelledout = hangul_number(num, sino=False)
|
| 269 |
-
else:
|
| 270 |
-
spelledout = hangul_number(num, sino=True)
|
| 271 |
-
text = text.replace(f"{num}{classifier}", f"{spelledout}{classifier}")
|
| 272 |
-
# digit by digit for remaining digits
|
| 273 |
-
digits = "0123456789"
|
| 274 |
-
names = "영일이삼사오육칠팔구"
|
| 275 |
-
for d, n in zip(digits, names):
|
| 276 |
-
text = text.replace(d, n)
|
| 277 |
-
return text
|
| 278 |
-
|
| 279 |
-
|
| 280 |
-
def korean_to_lazy_ipa(text):
|
| 281 |
-
text = latin_to_hangul(text)
|
| 282 |
-
text = number_to_hangul(text)
|
| 283 |
-
text = re.sub("[\uac00-\ud7af]+", lambda x: ko_pron.romanise(x.group(0), "ipa").split("] ~ [")[0], text)
|
| 284 |
-
for regex, replacement in _ipa_to_lazy_ipa:
|
| 285 |
-
text = re.sub(regex, replacement, text)
|
| 286 |
-
return text
|
| 287 |
-
|
| 288 |
-
|
| 289 |
-
_g2p = G2p()
|
| 290 |
-
|
| 291 |
-
|
| 292 |
-
def korean_to_ipa(text):
|
| 293 |
-
text = latin_to_hangul(text)
|
| 294 |
-
text = number_to_hangul(text)
|
| 295 |
-
text = _g2p(text)
|
| 296 |
-
text = fix_g2pk2_error(text)
|
| 297 |
-
text = korean_to_lazy_ipa(text)
|
| 298 |
-
return text.replace("ʧ", "tʃ").replace("ʥ", "dʑ")
|
| 299 |
-
|
| 300 |
-
|
| 301 |
-
def post_replace_ph(ph):
|
| 302 |
-
rep_map = {
|
| 303 |
-
"：": ",",
|
| 304 |
-
"；": ",",
|
| 305 |
-
"，": ",",
|
| 306 |
-
"。": ".",
|
| 307 |
-
"！": "!",
|
| 308 |
-
"？": "?",
|
| 309 |
-
"\n": ".",
|
| 310 |
-
"·": ",",
|
| 311 |
-
"、": ",",
|
| 312 |
-
"...": "…",
|
| 313 |
-
" ": "空",
|
| 314 |
-
}
|
| 315 |
-
if ph in rep_map.keys():
|
| 316 |
-
ph = rep_map[ph]
|
| 317 |
-
if ph in symbols:
|
| 318 |
-
return ph
|
| 319 |
-
if ph not in symbols:
|
| 320 |
-
ph = "停"
|
| 321 |
-
return ph
|
| 322 |
-
|
| 323 |
-
|
| 324 |
-
def g2p(text):
|
| 325 |
-
text = latin_to_hangul(text)
|
| 326 |
-
text = _g2p(text)
|
| 327 |
-
text = divide_hangul(text)
|
| 328 |
-
text = fix_g2pk2_error(text)
|
| 329 |
-
text = re.sub(r"([\u3131-\u3163])$", r"\1.", text)
|
| 330 |
-
# text = "".join([post_replace_ph(i) for i in text])
|
| 331 |
-
text = [post_replace_ph(i) for i in text]
|
| 332 |
-
return text
|
| 333 |
-
|
| 334 |
-
|
| 335 |
-
if __name__ == "__main__":
|
| 336 |
-
text = "안녕하세요"
|
| 337 |
-
print(g2p(text))
|
|
|
|
|
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|
text/opencpop-strict.txt
DELETED
|
@@ -1,429 +0,0 @@
|
|
| 1 |
-
a AA a
|
| 2 |
-
ai AA ai
|
| 3 |
-
an AA an
|
| 4 |
-
ang AA ang
|
| 5 |
-
ao AA ao
|
| 6 |
-
ba b a
|
| 7 |
-
bai b ai
|
| 8 |
-
ban b an
|
| 9 |
-
bang b ang
|
| 10 |
-
bao b ao
|
| 11 |
-
bei b ei
|
| 12 |
-
ben b en
|
| 13 |
-
beng b eng
|
| 14 |
-
bi b i
|
| 15 |
-
bian b ian
|
| 16 |
-
biao b iao
|
| 17 |
-
bie b ie
|
| 18 |
-
bin b in
|
| 19 |
-
bing b ing
|
| 20 |
-
bo b o
|
| 21 |
-
bu b u
|
| 22 |
-
ca c a
|
| 23 |
-
cai c ai
|
| 24 |
-
can c an
|
| 25 |
-
cang c ang
|
| 26 |
-
cao c ao
|
| 27 |
-
ce c e
|
| 28 |
-
cei c ei
|
| 29 |
-
cen c en
|
| 30 |
-
ceng c eng
|
| 31 |
-
cha ch a
|
| 32 |
-
chai ch ai
|
| 33 |
-
chan ch an
|
| 34 |
-
chang ch ang
|
| 35 |
-
chao ch ao
|
| 36 |
-
che ch e
|
| 37 |
-
chen ch en
|
| 38 |
-
cheng ch eng
|
| 39 |
-
chi ch ir
|
| 40 |
-
chong ch ong
|
| 41 |
-
chou ch ou
|
| 42 |
-
chu ch u
|
| 43 |
-
chua ch ua
|
| 44 |
-
chuai ch uai
|
| 45 |
-
chuan ch uan
|
| 46 |
-
chuang ch uang
|
| 47 |
-
chui ch ui
|
| 48 |
-
chun ch un
|
| 49 |
-
chuo ch uo
|
| 50 |
-
ci c i0
|
| 51 |
-
cong c ong
|
| 52 |
-
cou c ou
|
| 53 |
-
cu c u
|
| 54 |
-
cuan c uan
|
| 55 |
-
cui c ui
|
| 56 |
-
cun c un
|
| 57 |
-
cuo c uo
|
| 58 |
-
da d a
|
| 59 |
-
dai d ai
|
| 60 |
-
dan d an
|
| 61 |
-
dang d ang
|
| 62 |
-
dao d ao
|
| 63 |
-
de d e
|
| 64 |
-
dei d ei
|
| 65 |
-
den d en
|
| 66 |
-
deng d eng
|
| 67 |
-
di d i
|
| 68 |
-
dia d ia
|
| 69 |
-
dian d ian
|
| 70 |
-
diao d iao
|
| 71 |
-
die d ie
|
| 72 |
-
ding d ing
|
| 73 |
-
diu d iu
|
| 74 |
-
dong d ong
|
| 75 |
-
dou d ou
|
| 76 |
-
du d u
|
| 77 |
-
duan d uan
|
| 78 |
-
dui d ui
|
| 79 |
-
dun d un
|
| 80 |
-
duo d uo
|
| 81 |
-
e EE e
|
| 82 |
-
ei EE ei
|
| 83 |
-
en EE en
|
| 84 |
-
eng EE eng
|
| 85 |
-
er EE er
|
| 86 |
-
fa f a
|
| 87 |
-
fan f an
|
| 88 |
-
fang f ang
|
| 89 |
-
fei f ei
|
| 90 |
-
fen f en
|
| 91 |
-
feng f eng
|
| 92 |
-
fo f o
|
| 93 |
-
fou f ou
|
| 94 |
-
fu f u
|
| 95 |
-
ga g a
|
| 96 |
-
gai g ai
|
| 97 |
-
gan g an
|
| 98 |
-
gang g ang
|
| 99 |
-
gao g ao
|
| 100 |
-
ge g e
|
| 101 |
-
gei g ei
|
| 102 |
-
gen g en
|
| 103 |
-
geng g eng
|
| 104 |
-
gong g ong
|
| 105 |
-
gou g ou
|
| 106 |
-
gu g u
|
| 107 |
-
gua g ua
|
| 108 |
-
guai g uai
|
| 109 |
-
guan g uan
|
| 110 |
-
guang g uang
|
| 111 |
-
gui g ui
|
| 112 |
-
gun g un
|
| 113 |
-
guo g uo
|
| 114 |
-
ha h a
|
| 115 |
-
hai h ai
|
| 116 |
-
han h an
|
| 117 |
-
hang h ang
|
| 118 |
-
hao h ao
|
| 119 |
-
he h e
|
| 120 |
-
hei h ei
|
| 121 |
-
hen h en
|
| 122 |
-
heng h eng
|
| 123 |
-
hong h ong
|
| 124 |
-
hou h ou
|
| 125 |
-
hu h u
|
| 126 |
-
hua h ua
|
| 127 |
-
huai h uai
|
| 128 |
-
huan h uan
|
| 129 |
-
huang h uang
|
| 130 |
-
hui h ui
|
| 131 |
-
hun h un
|
| 132 |
-
huo h uo
|
| 133 |
-
ji j i
|
| 134 |
-
jia j ia
|
| 135 |
-
jian j ian
|
| 136 |
-
jiang j iang
|
| 137 |
-
jiao j iao
|
| 138 |
-
jie j ie
|
| 139 |
-
jin j in
|
| 140 |
-
jing j ing
|
| 141 |
-
jiong j iong
|
| 142 |
-
jiu j iu
|
| 143 |
-
ju j v
|
| 144 |
-
jv j v
|
| 145 |
-
juan j van
|
| 146 |
-
jvan j van
|
| 147 |
-
jue j ve
|
| 148 |
-
jve j ve
|
| 149 |
-
jun j vn
|
| 150 |
-
jvn j vn
|
| 151 |
-
ka k a
|
| 152 |
-
kai k ai
|
| 153 |
-
kan k an
|
| 154 |
-
kang k ang
|
| 155 |
-
kao k ao
|
| 156 |
-
ke k e
|
| 157 |
-
kei k ei
|
| 158 |
-
ken k en
|
| 159 |
-
keng k eng
|
| 160 |
-
kong k ong
|
| 161 |
-
kou k ou
|
| 162 |
-
ku k u
|
| 163 |
-
kua k ua
|
| 164 |
-
kuai k uai
|
| 165 |
-
kuan k uan
|
| 166 |
-
kuang k uang
|
| 167 |
-
kui k ui
|
| 168 |
-
kun k un
|
| 169 |
-
kuo k uo
|
| 170 |
-
la l a
|
| 171 |
-
lai l ai
|
| 172 |
-
lan l an
|
| 173 |
-
lang l ang
|
| 174 |
-
lao l ao
|
| 175 |
-
le l e
|
| 176 |
-
lei l ei
|
| 177 |
-
leng l eng
|
| 178 |
-
li l i
|
| 179 |
-
lia l ia
|
| 180 |
-
lian l ian
|
| 181 |
-
liang l iang
|
| 182 |
-
liao l iao
|
| 183 |
-
lie l ie
|
| 184 |
-
lin l in
|
| 185 |
-
ling l ing
|
| 186 |
-
liu l iu
|
| 187 |
-
lo l o
|
| 188 |
-
long l ong
|
| 189 |
-
lou l ou
|
| 190 |
-
lu l u
|
| 191 |
-
luan l uan
|
| 192 |
-
lun l un
|
| 193 |
-
luo l uo
|
| 194 |
-
lv l v
|
| 195 |
-
lve l ve
|
| 196 |
-
ma m a
|
| 197 |
-
mai m ai
|
| 198 |
-
man m an
|
| 199 |
-
mang m ang
|
| 200 |
-
mao m ao
|
| 201 |
-
me m e
|
| 202 |
-
mei m ei
|
| 203 |
-
men m en
|
| 204 |
-
meng m eng
|
| 205 |
-
mi m i
|
| 206 |
-
mian m ian
|
| 207 |
-
miao m iao
|
| 208 |
-
mie m ie
|
| 209 |
-
min m in
|
| 210 |
-
ming m ing
|
| 211 |
-
miu m iu
|
| 212 |
-
mo m o
|
| 213 |
-
mou m ou
|
| 214 |
-
mu m u
|
| 215 |
-
na n a
|
| 216 |
-
nai n ai
|
| 217 |
-
nan n an
|
| 218 |
-
nang n ang
|
| 219 |
-
nao n ao
|
| 220 |
-
ne n e
|
| 221 |
-
nei n ei
|
| 222 |
-
nen n en
|
| 223 |
-
neng n eng
|
| 224 |
-
ni n i
|
| 225 |
-
nian n ian
|
| 226 |
-
niang n iang
|
| 227 |
-
niao n iao
|
| 228 |
-
nie n ie
|
| 229 |
-
nin n in
|
| 230 |
-
ning n ing
|
| 231 |
-
niu n iu
|
| 232 |
-
nong n ong
|
| 233 |
-
nou n ou
|
| 234 |
-
nu n u
|
| 235 |
-
nuan n uan
|
| 236 |
-
nun n un
|
| 237 |
-
nuo n uo
|
| 238 |
-
nv n v
|
| 239 |
-
nve n ve
|
| 240 |
-
o OO o
|
| 241 |
-
ou OO ou
|
| 242 |
-
pa p a
|
| 243 |
-
pai p ai
|
| 244 |
-
pan p an
|
| 245 |
-
pang p ang
|
| 246 |
-
pao p ao
|
| 247 |
-
pei p ei
|
| 248 |
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pen p en
|
| 249 |
-
peng p eng
|
| 250 |
-
pi p i
|
| 251 |
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pian p ian
|
| 252 |
-
piao p iao
|
| 253 |
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pie p ie
|
| 254 |
-
pin p in
|
| 255 |
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ping p ing
|
| 256 |
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po p o
|
| 257 |
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pou p ou
|
| 258 |
-
pu p u
|
| 259 |
-
qi q i
|
| 260 |
-
qia q ia
|
| 261 |
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qian q ian
|
| 262 |
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qiang q iang
|
| 263 |
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qiao q iao
|
| 264 |
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qie q ie
|
| 265 |
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qin q in
|
| 266 |
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qing q ing
|
| 267 |
-
qiong q iong
|
| 268 |
-
qiu q iu
|
| 269 |
-
qu q v
|
| 270 |
-
qv q v
|
| 271 |
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quan q van
|
| 272 |
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qvan q van
|
| 273 |
-
que q ve
|
| 274 |
-
qve q ve
|
| 275 |
-
qun q vn
|
| 276 |
-
qvn q vn
|
| 277 |
-
ran r an
|
| 278 |
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rang r ang
|
| 279 |
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rao r ao
|
| 280 |
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re r e
|
| 281 |
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ren r en
|
| 282 |
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reng r eng
|
| 283 |
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ri r ir
|
| 284 |
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rong r ong
|
| 285 |
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rou r ou
|
| 286 |
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ru r u
|
| 287 |
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rua r ua
|
| 288 |
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ruan r uan
|
| 289 |
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rui r ui
|
| 290 |
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run r un
|
| 291 |
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ruo r uo
|
| 292 |
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sa s a
|
| 293 |
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sai s ai
|
| 294 |
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san s an
|
| 295 |
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sang s ang
|
| 296 |
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sao s ao
|
| 297 |
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se s e
|
| 298 |
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sen s en
|
| 299 |
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seng s eng
|
| 300 |
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sha sh a
|
| 301 |
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shai sh ai
|
| 302 |
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shan sh an
|
| 303 |
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shang sh ang
|
| 304 |
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shao sh ao
|
| 305 |
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she sh e
|
| 306 |
-
shei sh ei
|
| 307 |
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shen sh en
|
| 308 |
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sheng sh eng
|
| 309 |
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shi sh ir
|
| 310 |
-
shou sh ou
|
| 311 |
-
shu sh u
|
| 312 |
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shua sh ua
|
| 313 |
-
shuai sh uai
|
| 314 |
-
shuan sh uan
|
| 315 |
-
shuang sh uang
|
| 316 |
-
shui sh ui
|
| 317 |
-
shun sh un
|
| 318 |
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shuo sh uo
|
| 319 |
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si s i0
|
| 320 |
-
song s ong
|
| 321 |
-
sou s ou
|
| 322 |
-
su s u
|
| 323 |
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suan s uan
|
| 324 |
-
sui s ui
|
| 325 |
-
sun s un
|
| 326 |
-
suo s uo
|
| 327 |
-
ta t a
|
| 328 |
-
tai t ai
|
| 329 |
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tan t an
|
| 330 |
-
tang t ang
|
| 331 |
-
tao t ao
|
| 332 |
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te t e
|
| 333 |
-
tei t ei
|
| 334 |
-
teng t eng
|
| 335 |
-
ti t i
|
| 336 |
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tian t ian
|
| 337 |
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tiao t iao
|
| 338 |
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tie t ie
|
| 339 |
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ting t ing
|
| 340 |
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tong t ong
|
| 341 |
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tou t ou
|
| 342 |
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tu t u
|
| 343 |
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tuan t uan
|
| 344 |
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tui t ui
|
| 345 |
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tun t un
|
| 346 |
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tuo t uo
|
| 347 |
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wa w a
|
| 348 |
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wai w ai
|
| 349 |
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wan w an
|
| 350 |
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wang w ang
|
| 351 |
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wei w ei
|
| 352 |
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wen w en
|
| 353 |
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weng w eng
|
| 354 |
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wo w o
|
| 355 |
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wu w u
|
| 356 |
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xi x i
|
| 357 |
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xia x ia
|
| 358 |
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xian x ian
|
| 359 |
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xiang x iang
|
| 360 |
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xiao x iao
|
| 361 |
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xie x ie
|
| 362 |
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xin x in
|
| 363 |
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xing x ing
|
| 364 |
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xiong x iong
|
| 365 |
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xiu x iu
|
| 366 |
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xu x v
|
| 367 |
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xv x v
|
| 368 |
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xuan x van
|
| 369 |
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xvan x van
|
| 370 |
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xue x ve
|
| 371 |
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xve x ve
|
| 372 |
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xun x vn
|
| 373 |
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xvn x vn
|
| 374 |
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ya y a
|
| 375 |
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yan y En
|
| 376 |
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yang y ang
|
| 377 |
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yao y ao
|
| 378 |
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ye y E
|
| 379 |
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yi y i
|
| 380 |
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yin y in
|
| 381 |
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ying y ing
|
| 382 |
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yo y o
|
| 383 |
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yong y ong
|
| 384 |
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you y ou
|
| 385 |
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yu y v
|
| 386 |
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yv y v
|
| 387 |
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yuan y van
|
| 388 |
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yvan y van
|
| 389 |
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yue y ve
|
| 390 |
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yve y ve
|
| 391 |
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yun y vn
|
| 392 |
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yvn y vn
|
| 393 |
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za z a
|
| 394 |
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zai z ai
|
| 395 |
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zan z an
|
| 396 |
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zang z ang
|
| 397 |
-
zao z ao
|
| 398 |
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ze z e
|
| 399 |
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zei z ei
|
| 400 |
-
zen z en
|
| 401 |
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zeng z eng
|
| 402 |
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zha zh a
|
| 403 |
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zhai zh ai
|
| 404 |
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zhan zh an
|
| 405 |
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zhang zh ang
|
| 406 |
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zhao zh ao
|
| 407 |
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zhe zh e
|
| 408 |
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zhei zh ei
|
| 409 |
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zhen zh en
|
| 410 |
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zheng zh eng
|
| 411 |
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zhi zh ir
|
| 412 |
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zhong zh ong
|
| 413 |
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zhou zh ou
|
| 414 |
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zhu zh u
|
| 415 |
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zhua zh ua
|
| 416 |
-
zhuai zh uai
|
| 417 |
-
zhuan zh uan
|
| 418 |
-
zhuang zh uang
|
| 419 |
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zhui zh ui
|
| 420 |
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zhun zh un
|
| 421 |
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zhuo zh uo
|
| 422 |
-
zi z i0
|
| 423 |
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zong z ong
|
| 424 |
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zou z ou
|
| 425 |
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zu z u
|
| 426 |
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zuan z uan
|
| 427 |
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zui z ui
|
| 428 |
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zun z un
|
| 429 |
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zuo z uo
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|
text/symbols.py
DELETED
|
@@ -1,399 +0,0 @@
|
|
| 1 |
-
# punctuation = ['!', '?', '…', ",", ".","@"]#@是SP停顿
|
| 2 |
-
punctuation = ["!", "?", "…", ",", "."] # @是SP停顿
|
| 3 |
-
punctuation.append("-")
|
| 4 |
-
pu_symbols = punctuation + ["SP", "SP2", "SP3", "UNK"]
|
| 5 |
-
# pu_symbols = punctuation + ["SP", 'SP2', 'SP3','SP4', "UNK"]
|
| 6 |
-
pad = "_"
|
| 7 |
-
|
| 8 |
-
c = [
|
| 9 |
-
"AA",
|
| 10 |
-
"EE",
|
| 11 |
-
"OO",
|
| 12 |
-
"b",
|
| 13 |
-
"c",
|
| 14 |
-
"ch",
|
| 15 |
-
"d",
|
| 16 |
-
"f",
|
| 17 |
-
"g",
|
| 18 |
-
"h",
|
| 19 |
-
"j",
|
| 20 |
-
"k",
|
| 21 |
-
"l",
|
| 22 |
-
"m",
|
| 23 |
-
"n",
|
| 24 |
-
"p",
|
| 25 |
-
"q",
|
| 26 |
-
"r",
|
| 27 |
-
"s",
|
| 28 |
-
"sh",
|
| 29 |
-
"t",
|
| 30 |
-
"w",
|
| 31 |
-
"x",
|
| 32 |
-
"y",
|
| 33 |
-
"z",
|
| 34 |
-
"zh",
|
| 35 |
-
]
|
| 36 |
-
v = [
|
| 37 |
-
"E1",
|
| 38 |
-
"En1",
|
| 39 |
-
"a1",
|
| 40 |
-
"ai1",
|
| 41 |
-
"an1",
|
| 42 |
-
"ang1",
|
| 43 |
-
"ao1",
|
| 44 |
-
"e1",
|
| 45 |
-
"ei1",
|
| 46 |
-
"en1",
|
| 47 |
-
"eng1",
|
| 48 |
-
"er1",
|
| 49 |
-
"i1",
|
| 50 |
-
"i01",
|
| 51 |
-
"ia1",
|
| 52 |
-
"ian1",
|
| 53 |
-
"iang1",
|
| 54 |
-
"iao1",
|
| 55 |
-
"ie1",
|
| 56 |
-
"in1",
|
| 57 |
-
"ing1",
|
| 58 |
-
"iong1",
|
| 59 |
-
"ir1",
|
| 60 |
-
"iu1",
|
| 61 |
-
"o1",
|
| 62 |
-
"ong1",
|
| 63 |
-
"ou1",
|
| 64 |
-
"u1",
|
| 65 |
-
"ua1",
|
| 66 |
-
"uai1",
|
| 67 |
-
"uan1",
|
| 68 |
-
"uang1",
|
| 69 |
-
"ui1",
|
| 70 |
-
"un1",
|
| 71 |
-
"uo1",
|
| 72 |
-
"v1",
|
| 73 |
-
"van1",
|
| 74 |
-
"ve1",
|
| 75 |
-
"vn1",
|
| 76 |
-
"E2",
|
| 77 |
-
"En2",
|
| 78 |
-
"a2",
|
| 79 |
-
"ai2",
|
| 80 |
-
"an2",
|
| 81 |
-
"ang2",
|
| 82 |
-
"ao2",
|
| 83 |
-
"e2",
|
| 84 |
-
"ei2",
|
| 85 |
-
"en2",
|
| 86 |
-
"eng2",
|
| 87 |
-
"er2",
|
| 88 |
-
"i2",
|
| 89 |
-
"i02",
|
| 90 |
-
"ia2",
|
| 91 |
-
"ian2",
|
| 92 |
-
"iang2",
|
| 93 |
-
"iao2",
|
| 94 |
-
"ie2",
|
| 95 |
-
"in2",
|
| 96 |
-
"ing2",
|
| 97 |
-
"iong2",
|
| 98 |
-
"ir2",
|
| 99 |
-
"iu2",
|
| 100 |
-
"o2",
|
| 101 |
-
"ong2",
|
| 102 |
-
"ou2",
|
| 103 |
-
"u2",
|
| 104 |
-
"ua2",
|
| 105 |
-
"uai2",
|
| 106 |
-
"uan2",
|
| 107 |
-
"uang2",
|
| 108 |
-
"ui2",
|
| 109 |
-
"un2",
|
| 110 |
-
"uo2",
|
| 111 |
-
"v2",
|
| 112 |
-
"van2",
|
| 113 |
-
"ve2",
|
| 114 |
-
"vn2",
|
| 115 |
-
"E3",
|
| 116 |
-
"En3",
|
| 117 |
-
"a3",
|
| 118 |
-
"ai3",
|
| 119 |
-
"an3",
|
| 120 |
-
"ang3",
|
| 121 |
-
"ao3",
|
| 122 |
-
"e3",
|
| 123 |
-
"ei3",
|
| 124 |
-
"en3",
|
| 125 |
-
"eng3",
|
| 126 |
-
"er3",
|
| 127 |
-
"i3",
|
| 128 |
-
"i03",
|
| 129 |
-
"ia3",
|
| 130 |
-
"ian3",
|
| 131 |
-
"iang3",
|
| 132 |
-
"iao3",
|
| 133 |
-
"ie3",
|
| 134 |
-
"in3",
|
| 135 |
-
"ing3",
|
| 136 |
-
"iong3",
|
| 137 |
-
"ir3",
|
| 138 |
-
"iu3",
|
| 139 |
-
"o3",
|
| 140 |
-
"ong3",
|
| 141 |
-
"ou3",
|
| 142 |
-
"u3",
|
| 143 |
-
"ua3",
|
| 144 |
-
"uai3",
|
| 145 |
-
"uan3",
|
| 146 |
-
"uang3",
|
| 147 |
-
"ui3",
|
| 148 |
-
"un3",
|
| 149 |
-
"uo3",
|
| 150 |
-
"v3",
|
| 151 |
-
"van3",
|
| 152 |
-
"ve3",
|
| 153 |
-
"vn3",
|
| 154 |
-
"E4",
|
| 155 |
-
"En4",
|
| 156 |
-
"a4",
|
| 157 |
-
"ai4",
|
| 158 |
-
"an4",
|
| 159 |
-
"ang4",
|
| 160 |
-
"ao4",
|
| 161 |
-
"e4",
|
| 162 |
-
"ei4",
|
| 163 |
-
"en4",
|
| 164 |
-
"eng4",
|
| 165 |
-
"er4",
|
| 166 |
-
"i4",
|
| 167 |
-
"i04",
|
| 168 |
-
"ia4",
|
| 169 |
-
"ian4",
|
| 170 |
-
"iang4",
|
| 171 |
-
"iao4",
|
| 172 |
-
"ie4",
|
| 173 |
-
"in4",
|
| 174 |
-
"ing4",
|
| 175 |
-
"iong4",
|
| 176 |
-
"ir4",
|
| 177 |
-
"iu4",
|
| 178 |
-
"o4",
|
| 179 |
-
"ong4",
|
| 180 |
-
"ou4",
|
| 181 |
-
"u4",
|
| 182 |
-
"ua4",
|
| 183 |
-
"uai4",
|
| 184 |
-
"uan4",
|
| 185 |
-
"uang4",
|
| 186 |
-
"ui4",
|
| 187 |
-
"un4",
|
| 188 |
-
"uo4",
|
| 189 |
-
"v4",
|
| 190 |
-
"van4",
|
| 191 |
-
"ve4",
|
| 192 |
-
"vn4",
|
| 193 |
-
"E5",
|
| 194 |
-
"En5",
|
| 195 |
-
"a5",
|
| 196 |
-
"ai5",
|
| 197 |
-
"an5",
|
| 198 |
-
"ang5",
|
| 199 |
-
"ao5",
|
| 200 |
-
"e5",
|
| 201 |
-
"ei5",
|
| 202 |
-
"en5",
|
| 203 |
-
"eng5",
|
| 204 |
-
"er5",
|
| 205 |
-
"i5",
|
| 206 |
-
"i05",
|
| 207 |
-
"ia5",
|
| 208 |
-
"ian5",
|
| 209 |
-
"iang5",
|
| 210 |
-
"iao5",
|
| 211 |
-
"ie5",
|
| 212 |
-
"in5",
|
| 213 |
-
"ing5",
|
| 214 |
-
"iong5",
|
| 215 |
-
"ir5",
|
| 216 |
-
"iu5",
|
| 217 |
-
"o5",
|
| 218 |
-
"ong5",
|
| 219 |
-
"ou5",
|
| 220 |
-
"u5",
|
| 221 |
-
"ua5",
|
| 222 |
-
"uai5",
|
| 223 |
-
"uan5",
|
| 224 |
-
"uang5",
|
| 225 |
-
"ui5",
|
| 226 |
-
"un5",
|
| 227 |
-
"uo5",
|
| 228 |
-
"v5",
|
| 229 |
-
"van5",
|
| 230 |
-
"ve5",
|
| 231 |
-
"vn5",
|
| 232 |
-
]
|
| 233 |
-
|
| 234 |
-
v_without_tone = [
|
| 235 |
-
"E",
|
| 236 |
-
"En",
|
| 237 |
-
"a",
|
| 238 |
-
"ai",
|
| 239 |
-
"an",
|
| 240 |
-
"ang",
|
| 241 |
-
"ao",
|
| 242 |
-
"e",
|
| 243 |
-
"ei",
|
| 244 |
-
"en",
|
| 245 |
-
"eng",
|
| 246 |
-
"er",
|
| 247 |
-
"i",
|
| 248 |
-
"i0",
|
| 249 |
-
"ia",
|
| 250 |
-
"ian",
|
| 251 |
-
"iang",
|
| 252 |
-
"iao",
|
| 253 |
-
"ie",
|
| 254 |
-
"in",
|
| 255 |
-
"ing",
|
| 256 |
-
"iong",
|
| 257 |
-
"ir",
|
| 258 |
-
"iu",
|
| 259 |
-
"o",
|
| 260 |
-
"ong",
|
| 261 |
-
"ou",
|
| 262 |
-
"u",
|
| 263 |
-
"ua",
|
| 264 |
-
"uai",
|
| 265 |
-
"uan",
|
| 266 |
-
"uang",
|
| 267 |
-
"ui",
|
| 268 |
-
"un",
|
| 269 |
-
"uo",
|
| 270 |
-
"v",
|
| 271 |
-
"van",
|
| 272 |
-
"ve",
|
| 273 |
-
"vn",
|
| 274 |
-
]
|
| 275 |
-
|
| 276 |
-
# japanese
|
| 277 |
-
ja_symbols = [
|
| 278 |
-
"I",
|
| 279 |
-
"N",
|
| 280 |
-
"U",
|
| 281 |
-
"a",
|
| 282 |
-
"b",
|
| 283 |
-
"by",
|
| 284 |
-
"ch",
|
| 285 |
-
"cl",
|
| 286 |
-
"d",
|
| 287 |
-
"dy",
|
| 288 |
-
"e",
|
| 289 |
-
"f",
|
| 290 |
-
"g",
|
| 291 |
-
"gy",
|
| 292 |
-
"h",
|
| 293 |
-
"hy",
|
| 294 |
-
"i",
|
| 295 |
-
"j",
|
| 296 |
-
"k",
|
| 297 |
-
"ky",
|
| 298 |
-
"m",
|
| 299 |
-
"my",
|
| 300 |
-
"n",
|
| 301 |
-
"ny",
|
| 302 |
-
"o",
|
| 303 |
-
"p",
|
| 304 |
-
"py",
|
| 305 |
-
"r",
|
| 306 |
-
"ry",
|
| 307 |
-
"s",
|
| 308 |
-
"sh",
|
| 309 |
-
"t",
|
| 310 |
-
"ts",
|
| 311 |
-
"u",
|
| 312 |
-
"v",
|
| 313 |
-
"w",
|
| 314 |
-
"y",
|
| 315 |
-
"z",
|
| 316 |
-
# "[", #上升调型
|
| 317 |
-
# "]", #下降调型
|
| 318 |
-
# "$", #结束符
|
| 319 |
-
# "^", #开始符
|
| 320 |
-
]
|
| 321 |
-
|
| 322 |
-
arpa = {
|
| 323 |
-
"AH0",
|
| 324 |
-
"S",
|
| 325 |
-
"AH1",
|
| 326 |
-
"EY2",
|
| 327 |
-
"AE2",
|
| 328 |
-
"EH0",
|
| 329 |
-
"OW2",
|
| 330 |
-
"UH0",
|
| 331 |
-
"NG",
|
| 332 |
-
"B",
|
| 333 |
-
"G",
|
| 334 |
-
"AY0",
|
| 335 |
-
"M",
|
| 336 |
-
"AA0",
|
| 337 |
-
"F",
|
| 338 |
-
"AO0",
|
| 339 |
-
"ER2",
|
| 340 |
-
"UH1",
|
| 341 |
-
"IY1",
|
| 342 |
-
"AH2",
|
| 343 |
-
"DH",
|
| 344 |
-
"IY0",
|
| 345 |
-
"EY1",
|
| 346 |
-
"IH0",
|
| 347 |
-
"K",
|
| 348 |
-
"N",
|
| 349 |
-
"W",
|
| 350 |
-
"IY2",
|
| 351 |
-
"T",
|
| 352 |
-
"AA1",
|
| 353 |
-
"ER1",
|
| 354 |
-
"EH2",
|
| 355 |
-
"OY0",
|
| 356 |
-
"UH2",
|
| 357 |
-
"UW1",
|
| 358 |
-
"Z",
|
| 359 |
-
"AW2",
|
| 360 |
-
"AW1",
|
| 361 |
-
"V",
|
| 362 |
-
"UW2",
|
| 363 |
-
"AA2",
|
| 364 |
-
"ER",
|
| 365 |
-
"AW0",
|
| 366 |
-
"UW0",
|
| 367 |
-
"R",
|
| 368 |
-
"OW1",
|
| 369 |
-
"EH1",
|
| 370 |
-
"ZH",
|
| 371 |
-
"AE0",
|
| 372 |
-
"IH2",
|
| 373 |
-
"IH",
|
| 374 |
-
"Y",
|
| 375 |
-
"JH",
|
| 376 |
-
"P",
|
| 377 |
-
"AY1",
|
| 378 |
-
"EY0",
|
| 379 |
-
"OY2",
|
| 380 |
-
"TH",
|
| 381 |
-
"HH",
|
| 382 |
-
"D",
|
| 383 |
-
"ER0",
|
| 384 |
-
"CH",
|
| 385 |
-
"AO1",
|
| 386 |
-
"AE1",
|
| 387 |
-
"AO2",
|
| 388 |
-
"OY1",
|
| 389 |
-
"AY2",
|
| 390 |
-
"IH1",
|
| 391 |
-
"OW0",
|
| 392 |
-
"L",
|
| 393 |
-
"SH",
|
| 394 |
-
}
|
| 395 |
-
|
| 396 |
-
symbols = [pad] + c + v + ja_symbols + pu_symbols + list(arpa)
|
| 397 |
-
symbols = sorted(set(symbols))
|
| 398 |
-
if __name__ == "__main__":
|
| 399 |
-
print(len(symbols))
|
|
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|
text/symbols2.py
DELETED
|
@@ -1,797 +0,0 @@
|
|
| 1 |
-
# punctuation = ['!', '?', '…', ",", ".","@"]#@是SP停顿
|
| 2 |
-
punctuation = ["!", "?", "…", ",", "."] # @是SP停顿
|
| 3 |
-
punctuation.append("-")
|
| 4 |
-
pu_symbols = punctuation + ["SP", "SP2", "SP3", "UNK"]
|
| 5 |
-
# pu_symbols = punctuation + ["SP", 'SP2', 'SP3','SP4', "UNK"]
|
| 6 |
-
pad = "_"
|
| 7 |
-
|
| 8 |
-
c = [
|
| 9 |
-
"AA",
|
| 10 |
-
"EE",
|
| 11 |
-
"OO",
|
| 12 |
-
"b",
|
| 13 |
-
"c",
|
| 14 |
-
"ch",
|
| 15 |
-
"d",
|
| 16 |
-
"f",
|
| 17 |
-
"g",
|
| 18 |
-
"h",
|
| 19 |
-
"j",
|
| 20 |
-
"k",
|
| 21 |
-
"l",
|
| 22 |
-
"m",
|
| 23 |
-
"n",
|
| 24 |
-
"p",
|
| 25 |
-
"q",
|
| 26 |
-
"r",
|
| 27 |
-
"s",
|
| 28 |
-
"sh",
|
| 29 |
-
"t",
|
| 30 |
-
"w",
|
| 31 |
-
"x",
|
| 32 |
-
"y",
|
| 33 |
-
"z",
|
| 34 |
-
"zh",
|
| 35 |
-
]
|
| 36 |
-
v = [
|
| 37 |
-
"E1",
|
| 38 |
-
"En1",
|
| 39 |
-
"a1",
|
| 40 |
-
"ai1",
|
| 41 |
-
"an1",
|
| 42 |
-
"ang1",
|
| 43 |
-
"ao1",
|
| 44 |
-
"e1",
|
| 45 |
-
"ei1",
|
| 46 |
-
"en1",
|
| 47 |
-
"eng1",
|
| 48 |
-
"er1",
|
| 49 |
-
"i1",
|
| 50 |
-
"i01",
|
| 51 |
-
"ia1",
|
| 52 |
-
"ian1",
|
| 53 |
-
"iang1",
|
| 54 |
-
"iao1",
|
| 55 |
-
"ie1",
|
| 56 |
-
"in1",
|
| 57 |
-
"ing1",
|
| 58 |
-
"iong1",
|
| 59 |
-
"ir1",
|
| 60 |
-
"iu1",
|
| 61 |
-
"o1",
|
| 62 |
-
"ong1",
|
| 63 |
-
"ou1",
|
| 64 |
-
"u1",
|
| 65 |
-
"ua1",
|
| 66 |
-
"uai1",
|
| 67 |
-
"uan1",
|
| 68 |
-
"uang1",
|
| 69 |
-
"ui1",
|
| 70 |
-
"un1",
|
| 71 |
-
"uo1",
|
| 72 |
-
"v1",
|
| 73 |
-
"van1",
|
| 74 |
-
"ve1",
|
| 75 |
-
"vn1",
|
| 76 |
-
"E2",
|
| 77 |
-
"En2",
|
| 78 |
-
"a2",
|
| 79 |
-
"ai2",
|
| 80 |
-
"an2",
|
| 81 |
-
"ang2",
|
| 82 |
-
"ao2",
|
| 83 |
-
"e2",
|
| 84 |
-
"ei2",
|
| 85 |
-
"en2",
|
| 86 |
-
"eng2",
|
| 87 |
-
"er2",
|
| 88 |
-
"i2",
|
| 89 |
-
"i02",
|
| 90 |
-
"ia2",
|
| 91 |
-
"ian2",
|
| 92 |
-
"iang2",
|
| 93 |
-
"iao2",
|
| 94 |
-
"ie2",
|
| 95 |
-
"in2",
|
| 96 |
-
"ing2",
|
| 97 |
-
"iong2",
|
| 98 |
-
"ir2",
|
| 99 |
-
"iu2",
|
| 100 |
-
"o2",
|
| 101 |
-
"ong2",
|
| 102 |
-
"ou2",
|
| 103 |
-
"u2",
|
| 104 |
-
"ua2",
|
| 105 |
-
"uai2",
|
| 106 |
-
"uan2",
|
| 107 |
-
"uang2",
|
| 108 |
-
"ui2",
|
| 109 |
-
"un2",
|
| 110 |
-
"uo2",
|
| 111 |
-
"v2",
|
| 112 |
-
"van2",
|
| 113 |
-
"ve2",
|
| 114 |
-
"vn2",
|
| 115 |
-
"E3",
|
| 116 |
-
"En3",
|
| 117 |
-
"a3",
|
| 118 |
-
"ai3",
|
| 119 |
-
"an3",
|
| 120 |
-
"ang3",
|
| 121 |
-
"ao3",
|
| 122 |
-
"e3",
|
| 123 |
-
"ei3",
|
| 124 |
-
"en3",
|
| 125 |
-
"eng3",
|
| 126 |
-
"er3",
|
| 127 |
-
"i3",
|
| 128 |
-
"i03",
|
| 129 |
-
"ia3",
|
| 130 |
-
"ian3",
|
| 131 |
-
"iang3",
|
| 132 |
-
"iao3",
|
| 133 |
-
"ie3",
|
| 134 |
-
"in3",
|
| 135 |
-
"ing3",
|
| 136 |
-
"iong3",
|
| 137 |
-
"ir3",
|
| 138 |
-
"iu3",
|
| 139 |
-
"o3",
|
| 140 |
-
"ong3",
|
| 141 |
-
"ou3",
|
| 142 |
-
"u3",
|
| 143 |
-
"ua3",
|
| 144 |
-
"uai3",
|
| 145 |
-
"uan3",
|
| 146 |
-
"uang3",
|
| 147 |
-
"ui3",
|
| 148 |
-
"un3",
|
| 149 |
-
"uo3",
|
| 150 |
-
"v3",
|
| 151 |
-
"van3",
|
| 152 |
-
"ve3",
|
| 153 |
-
"vn3",
|
| 154 |
-
"E4",
|
| 155 |
-
"En4",
|
| 156 |
-
"a4",
|
| 157 |
-
"ai4",
|
| 158 |
-
"an4",
|
| 159 |
-
"ang4",
|
| 160 |
-
"ao4",
|
| 161 |
-
"e4",
|
| 162 |
-
"ei4",
|
| 163 |
-
"en4",
|
| 164 |
-
"eng4",
|
| 165 |
-
"er4",
|
| 166 |
-
"i4",
|
| 167 |
-
"i04",
|
| 168 |
-
"ia4",
|
| 169 |
-
"ian4",
|
| 170 |
-
"iang4",
|
| 171 |
-
"iao4",
|
| 172 |
-
"ie4",
|
| 173 |
-
"in4",
|
| 174 |
-
"ing4",
|
| 175 |
-
"iong4",
|
| 176 |
-
"ir4",
|
| 177 |
-
"iu4",
|
| 178 |
-
"o4",
|
| 179 |
-
"ong4",
|
| 180 |
-
"ou4",
|
| 181 |
-
"u4",
|
| 182 |
-
"ua4",
|
| 183 |
-
"uai4",
|
| 184 |
-
"uan4",
|
| 185 |
-
"uang4",
|
| 186 |
-
"ui4",
|
| 187 |
-
"un4",
|
| 188 |
-
"uo4",
|
| 189 |
-
"v4",
|
| 190 |
-
"van4",
|
| 191 |
-
"ve4",
|
| 192 |
-
"vn4",
|
| 193 |
-
"E5",
|
| 194 |
-
"En5",
|
| 195 |
-
"a5",
|
| 196 |
-
"ai5",
|
| 197 |
-
"an5",
|
| 198 |
-
"ang5",
|
| 199 |
-
"ao5",
|
| 200 |
-
"e5",
|
| 201 |
-
"ei5",
|
| 202 |
-
"en5",
|
| 203 |
-
"eng5",
|
| 204 |
-
"er5",
|
| 205 |
-
"i5",
|
| 206 |
-
"i05",
|
| 207 |
-
"ia5",
|
| 208 |
-
"ian5",
|
| 209 |
-
"iang5",
|
| 210 |
-
"iao5",
|
| 211 |
-
"ie5",
|
| 212 |
-
"in5",
|
| 213 |
-
"ing5",
|
| 214 |
-
"iong5",
|
| 215 |
-
"ir5",
|
| 216 |
-
"iu5",
|
| 217 |
-
"o5",
|
| 218 |
-
"ong5",
|
| 219 |
-
"ou5",
|
| 220 |
-
"u5",
|
| 221 |
-
"ua5",
|
| 222 |
-
"uai5",
|
| 223 |
-
"uan5",
|
| 224 |
-
"uang5",
|
| 225 |
-
"ui5",
|
| 226 |
-
"un5",
|
| 227 |
-
"uo5",
|
| 228 |
-
"v5",
|
| 229 |
-
"van5",
|
| 230 |
-
"ve5",
|
| 231 |
-
"vn5",
|
| 232 |
-
]
|
| 233 |
-
|
| 234 |
-
v_without_tone = [
|
| 235 |
-
"E",
|
| 236 |
-
"En",
|
| 237 |
-
"a",
|
| 238 |
-
"ai",
|
| 239 |
-
"an",
|
| 240 |
-
"ang",
|
| 241 |
-
"ao",
|
| 242 |
-
"e",
|
| 243 |
-
"ei",
|
| 244 |
-
"en",
|
| 245 |
-
"eng",
|
| 246 |
-
"er",
|
| 247 |
-
"i",
|
| 248 |
-
"i0",
|
| 249 |
-
"ia",
|
| 250 |
-
"ian",
|
| 251 |
-
"iang",
|
| 252 |
-
"iao",
|
| 253 |
-
"ie",
|
| 254 |
-
"in",
|
| 255 |
-
"ing",
|
| 256 |
-
"iong",
|
| 257 |
-
"ir",
|
| 258 |
-
"iu",
|
| 259 |
-
"o",
|
| 260 |
-
"ong",
|
| 261 |
-
"ou",
|
| 262 |
-
"u",
|
| 263 |
-
"ua",
|
| 264 |
-
"uai",
|
| 265 |
-
"uan",
|
| 266 |
-
"uang",
|
| 267 |
-
"ui",
|
| 268 |
-
"un",
|
| 269 |
-
"uo",
|
| 270 |
-
"v",
|
| 271 |
-
"van",
|
| 272 |
-
"ve",
|
| 273 |
-
"vn",
|
| 274 |
-
]
|
| 275 |
-
|
| 276 |
-
# japanese
|
| 277 |
-
ja_symbols = [
|
| 278 |
-
"I",
|
| 279 |
-
"N",
|
| 280 |
-
"U",
|
| 281 |
-
"a",
|
| 282 |
-
"b",
|
| 283 |
-
"by",
|
| 284 |
-
"ch",
|
| 285 |
-
"cl",
|
| 286 |
-
"d",
|
| 287 |
-
"dy",
|
| 288 |
-
"e",
|
| 289 |
-
"f",
|
| 290 |
-
"g",
|
| 291 |
-
"gy",
|
| 292 |
-
"h",
|
| 293 |
-
"hy",
|
| 294 |
-
"i",
|
| 295 |
-
"j",
|
| 296 |
-
"k",
|
| 297 |
-
"ky",
|
| 298 |
-
"m",
|
| 299 |
-
"my",
|
| 300 |
-
"n",
|
| 301 |
-
"ny",
|
| 302 |
-
"o",
|
| 303 |
-
"p",
|
| 304 |
-
"py",
|
| 305 |
-
"r",
|
| 306 |
-
"ry",
|
| 307 |
-
"s",
|
| 308 |
-
"sh",
|
| 309 |
-
"t",
|
| 310 |
-
"ts",
|
| 311 |
-
"u",
|
| 312 |
-
"v",
|
| 313 |
-
"w",
|
| 314 |
-
"y",
|
| 315 |
-
"z",
|
| 316 |
-
###楼下2个留到后面加
|
| 317 |
-
# "[", #上升调型
|
| 318 |
-
# "]", #下降调型
|
| 319 |
-
# "$", #结束符
|
| 320 |
-
# "^", #开始符
|
| 321 |
-
]
|
| 322 |
-
|
| 323 |
-
arpa = {
|
| 324 |
-
"AH0",
|
| 325 |
-
"S",
|
| 326 |
-
"AH1",
|
| 327 |
-
"EY2",
|
| 328 |
-
"AE2",
|
| 329 |
-
"EH0",
|
| 330 |
-
"OW2",
|
| 331 |
-
"UH0",
|
| 332 |
-
"NG",
|
| 333 |
-
"B",
|
| 334 |
-
"G",
|
| 335 |
-
"AY0",
|
| 336 |
-
"M",
|
| 337 |
-
"AA0",
|
| 338 |
-
"F",
|
| 339 |
-
"AO0",
|
| 340 |
-
"ER2",
|
| 341 |
-
"UH1",
|
| 342 |
-
"IY1",
|
| 343 |
-
"AH2",
|
| 344 |
-
"DH",
|
| 345 |
-
"IY0",
|
| 346 |
-
"EY1",
|
| 347 |
-
"IH0",
|
| 348 |
-
"K",
|
| 349 |
-
"N",
|
| 350 |
-
"W",
|
| 351 |
-
"IY2",
|
| 352 |
-
"T",
|
| 353 |
-
"AA1",
|
| 354 |
-
"ER1",
|
| 355 |
-
"EH2",
|
| 356 |
-
"OY0",
|
| 357 |
-
"UH2",
|
| 358 |
-
"UW1",
|
| 359 |
-
"Z",
|
| 360 |
-
"AW2",
|
| 361 |
-
"AW1",
|
| 362 |
-
"V",
|
| 363 |
-
"UW2",
|
| 364 |
-
"AA2",
|
| 365 |
-
"ER",
|
| 366 |
-
"AW0",
|
| 367 |
-
"UW0",
|
| 368 |
-
"R",
|
| 369 |
-
"OW1",
|
| 370 |
-
"EH1",
|
| 371 |
-
"ZH",
|
| 372 |
-
"AE0",
|
| 373 |
-
"IH2",
|
| 374 |
-
"IH",
|
| 375 |
-
"Y",
|
| 376 |
-
"JH",
|
| 377 |
-
"P",
|
| 378 |
-
"AY1",
|
| 379 |
-
"EY0",
|
| 380 |
-
"OY2",
|
| 381 |
-
"TH",
|
| 382 |
-
"HH",
|
| 383 |
-
"D",
|
| 384 |
-
"ER0",
|
| 385 |
-
"CH",
|
| 386 |
-
"AO1",
|
| 387 |
-
"AE1",
|
| 388 |
-
"AO2",
|
| 389 |
-
"OY1",
|
| 390 |
-
"AY2",
|
| 391 |
-
"IH1",
|
| 392 |
-
"OW0",
|
| 393 |
-
"L",
|
| 394 |
-
"SH",
|
| 395 |
-
}
|
| 396 |
-
|
| 397 |
-
ko_symbols = "ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ空停"
|
| 398 |
-
# ko_symbols='ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ '
|
| 399 |
-
|
| 400 |
-
yue_symbols = {
|
| 401 |
-
"Yeot3",
|
| 402 |
-
"Yip1",
|
| 403 |
-
"Yyu3",
|
| 404 |
-
"Yeng4",
|
| 405 |
-
"Yut5",
|
| 406 |
-
"Yaan5",
|
| 407 |
-
"Ym5",
|
| 408 |
-
"Yaan6",
|
| 409 |
-
"Yang1",
|
| 410 |
-
"Yun4",
|
| 411 |
-
"Yon2",
|
| 412 |
-
"Yui5",
|
| 413 |
-
"Yun2",
|
| 414 |
-
"Yat3",
|
| 415 |
-
"Ye",
|
| 416 |
-
"Yeot1",
|
| 417 |
-
"Yoeng5",
|
| 418 |
-
"Yoek2",
|
| 419 |
-
"Yam2",
|
| 420 |
-
"Yeon6",
|
| 421 |
-
"Yu6",
|
| 422 |
-
"Yiu3",
|
| 423 |
-
"Yaang6",
|
| 424 |
-
"Yp5",
|
| 425 |
-
"Yai4",
|
| 426 |
-
"Yoek4",
|
| 427 |
-
"Yit6",
|
| 428 |
-
"Yam5",
|
| 429 |
-
"Yoeng6",
|
| 430 |
-
"Yg1",
|
| 431 |
-
"Yk3",
|
| 432 |
-
"Yoe4",
|
| 433 |
-
"Yam3",
|
| 434 |
-
"Yc",
|
| 435 |
-
"Yyu4",
|
| 436 |
-
"Yyut1",
|
| 437 |
-
"Yiu4",
|
| 438 |
-
"Ying3",
|
| 439 |
-
"Yip3",
|
| 440 |
-
"Yaap3",
|
| 441 |
-
"Yau3",
|
| 442 |
-
"Yan4",
|
| 443 |
-
"Yau1",
|
| 444 |
-
"Yap4",
|
| 445 |
-
"Yk6",
|
| 446 |
-
"Yok3",
|
| 447 |
-
"Yai1",
|
| 448 |
-
"Yeot6",
|
| 449 |
-
"Yan2",
|
| 450 |
-
"Yoek6",
|
| 451 |
-
"Yt1",
|
| 452 |
-
"Yoi1",
|
| 453 |
-
"Yit5",
|
| 454 |
-
"Yn4",
|
| 455 |
-
"Yaau3",
|
| 456 |
-
"Yau4",
|
| 457 |
-
"Yuk6",
|
| 458 |
-
"Ys",
|
| 459 |
-
"Yuk",
|
| 460 |
-
"Yin6",
|
| 461 |
-
"Yung6",
|
| 462 |
-
"Ya",
|
| 463 |
-
"You",
|
| 464 |
-
"Yaai5",
|
| 465 |
-
"Yau5",
|
| 466 |
-
"Yoi3",
|
| 467 |
-
"Yaak3",
|
| 468 |
-
"Yaat3",
|
| 469 |
-
"Ying2",
|
| 470 |
-
"Yok5",
|
| 471 |
-
"Yeng2",
|
| 472 |
-
"Yyut3",
|
| 473 |
-
"Yam1",
|
| 474 |
-
"Yip5",
|
| 475 |
-
"You1",
|
| 476 |
-
"Yam6",
|
| 477 |
-
"Yaa5",
|
| 478 |
-
"Yi6",
|
| 479 |
-
"Yek4",
|
| 480 |
-
"Yyu2",
|
| 481 |
-
"Yuk5",
|
| 482 |
-
"Yaam1",
|
| 483 |
-
"Yang2",
|
| 484 |
-
"Yai",
|
| 485 |
-
"Yiu6",
|
| 486 |
-
"Yin4",
|
| 487 |
-
"Yok4",
|
| 488 |
-
"Yot3",
|
| 489 |
-
"Yui2",
|
| 490 |
-
"Yeoi5",
|
| 491 |
-
"Yyun6",
|
| 492 |
-
"Yyu5",
|
| 493 |
-
"Yoi5",
|
| 494 |
-
"Yeot2",
|
| 495 |
-
"Yim4",
|
| 496 |
-
"Yeoi2",
|
| 497 |
-
"Yaan1",
|
| 498 |
-
"Yang6",
|
| 499 |
-
"Yong1",
|
| 500 |
-
"Yaang4",
|
| 501 |
-
"Yung5",
|
| 502 |
-
"Yeon1",
|
| 503 |
-
"Yin2",
|
| 504 |
-
"Ya3",
|
| 505 |
-
"Yaang3",
|
| 506 |
-
"Yg",
|
| 507 |
-
"Yk2",
|
| 508 |
-
"Yaau5",
|
| 509 |
-
"Yut1",
|
| 510 |
-
"Yt5",
|
| 511 |
-
"Yip4",
|
| 512 |
-
"Yung4",
|
| 513 |
-
"Yj",
|
| 514 |
-
"Yong3",
|
| 515 |
-
"Ya1",
|
| 516 |
-
"Yg6",
|
| 517 |
-
"Yaau6",
|
| 518 |
-
"Yit3",
|
| 519 |
-
"Yun3",
|
| 520 |
-
"Ying1",
|
| 521 |
-
"Yn2",
|
| 522 |
-
"Yg4",
|
| 523 |
-
"Yl",
|
| 524 |
-
"Yp3",
|
| 525 |
-
"Yn3",
|
| 526 |
-
"Yak1",
|
| 527 |
-
"Yang5",
|
| 528 |
-
"Yoe6",
|
| 529 |
-
"You2",
|
| 530 |
-
"Yap2",
|
| 531 |
-
"Yak2",
|
| 532 |
-
"Yt3",
|
| 533 |
-
"Yot5",
|
| 534 |
-
"Yim2",
|
| 535 |
-
"Yi1",
|
| 536 |
-
"Yn6",
|
| 537 |
-
"Yaat5",
|
| 538 |
-
"Yaam3",
|
| 539 |
-
"Yoek5",
|
| 540 |
-
"Ye3",
|
| 541 |
-
"Yeon4",
|
| 542 |
-
"Yaa2",
|
| 543 |
-
"Yu3",
|
| 544 |
-
"Yim6",
|
| 545 |
-
"Ym",
|
| 546 |
-
"Yoe3",
|
| 547 |
-
"Yaai2",
|
| 548 |
-
"Ym2",
|
| 549 |
-
"Ya6",
|
| 550 |
-
"Yeng6",
|
| 551 |
-
"Yik4",
|
| 552 |
-
"Yot4",
|
| 553 |
-
"Yaai4",
|
| 554 |
-
"Yyun3",
|
| 555 |
-
"Yu1",
|
| 556 |
-
"Yoeng1",
|
| 557 |
-
"Yaap2",
|
| 558 |
-
"Yuk3",
|
| 559 |
-
"Yoek3",
|
| 560 |
-
"Yeng5",
|
| 561 |
-
"Yeoi1",
|
| 562 |
-
"Yiu2",
|
| 563 |
-
"Yok1",
|
| 564 |
-
"Yo1",
|
| 565 |
-
"Yoek1",
|
| 566 |
-
"Yoeng2",
|
| 567 |
-
"Yeon5",
|
| 568 |
-
"Yiu1",
|
| 569 |
-
"Yoeng4",
|
| 570 |
-
"Yuk2",
|
| 571 |
-
"Yat4",
|
| 572 |
-
"Yg5",
|
| 573 |
-
"Yut4",
|
| 574 |
-
"Yan6",
|
| 575 |
-
"Yin3",
|
| 576 |
-
"Yaa6",
|
| 577 |
-
"Yap1",
|
| 578 |
-
"Yg2",
|
| 579 |
-
"Yoe5",
|
| 580 |
-
"Yt4",
|
| 581 |
-
"Ya5",
|
| 582 |
-
"Yo4",
|
| 583 |
-
"Yyu1",
|
| 584 |
-
"Yak3",
|
| 585 |
-
"Yeon2",
|
| 586 |
-
"Yong4",
|
| 587 |
-
"Ym1",
|
| 588 |
-
"Ye2",
|
| 589 |
-
"Yaang5",
|
| 590 |
-
"Yoi2",
|
| 591 |
-
"Yeng3",
|
| 592 |
-
"Yn",
|
| 593 |
-
"Yyut4",
|
| 594 |
-
"Yau",
|
| 595 |
-
"Yaak2",
|
| 596 |
-
"Yaan4",
|
| 597 |
-
"Yek2",
|
| 598 |
-
"Yin1",
|
| 599 |
-
"Yi5",
|
| 600 |
-
"Yoe2",
|
| 601 |
-
"Yei5",
|
| 602 |
-
"Yaat6",
|
| 603 |
-
"Yak5",
|
| 604 |
-
"Yp6",
|
| 605 |
-
"Yok6",
|
| 606 |
-
"Yei2",
|
| 607 |
-
"Yaap1",
|
| 608 |
-
"Yyut5",
|
| 609 |
-
"Yi4",
|
| 610 |
-
"Yim1",
|
| 611 |
-
"Yk5",
|
| 612 |
-
"Ye4",
|
| 613 |
-
"Yok2",
|
| 614 |
-
"Yaam6",
|
| 615 |
-
"Yat2",
|
| 616 |
-
"Yon6",
|
| 617 |
-
"Yei3",
|
| 618 |
-
"Yyu6",
|
| 619 |
-
"Yeot5",
|
| 620 |
-
"Yk4",
|
| 621 |
-
"Yai6",
|
| 622 |
-
"Yd",
|
| 623 |
-
"Yg3",
|
| 624 |
-
"Yei6",
|
| 625 |
-
"Yau2",
|
| 626 |
-
"Yok",
|
| 627 |
-
"Yau6",
|
| 628 |
-
"Yung3",
|
| 629 |
-
"Yim5",
|
| 630 |
-
"Yut6",
|
| 631 |
-
"Yit1",
|
| 632 |
-
"Yon3",
|
| 633 |
-
"Yat1",
|
| 634 |
-
"Yaam2",
|
| 635 |
-
"Yyut2",
|
| 636 |
-
"Yui6",
|
| 637 |
-
"Yt2",
|
| 638 |
-
"Yek6",
|
| 639 |
-
"Yt",
|
| 640 |
-
"Ye6",
|
| 641 |
-
"Yang3",
|
| 642 |
-
"Ying6",
|
| 643 |
-
"Yaau1",
|
| 644 |
-
"Yeon3",
|
| 645 |
-
"Yng",
|
| 646 |
-
"Yh",
|
| 647 |
-
"Yang4",
|
| 648 |
-
"Ying5",
|
| 649 |
-
"Yaap6",
|
| 650 |
-
"Yoeng3",
|
| 651 |
-
"Yyun4",
|
| 652 |
-
"You3",
|
| 653 |
-
"Yan5",
|
| 654 |
-
"Yat5",
|
| 655 |
-
"Yot1",
|
| 656 |
-
"Yun1",
|
| 657 |
-
"Yi3",
|
| 658 |
-
"Yaa1",
|
| 659 |
-
"Yaap4",
|
| 660 |
-
"You6",
|
| 661 |
-
"Yaang2",
|
| 662 |
-
"Yaap5",
|
| 663 |
-
"Yaa3",
|
| 664 |
-
"Yaak6",
|
| 665 |
-
"Yeng1",
|
| 666 |
-
"Yaak1",
|
| 667 |
-
"Yo5",
|
| 668 |
-
"Yoi4",
|
| 669 |
-
"Yam4",
|
| 670 |
-
"Yik1",
|
| 671 |
-
"Ye1",
|
| 672 |
-
"Yai5",
|
| 673 |
-
"Yung1",
|
| 674 |
-
"Yp2",
|
| 675 |
-
"Yui4",
|
| 676 |
-
"Yaak4",
|
| 677 |
-
"Yung2",
|
| 678 |
-
"Yak4",
|
| 679 |
-
"Yaat4",
|
| 680 |
-
"Yeoi4",
|
| 681 |
-
"Yut2",
|
| 682 |
-
"Yin5",
|
| 683 |
-
"Yaau4",
|
| 684 |
-
"Yap6",
|
| 685 |
-
"Yb",
|
| 686 |
-
"Yaam4",
|
| 687 |
-
"Yw",
|
| 688 |
-
"Yut3",
|
| 689 |
-
"Yong2",
|
| 690 |
-
"Yt6",
|
| 691 |
-
"Yaai6",
|
| 692 |
-
"Yap5",
|
| 693 |
-
"Yik5",
|
| 694 |
-
"Yun6",
|
| 695 |
-
"Yaam5",
|
| 696 |
-
"Yun5",
|
| 697 |
-
"Yik3",
|
| 698 |
-
"Ya2",
|
| 699 |
-
"Yyut6",
|
| 700 |
-
"Yon4",
|
| 701 |
-
"Yk1",
|
| 702 |
-
"Yit4",
|
| 703 |
-
"Yak6",
|
| 704 |
-
"Yaan2",
|
| 705 |
-
"Yuk1",
|
| 706 |
-
"Yai2",
|
| 707 |
-
"Yik2",
|
| 708 |
-
"Yaat2",
|
| 709 |
-
"Yo3",
|
| 710 |
-
"Ykw",
|
| 711 |
-
"Yn5",
|
| 712 |
-
"Yaa",
|
| 713 |
-
"Ye5",
|
| 714 |
-
"Yu4",
|
| 715 |
-
"Yei1",
|
| 716 |
-
"Yai3",
|
| 717 |
-
"Yyun5",
|
| 718 |
-
"Yip2",
|
| 719 |
-
"Yaau2",
|
| 720 |
-
"Yiu5",
|
| 721 |
-
"Ym4",
|
| 722 |
-
"Yeoi6",
|
| 723 |
-
"Yk",
|
| 724 |
-
"Ym6",
|
| 725 |
-
"Yoe1",
|
| 726 |
-
"Yeoi3",
|
| 727 |
-
"Yon",
|
| 728 |
-
"Yuk4",
|
| 729 |
-
"Yaai3",
|
| 730 |
-
"Yaa4",
|
| 731 |
-
"Yot6",
|
| 732 |
-
"Yaang1",
|
| 733 |
-
"Yei4",
|
| 734 |
-
"Yek1",
|
| 735 |
-
"Yo",
|
| 736 |
-
"Yp",
|
| 737 |
-
"Yo6",
|
| 738 |
-
"Yp4",
|
| 739 |
-
"Yan3",
|
| 740 |
-
"Yoi",
|
| 741 |
-
"Yap3",
|
| 742 |
-
"Yek3",
|
| 743 |
-
"Yim3",
|
| 744 |
-
"Yz",
|
| 745 |
-
"Yot2",
|
| 746 |
-
"Yoi6",
|
| 747 |
-
"Yit2",
|
| 748 |
-
"Yu5",
|
| 749 |
-
"Yaan3",
|
| 750 |
-
"Yan1",
|
| 751 |
-
"Yon5",
|
| 752 |
-
"Yp1",
|
| 753 |
-
"Yong5",
|
| 754 |
-
"Ygw",
|
| 755 |
-
"Yak",
|
| 756 |
-
"Yat6",
|
| 757 |
-
"Ying4",
|
| 758 |
-
"Yu2",
|
| 759 |
-
"Yf",
|
| 760 |
-
"Ya4",
|
| 761 |
-
"Yon1",
|
| 762 |
-
"You4",
|
| 763 |
-
"Yik6",
|
| 764 |
-
"Yui1",
|
| 765 |
-
"Yaat1",
|
| 766 |
-
"Yeot4",
|
| 767 |
-
"Yi2",
|
| 768 |
-
"Yaai1",
|
| 769 |
-
"Yek5",
|
| 770 |
-
"Ym3",
|
| 771 |
-
"Yong6",
|
| 772 |
-
"You5",
|
| 773 |
-
"Yyun1",
|
| 774 |
-
"Yn1",
|
| 775 |
-
"Yo2",
|
| 776 |
-
"Yip6",
|
| 777 |
-
"Yui3",
|
| 778 |
-
"Yaak5",
|
| 779 |
-
"Yyun2",
|
| 780 |
-
}
|
| 781 |
-
|
| 782 |
-
# symbols = [pad] + c + v + ja_symbols + pu_symbols + list(arpa)+list(ko_symbols)#+list(yue_symbols)###直接这么加yue顺序乱了
|
| 783 |
-
symbols = [pad] + c + v + ja_symbols + pu_symbols + list(arpa)
|
| 784 |
-
symbols = sorted(set(symbols))
|
| 785 |
-
# print(len(symbols))
|
| 786 |
-
symbols += ["[", "]"] ##日文新增上升下降调型
|
| 787 |
-
symbols += sorted(list(ko_symbols))
|
| 788 |
-
symbols += sorted(list(yue_symbols)) ##新加的yue统一摆在后头#已查过开头加Y后没有重复，韩文显然不会重复
|
| 789 |
-
# print(len(symbols))
|
| 790 |
-
if __name__ == "__main__":
|
| 791 |
-
print(len(symbols))
|
| 792 |
-
"""
|
| 793 |
-
粤语：
|
| 794 |
-
732-353=379
|
| 795 |
-
韩文+粤语：
|
| 796 |
-
732-322=410
|
| 797 |
-
"""
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|
text/tone_sandhi.py
DELETED
|
@@ -1,778 +0,0 @@
|
|
| 1 |
-
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
| 2 |
-
#
|
| 3 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 4 |
-
# you may not use this file except in compliance with the License.
|
| 5 |
-
# You may obtain a copy of the License at
|
| 6 |
-
#
|
| 7 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
| 8 |
-
#
|
| 9 |
-
# Unless required by applicable law or agreed to in writing, software
|
| 10 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 11 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 12 |
-
# See the License for the specific language governing permissions and
|
| 13 |
-
# limitations under the License.
|
| 14 |
-
from typing import List
|
| 15 |
-
from typing import Tuple
|
| 16 |
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|
| 17 |
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import jieba_fast as jieba
|
| 18 |
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from pypinyin import lazy_pinyin
|
| 19 |
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from pypinyin import Style
|
| 20 |
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|
| 21 |
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|
| 22 |
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class ToneSandhi:
|
| 23 |
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def __init__(self):
|
| 24 |
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self.must_neural_tone_words = {
|
| 25 |
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"麻烦",
|
| 26 |
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"麻利",
|
| 27 |
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"鸳鸯",
|
| 28 |
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"高粱",
|
| 29 |
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"骨头",
|
| 30 |
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"骆驼",
|
| 31 |
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"马虎",
|
| 32 |
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"首饰",
|
| 33 |
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"馒头",
|
| 34 |
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"馄饨",
|
| 35 |
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"风筝",
|
| 36 |
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"难为",
|
| 37 |
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"队伍",
|
| 38 |
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"阔气",
|
| 39 |
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"闺女",
|
| 40 |
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"门道",
|
| 41 |
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"锄头",
|
| 42 |
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"铺盖",
|
| 43 |
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"铃铛",
|
| 44 |
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"铁匠",
|
| 45 |
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"钥匙",
|
| 46 |
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"里脊",
|
| 47 |
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"里头",
|
| 48 |
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"部分",
|
| 49 |
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"那么",
|
| 50 |
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"道士",
|
| 51 |
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"造化",
|
| 52 |
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"迷糊",
|
| 53 |
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"连累",
|
| 54 |
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"这么",
|
| 55 |
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"这个",
|
| 56 |
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"运气",
|
| 57 |
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"过去",
|
| 58 |
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"软和",
|
| 59 |
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"转悠",
|
| 60 |
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"踏实",
|
| 61 |
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"跳蚤",
|
| 62 |
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"跟头",
|
| 63 |
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"趔趄",
|
| 64 |
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"财主",
|
| 65 |
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"豆腐",
|
| 66 |
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"讲究",
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| 67 |
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"记性",
|
| 68 |
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"记号",
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| 69 |
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"认识",
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| 70 |
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"规矩",
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| 71 |
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"见识",
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| 72 |
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"裁缝",
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| 73 |
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"补丁",
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| 74 |
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"衣裳",
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| 75 |
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"衣服",
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| 76 |
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"衙门",
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| 77 |
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"街坊",
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| 78 |
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"行李",
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| 79 |
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"行当",
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| 80 |
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"蛤蟆",
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| 81 |
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"蘑菇",
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| 82 |
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"薄荷",
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| 83 |
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"葫芦",
|
| 84 |
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"葡萄",
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| 85 |
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"萝卜",
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| 86 |
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"荸荠",
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| 87 |
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"苗条",
|
| 88 |
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"苗头",
|
| 89 |
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"苍蝇",
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| 90 |
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"芝麻",
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| 91 |
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"舒服",
|
| 92 |
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"舒坦",
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| 93 |
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"舌头",
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| 94 |
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"自在",
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| 95 |
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"膏药",
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| 96 |
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"脾气",
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| 97 |
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"脑袋",
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| 98 |
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"脊梁",
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| 99 |
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"能耐",
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| 100 |
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"胳膊",
|
| 101 |
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"胭脂",
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| 102 |
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"胡萝",
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| 103 |
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"胡琴",
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| 104 |
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"胡同",
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| 105 |
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"聪明",
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| 106 |
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"耽误",
|
| 107 |
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"耽搁",
|
| 108 |
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"耷拉",
|
| 109 |
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"耳朵",
|
| 110 |
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"老爷",
|
| 111 |
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"老实",
|
| 112 |
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"老婆",
|
| 113 |
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"老头",
|
| 114 |
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"老太",
|
| 115 |
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"翻腾",
|
| 116 |
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"罗嗦",
|
| 117 |
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"罐头",
|
| 118 |
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"编辑",
|
| 119 |
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"结实",
|
| 120 |
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"红火",
|
| 121 |
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"累赘",
|
| 122 |
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"糨糊",
|
| 123 |
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"糊涂",
|
| 124 |
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"精神",
|
| 125 |
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"粮食",
|
| 126 |
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"簸箕",
|
| 127 |
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"篱笆",
|
| 128 |
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"算计",
|
| 129 |
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"算盘",
|
| 130 |
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"答应",
|
| 131 |
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"笤帚",
|
| 132 |
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"笑语",
|
| 133 |
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"笑话",
|
| 134 |
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"窟窿",
|
| 135 |
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"窝囊",
|
| 136 |
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"窗户",
|
| 137 |
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"稳当",
|
| 138 |
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"稀罕",
|
| 139 |
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"称呼",
|
| 140 |
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"秧歌",
|
| 141 |
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"秀气",
|
| 142 |
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"秀才",
|
| 143 |
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"福气",
|
| 144 |
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"祖宗",
|
| 145 |
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"砚台",
|
| 146 |
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"码头",
|
| 147 |
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"石榴",
|
| 148 |
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"石头",
|
| 149 |
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"石匠",
|
| 150 |
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"知识",
|
| 151 |
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"眼睛",
|
| 152 |
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"眯缝",
|
| 153 |
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"眨巴",
|
| 154 |
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"眉毛",
|
| 155 |
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"相声",
|
| 156 |
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"盘算",
|
| 157 |
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"白净",
|
| 158 |
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"痢疾",
|
| 159 |
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"痛快",
|
| 160 |
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"疟疾",
|
| 161 |
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"疙瘩",
|
| 162 |
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"疏忽",
|
| 163 |
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"畜生",
|
| 164 |
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"生意",
|
| 165 |
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"甘蔗",
|
| 166 |
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"琵琶",
|
| 167 |
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"琢磨",
|
| 168 |
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"琉璃",
|
| 169 |
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"玻璃",
|
| 170 |
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"玫瑰",
|
| 171 |
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"玄乎",
|
| 172 |
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"狐狸",
|
| 173 |
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"状元",
|
| 174 |
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"特务",
|
| 175 |
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"牲口",
|
| 176 |
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"牙碜",
|
| 177 |
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"牌楼",
|
| 178 |
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"爽快",
|
| 179 |
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"爱人",
|
| 180 |
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"热闹",
|
| 181 |
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"烧饼",
|
| 182 |
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"烟筒",
|
| 183 |
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"烂糊",
|
| 184 |
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"点心",
|
| 185 |
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"炊帚",
|
| 186 |
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"灯笼",
|
| 187 |
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"火候",
|
| 188 |
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"漂亮",
|
| 189 |
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"滑溜",
|
| 190 |
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"溜达",
|
| 191 |
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"温和",
|
| 192 |
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"清楚",
|
| 193 |
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"消息",
|
| 194 |
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"浪头",
|
| 195 |
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"活泼",
|
| 196 |
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"比方",
|
| 197 |
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"正经",
|
| 198 |
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"欺负",
|
| 199 |
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"模糊",
|
| 200 |
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"槟榔",
|
| 201 |
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"棺材",
|
| 202 |
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"棒槌",
|
| 203 |
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"棉花",
|
| 204 |
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"核桃",
|
| 205 |
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"栅栏",
|
| 206 |
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"柴火",
|
| 207 |
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"架势",
|
| 208 |
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"枕头",
|
| 209 |
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"���杷",
|
| 210 |
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"机灵",
|
| 211 |
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"本事",
|
| 212 |
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"木头",
|
| 213 |
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"木匠",
|
| 214 |
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"朋友",
|
| 215 |
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"月饼",
|
| 216 |
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"月亮",
|
| 217 |
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"暖和",
|
| 218 |
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"明白",
|
| 219 |
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"时候",
|
| 220 |
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"新鲜",
|
| 221 |
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"故事",
|
| 222 |
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"收拾",
|
| 223 |
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"收成",
|
| 224 |
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"提防",
|
| 225 |
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"挖苦",
|
| 226 |
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"挑剔",
|
| 227 |
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"指甲",
|
| 228 |
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"指头",
|
| 229 |
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"拾掇",
|
| 230 |
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"拳头",
|
| 231 |
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"拨弄",
|
| 232 |
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"招牌",
|
| 233 |
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"招呼",
|
| 234 |
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"抬举",
|
| 235 |
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"护士",
|
| 236 |
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"折腾",
|
| 237 |
-
"扫帚",
|
| 238 |
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"打量",
|
| 239 |
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"打算",
|
| 240 |
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"打点",
|
| 241 |
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"打扮",
|
| 242 |
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"打听",
|
| 243 |
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"打发",
|
| 244 |
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"扎实",
|
| 245 |
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"扁担",
|
| 246 |
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"戒指",
|
| 247 |
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"懒得",
|
| 248 |
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"意识",
|
| 249 |
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"意思",
|
| 250 |
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"情形",
|
| 251 |
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"悟性",
|
| 252 |
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"怪物",
|
| 253 |
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"思量",
|
| 254 |
-
"怎么",
|
| 255 |
-
"念头",
|
| 256 |
-
"念叨",
|
| 257 |
-
"快活",
|
| 258 |
-
"忙活",
|
| 259 |
-
"志气",
|
| 260 |
-
"心思",
|
| 261 |
-
"得罪",
|
| 262 |
-
"张罗",
|
| 263 |
-
"弟兄",
|
| 264 |
-
"开通",
|
| 265 |
-
"应酬",
|
| 266 |
-
"庄稼",
|
| 267 |
-
"干事",
|
| 268 |
-
"帮手",
|
| 269 |
-
"帐篷",
|
| 270 |
-
"希罕",
|
| 271 |
-
"师父",
|
| 272 |
-
"师傅",
|
| 273 |
-
"巴结",
|
| 274 |
-
"巴掌",
|
| 275 |
-
"差事",
|
| 276 |
-
"工夫",
|
| 277 |
-
"岁数",
|
| 278 |
-
"屁股",
|
| 279 |
-
"尾巴",
|
| 280 |
-
"少爷",
|
| 281 |
-
"小气",
|
| 282 |
-
"小伙",
|
| 283 |
-
"将就",
|
| 284 |
-
"对头",
|
| 285 |
-
"对付",
|
| 286 |
-
"寡妇",
|
| 287 |
-
"家伙",
|
| 288 |
-
"客气",
|
| 289 |
-
"实在",
|
| 290 |
-
"官司",
|
| 291 |
-
"学问",
|
| 292 |
-
"学生",
|
| 293 |
-
"字号",
|
| 294 |
-
"嫁妆",
|
| 295 |
-
"媳妇",
|
| 296 |
-
"媒人",
|
| 297 |
-
"婆家",
|
| 298 |
-
"娘家",
|
| 299 |
-
"委屈",
|
| 300 |
-
"姑娘",
|
| 301 |
-
"姐夫",
|
| 302 |
-
"妯娌",
|
| 303 |
-
"妥当",
|
| 304 |
-
"妖精",
|
| 305 |
-
"奴才",
|
| 306 |
-
"女婿",
|
| 307 |
-
"头发",
|
| 308 |
-
"太阳",
|
| 309 |
-
"大爷",
|
| 310 |
-
"大方",
|
| 311 |
-
"大意",
|
| 312 |
-
"大夫",
|
| 313 |
-
"多少",
|
| 314 |
-
"多么",
|
| 315 |
-
"外甥",
|
| 316 |
-
"壮实",
|
| 317 |
-
"地道",
|
| 318 |
-
"地方",
|
| 319 |
-
"在乎",
|
| 320 |
-
"困难",
|
| 321 |
-
"嘴巴",
|
| 322 |
-
"嘱咐",
|
| 323 |
-
"嘟囔",
|
| 324 |
-
"嘀咕",
|
| 325 |
-
"喜欢",
|
| 326 |
-
"喇嘛",
|
| 327 |
-
"喇叭",
|
| 328 |
-
"商量",
|
| 329 |
-
"唾沫",
|
| 330 |
-
"哑巴",
|
| 331 |
-
"哈欠",
|
| 332 |
-
"哆嗦",
|
| 333 |
-
"咳嗽",
|
| 334 |
-
"和尚",
|
| 335 |
-
"告诉",
|
| 336 |
-
"告示",
|
| 337 |
-
"含糊",
|
| 338 |
-
"吓唬",
|
| 339 |
-
"后头",
|
| 340 |
-
"名字",
|
| 341 |
-
"名堂",
|
| 342 |
-
"合同",
|
| 343 |
-
"吆喝",
|
| 344 |
-
"叫唤",
|
| 345 |
-
"口袋",
|
| 346 |
-
"厚道",
|
| 347 |
-
"厉害",
|
| 348 |
-
"千斤",
|
| 349 |
-
"包袱",
|
| 350 |
-
"包涵",
|
| 351 |
-
"匀称",
|
| 352 |
-
"勤快",
|
| 353 |
-
"动静",
|
| 354 |
-
"动弹",
|
| 355 |
-
"功夫",
|
| 356 |
-
"力气",
|
| 357 |
-
"前头",
|
| 358 |
-
"刺猬",
|
| 359 |
-
"刺激",
|
| 360 |
-
"别扭",
|
| 361 |
-
"利落",
|
| 362 |
-
"利索",
|
| 363 |
-
"利害",
|
| 364 |
-
"分析",
|
| 365 |
-
"出息",
|
| 366 |
-
"凑合",
|
| 367 |
-
"凉快",
|
| 368 |
-
"冷战",
|
| 369 |
-
"冤枉",
|
| 370 |
-
"冒失",
|
| 371 |
-
"养活",
|
| 372 |
-
"关系",
|
| 373 |
-
"先生",
|
| 374 |
-
"兄弟",
|
| 375 |
-
"便宜",
|
| 376 |
-
"使唤",
|
| 377 |
-
"佩服",
|
| 378 |
-
"作坊",
|
| 379 |
-
"体面",
|
| 380 |
-
"位置",
|
| 381 |
-
"似的",
|
| 382 |
-
"伙计",
|
| 383 |
-
"休息",
|
| 384 |
-
"什么",
|
| 385 |
-
"人家",
|
| 386 |
-
"亲戚",
|
| 387 |
-
"亲家",
|
| 388 |
-
"交情",
|
| 389 |
-
"云彩",
|
| 390 |
-
"事情",
|
| 391 |
-
"买卖",
|
| 392 |
-
"主意",
|
| 393 |
-
"丫头",
|
| 394 |
-
"丧气",
|
| 395 |
-
"两口",
|
| 396 |
-
"东西",
|
| 397 |
-
"东家",
|
| 398 |
-
"世故",
|
| 399 |
-
"不由",
|
| 400 |
-
"不在",
|
| 401 |
-
"下水",
|
| 402 |
-
"下巴",
|
| 403 |
-
"上头",
|
| 404 |
-
"上司",
|
| 405 |
-
"丈夫",
|
| 406 |
-
"丈人",
|
| 407 |
-
"一辈",
|
| 408 |
-
"那个",
|
| 409 |
-
"菩萨",
|
| 410 |
-
"父亲",
|
| 411 |
-
"母亲",
|
| 412 |
-
"咕噜",
|
| 413 |
-
"邋遢",
|
| 414 |
-
"费用",
|
| 415 |
-
"冤家",
|
| 416 |
-
"甜头",
|
| 417 |
-
"介绍",
|
| 418 |
-
"荒唐",
|
| 419 |
-
"大人",
|
| 420 |
-
"泥鳅",
|
| 421 |
-
"幸福",
|
| 422 |
-
"熟悉",
|
| 423 |
-
"计划",
|
| 424 |
-
"扑腾",
|
| 425 |
-
"蜡烛",
|
| 426 |
-
"姥爷",
|
| 427 |
-
"照顾",
|
| 428 |
-
"喉咙",
|
| 429 |
-
"吉他",
|
| 430 |
-
"弄堂",
|
| 431 |
-
"蚂蚱",
|
| 432 |
-
"凤凰",
|
| 433 |
-
"拖沓",
|
| 434 |
-
"寒碜",
|
| 435 |
-
"糟蹋",
|
| 436 |
-
"倒腾",
|
| 437 |
-
"报复",
|
| 438 |
-
"逻辑",
|
| 439 |
-
"盘缠",
|
| 440 |
-
"喽啰",
|
| 441 |
-
"牢骚",
|
| 442 |
-
"咖喱",
|
| 443 |
-
"扫把",
|
| 444 |
-
"惦记",
|
| 445 |
-
}
|
| 446 |
-
self.must_not_neural_tone_words = {
|
| 447 |
-
"男子",
|
| 448 |
-
"女子",
|
| 449 |
-
"分子",
|
| 450 |
-
"原子",
|
| 451 |
-
"量子",
|
| 452 |
-
"莲子",
|
| 453 |
-
"石子",
|
| 454 |
-
"瓜子",
|
| 455 |
-
"电子",
|
| 456 |
-
"人人",
|
| 457 |
-
"虎虎",
|
| 458 |
-
"幺幺",
|
| 459 |
-
"干嘛",
|
| 460 |
-
"学子",
|
| 461 |
-
"哈哈",
|
| 462 |
-
"数数",
|
| 463 |
-
"袅袅",
|
| 464 |
-
"局地",
|
| 465 |
-
"以下",
|
| 466 |
-
"娃哈哈",
|
| 467 |
-
"花花草草",
|
| 468 |
-
"留得",
|
| 469 |
-
"耕地",
|
| 470 |
-
"想想",
|
| 471 |
-
"熙熙",
|
| 472 |
-
"攘攘",
|
| 473 |
-
"卵子",
|
| 474 |
-
"死死",
|
| 475 |
-
"冉冉",
|
| 476 |
-
"恳恳",
|
| 477 |
-
"佼佼",
|
| 478 |
-
"吵吵",
|
| 479 |
-
"打打",
|
| 480 |
-
"考考",
|
| 481 |
-
"整整",
|
| 482 |
-
"莘莘",
|
| 483 |
-
"落地",
|
| 484 |
-
"算子",
|
| 485 |
-
"家家户户",
|
| 486 |
-
"青青",
|
| 487 |
-
}
|
| 488 |
-
self.punc = "：，；。？！“”‘’':,;.?!"
|
| 489 |
-
|
| 490 |
-
# the meaning of jieba pos tag: https://blog.csdn.net/weixin_44174352/article/details/113731041
|
| 491 |
-
# e.g.
|
| 492 |
-
# word: "家里"
|
| 493 |
-
# pos: "s"
|
| 494 |
-
# finals: ['ia1', 'i3']
|
| 495 |
-
def _neural_sandhi(self, word: str, pos: str, finals: List[str]) -> List[str]:
|
| 496 |
-
# reduplication words for n. and v. e.g. 奶奶, 试试, 旺旺
|
| 497 |
-
for j, item in enumerate(word):
|
| 498 |
-
if (
|
| 499 |
-
j - 1 >= 0
|
| 500 |
-
and item == word[j - 1]
|
| 501 |
-
and pos[0] in {"n", "v", "a"}
|
| 502 |
-
and word not in self.must_not_neural_tone_words
|
| 503 |
-
):
|
| 504 |
-
finals[j] = finals[j][:-1] + "5"
|
| 505 |
-
ge_idx = word.find("个")
|
| 506 |
-
if len(word) >= 1 and word[-1] in "吧呢哈啊呐噻嘛吖嗨呐哦哒额滴哩哟喽啰耶喔诶":
|
| 507 |
-
finals[-1] = finals[-1][:-1] + "5"
|
| 508 |
-
elif len(word) >= 1 and word[-1] in "的地得":
|
| 509 |
-
finals[-1] = finals[-1][:-1] + "5"
|
| 510 |
-
# e.g. 走了, 看着, 去过
|
| 511 |
-
elif len(word) == 1 and word in "了着过" and pos in {"ul", "uz", "ug"}:
|
| 512 |
-
finals[-1] = finals[-1][:-1] + "5"
|
| 513 |
-
elif len(word) > 1 and word[-1] in "们子" and pos in {"r", "n"} and word not in self.must_not_neural_tone_words:
|
| 514 |
-
finals[-1] = finals[-1][:-1] + "5"
|
| 515 |
-
# e.g. 桌上, 地下, 家里
|
| 516 |
-
elif len(word) > 1 and word[-1] in "上下里" and pos in {"s", "l", "f"}:
|
| 517 |
-
finals[-1] = finals[-1][:-1] + "5"
|
| 518 |
-
# e.g. 上来, 下去
|
| 519 |
-
elif len(word) > 1 and word[-1] in "来去" and word[-2] in "上下进出回过起开":
|
| 520 |
-
finals[-1] = finals[-1][:-1] + "5"
|
| 521 |
-
# 个做量词
|
| 522 |
-
elif (
|
| 523 |
-
ge_idx >= 1 and (word[ge_idx - 1].isnumeric() or word[ge_idx - 1] in "几有两半多各整每做是")
|
| 524 |
-
) or word == "个":
|
| 525 |
-
finals[ge_idx] = finals[ge_idx][:-1] + "5"
|
| 526 |
-
else:
|
| 527 |
-
if word in self.must_neural_tone_words or word[-2:] in self.must_neural_tone_words:
|
| 528 |
-
finals[-1] = finals[-1][:-1] + "5"
|
| 529 |
-
|
| 530 |
-
word_list = self._split_word(word)
|
| 531 |
-
finals_list = [finals[: len(word_list[0])], finals[len(word_list[0]) :]]
|
| 532 |
-
for i, word in enumerate(word_list):
|
| 533 |
-
# conventional neural in Chinese
|
| 534 |
-
if word in self.must_neural_tone_words or word[-2:] in self.must_neural_tone_words:
|
| 535 |
-
finals_list[i][-1] = finals_list[i][-1][:-1] + "5"
|
| 536 |
-
finals = sum(finals_list, [])
|
| 537 |
-
return finals
|
| 538 |
-
|
| 539 |
-
def _bu_sandhi(self, word: str, finals: List[str]) -> List[str]:
|
| 540 |
-
# e.g. 看不懂
|
| 541 |
-
if len(word) == 3 and word[1] == "不":
|
| 542 |
-
finals[1] = finals[1][:-1] + "5"
|
| 543 |
-
else:
|
| 544 |
-
for i, char in enumerate(word):
|
| 545 |
-
# "不" before tone4 should be bu2, e.g. 不怕
|
| 546 |
-
if char == "不" and i + 1 < len(word) and finals[i + 1][-1] == "4":
|
| 547 |
-
finals[i] = finals[i][:-1] + "2"
|
| 548 |
-
return finals
|
| 549 |
-
|
| 550 |
-
def _yi_sandhi(self, word: str, finals: List[str]) -> List[str]:
|
| 551 |
-
# "一" in number sequences, e.g. 一零零, 二一零
|
| 552 |
-
if word.find("一") != -1 and all([item.isnumeric() for item in word if item != "一"]):
|
| 553 |
-
return finals
|
| 554 |
-
# "一" between reduplication words shold be yi5, e.g. 看一看
|
| 555 |
-
elif len(word) == 3 and word[1] == "一" and word[0] == word[-1]:
|
| 556 |
-
finals[1] = finals[1][:-1] + "5"
|
| 557 |
-
# when "一" is ordinal word, it should be yi1
|
| 558 |
-
elif word.startswith("第一"):
|
| 559 |
-
finals[1] = finals[1][:-1] + "1"
|
| 560 |
-
else:
|
| 561 |
-
for i, char in enumerate(word):
|
| 562 |
-
if char == "一" and i + 1 < len(word):
|
| 563 |
-
# "一" before tone4 should be yi2, e.g. 一段
|
| 564 |
-
if finals[i + 1][-1] == "4":
|
| 565 |
-
finals[i] = finals[i][:-1] + "2"
|
| 566 |
-
# "一" before non-tone4 should be yi4, e.g. 一天
|
| 567 |
-
else:
|
| 568 |
-
# "一" 后面如果是标点，还读一声
|
| 569 |
-
if word[i + 1] not in self.punc:
|
| 570 |
-
finals[i] = finals[i][:-1] + "4"
|
| 571 |
-
return finals
|
| 572 |
-
|
| 573 |
-
def _split_word(self, word: str) -> List[str]:
|
| 574 |
-
word_list = jieba.cut_for_search(word)
|
| 575 |
-
word_list = sorted(word_list, key=lambda i: len(i), reverse=False)
|
| 576 |
-
first_subword = word_list[0]
|
| 577 |
-
first_begin_idx = word.find(first_subword)
|
| 578 |
-
if first_begin_idx == 0:
|
| 579 |
-
second_subword = word[len(first_subword) :]
|
| 580 |
-
new_word_list = [first_subword, second_subword]
|
| 581 |
-
else:
|
| 582 |
-
second_subword = word[: -len(first_subword)]
|
| 583 |
-
new_word_list = [second_subword, first_subword]
|
| 584 |
-
return new_word_list
|
| 585 |
-
|
| 586 |
-
def _three_sandhi(self, word: str, finals: List[str]) -> List[str]:
|
| 587 |
-
if len(word) == 2 and self._all_tone_three(finals):
|
| 588 |
-
finals[0] = finals[0][:-1] + "2"
|
| 589 |
-
elif len(word) == 3:
|
| 590 |
-
word_list = self._split_word(word)
|
| 591 |
-
if self._all_tone_three(finals):
|
| 592 |
-
# disyllabic + monosyllabic, e.g. 蒙古/包
|
| 593 |
-
if len(word_list[0]) == 2:
|
| 594 |
-
finals[0] = finals[0][:-1] + "2"
|
| 595 |
-
finals[1] = finals[1][:-1] + "2"
|
| 596 |
-
# monosyllabic + disyllabic, e.g. 纸/老虎
|
| 597 |
-
elif len(word_list[0]) == 1:
|
| 598 |
-
finals[1] = finals[1][:-1] + "2"
|
| 599 |
-
else:
|
| 600 |
-
finals_list = [finals[: len(word_list[0])], finals[len(word_list[0]) :]]
|
| 601 |
-
if len(finals_list) == 2:
|
| 602 |
-
for i, sub in enumerate(finals_list):
|
| 603 |
-
# e.g. 所有/人
|
| 604 |
-
if self._all_tone_three(sub) and len(sub) == 2:
|
| 605 |
-
finals_list[i][0] = finals_list[i][0][:-1] + "2"
|
| 606 |
-
# e.g. 好/喜欢
|
| 607 |
-
elif (
|
| 608 |
-
i == 1
|
| 609 |
-
and not self._all_tone_three(sub)
|
| 610 |
-
and finals_list[i][0][-1] == "3"
|
| 611 |
-
and finals_list[0][-1][-1] == "3"
|
| 612 |
-
):
|
| 613 |
-
finals_list[0][-1] = finals_list[0][-1][:-1] + "2"
|
| 614 |
-
finals = sum(finals_list, [])
|
| 615 |
-
# split idiom into two words who's length is 2
|
| 616 |
-
elif len(word) == 4:
|
| 617 |
-
finals_list = [finals[:2], finals[2:]]
|
| 618 |
-
finals = []
|
| 619 |
-
for sub in finals_list:
|
| 620 |
-
if self._all_tone_three(sub):
|
| 621 |
-
sub[0] = sub[0][:-1] + "2"
|
| 622 |
-
finals += sub
|
| 623 |
-
|
| 624 |
-
return finals
|
| 625 |
-
|
| 626 |
-
def _all_tone_three(self, finals: List[str]) -> bool:
|
| 627 |
-
return all(x[-1] == "3" for x in finals)
|
| 628 |
-
|
| 629 |
-
# merge "不" and the word behind it
|
| 630 |
-
# if don't merge, "不" sometimes appears alone according to jieba, which may occur sandhi error
|
| 631 |
-
def _merge_bu(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
|
| 632 |
-
new_seg = []
|
| 633 |
-
last_word = ""
|
| 634 |
-
for word, pos in seg:
|
| 635 |
-
if last_word == "不":
|
| 636 |
-
word = last_word + word
|
| 637 |
-
if word != "不":
|
| 638 |
-
new_seg.append((word, pos))
|
| 639 |
-
last_word = word[:]
|
| 640 |
-
if last_word == "不":
|
| 641 |
-
new_seg.append((last_word, "d"))
|
| 642 |
-
last_word = ""
|
| 643 |
-
return new_seg
|
| 644 |
-
|
| 645 |
-
# function 1: merge "一" and reduplication words in it's left and right, e.g. "听","一","听" ->"听一听"
|
| 646 |
-
# function 2: merge single "一" and the word behind it
|
| 647 |
-
# if don't merge, "一" sometimes appears alone according to jieba, which may occur sandhi error
|
| 648 |
-
# e.g.
|
| 649 |
-
# input seg: [('听', 'v'), ('一', 'm'), ('听', 'v')]
|
| 650 |
-
# output seg: [['听一听', 'v']]
|
| 651 |
-
def _merge_yi(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
|
| 652 |
-
new_seg = []
|
| 653 |
-
i = 0
|
| 654 |
-
# function 1
|
| 655 |
-
while i < len(seg):
|
| 656 |
-
word, pos = seg[i]
|
| 657 |
-
merged = False
|
| 658 |
-
if (
|
| 659 |
-
i - 1 >= 0
|
| 660 |
-
and word == "一"
|
| 661 |
-
and i + 1 < len(seg)
|
| 662 |
-
):
|
| 663 |
-
last = new_seg[-1] if new_seg else seg[i - 1]
|
| 664 |
-
if last[0] == seg[i + 1][0] and last[1] == "v" and seg[i + 1][1] == "v":
|
| 665 |
-
combined = last[0] + "一" + seg[i + 1][0]
|
| 666 |
-
new_seg[-1] = [combined, last[1]]
|
| 667 |
-
i += 2
|
| 668 |
-
merged = True
|
| 669 |
-
if not merged:
|
| 670 |
-
new_seg.append([word, pos])
|
| 671 |
-
i += 1
|
| 672 |
-
seg = new_seg
|
| 673 |
-
new_seg = []
|
| 674 |
-
# function 2
|
| 675 |
-
for word, pos in seg:
|
| 676 |
-
if new_seg and new_seg[-1][0] == "一":
|
| 677 |
-
new_seg[-1][0] = new_seg[-1][0] + word
|
| 678 |
-
else:
|
| 679 |
-
new_seg.append([word, pos])
|
| 680 |
-
return new_seg
|
| 681 |
-
|
| 682 |
-
# the first and the second words are all_tone_three
|
| 683 |
-
def _merge_continuous_three_tones(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
|
| 684 |
-
new_seg = []
|
| 685 |
-
sub_finals_list = [
|
| 686 |
-
lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3) for (word, pos) in seg
|
| 687 |
-
]
|
| 688 |
-
assert len(sub_finals_list) == len(seg)
|
| 689 |
-
merge_last = [False] * len(seg)
|
| 690 |
-
for i, (word, pos) in enumerate(seg):
|
| 691 |
-
if (
|
| 692 |
-
i - 1 >= 0
|
| 693 |
-
and self._all_tone_three(sub_finals_list[i - 1])
|
| 694 |
-
and self._all_tone_three(sub_finals_list[i])
|
| 695 |
-
and not merge_last[i - 1]
|
| 696 |
-
):
|
| 697 |
-
# if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
|
| 698 |
-
if not self._is_reduplication(seg[i - 1][0]) and len(seg[i - 1][0]) + len(seg[i][0]) <= 3:
|
| 699 |
-
new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
|
| 700 |
-
merge_last[i] = True
|
| 701 |
-
else:
|
| 702 |
-
new_seg.append([word, pos])
|
| 703 |
-
else:
|
| 704 |
-
new_seg.append([word, pos])
|
| 705 |
-
|
| 706 |
-
return new_seg
|
| 707 |
-
|
| 708 |
-
def _is_reduplication(self, word: str) -> bool:
|
| 709 |
-
return len(word) == 2 and word[0] == word[1]
|
| 710 |
-
|
| 711 |
-
# the last char of first word and the first char of second word is tone_three
|
| 712 |
-
def _merge_continuous_three_tones_2(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
|
| 713 |
-
new_seg = []
|
| 714 |
-
sub_finals_list = [
|
| 715 |
-
lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3) for (word, pos) in seg
|
| 716 |
-
]
|
| 717 |
-
assert len(sub_finals_list) == len(seg)
|
| 718 |
-
merge_last = [False] * len(seg)
|
| 719 |
-
for i, (word, pos) in enumerate(seg):
|
| 720 |
-
if (
|
| 721 |
-
i - 1 >= 0
|
| 722 |
-
and sub_finals_list[i - 1][-1][-1] == "3"
|
| 723 |
-
and sub_finals_list[i][0][-1] == "3"
|
| 724 |
-
and not merge_last[i - 1]
|
| 725 |
-
):
|
| 726 |
-
# if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
|
| 727 |
-
if not self._is_reduplication(seg[i - 1][0]) and len(seg[i - 1][0]) + len(seg[i][0]) <= 3:
|
| 728 |
-
new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
|
| 729 |
-
merge_last[i] = True
|
| 730 |
-
else:
|
| 731 |
-
new_seg.append([word, pos])
|
| 732 |
-
else:
|
| 733 |
-
new_seg.append([word, pos])
|
| 734 |
-
return new_seg
|
| 735 |
-
|
| 736 |
-
def _merge_er(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
|
| 737 |
-
new_seg = []
|
| 738 |
-
for i, (word, pos) in enumerate(seg):
|
| 739 |
-
if i - 1 >= 0 and word == "儿" and seg[i - 1][0] != "#":
|
| 740 |
-
new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
|
| 741 |
-
else:
|
| 742 |
-
new_seg.append([word, pos])
|
| 743 |
-
return new_seg
|
| 744 |
-
|
| 745 |
-
def _merge_reduplication(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
|
| 746 |
-
new_seg = []
|
| 747 |
-
for i, (word, pos) in enumerate(seg):
|
| 748 |
-
if new_seg and word == new_seg[-1][0]:
|
| 749 |
-
new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
|
| 750 |
-
else:
|
| 751 |
-
new_seg.append([word, pos])
|
| 752 |
-
return new_seg
|
| 753 |
-
|
| 754 |
-
def pre_merge_for_modify(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
|
| 755 |
-
seg = self._merge_bu(seg)
|
| 756 |
-
try:
|
| 757 |
-
seg = self._merge_yi(seg)
|
| 758 |
-
except:
|
| 759 |
-
print("_merge_yi failed")
|
| 760 |
-
seg = self._merge_reduplication(seg)
|
| 761 |
-
try:
|
| 762 |
-
seg = self._merge_continuous_three_tones(seg)
|
| 763 |
-
except:
|
| 764 |
-
print("_merge_continuous_three_tones failed")
|
| 765 |
-
try:
|
| 766 |
-
seg = self._merge_continuous_three_tones_2(seg)
|
| 767 |
-
except:
|
| 768 |
-
print("_merge_continuous_three_tones_2 failed")
|
| 769 |
-
|
| 770 |
-
seg = self._merge_er(seg)
|
| 771 |
-
return seg
|
| 772 |
-
|
| 773 |
-
def modified_tone(self, word: str, pos: str, finals: List[str]) -> List[str]:
|
| 774 |
-
finals = self._bu_sandhi(word, finals)
|
| 775 |
-
finals = self._yi_sandhi(word, finals)
|
| 776 |
-
finals = self._neural_sandhi(word, pos, finals)
|
| 777 |
-
finals = self._three_sandhi(word, finals)
|
| 778 |
-
return finals
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|
text/zh_normalization/README.md
DELETED
|
@@ -1,16 +0,0 @@
|
|
| 1 |
-
## Supported NSW (Non-Standard-Word) Normalization
|
| 2 |
-
|
| 3 |
-
|NSW type|raw|normalized|
|
| 4 |
-
|:--|:-|:-|
|
| 5 |
-
|serial number|电影中梁朝伟扮演的陈永仁的编号27149|电影中梁朝伟扮演的陈永仁的编号二七一四九|
|
| 6 |
-
|cardinal|这块黄金重达324.75克<br>我们班的最高总分为583分|这块黄金重达三百二十四点七五克<br>我们班的最高总分为五百八十三分|
|
| 7 |
-
|numeric range |12\~23<br>-1.5\~2|十二到二十三<br>负一点五到二|
|
| 8 |
-
|date|她出生于86年8月18日，她弟弟出生于1995年3月1日|她出生于八六年八月十八日， 她弟弟出生于一九九五年三月一日|
|
| 9 |
-
|time|等会请在12:05请通知我|等会请在十二点零五分请通知我
|
| 10 |
-
|temperature|今天的最低气温达到-10°C|今天的最低气温达到零下十度
|
| 11 |
-
|fraction|现场有7/12的观众投出了赞成票|现场有十二分之七的观众投出了赞成票|
|
| 12 |
-
|percentage|明天有62％的概率降雨|明天有百分之六十二的概率降雨|
|
| 13 |
-
|money|随便来几个价格12块5，34.5元，20.1万|随便来几个价格十二块五，三十四点五元，二十点一万|
|
| 14 |
-
|telephone|这是固话0421-33441122<br>这是手机+86 18544139121|这是固话零四二一三三四四一一二二<br>这是手机八六一八五四四一三九一二一|
|
| 15 |
-
## References
|
| 16 |
-
[Pull requests #658 of DeepSpeech](https://github.com/PaddlePaddle/DeepSpeech/pull/658/files)
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|
text/zh_normalization/__init__.py
DELETED
|
@@ -1,14 +0,0 @@
|
|
| 1 |
-
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
| 2 |
-
#
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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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from text.zh_normalization.text_normlization import *
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text/zh_normalization/char_convert.py
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@@ -1,44 +0,0 @@
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# coding=utf-8
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# Copyright (c) 2020 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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| 11 |
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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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| 15 |
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"""Traditional and simplified Chinese conversion, a simplified character may correspond to multiple traditional characters."""
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-
simplified_charcters = 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traditional_characters = 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鐃鐄鐇鐏鐒鐓鐔鐗馗鐙鐝鐠鐡鐦鐨鐩鐫鐬鐱鐳鐶鐻鐽鐿鑀鑅鑌鑐鑕鑚鑛鑢鑤鑥鑪鑭鑯鑱鑴鑵鑷钁钃镻閆閈閌閎閒閔閗閟閡関閤閤閧閬閲閹閺閻閼閽閿闇闉闋闐闑闒闓闘闚闞闟闠闤闥阞阢阤阨阬阯阹阼阽陁陑陔陛陜陡陥陬騭陴険陼陾隂隃隈隒隗隞隠隣隤隩隮隰顴隳隷隹雂雈雉雊雎雑雒雗雘雚雝雟雩雰雱驛霂霅霈霊霑霒霓霙霝霢霣霤霨霩霪霫霮靁靆靉靑靚靣靦靪靮靰靳靷靸靺靼靿鞀鞃鞄鞌鞗鞙鞚鞝鞞鞡鞣鞨鞫鞬鞮鞶鞹鞾韃韅韉馱韍韎韔韖韘韝韞韡韣韭韮韱韹韺頀颳頄頇頊頍頎頏頒頖頞頠頫頬顱頯頲頴頼顇顋顑顒顓顔顕顚顜顢顣顬顳颭颮颱颶颸颺颻颽颾颿飀飂飈飌飜飡飣飤飥飩飫飮飱飶餀餂餄餎餇餈餑餔餕餖餗餚餛餜餟餠餤餧餩餪餫餬餮餱餲餳餺餻餼餽餿饁饅饇饉饊饍饎饐饘饟饢馘馥馝馡馣騮騾馵馹駃駄駅駆駉駋駑駓駔駗駘駙駜駡駢駪駬駰駴駸駹駽駾騂騄騅騆騉騋騍騏驎騑騒験騕騖騠騢騣騤騧驤騵騶騸騺驀驂驃驄驆驈驊驌驍驎驏驒驔驖驙驦驩驫骺鯁骫骭骯骱骴骶骷髏骾髁髂髄髆髈髐髑髕髖髙髝髞髟髡髣髧髪髫髭髯髲髳髹髺髽髾鬁鬃鬅鬈鬋鬎鬏鬐鬑鬒鬖鬗鬘鬙鬠鬣鬪鬫鬬鬮鬯鬰鬲鬵鬷魆魈魊魋魍魎魑魖鰾魛魟魣魦魨魬魴魵魸鮀鮁鮆鮌鮎鮑鮒鮓鮚鮞鮟鱇鮠鮦鮨鮪鮭鮶鮸鮿鯀鯄鯆鯇鯈鯔鯕鯖鯗鯙鯠鯤鯥鯫鯰鯷鯸鯿鰂鰆鶼鰉鰋鰐鰒鰕鰛鰜鰣鰤鰥鰦鰨鰩鰮鰳鰶鰷鱺鰼鰽鱀鱄鱅鱆鱈鱎鱐鱓鱔鱖鱘鱟鱠鱣鱨鱭鱮鱲鱵鱻鲅鳦鳧鳯鳲鳷鳻鴂鴃鴄鴆鴈鴎鴒鴔鴗鴛鴦鴝鵒鴟鴠鴢鴣鴥鴯鶓鴳鴴鴷鴽鵀鵁鵂鵓鵖鵙鵜鶘鵞鵟鵩鵪鵫鵵鵷鵻鵾鶂鶊鶏鶒鶖鶗鶡鶤鶦鶬鶱鶲鶵鶸鶹鶺鶿鷀鷁鷃鷄鷇鷈鷉鷊鷏鷓鷕鷖鷙鷞鷟鷥鷦鷯鷩鷫鷭鷳鷴鷽鷾鷿鸂鸇鸊鸏鸑鸒鸓鸕鸛鸜鸝鹸鹹鹺麀麂麃麄麇麋麌麐麑麒麚麛麝麤麩麪麫麮麯麰麺麾黁黈黌黢黒黓黕黙黝黟黥黦黧黮黰黱黲黶黹黻黼黽黿鼂鼃鼅鼈鼉鼏鼐鼒鼕鼖鼙鼚鼛鼡鼩鼱鼪鼫鼯鼷鼽齁齆齇齈齉齌齎齏齔齕齗齙齚齜齞齟齬齠齢齣齧齩齮齯齰齱齵齾龎龑龒龔龖龘龝龡龢龤"
|
| 20 |
-
|
| 21 |
-
assert len(simplified_charcters) == len(simplified_charcters)
|
| 22 |
-
|
| 23 |
-
s2t_dict = {}
|
| 24 |
-
t2s_dict = {}
|
| 25 |
-
for i, item in enumerate(simplified_charcters):
|
| 26 |
-
s2t_dict[item] = traditional_characters[i]
|
| 27 |
-
t2s_dict[traditional_characters[i]] = item
|
| 28 |
-
|
| 29 |
-
|
| 30 |
-
def tranditional_to_simplified(text: str) -> str:
|
| 31 |
-
return "".join([t2s_dict[item] if item in t2s_dict else item for item in text])
|
| 32 |
-
|
| 33 |
-
|
| 34 |
-
def simplified_to_traditional(text: str) -> str:
|
| 35 |
-
return "".join([s2t_dict[item] if item in s2t_dict else item for item in text])
|
| 36 |
-
|
| 37 |
-
|
| 38 |
-
if __name__ == "__main__":
|
| 39 |
-
text = "一般是指存取一個應用程式啟動時始終顯示在網站或網頁瀏覽器中的一個或多個初始網頁等畫面存在的站點"
|
| 40 |
-
print(text)
|
| 41 |
-
text_simple = tranditional_to_simplified(text)
|
| 42 |
-
print(text_simple)
|
| 43 |
-
text_traditional = simplified_to_traditional(text_simple)
|
| 44 |
-
print(text_traditional)
|
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text/zh_normalization/chronology.py
DELETED
|
@@ -1,139 +0,0 @@
|
|
| 1 |
-
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
| 2 |
-
#
|
| 3 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 4 |
-
# you may not use this file except in compliance with the License.
|
| 5 |
-
# You may obtain a copy of the License at
|
| 6 |
-
#
|
| 7 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
| 8 |
-
#
|
| 9 |
-
# Unless required by applicable law or agreed to in writing, software
|
| 10 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 11 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 12 |
-
# See the License for the specific language governing permissions and
|
| 13 |
-
# limitations under the License.
|
| 14 |
-
import re
|
| 15 |
-
|
| 16 |
-
from .num import DIGITS
|
| 17 |
-
from .num import num2str
|
| 18 |
-
from .num import verbalize_cardinal
|
| 19 |
-
from .num import verbalize_digit
|
| 20 |
-
|
| 21 |
-
|
| 22 |
-
def _time_num2str(num_string: str) -> str:
|
| 23 |
-
"""A special case for verbalizing number in time."""
|
| 24 |
-
result = num2str(num_string.lstrip("0"))
|
| 25 |
-
if num_string.startswith("0"):
|
| 26 |
-
result = DIGITS["0"] + result
|
| 27 |
-
return result
|
| 28 |
-
|
| 29 |
-
|
| 30 |
-
# 时刻表达式
|
| 31 |
-
RE_TIME = re.compile(
|
| 32 |
-
r"([0-1]?[0-9]|2[0-3])"
|
| 33 |
-
r":([0-5][0-9])"
|
| 34 |
-
r"(:([0-5][0-9]))?"
|
| 35 |
-
)
|
| 36 |
-
|
| 37 |
-
# 时间范围，如8:30-12:30
|
| 38 |
-
RE_TIME_RANGE = re.compile(
|
| 39 |
-
r"([0-1]?[0-9]|2[0-3])"
|
| 40 |
-
r":([0-5][0-9])"
|
| 41 |
-
r"(:([0-5][0-9]))?"
|
| 42 |
-
r"(~|-)"
|
| 43 |
-
r"([0-1]?[0-9]|2[0-3])"
|
| 44 |
-
r":([0-5][0-9])"
|
| 45 |
-
r"(:([0-5][0-9]))?"
|
| 46 |
-
)
|
| 47 |
-
|
| 48 |
-
|
| 49 |
-
def replace_time(match) -> str:
|
| 50 |
-
"""
|
| 51 |
-
Args:
|
| 52 |
-
match (re.Match)
|
| 53 |
-
Returns:
|
| 54 |
-
str
|
| 55 |
-
"""
|
| 56 |
-
|
| 57 |
-
is_range = len(match.groups()) > 5
|
| 58 |
-
|
| 59 |
-
hour = match.group(1)
|
| 60 |
-
minute = match.group(2)
|
| 61 |
-
second = match.group(4)
|
| 62 |
-
|
| 63 |
-
if is_range:
|
| 64 |
-
hour_2 = match.group(6)
|
| 65 |
-
minute_2 = match.group(7)
|
| 66 |
-
second_2 = match.group(9)
|
| 67 |
-
|
| 68 |
-
result = f"{num2str(hour)}点"
|
| 69 |
-
if minute.lstrip("0"):
|
| 70 |
-
if int(minute) == 30:
|
| 71 |
-
result += "半"
|
| 72 |
-
else:
|
| 73 |
-
result += f"{_time_num2str(minute)}分"
|
| 74 |
-
if second and second.lstrip("0"):
|
| 75 |
-
result += f"{_time_num2str(second)}秒"
|
| 76 |
-
|
| 77 |
-
if is_range:
|
| 78 |
-
result += "至"
|
| 79 |
-
result += f"{num2str(hour_2)}点"
|
| 80 |
-
if minute_2.lstrip("0"):
|
| 81 |
-
if int(minute) == 30:
|
| 82 |
-
result += "半"
|
| 83 |
-
else:
|
| 84 |
-
result += f"{_time_num2str(minute_2)}分"
|
| 85 |
-
if second_2 and second_2.lstrip("0"):
|
| 86 |
-
result += f"{_time_num2str(second_2)}秒"
|
| 87 |
-
|
| 88 |
-
return result
|
| 89 |
-
|
| 90 |
-
|
| 91 |
-
RE_DATE = re.compile(
|
| 92 |
-
r"(\d{4}|\d{2})年"
|
| 93 |
-
r"((0?[1-9]|1[0-2])月)?"
|
| 94 |
-
r"(((0?[1-9])|((1|2)[0-9])|30|31)([日号]))?"
|
| 95 |
-
)
|
| 96 |
-
|
| 97 |
-
|
| 98 |
-
def replace_date(match) -> str:
|
| 99 |
-
"""
|
| 100 |
-
Args:
|
| 101 |
-
match (re.Match)
|
| 102 |
-
Returns:
|
| 103 |
-
str
|
| 104 |
-
"""
|
| 105 |
-
year = match.group(1)
|
| 106 |
-
month = match.group(3)
|
| 107 |
-
day = match.group(5)
|
| 108 |
-
result = ""
|
| 109 |
-
if year:
|
| 110 |
-
result += f"{verbalize_digit(year)}年"
|
| 111 |
-
if month:
|
| 112 |
-
result += f"{verbalize_cardinal(month)}月"
|
| 113 |
-
if day:
|
| 114 |
-
result += f"{verbalize_cardinal(day)}{match.group(9)}"
|
| 115 |
-
return result
|
| 116 |
-
|
| 117 |
-
|
| 118 |
-
# 用 / 或者 - 分隔的 YY/MM/DD 或者 YY-MM-DD 日期
|
| 119 |
-
RE_DATE2 = re.compile(r"(\d{4})([- /.])(0[1-9]|1[012])\2(0[1-9]|[12][0-9]|3[01])")
|
| 120 |
-
|
| 121 |
-
|
| 122 |
-
def replace_date2(match) -> str:
|
| 123 |
-
"""
|
| 124 |
-
Args:
|
| 125 |
-
match (re.Match)
|
| 126 |
-
Returns:
|
| 127 |
-
str
|
| 128 |
-
"""
|
| 129 |
-
year = match.group(1)
|
| 130 |
-
month = match.group(3)
|
| 131 |
-
day = match.group(4)
|
| 132 |
-
result = ""
|
| 133 |
-
if year:
|
| 134 |
-
result += f"{verbalize_digit(year)}年"
|
| 135 |
-
if month:
|
| 136 |
-
result += f"{verbalize_cardinal(month)}月"
|
| 137 |
-
if day:
|
| 138 |
-
result += f"{verbalize_cardinal(day)}日"
|
| 139 |
-
return result
|
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|
text/zh_normalization/constants.py
DELETED
|
@@ -1,62 +0,0 @@
|
|
| 1 |
-
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
| 2 |
-
#
|
| 3 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 4 |
-
# you may not use this file except in compliance with the License.
|
| 5 |
-
# You may obtain a copy of the License at
|
| 6 |
-
#
|
| 7 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
| 8 |
-
#
|
| 9 |
-
# Unless required by applicable law or agreed to in writing, software
|
| 10 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 11 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 12 |
-
# See the License for the specific language governing permissions and
|
| 13 |
-
# limitations under the License.
|
| 14 |
-
import re
|
| 15 |
-
import string
|
| 16 |
-
|
| 17 |
-
from pypinyin.constants import SUPPORT_UCS4
|
| 18 |
-
|
| 19 |
-
# 全角半角转换
|
| 20 |
-
# 英文字符全角 -> 半角映射表 (num: 52)
|
| 21 |
-
F2H_ASCII_LETTERS = {ord(char) + 65248: ord(char) for char in string.ascii_letters}
|
| 22 |
-
|
| 23 |
-
# 英文字符半角 -> 全角映射表
|
| 24 |
-
H2F_ASCII_LETTERS = {value: key for key, value in F2H_ASCII_LETTERS.items()}
|
| 25 |
-
|
| 26 |
-
# 数字字符全角 -> 半角映射表 (num: 10)
|
| 27 |
-
F2H_DIGITS = {ord(char) + 65248: ord(char) for char in string.digits}
|
| 28 |
-
# 数字字符半角 -> 全角映射表
|
| 29 |
-
H2F_DIGITS = {value: key for key, value in F2H_DIGITS.items()}
|
| 30 |
-
|
| 31 |
-
# 标点符号全角 -> 半角映射表 (num: 32)
|
| 32 |
-
F2H_PUNCTUATIONS = {ord(char) + 65248: ord(char) for char in string.punctuation}
|
| 33 |
-
# 标点符号半角 -> 全角映射表
|
| 34 |
-
H2F_PUNCTUATIONS = {value: key for key, value in F2H_PUNCTUATIONS.items()}
|
| 35 |
-
|
| 36 |
-
# 空格 (num: 1)
|
| 37 |
-
F2H_SPACE = {"\u3000": " "}
|
| 38 |
-
H2F_SPACE = {" ": "\u3000"}
|
| 39 |
-
|
| 40 |
-
# 非"有拼音的汉字"的字符串，可用于NSW提取
|
| 41 |
-
if SUPPORT_UCS4:
|
| 42 |
-
RE_NSW = re.compile(
|
| 43 |
-
r"(?:[^"
|
| 44 |
-
r"\u3007" # 〇
|
| 45 |
-
r"\u3400-\u4dbf" # CJK扩展A:[3400-4DBF]
|
| 46 |
-
r"\u4e00-\u9fff" # CJK基本:[4E00-9FFF]
|
| 47 |
-
r"\uf900-\ufaff" # CJK兼容:[F900-FAFF]
|
| 48 |
-
r"\U00020000-\U0002A6DF" # CJK扩展B:[20000-2A6DF]
|
| 49 |
-
r"\U0002A703-\U0002B73F" # CJK扩展C:[2A700-2B73F]
|
| 50 |
-
r"\U0002B740-\U0002B81D" # CJK扩展D:[2B740-2B81D]
|
| 51 |
-
r"\U0002F80A-\U0002FA1F" # CJK兼容扩展:[2F800-2FA1F]
|
| 52 |
-
r"])+"
|
| 53 |
-
)
|
| 54 |
-
else:
|
| 55 |
-
RE_NSW = re.compile( # pragma: no cover
|
| 56 |
-
r"(?:[^"
|
| 57 |
-
r"\u3007" # 〇
|
| 58 |
-
r"\u3400-\u4dbf" # CJK扩展A:[3400-4DBF]
|
| 59 |
-
r"\u4e00-\u9fff" # CJK基本:[4E00-9FFF]
|
| 60 |
-
r"\uf900-\ufaff" # CJK兼容:[F900-FAFF]
|
| 61 |
-
r"])+"
|
| 62 |
-
)
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text/zh_normalization/num.py
DELETED
|
@@ -1,317 +0,0 @@
|
|
| 1 |
-
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
| 2 |
-
#
|
| 3 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 4 |
-
# you may not use this file except in compliance with the License.
|
| 5 |
-
# You may obtain a copy of the License at
|
| 6 |
-
#
|
| 7 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
| 8 |
-
#
|
| 9 |
-
# Unless required by applicable law or agreed to in writing, software
|
| 10 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 11 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 12 |
-
# See the License for the specific language governing permissions and
|
| 13 |
-
# limitations under the License.
|
| 14 |
-
"""
|
| 15 |
-
Rules to verbalize numbers into Chinese characters.
|
| 16 |
-
https://zh.wikipedia.org/wiki/中文数字#現代中文
|
| 17 |
-
"""
|
| 18 |
-
|
| 19 |
-
import re
|
| 20 |
-
from collections import OrderedDict
|
| 21 |
-
from typing import List
|
| 22 |
-
|
| 23 |
-
DIGITS = {str(i): tran for i, tran in enumerate("零一二三四五六七八九")}
|
| 24 |
-
UNITS = OrderedDict(
|
| 25 |
-
{
|
| 26 |
-
1: "十",
|
| 27 |
-
2: "百",
|
| 28 |
-
3: "千",
|
| 29 |
-
4: "万",
|
| 30 |
-
8: "亿",
|
| 31 |
-
}
|
| 32 |
-
)
|
| 33 |
-
|
| 34 |
-
COM_QUANTIFIERS = "(处|台|架|枚|趟|幅|平|方|堵|间|床|株|批|项|例|列|篇|栋|注|亩|封|艘|把|目|套|段|人|所|朵|匹|张|座|回|场|尾|条|个|首|阙|阵|网|炮|顶|丘|棵|只|支|袭|辆|挑|担|颗|壳|窠|曲|墙|群|腔|砣|座|客|贯|扎|捆|刀|令|打|手|罗|坡|山|岭|江|溪|钟|队|单|双|对|出|口|头|脚|板|跳|枝|件|贴|针|线|管|名|位|身|堂|课|本|页|家|户|层|丝|毫|厘|分|钱|两|斤|担|铢|石|钧|锱|忽|(千|毫|微)克|毫|厘|(公)分|分|寸|尺|丈|里|寻|常|铺|程|(千|分|厘|毫|微)米|米|撮|勺|合|升|斗|石|盘|碗|碟|叠|桶|笼|盆|盒|杯|钟|斛|锅|簋|篮|盘|桶|罐|瓶|壶|卮|盏|箩|箱|煲|啖|袋|钵|年|月|日|季|刻|时|周|天|秒|分|小时|旬|纪|岁|世|更|夜|春|夏|秋|冬|代|伏|辈|丸|泡|粒|颗|幢|堆|条|根|支|道|面|片|张|颗|块|元|(亿|千万|百万|万|千|百)|(亿|千万|百万|万|千|百|美|)元|(亿|千万|百万|万|千|百|十|)吨|(亿|千万|百万|万|千|百|)块|角|毛|分)"
|
| 35 |
-
|
| 36 |
-
# 分数表达式
|
| 37 |
-
RE_FRAC = re.compile(r"(-?)(\d+)/(\d+)")
|
| 38 |
-
|
| 39 |
-
|
| 40 |
-
def replace_frac(match) -> str:
|
| 41 |
-
"""
|
| 42 |
-
Args:
|
| 43 |
-
match (re.Match)
|
| 44 |
-
Returns:
|
| 45 |
-
str
|
| 46 |
-
"""
|
| 47 |
-
sign = match.group(1)
|
| 48 |
-
nominator = match.group(2)
|
| 49 |
-
denominator = match.group(3)
|
| 50 |
-
sign: str = "负" if sign else ""
|
| 51 |
-
nominator: str = num2str(nominator)
|
| 52 |
-
denominator: str = num2str(denominator)
|
| 53 |
-
result = f"{sign}{denominator}分之{nominator}"
|
| 54 |
-
return result
|
| 55 |
-
|
| 56 |
-
|
| 57 |
-
# 百分数表达式
|
| 58 |
-
RE_PERCENTAGE = re.compile(r"(-?)(\d+(\.\d+)?)%")
|
| 59 |
-
|
| 60 |
-
|
| 61 |
-
def replace_percentage(match) -> str:
|
| 62 |
-
"""
|
| 63 |
-
Args:
|
| 64 |
-
match (re.Match)
|
| 65 |
-
Returns:
|
| 66 |
-
str
|
| 67 |
-
"""
|
| 68 |
-
sign = match.group(1)
|
| 69 |
-
percent = match.group(2)
|
| 70 |
-
sign: str = "负" if sign else ""
|
| 71 |
-
percent: str = num2str(percent)
|
| 72 |
-
result = f"{sign}百分之{percent}"
|
| 73 |
-
return result
|
| 74 |
-
|
| 75 |
-
|
| 76 |
-
# 整数表达式
|
| 77 |
-
# 带负号的整数 -10
|
| 78 |
-
RE_INTEGER = re.compile(r"(-)" r"(\d+)")
|
| 79 |
-
|
| 80 |
-
|
| 81 |
-
def replace_negative_num(match) -> str:
|
| 82 |
-
"""
|
| 83 |
-
Args:
|
| 84 |
-
match (re.Match)
|
| 85 |
-
Returns:
|
| 86 |
-
str
|
| 87 |
-
"""
|
| 88 |
-
sign = match.group(1)
|
| 89 |
-
number = match.group(2)
|
| 90 |
-
sign: str = "负" if sign else ""
|
| 91 |
-
number: str = num2str(number)
|
| 92 |
-
result = f"{sign}{number}"
|
| 93 |
-
return result
|
| 94 |
-
|
| 95 |
-
|
| 96 |
-
# 编号-无符号整形
|
| 97 |
-
# 00078
|
| 98 |
-
RE_DEFAULT_NUM = re.compile(r"\d{3}\d*")
|
| 99 |
-
|
| 100 |
-
|
| 101 |
-
def replace_default_num(match):
|
| 102 |
-
"""
|
| 103 |
-
Args:
|
| 104 |
-
match (re.Match)
|
| 105 |
-
Returns:
|
| 106 |
-
str
|
| 107 |
-
"""
|
| 108 |
-
number = match.group(0)
|
| 109 |
-
return verbalize_digit(number, alt_one=True)
|
| 110 |
-
|
| 111 |
-
|
| 112 |
-
# 加减乘除
|
| 113 |
-
# RE_ASMD = re.compile(
|
| 114 |
-
# r'((-?)((\d+)(\.\d+)?)|(\.(\d+)))([\+\-\×÷=])((-?)((\d+)(\.\d+)?)|(\.(\d+)))')
|
| 115 |
-
RE_ASMD = re.compile(
|
| 116 |
-
r"((-?)((\d+)(\.\d+)?[⁰¹²³⁴⁵⁶⁷⁸⁹ˣʸⁿ]*)|(\.\d+[⁰¹²³⁴⁵⁶⁷⁸⁹ˣʸⁿ]*)|([A-Za-z][⁰¹²³⁴⁵⁶⁷⁸⁹ˣʸⁿ]*))([\+\-\×÷=])((-?)((\d+)(\.\d+)?[⁰¹²³⁴⁵⁶⁷⁸⁹ˣʸⁿ]*)|(\.\d+[⁰¹²³⁴⁵⁶⁷⁸⁹ˣʸⁿ]*)|([A-Za-z][⁰¹²³⁴⁵⁶⁷⁸⁹ˣʸⁿ]*))"
|
| 117 |
-
)
|
| 118 |
-
|
| 119 |
-
asmd_map = {"+": "加", "-": "减", "×": "乘", "÷": "除", "=": "等于"}
|
| 120 |
-
|
| 121 |
-
|
| 122 |
-
def replace_asmd(match) -> str:
|
| 123 |
-
"""
|
| 124 |
-
Args:
|
| 125 |
-
match (re.Match)
|
| 126 |
-
Returns:
|
| 127 |
-
str
|
| 128 |
-
"""
|
| 129 |
-
result = match.group(1) + asmd_map[match.group(8)] + match.group(9)
|
| 130 |
-
return result
|
| 131 |
-
|
| 132 |
-
|
| 133 |
-
# 次方专项
|
| 134 |
-
RE_POWER = re.compile(r"[⁰¹²³⁴⁵⁶⁷⁸⁹ˣʸⁿ]+")
|
| 135 |
-
|
| 136 |
-
power_map = {
|
| 137 |
-
"⁰": "0",
|
| 138 |
-
"¹": "1",
|
| 139 |
-
"²": "2",
|
| 140 |
-
"³": "3",
|
| 141 |
-
"⁴": "4",
|
| 142 |
-
"⁵": "5",
|
| 143 |
-
"⁶": "6",
|
| 144 |
-
"⁷": "7",
|
| 145 |
-
"⁸": "8",
|
| 146 |
-
"⁹": "9",
|
| 147 |
-
"ˣ": "x",
|
| 148 |
-
"ʸ": "y",
|
| 149 |
-
"ⁿ": "n",
|
| 150 |
-
}
|
| 151 |
-
|
| 152 |
-
|
| 153 |
-
def replace_power(match) -> str:
|
| 154 |
-
"""
|
| 155 |
-
Args:
|
| 156 |
-
match (re.Match)
|
| 157 |
-
Returns:
|
| 158 |
-
str
|
| 159 |
-
"""
|
| 160 |
-
power_num = ""
|
| 161 |
-
for m in match.group(0):
|
| 162 |
-
power_num += power_map[m]
|
| 163 |
-
result = "的" + power_num + "次方"
|
| 164 |
-
return result
|
| 165 |
-
|
| 166 |
-
|
| 167 |
-
# 数字表达式
|
| 168 |
-
# 纯小数
|
| 169 |
-
RE_DECIMAL_NUM = re.compile(r"(-?)((\d+)(\.\d+))" r"|(\.(\d+))")
|
| 170 |
-
# 正整数 + ��词
|
| 171 |
-
RE_POSITIVE_QUANTIFIERS = re.compile(r"(\d+)([多余几\+])?" + COM_QUANTIFIERS)
|
| 172 |
-
RE_NUMBER = re.compile(r"(-?)((\d+)(\.\d+)?)" r"|(\.(\d+))")
|
| 173 |
-
|
| 174 |
-
|
| 175 |
-
def replace_positive_quantifier(match) -> str:
|
| 176 |
-
"""
|
| 177 |
-
Args:
|
| 178 |
-
match (re.Match)
|
| 179 |
-
Returns:
|
| 180 |
-
str
|
| 181 |
-
"""
|
| 182 |
-
number = match.group(1)
|
| 183 |
-
match_2 = match.group(2)
|
| 184 |
-
if match_2 == "+":
|
| 185 |
-
match_2 = "多"
|
| 186 |
-
match_2: str = match_2 if match_2 else ""
|
| 187 |
-
quantifiers: str = match.group(3)
|
| 188 |
-
number: str = num2str(number)
|
| 189 |
-
number = "两" if number == "二" else number
|
| 190 |
-
result = f"{number}{match_2}{quantifiers}"
|
| 191 |
-
return result
|
| 192 |
-
|
| 193 |
-
|
| 194 |
-
def replace_number(match) -> str:
|
| 195 |
-
"""
|
| 196 |
-
Args:
|
| 197 |
-
match (re.Match)
|
| 198 |
-
Returns:
|
| 199 |
-
str
|
| 200 |
-
"""
|
| 201 |
-
sign = match.group(1)
|
| 202 |
-
number = match.group(2)
|
| 203 |
-
pure_decimal = match.group(5)
|
| 204 |
-
if pure_decimal:
|
| 205 |
-
result = num2str(pure_decimal)
|
| 206 |
-
else:
|
| 207 |
-
sign: str = "负" if sign else ""
|
| 208 |
-
number: str = num2str(number)
|
| 209 |
-
result = f"{sign}{number}"
|
| 210 |
-
return result
|
| 211 |
-
|
| 212 |
-
|
| 213 |
-
# 范围表达式
|
| 214 |
-
# match.group(1) and match.group(8) are copy from RE_NUMBER
|
| 215 |
-
|
| 216 |
-
RE_RANGE = re.compile(
|
| 217 |
-
r"""
|
| 218 |
-
(?<![\d\+\-\×÷=]) # 使用反向前瞻以确保数字范围之前没有其他数字和操作符
|
| 219 |
-
((-?)((\d+)(\.\d+)?)) # 匹配范围起始的负数或正数（整数或小数）
|
| 220 |
-
[-~] # 匹配范围分隔符
|
| 221 |
-
((-?)((\d+)(\.\d+)?)) # 匹配范围结束的负数或正数（整数或小数）
|
| 222 |
-
(?![\d\+\-\×÷=]) # 使用正向前瞻以确保数字范围之后没有其他数字和操作符
|
| 223 |
-
""",
|
| 224 |
-
re.VERBOSE,
|
| 225 |
-
)
|
| 226 |
-
|
| 227 |
-
|
| 228 |
-
def replace_range(match) -> str:
|
| 229 |
-
"""
|
| 230 |
-
Args:
|
| 231 |
-
match (re.Match)
|
| 232 |
-
Returns:
|
| 233 |
-
str
|
| 234 |
-
"""
|
| 235 |
-
first, second = match.group(1), match.group(6)
|
| 236 |
-
first = RE_NUMBER.sub(replace_number, first)
|
| 237 |
-
second = RE_NUMBER.sub(replace_number, second)
|
| 238 |
-
result = f"{first}到{second}"
|
| 239 |
-
return result
|
| 240 |
-
|
| 241 |
-
|
| 242 |
-
# ~至表达式
|
| 243 |
-
RE_TO_RANGE = re.compile(
|
| 244 |
-
r"((-?)((\d+)(\.\d+)?)|(\.(\d+)))(%|°C|℃|度|摄氏度|cm2|cm²|cm3|cm³|cm|db|ds|kg|km|m2|m²|m³|m3|ml|m|mm|s)[~]((-?)((\d+)(\.\d+)?)|(\.(\d+)))(%|°C|℃|度|摄氏度|cm2|cm²|cm3|cm³|cm|db|ds|kg|km|m2|m²|m³|m3|ml|m|mm|s)"
|
| 245 |
-
)
|
| 246 |
-
|
| 247 |
-
|
| 248 |
-
def replace_to_range(match) -> str:
|
| 249 |
-
"""
|
| 250 |
-
Args:
|
| 251 |
-
match (re.Match)
|
| 252 |
-
Returns:
|
| 253 |
-
str
|
| 254 |
-
"""
|
| 255 |
-
result = match.group(0).replace("~", "至")
|
| 256 |
-
return result
|
| 257 |
-
|
| 258 |
-
|
| 259 |
-
def _get_value(value_string: str, use_zero: bool = True) -> List[str]:
|
| 260 |
-
stripped = value_string.lstrip("0")
|
| 261 |
-
if len(stripped) == 0:
|
| 262 |
-
return []
|
| 263 |
-
elif len(stripped) == 1:
|
| 264 |
-
if use_zero and len(stripped) < len(value_string):
|
| 265 |
-
return [DIGITS["0"], DIGITS[stripped]]
|
| 266 |
-
else:
|
| 267 |
-
return [DIGITS[stripped]]
|
| 268 |
-
else:
|
| 269 |
-
largest_unit = next(power for power in reversed(UNITS.keys()) if power < len(stripped))
|
| 270 |
-
first_part = value_string[:-largest_unit]
|
| 271 |
-
second_part = value_string[-largest_unit:]
|
| 272 |
-
return _get_value(first_part) + [UNITS[largest_unit]] + _get_value(second_part)
|
| 273 |
-
|
| 274 |
-
|
| 275 |
-
def verbalize_cardinal(value_string: str) -> str:
|
| 276 |
-
if not value_string:
|
| 277 |
-
return ""
|
| 278 |
-
|
| 279 |
-
# 000 -> '零' , 0 -> '零'
|
| 280 |
-
value_string = value_string.lstrip("0")
|
| 281 |
-
if len(value_string) == 0:
|
| 282 |
-
return DIGITS["0"]
|
| 283 |
-
|
| 284 |
-
result_symbols = _get_value(value_string)
|
| 285 |
-
# verbalized number starting with '一十*' is abbreviated as `十*`
|
| 286 |
-
if len(result_symbols) >= 2 and result_symbols[0] == DIGITS["1"] and result_symbols[1] == UNITS[1]:
|
| 287 |
-
result_symbols = result_symbols[1:]
|
| 288 |
-
return "".join(result_symbols)
|
| 289 |
-
|
| 290 |
-
|
| 291 |
-
def verbalize_digit(value_string: str, alt_one=False) -> str:
|
| 292 |
-
result_symbols = [DIGITS[digit] for digit in value_string]
|
| 293 |
-
result = "".join(result_symbols)
|
| 294 |
-
if alt_one:
|
| 295 |
-
result = result.replace("一", "幺")
|
| 296 |
-
return result
|
| 297 |
-
|
| 298 |
-
|
| 299 |
-
def num2str(value_string: str) -> str:
|
| 300 |
-
integer_decimal = value_string.split(".")
|
| 301 |
-
if len(integer_decimal) == 1:
|
| 302 |
-
integer = integer_decimal[0]
|
| 303 |
-
decimal = ""
|
| 304 |
-
elif len(integer_decimal) == 2:
|
| 305 |
-
integer, decimal = integer_decimal
|
| 306 |
-
else:
|
| 307 |
-
raise ValueError(f"The value string: '${value_string}' has more than one point in it.")
|
| 308 |
-
|
| 309 |
-
result = verbalize_cardinal(integer)
|
| 310 |
-
|
| 311 |
-
decimal = decimal.rstrip("0")
|
| 312 |
-
if decimal:
|
| 313 |
-
# '.22' is verbalized as '零点二二'
|
| 314 |
-
# '3.20' is verbalized as '三点二
|
| 315 |
-
result = result if result else "零"
|
| 316 |
-
result += "点" + verbalize_digit(decimal)
|
| 317 |
-
return result
|
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|
text/zh_normalization/phonecode.py
DELETED
|
@@ -1,59 +0,0 @@
|
|
| 1 |
-
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
| 2 |
-
#
|
| 3 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 4 |
-
# you may not use this file except in compliance with the License.
|
| 5 |
-
# You may obtain a copy of the License at
|
| 6 |
-
#
|
| 7 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
| 8 |
-
#
|
| 9 |
-
# Unless required by applicable law or agreed to in writing, software
|
| 10 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 11 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 12 |
-
# See the License for the specific language governing permissions and
|
| 13 |
-
# limitations under the License.
|
| 14 |
-
import re
|
| 15 |
-
|
| 16 |
-
from .num import verbalize_digit
|
| 17 |
-
|
| 18 |
-
# 规范化固话/手机号码
|
| 19 |
-
# 手机
|
| 20 |
-
# http://www.jihaoba.com/news/show/13680
|
| 21 |
-
# 移动：139、138、137、136、135、134、159、158、157、150、151、152、188、187、182、183、184、178、198
|
| 22 |
-
# 联通：130、131、132、156、155、186、185、176
|
| 23 |
-
# 电信：133、153、189、180、181、177
|
| 24 |
-
RE_MOBILE_PHONE = re.compile(r"(?<!\d)((\+?86 ?)?1([38]\d|5[0-35-9]|7[678]|9[89])\d{8})(?!\d)")
|
| 25 |
-
RE_TELEPHONE = re.compile(r"(?<!\d)((0(10|2[1-3]|[3-9]\d{2})-?)?[1-9]\d{6,7})(?!\d)")
|
| 26 |
-
|
| 27 |
-
# 全国统一的号码400开头
|
| 28 |
-
RE_NATIONAL_UNIFORM_NUMBER = re.compile(r"(400)(-)?\d{3}(-)?\d{4}")
|
| 29 |
-
|
| 30 |
-
|
| 31 |
-
def phone2str(phone_string: str, mobile=True) -> str:
|
| 32 |
-
if mobile:
|
| 33 |
-
sp_parts = phone_string.strip("+").split()
|
| 34 |
-
result = "，".join([verbalize_digit(part, alt_one=True) for part in sp_parts])
|
| 35 |
-
return result
|
| 36 |
-
else:
|
| 37 |
-
sil_parts = phone_string.split("-")
|
| 38 |
-
result = "，".join([verbalize_digit(part, alt_one=True) for part in sil_parts])
|
| 39 |
-
return result
|
| 40 |
-
|
| 41 |
-
|
| 42 |
-
def replace_phone(match) -> str:
|
| 43 |
-
"""
|
| 44 |
-
Args:
|
| 45 |
-
match (re.Match)
|
| 46 |
-
Returns:
|
| 47 |
-
str
|
| 48 |
-
"""
|
| 49 |
-
return phone2str(match.group(0), mobile=False)
|
| 50 |
-
|
| 51 |
-
|
| 52 |
-
def replace_mobile(match) -> str:
|
| 53 |
-
"""
|
| 54 |
-
Args:
|
| 55 |
-
match (re.Match)
|
| 56 |
-
Returns:
|
| 57 |
-
str
|
| 58 |
-
"""
|
| 59 |
-
return phone2str(match.group(0))
|
|
|
|
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|
text/zh_normalization/quantifier.py
DELETED
|
@@ -1,63 +0,0 @@
|
|
| 1 |
-
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
| 2 |
-
#
|
| 3 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 4 |
-
# you may not use this file except in compliance with the License.
|
| 5 |
-
# You may obtain a copy of the License at
|
| 6 |
-
#
|
| 7 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
| 8 |
-
#
|
| 9 |
-
# Unless required by applicable law or agreed to in writing, software
|
| 10 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 11 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 12 |
-
# See the License for the specific language governing permissions and
|
| 13 |
-
# limitations under the License.
|
| 14 |
-
import re
|
| 15 |
-
|
| 16 |
-
from .num import num2str
|
| 17 |
-
|
| 18 |
-
# 温度表达式，温度会影响负号的读法
|
| 19 |
-
# -3°C 零下三度
|
| 20 |
-
RE_TEMPERATURE = re.compile(r"(-?)(\d+(\.\d+)?)(°C|℃|度|摄氏度)")
|
| 21 |
-
measure_dict = {
|
| 22 |
-
"cm2": "平方厘米",
|
| 23 |
-
"cm²": "平方厘米",
|
| 24 |
-
"cm3": "立方厘米",
|
| 25 |
-
"cm³": "立方厘米",
|
| 26 |
-
"cm": "厘米",
|
| 27 |
-
"db": "分贝",
|
| 28 |
-
"ds": "毫秒",
|
| 29 |
-
"kg": "千克",
|
| 30 |
-
"km": "千米",
|
| 31 |
-
"m2": "平方米",
|
| 32 |
-
"m²": "平方米",
|
| 33 |
-
"m³": "立方米",
|
| 34 |
-
"m3": "立方米",
|
| 35 |
-
"ml": "毫升",
|
| 36 |
-
"m": "米",
|
| 37 |
-
"mm": "毫米",
|
| 38 |
-
"s": "秒",
|
| 39 |
-
}
|
| 40 |
-
|
| 41 |
-
|
| 42 |
-
def replace_temperature(match) -> str:
|
| 43 |
-
"""
|
| 44 |
-
Args:
|
| 45 |
-
match (re.Match)
|
| 46 |
-
Returns:
|
| 47 |
-
str
|
| 48 |
-
"""
|
| 49 |
-
sign = match.group(1)
|
| 50 |
-
temperature = match.group(2)
|
| 51 |
-
unit = match.group(3)
|
| 52 |
-
sign: str = "零下" if sign else ""
|
| 53 |
-
temperature: str = num2str(temperature)
|
| 54 |
-
unit: str = "摄氏度" if unit == "摄氏度" else "度"
|
| 55 |
-
result = f"{sign}{temperature}{unit}"
|
| 56 |
-
return result
|
| 57 |
-
|
| 58 |
-
|
| 59 |
-
def replace_measure(sentence) -> str:
|
| 60 |
-
for q_notation in measure_dict:
|
| 61 |
-
if q_notation in sentence:
|
| 62 |
-
sentence = sentence.replace(q_notation, measure_dict[q_notation])
|
| 63 |
-
return sentence
|
|
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|
text/zh_normalization/text_normlization.py
DELETED
|
@@ -1,172 +0,0 @@
|
|
| 1 |
-
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
| 2 |
-
#
|
| 3 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 4 |
-
# you may not use this file except in compliance with the License.
|
| 5 |
-
# You may obtain a copy of the License at
|
| 6 |
-
#
|
| 7 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
| 8 |
-
#
|
| 9 |
-
# Unless required by applicable law or agreed to in writing, software
|
| 10 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 11 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 12 |
-
# See the License for the specific language governing permissions and
|
| 13 |
-
# limitations under the License.
|
| 14 |
-
import re
|
| 15 |
-
from typing import List
|
| 16 |
-
|
| 17 |
-
from .char_convert import tranditional_to_simplified
|
| 18 |
-
from .chronology import RE_DATE
|
| 19 |
-
from .chronology import RE_DATE2
|
| 20 |
-
from .chronology import RE_TIME
|
| 21 |
-
from .chronology import RE_TIME_RANGE
|
| 22 |
-
from .chronology import replace_date
|
| 23 |
-
from .chronology import replace_date2
|
| 24 |
-
from .chronology import replace_time
|
| 25 |
-
from .constants import F2H_ASCII_LETTERS
|
| 26 |
-
from .constants import F2H_DIGITS
|
| 27 |
-
from .constants import F2H_SPACE
|
| 28 |
-
from .num import RE_DECIMAL_NUM
|
| 29 |
-
from .num import RE_DEFAULT_NUM
|
| 30 |
-
from .num import RE_FRAC
|
| 31 |
-
from .num import RE_INTEGER
|
| 32 |
-
from .num import RE_NUMBER
|
| 33 |
-
from .num import RE_PERCENTAGE
|
| 34 |
-
from .num import RE_POSITIVE_QUANTIFIERS
|
| 35 |
-
from .num import RE_RANGE
|
| 36 |
-
from .num import RE_TO_RANGE
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| 37 |
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from .num import RE_ASMD
|
| 38 |
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from .num import RE_POWER
|
| 39 |
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from .num import replace_default_num
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| 40 |
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from .num import replace_frac
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| 41 |
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from .num import replace_negative_num
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| 42 |
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from .num import replace_number
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| 43 |
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from .num import replace_percentage
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| 44 |
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from .num import replace_positive_quantifier
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| 45 |
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from .num import replace_range
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| 46 |
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from .num import replace_to_range
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| 47 |
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from .num import replace_asmd
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| 48 |
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from .num import replace_power
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| 49 |
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from .phonecode import RE_MOBILE_PHONE
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| 50 |
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from .phonecode import RE_NATIONAL_UNIFORM_NUMBER
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| 51 |
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from .phonecode import RE_TELEPHONE
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| 52 |
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from .phonecode import replace_mobile
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| 53 |
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from .phonecode import replace_phone
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| 54 |
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from .quantifier import RE_TEMPERATURE
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| 55 |
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from .quantifier import replace_measure
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| 56 |
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from .quantifier import replace_temperature
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| 57 |
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|
| 58 |
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|
| 59 |
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class TextNormalizer:
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| 60 |
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def __init__(self):
|
| 61 |
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self.SENTENCE_SPLITOR = re.compile(r"([：、，；。？！,;?!][”’]?)")
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| 62 |
-
|
| 63 |
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def _split(self, text: str, lang="zh") -> List[str]:
|
| 64 |
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"""Split long text into sentences with sentence-splitting punctuations.
|
| 65 |
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Args:
|
| 66 |
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text (str): The input text.
|
| 67 |
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Returns:
|
| 68 |
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List[str]: Sentences.
|
| 69 |
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"""
|
| 70 |
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# Only for pure Chinese here
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| 71 |
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if lang == "zh":
|
| 72 |
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text = text.replace(" ", "")
|
| 73 |
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# 过滤掉特殊字符
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| 74 |
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text = re.sub(r"[——《》【】<>{}()（）#&@“”^_|\\]", "", text)
|
| 75 |
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text = self.SENTENCE_SPLITOR.sub(r"\1\n", text)
|
| 76 |
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text = text.strip()
|
| 77 |
-
sentences = [sentence.strip() for sentence in re.split(r"\n+", text)]
|
| 78 |
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return sentences
|
| 79 |
-
|
| 80 |
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def _post_replace(self, sentence: str) -> str:
|
| 81 |
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sentence = sentence.replace("/", "每")
|
| 82 |
-
# sentence = sentence.replace('~', '至')
|
| 83 |
-
# sentence = sentence.replace('～', '至')
|
| 84 |
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sentence = sentence.replace("①", "一")
|
| 85 |
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sentence = sentence.replace("②", "二")
|
| 86 |
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sentence = sentence.replace("③", "三")
|
| 87 |
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sentence = sentence.replace("④", "四")
|
| 88 |
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sentence = sentence.replace("⑤", "五")
|
| 89 |
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sentence = sentence.replace("⑥", "六")
|
| 90 |
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sentence = sentence.replace("⑦", "七")
|
| 91 |
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sentence = sentence.replace("⑧", "八")
|
| 92 |
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sentence = sentence.replace("⑨", "九")
|
| 93 |
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sentence = sentence.replace("⑩", "十")
|
| 94 |
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sentence = sentence.replace("α", "阿尔法")
|
| 95 |
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sentence = sentence.replace("β", "贝塔")
|
| 96 |
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sentence = sentence.replace("γ", "伽玛").replace("Γ", "伽玛")
|
| 97 |
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sentence = sentence.replace("δ", "德尔塔").replace("Δ", "德尔塔")
|
| 98 |
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sentence = sentence.replace("ε", "艾普西龙")
|
| 99 |
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sentence = sentence.replace("ζ", "捷塔")
|
| 100 |
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sentence = sentence.replace("η", "依塔")
|
| 101 |
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sentence = sentence.replace("θ", "西塔").replace("Θ", "西塔")
|
| 102 |
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sentence = sentence.replace("ι", "艾欧塔")
|
| 103 |
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sentence = sentence.replace("κ", "喀帕")
|
| 104 |
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sentence = sentence.replace("λ", "拉姆达").replace("Λ", "拉姆达")
|
| 105 |
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sentence = sentence.replace("μ", "缪")
|
| 106 |
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sentence = sentence.replace("ν", "拗")
|
| 107 |
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sentence = sentence.replace("ξ", "克西").replace("Ξ", "克西")
|
| 108 |
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sentence = sentence.replace("ο", "欧米克伦")
|
| 109 |
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sentence = sentence.replace("π", "派").replace("Π", "派")
|
| 110 |
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sentence = sentence.replace("ρ", "肉")
|
| 111 |
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sentence = sentence.replace("ς", "西格玛").replace("Σ", "西格玛").replace("σ", "西格玛")
|
| 112 |
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sentence = sentence.replace("τ", "套")
|
| 113 |
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sentence = sentence.replace("υ", "宇普西龙")
|
| 114 |
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sentence = sentence.replace("φ", "服艾").replace("Φ", "服艾")
|
| 115 |
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sentence = sentence.replace("χ", "器")
|
| 116 |
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sentence = sentence.replace("ψ", "普赛").replace("Ψ", "普赛")
|
| 117 |
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sentence = sentence.replace("ω", "欧米伽").replace("Ω", "欧米伽")
|
| 118 |
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# 兜底数学运算，顺便兼容懒人用语
|
| 119 |
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sentence = sentence.replace("+", "加")
|
| 120 |
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sentence = sentence.replace("-", "减")
|
| 121 |
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sentence = sentence.replace("×", "乘")
|
| 122 |
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sentence = sentence.replace("÷", "除")
|
| 123 |
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sentence = sentence.replace("=", "等")
|
| 124 |
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# re filter special characters, have one more character "-" than line 68
|
| 125 |
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sentence = re.sub(r"[-——《》【】<=>{}()（）#&@“”^_|\\]", "", sentence)
|
| 126 |
-
return sentence
|
| 127 |
-
|
| 128 |
-
def normalize_sentence(self, sentence: str) -> str:
|
| 129 |
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# basic character conversions
|
| 130 |
-
sentence = tranditional_to_simplified(sentence)
|
| 131 |
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sentence = sentence.translate(F2H_ASCII_LETTERS).translate(F2H_DIGITS).translate(F2H_SPACE)
|
| 132 |
-
|
| 133 |
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# number related NSW verbalization
|
| 134 |
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sentence = RE_DATE.sub(replace_date, sentence)
|
| 135 |
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sentence = RE_DATE2.sub(replace_date2, sentence)
|
| 136 |
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|
| 137 |
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# range first
|
| 138 |
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sentence = RE_TIME_RANGE.sub(replace_time, sentence)
|
| 139 |
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sentence = RE_TIME.sub(replace_time, sentence)
|
| 140 |
-
|
| 141 |
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# 处理~波浪号作为至的替换
|
| 142 |
-
sentence = RE_TO_RANGE.sub(replace_to_range, sentence)
|
| 143 |
-
sentence = RE_TEMPERATURE.sub(replace_temperature, sentence)
|
| 144 |
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sentence = replace_measure(sentence)
|
| 145 |
-
|
| 146 |
-
# 处理数学运算
|
| 147 |
-
while RE_ASMD.search(sentence):
|
| 148 |
-
sentence = RE_ASMD.sub(replace_asmd, sentence)
|
| 149 |
-
sentence = RE_POWER.sub(replace_power, sentence)
|
| 150 |
-
|
| 151 |
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sentence = RE_FRAC.sub(replace_frac, sentence)
|
| 152 |
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sentence = RE_PERCENTAGE.sub(replace_percentage, sentence)
|
| 153 |
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sentence = RE_MOBILE_PHONE.sub(replace_mobile, sentence)
|
| 154 |
-
|
| 155 |
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sentence = RE_TELEPHONE.sub(replace_phone, sentence)
|
| 156 |
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sentence = RE_NATIONAL_UNIFORM_NUMBER.sub(replace_phone, sentence)
|
| 157 |
-
|
| 158 |
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sentence = RE_RANGE.sub(replace_range, sentence)
|
| 159 |
-
|
| 160 |
-
sentence = RE_INTEGER.sub(replace_negative_num, sentence)
|
| 161 |
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sentence = RE_DECIMAL_NUM.sub(replace_number, sentence)
|
| 162 |
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sentence = RE_POSITIVE_QUANTIFIERS.sub(replace_positive_quantifier, sentence)
|
| 163 |
-
sentence = RE_DEFAULT_NUM.sub(replace_default_num, sentence)
|
| 164 |
-
sentence = RE_NUMBER.sub(replace_number, sentence)
|
| 165 |
-
sentence = self._post_replace(sentence)
|
| 166 |
-
|
| 167 |
-
return sentence
|
| 168 |
-
|
| 169 |
-
def normalize(self, text: str) -> List[str]:
|
| 170 |
-
sentences = self._split(text)
|
| 171 |
-
sentences = [self.normalize_sentence(sent) for sent in sentences]
|
| 172 |
-
return sentences
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