diff --git a/Model/vctk/vctk_base.json b/Model/vctk/vctk_base.json index f6d46f2f4e6e7ef7333c62fba5230a292b4f4e83..425e645d8452af087c80126ba2bc7f1fdb2a25d5 100644 --- a/Model/vctk/vctk_base.json +++ b/Model/vctk/vctk_base.json @@ -50,6 +50,6 @@ "use_spectral_norm": false, "gin_channels": 256 }, - "speakers": ["vctk1(english)","vctk2(english)","vctk3(english)"], + "speakers": ["vctk0(english)","vctk1(english)","vctk2(english)","vctk3(english)","vctk4(english)","vctk5(english)","vctk6(english)","vctk7(english)","vctk8(english)","vctk9(english)","vctk10(english)","vctk11(english)","vctk12(english)","vctk13(english)","vctk14(english)","vctk15(english)","vctk16(english)","vctk17(english)","vctk18(english)","vctk19(english)"], "symbols": ["_", ";", ":", ",", ".", "!", "?", "¡", "¿", "—", "…", "\"", "«", "»", "“", "”", " ", "A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "ɑ", "ɐ", "ɒ", "æ", "ɓ", "ʙ", "β", "ɔ", "ɕ", "ç", "ɗ", "ɖ", "ð", "ʤ", "ə", "ɘ", "ɚ", "ɛ", "ɜ", "ɝ", "ɞ", "ɟ", "ʄ", "ɡ", "ɠ", "ɢ", "ʛ", "ɦ", "ɧ", "ħ", "ɥ", "ʜ", "ɨ", "ɪ", "ʝ", "ɭ", "ɬ", "ɫ", "ɮ", "ʟ", "ɱ", "ɯ", "ɰ", "ŋ", "ɳ", "ɲ", "ɴ", "ø", "ɵ", "ɸ", "θ", "œ", "ɶ", "ʘ", "ɹ", "ɺ", "ɾ", "ɻ", "ʀ", "ʁ", "ɽ", "ʂ", "ʃ", "ʈ", "ʧ", "ʉ", "ʊ", "ʋ", "ⱱ", "ʌ", "ɣ", "ɤ", "ʍ", "χ", "ʎ", "ʏ", "ʑ", "ʐ", "ʒ", "ʔ", "ʡ", "ʕ", "ʢ", "ǀ", "ǁ", "ǂ", "ǃ", "ˈ", "ˌ", "ː", "ˑ", "ʼ", "ʴ", "ʰ", "ʱ", "ʲ", "ʷ", "ˠ", "ˤ", "˞", "↓", "↑", "→", "↗", "↘", "'", "̩", "'", "ᵻ"] } \ No newline at end of file diff --git a/Model/vits_chinese/bert_vits.json b/Model/vits_chinese/bert_vits.json new file mode 100644 index 0000000000000000000000000000000000000000..c5359d75384341b2aee0c32894dc09c368bfba55 --- /dev/null +++ b/Model/vits_chinese/bert_vits.json @@ -0,0 +1,55 @@ +{ + "train": { + "log_interval": 100, + "eval_interval": 10000, + "seed": 1234, + "epochs": 20000, + "learning_rate": 1e-4, + "betas": [0.8, 0.99], + "eps": 1e-9, + "batch_size": 8, + "fp16_run": false, + "lr_decay": 0.999875, + "segment_size": 12800, + "init_lr_ratio": 1, + "warmup_epochs": 0, + "c_mel": 45, + "c_kl": 1.0 + }, + "data": { + "training_files":"filelists/train.txt", + "validation_files":"filelists/valid.txt", + "text_cleaners":["bert_chinese_cleaners"], + "max_wav_value": 32768.0, + "sampling_rate": 16000, + "filter_length": 1024, + "hop_length": 256, + "win_length": 1024, + "n_mel_channels": 80, + "mel_fmin": 0.0, + "mel_fmax": null, + "add_blank": false, + "n_speakers": 0 + }, + "model": { + "inter_channels": 192, + "hidden_channels": 192, + "filter_channels": 768, + "n_heads": 2, + "n_layers": 6, + "kernel_size": 3, + "p_dropout": 0.1, + "resblock": "1", + "resblock_kernel_sizes": [3,7,11], + "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]], + "upsample_rates": [8,8,2,2], + "upsample_initial_channel": 512, + "upsample_kernel_sizes": [16,16,4,4], + "n_layers_q": 3, + "use_spectral_norm": false, + "use_sdp": false, + "bert_embedding": true + }, + "speakers": ["bert"], + "symbols": ["sil", "eos", "sp", "#0", "#1", "#2", "#3", "^", "b", "c", "ch", "d", "f", "g", "h", "j", "k", "l", "m", "n", "p", "q", "r", "s", "sh", "t", "x", "z", "zh", "a1", "a2", "a3", "a4", "a5", "ai1", "ai2", "ai3", "ai4", "ai5", "an1", "an2", "an3", "an4", "an5", "ang1", "ang2", "ang3", "ang4", "ang5", "ao1", "ao2", "ao3", "ao4", "ao5", "e1", "e2", "e3", "e4", "e5", "ei1", "ei2", "ei3", "ei4", "ei5", "en1", "en2", "en3", "en4", "en5", "eng1", "eng2", "eng3", "eng4", "eng5", "er1", "er2", "er3", "er4", "er5", "i1", "i2", "i3", "i4", "i5", "ia1", "ia2", "ia3", "ia4", "ia5", "ian1", "ian2", "ian3", "ian4", "ian5", "iang1", "iang2", "iang3", "iang4", "iang5", "iao1", "iao2", "iao3", "iao4", "iao5", "ie1", "ie2", "ie3", "ie4", "ie5", "ii1", "ii2", "ii3", "ii4", "ii5", "iii1", "iii2", "iii3", "iii4", "iii5", "in1", "in2", "in3", "in4", "in5", "ing1", "ing2", "ing3", "ing4", "ing5", "iong1", "iong2", "iong3", "iong4", "iong5", "iou1", "iou2", "iou3", "iou4", "iou5", "o1", "o2", "o3", "o4", "o5", "ong1", "ong2", "ong3", "ong4", "ong5", "ou1", "ou2", "ou3", "ou4", "ou5", "u1", "u2", "u3", "u4", "u5", "ua1", "ua2", "ua3", "ua4", "ua5", "uai1", "uai2", "uai3", "uai4", "uai5", "uan1", "uan2", "uan3", "uan4", "uan5", "uang1", "uang2", "uang3", "uang4", "uang5", "uei1", "uei2", "uei3", "uei4", "uei5", "uen1", "uen2", "uen3", "uen4", "uen5", "ueng1", "ueng2", "ueng3", "ueng4", "ueng5", "uo1", "uo2", "uo3", "uo4", "uo5", "v1", "v2", "v3", "v4", "v5", "van1", "van2", "van3", "van4", "van5", "ve1", "ve2", "ve3", "ve4", "ve5", "vn1", "vn2", "vn3", "vn4", "vn5"] +} diff --git a/Model/vits_chinese/vits_bert_model.pth b/Model/vits_chinese/vits_bert_model.pth new file mode 100644 index 0000000000000000000000000000000000000000..d3572fb31e86d10a8c60f401b952d45997619fa1 --- /dev/null +++ b/Model/vits_chinese/vits_bert_model.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:be0dcf53ffcd49d51fd9a710338a9ff7eed60d0c26ccbb03ebd5a9175f20dc39 +size 141822877 diff --git a/bert/ProsodyModel.py b/bert/ProsodyModel.py new file mode 100644 index 0000000000000000000000000000000000000000..5f305b41894a4a8cec05c23dcdd29a9b939b748b --- /dev/null +++ b/bert/ProsodyModel.py @@ -0,0 +1,75 @@ +import os +import torch +import torch.nn as nn +import torch.nn.functional as F + +from transformers import BertModel, BertConfig, BertTokenizer + + +class CharEmbedding(nn.Module): + def __init__(self, model_dir): + super().__init__() + self.tokenizer = BertTokenizer.from_pretrained(model_dir) + self.bert_config = BertConfig.from_pretrained(model_dir) + self.hidden_size = self.bert_config.hidden_size + self.bert = BertModel(self.bert_config) + self.proj = nn.Linear(self.hidden_size, 256) + self.linear = nn.Linear(256, 3) + + def text2Token(self, text): + token = self.tokenizer.tokenize(text) + txtid = self.tokenizer.convert_tokens_to_ids(token) + return txtid + + def forward(self, inputs_ids, inputs_masks, tokens_type_ids): + out_seq = self.bert(input_ids=inputs_ids, + attention_mask=inputs_masks, + token_type_ids=tokens_type_ids)[0] + out_seq = self.proj(out_seq) + return out_seq + + +class TTSProsody(object): + def __init__(self, path, device): + self.device = device + self.char_model = CharEmbedding(path) + self.char_model.load_state_dict( + torch.load( + os.path.join(path, 'prosody_model.pt'), + map_location="cpu" + ), + strict=False + ) + self.char_model.eval() + self.char_model.to(self.device) + + def get_char_embeds(self, text): + input_ids = self.char_model.text2Token(text) + input_masks = [1] * len(input_ids) + type_ids = [0] * len(input_ids) + input_ids = torch.LongTensor([input_ids]).to(self.device) + input_masks = torch.LongTensor([input_masks]).to(self.device) + type_ids = torch.LongTensor([type_ids]).to(self.device) + + with torch.no_grad(): + char_embeds = self.char_model( + input_ids, input_masks, type_ids).squeeze(0).cpu() + return char_embeds + + def expand_for_phone(self, char_embeds, length): # length of phones for char + assert char_embeds.size(0) == len(length) + expand_vecs = list() + for vec, leng in zip(char_embeds, length): + vec = vec.expand(leng, -1) + expand_vecs.append(vec) + expand_embeds = torch.cat(expand_vecs, 0) + assert expand_embeds.size(0) == sum(length) + return expand_embeds.numpy() + + +if __name__ == "__main__": + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + prosody = TTSProsody('./bert/', device) + while True: + text = input("请输入文本：") + prosody.get_char_embeds(text) diff --git a/bert/__init__.py b/bert/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..17e0516f5d1b75f228263f0050688c67ae3b7cdf --- /dev/null +++ b/bert/__init__.py @@ -0,0 +1,2 @@ +""" from https://github.com/PlayVoice/vits_chinese """ +from .ProsodyModel import TTSProsody \ No newline at end of file diff --git a/bert/config.json b/bert/config.json new file mode 100644 index 0000000000000000000000000000000000000000..adb75ff5aaacf4050df7a63916e863f14fb7f644 --- /dev/null +++ b/bert/config.json @@ -0,0 +1,19 @@ +{ + "attention_probs_dropout_prob": 0.1, + "directionality": "bidi", + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "initializer_range": 0.02, + "intermediate_size": 3072, + "max_position_embeddings": 512, + "num_attention_heads": 12, + "num_hidden_layers": 12, + "pooler_fc_size": 768, + "pooler_num_attention_heads": 12, + "pooler_num_fc_layers": 3, + "pooler_size_per_head": 128, + "pooler_type": "first_token_transform", + "type_vocab_size": 2, + "vocab_size": 21128 +} diff --git a/bert/prosody_model.pt b/bert/prosody_model.pt new file mode 100644 index 0000000000000000000000000000000000000000..3cbdc83d7c64fac50930137f24dde63e6a27ce1b --- /dev/null +++ b/bert/prosody_model.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3accec7a0d5cbfccaa8a42b96374a91d442a69801c6a01402baae3bf06b8c015 +size 409941419 diff --git a/bert/prosody_tool.py b/bert/prosody_tool.py new file mode 100644 index 0000000000000000000000000000000000000000..690a1bab4701b7912840c09447d8e0de8ebecb5a --- /dev/null +++ b/bert/prosody_tool.py @@ -0,0 +1,426 @@ +def is_chinese(uchar): + if uchar >= u'\u4e00' and uchar <= u'\u9fa5': + return True + else: + return False + + +pinyin_dict = { + "a": ("^", "a"), + "ai": ("^", "ai"), + "an": ("^", "an"), + "ang": ("^", "ang"), + "ao": ("^", "ao"), + "ba": ("b", "a"), + "bai": ("b", "ai"), + "ban": ("b", "an"), + "bang": ("b", "ang"), + "bao": ("b", "ao"), + "be": ("b", "e"), + "bei": ("b", "ei"), + "ben": ("b", "en"), + "beng": ("b", "eng"), + "bi": ("b", "i"), + "bian": ("b", "ian"), + "biao": ("b", "iao"), + "bie": ("b", "ie"), + "bin": ("b", "in"), + "bing": ("b", "ing"), + "bo": ("b", "o"), + "bu": ("b", "u"), + "ca": ("c", "a"), + "cai": ("c", "ai"), + "can": ("c", "an"), + "cang": ("c", "ang"), + "cao": ("c", "ao"), + "ce": ("c", "e"), + "cen": ("c", "en"), + "ceng": ("c", "eng"), + "cha": ("ch", "a"), + "chai": ("ch", "ai"), + "chan": ("ch", "an"), + "chang": ("ch", "ang"), + "chao": ("ch", "ao"), + "che": ("ch", "e"), + "chen": ("ch", "en"), + "cheng": ("ch", "eng"), + "chi": ("ch", "iii"), + "chong": ("ch", "ong"), + "chou": ("ch", "ou"), + "chu": ("ch", "u"), + "chua": ("ch", "ua"), + "chuai": ("ch", "uai"), + "chuan": ("ch", "uan"), + "chuang": ("ch", "uang"), + "chui": ("ch", "uei"), + "chun": ("ch", "uen"), + "chuo": ("ch", "uo"), + "ci": ("c", "ii"), + "cong": ("c", "ong"), + "cou": ("c", "ou"), + "cu": ("c", "u"), + "cuan": ("c", "uan"), + "cui": ("c", "uei"), + "cun": ("c", "uen"), + "cuo": ("c", "uo"), + "da": ("d", "a"), + "dai": ("d", "ai"), + "dan": ("d", "an"), + "dang": ("d", "ang"), + "dao": ("d", "ao"), + "de": ("d", "e"), + "dei": ("d", "ei"), + "den": ("d", "en"), + "deng": ("d", "eng"), + "di": ("d", "i"), + "dia": ("d", "ia"), + "dian": ("d", "ian"), + "diao": ("d", "iao"), + "die": ("d", "ie"), + "ding": ("d", "ing"), + "diu": ("d", "iou"), + "dong": ("d", "ong"), + "dou": ("d", "ou"), + "du": ("d", "u"), + "duan": ("d", "uan"), + "dui": ("d", "uei"), + "dun": ("d", "uen"), + "duo": ("d", "uo"), + "e": ("^", "e"), + "ei": ("^", "ei"), + "en": ("^", "en"), + "ng": ("^", "en"), + "eng": ("^", "eng"), + "er": ("^", "er"), + "fa": ("f", "a"), + "fan": ("f", "an"), + "fang": ("f", "ang"), + "fei": ("f", "ei"), + "fen": ("f", "en"), + "feng": ("f", "eng"), + "fo": ("f", "o"), + "fou": ("f", "ou"), + "fu": ("f", "u"), + "ga": ("g", "a"), + "gai": ("g", "ai"), + "gan": ("g", "an"), + "gang": ("g", "ang"), + "gao": ("g", "ao"), + "ge": ("g", "e"), + "gei": ("g", "ei"), + "gen": ("g", "en"), + "geng": ("g", "eng"), + "gong": ("g", "ong"), + "gou": ("g", "ou"), + "gu": ("g", "u"), + "gua": ("g", "ua"), + "guai": ("g", "uai"), + "guan": ("g", "uan"), + "guang": ("g", "uang"), + "gui": ("g", "uei"), + "gun": ("g", "uen"), + "guo": ("g", "uo"), + "ha": ("h", "a"), + "hai": ("h", "ai"), + "han": ("h", "an"), + "hang": ("h", "ang"), + "hao": ("h", "ao"), + "he": ("h", "e"), + "hei": ("h", "ei"), + "hen": ("h", "en"), + "heng": ("h", "eng"), + "hong": ("h", "ong"), + "hou": ("h", "ou"), + "hu": ("h", "u"), + "hua": ("h", "ua"), + "huai": ("h", "uai"), + "huan": ("h", "uan"), + "huang": ("h", "uang"), + "hui": ("h", "uei"), + "hun": ("h", "uen"), + "huo": ("h", "uo"), + "ji": ("j", "i"), + "jia": ("j", "ia"), + "jian": ("j", "ian"), + "jiang": ("j", "iang"), + "jiao": ("j", "iao"), + "jie": ("j", "ie"), + "jin": ("j", "in"), + "jing": ("j", "ing"), + "jiong": ("j", "iong"), + "jiu": ("j", "iou"), + "ju": ("j", "v"), + "juan": ("j", "van"), + "jue": ("j", "ve"), + "jun": ("j", "vn"), + "ka": ("k", "a"), + "kai": ("k", "ai"), + "kan": ("k", "an"), + "kang": ("k", "ang"), + "kao": ("k", "ao"), + "ke": ("k", "e"), + "kei": ("k", "ei"), + "ken": ("k", "en"), + "keng": ("k", "eng"), + "kong": ("k", "ong"), + "kou": ("k", "ou"), + "ku": ("k", "u"), + "kua": ("k", "ua"), + "kuai": ("k", "uai"), + "kuan": ("k", "uan"), + "kuang": ("k", "uang"), + "kui": ("k", "uei"), + "kun": ("k", "uen"), + "kuo": ("k", "uo"), + "la": ("l", "a"), + "lai": ("l", "ai"), + "lan": ("l", "an"), + "lang": ("l", "ang"), + "lao": ("l", "ao"), + "le": ("l", "e"), + "lei": ("l", "ei"), + "leng": ("l", "eng"), + "li": ("l", "i"), + "lia": ("l", "ia"), + "lian": ("l", "ian"), + "liang": ("l", "iang"), + "liao": ("l", "iao"), + "lie": ("l", "ie"), + "lin": ("l", "in"), + "ling": ("l", "ing"), + "liu": ("l", "iou"), + "lo": ("l", "o"), + "long": ("l", "ong"), + "lou": ("l", "ou"), + "lu": ("l", "u"), + "lv": ("l", "v"), + "luan": ("l", "uan"), + "lve": ("l", "ve"), + "lue": ("l", "ve"), + "lun": ("l", "uen"), + "luo": ("l", "uo"), + "ma": ("m", "a"), + "mai": ("m", "ai"), + "man": ("m", "an"), + "mang": ("m", "ang"), + "mao": ("m", "ao"), + "me": ("m", "e"), + "mei": ("m", "ei"), + "men": ("m", "en"), + "meng": ("m", "eng"), + "mi": ("m", "i"), + "mian": ("m", "ian"), + "miao": ("m", "iao"), + "mie": ("m", "ie"), + "min": ("m", "in"), + "ming": ("m", "ing"), + "miu": ("m", "iou"), + "mo": ("m", "o"), + "mou": ("m", "ou"), + "mu": ("m", "u"), + "na": ("n", "a"), + "nai": ("n", "ai"), + "nan": ("n", "an"), + "nang": ("n", "ang"), + "nao": ("n", "ao"), + "ne": ("n", "e"), + "nei": ("n", "ei"), + "nen": ("n", "en"), + "neng": ("n", "eng"), + "ni": ("n", "i"), + "nia": ("n", "ia"), + "nian": ("n", "ian"), + "niang": ("n", "iang"), + "niao": ("n", "iao"), + "nie": ("n", "ie"), + "nin": ("n", "in"), + "ning": ("n", "ing"), + "niu": ("n", "iou"), + "nong": ("n", "ong"), + "nou": ("n", "ou"), + "nu": ("n", "u"), + "nv": ("n", "v"), + "nuan": ("n", "uan"), + "nve": ("n", "ve"), + "nue": ("n", "ve"), + "nuo": ("n", "uo"), + "o": ("^", "o"), + "ou": ("^", "ou"), + "pa": ("p", "a"), + "pai": ("p", "ai"), + "pan": ("p", "an"), + "pang": ("p", "ang"), + "pao": ("p", "ao"), + "pe": ("p", "e"), + "pei": ("p", "ei"), + "pen": ("p", "en"), + "peng": ("p", "eng"), + "pi": ("p", "i"), + "pian": ("p", "ian"), + "piao": ("p", "iao"), + "pie": ("p", "ie"), + "pin": ("p", "in"), + "ping": ("p", "ing"), + "po": ("p", "o"), + "pou": ("p", "ou"), + "pu": ("p", "u"), + "qi": ("q", "i"), + "qia": ("q", "ia"), + "qian": ("q", "ian"), + "qiang": ("q", "iang"), + "qiao": ("q", "iao"), + "qie": ("q", "ie"), + "qin": ("q", "in"), + "qing": ("q", "ing"), + "qiong": ("q", "iong"), + "qiu": ("q", "iou"), + "qu": ("q", "v"), + "quan": ("q", "van"), + "que": ("q", "ve"), + "qun": ("q", "vn"), + "ran": ("r", "an"), + "rang": ("r", "ang"), + "rao": ("r", "ao"), + "re": ("r", "e"), + "ren": ("r", "en"), + "reng": ("r", "eng"), + "ri": ("r", "iii"), + "rong": ("r", "ong"), + "rou": ("r", "ou"), + "ru": ("r", "u"), + "rua": ("r", "ua"), + "ruan": ("r", "uan"), + "rui": ("r", "uei"), + "run": ("r", "uen"), + "ruo": ("r", "uo"), + "sa": ("s", "a"), + "sai": ("s", "ai"), + "san": ("s", "an"), + "sang": ("s", "ang"), + "sao": ("s", "ao"), + "se": ("s", "e"), + "sen": ("s", "en"), + "seng": ("s", "eng"), + "sha": ("sh", "a"), + "shai": ("sh", "ai"), + "shan": ("sh", "an"), + "shang": ("sh", "ang"), + "shao": ("sh", "ao"), + "she": ("sh", "e"), + "shei": ("sh", "ei"), + "shen": ("sh", "en"), + "sheng": ("sh", "eng"), + "shi": ("sh", "iii"), + "shou": ("sh", "ou"), + "shu": ("sh", "u"), + "shua": ("sh", "ua"), + "shuai": ("sh", "uai"), + "shuan": ("sh", "uan"), + "shuang": ("sh", "uang"), + "shui": ("sh", "uei"), + "shun": ("sh", "uen"), + "shuo": ("sh", "uo"), + "si": ("s", "ii"), + "song": ("s", "ong"), + "sou": ("s", "ou"), + "su": ("s", "u"), + "suan": ("s", "uan"), + "sui": ("s", "uei"), + "sun": ("s", "uen"), + "suo": ("s", "uo"), + "ta": ("t", "a"), + "tai": ("t", "ai"), + "tan": ("t", "an"), + "tang": ("t", "ang"), + "tao": ("t", "ao"), + "te": ("t", "e"), + "tei": ("t", "ei"), + "teng": ("t", "eng"), + "ti": ("t", "i"), + "tian": ("t", "ian"), + "tiao": ("t", "iao"), + "tie": ("t", "ie"), + "ting": ("t", "ing"), + "tong": ("t", "ong"), + "tou": ("t", "ou"), + "tu": ("t", "u"), + "tuan": ("t", "uan"), + "tui": ("t", "uei"), + "tun": ("t", "uen"), + "tuo": ("t", "uo"), + "wa": ("^", "ua"), + "wai": ("^", "uai"), + "wan": ("^", "uan"), + "wang": ("^", "uang"), + "wei": ("^", "uei"), + "wen": ("^", "uen"), + "weng": ("^", "ueng"), + "wo": ("^", "uo"), + "wu": ("^", "u"), + "xi": ("x", "i"), + "xia": ("x", "ia"), + "xian": ("x", "ian"), + "xiang": ("x", "iang"), + "xiao": ("x", "iao"), + "xie": ("x", "ie"), + "xin": ("x", "in"), + "xing": ("x", "ing"), + "xiong": ("x", "iong"), + "xiu": ("x", "iou"), + "xu": ("x", "v"), + "xuan": ("x", "van"), + "xue": ("x", "ve"), + "xun": ("x", "vn"), + "ya": ("^", "ia"), + "yan": ("^", "ian"), + "yang": ("^", "iang"), + "yao": ("^", "iao"), + "ye": ("^", "ie"), + "yi": ("^", "i"), + "yin": ("^", "in"), + "ying": ("^", "ing"), + "yo": ("^", "iou"), + "yong": ("^", "iong"), + "you": ("^", "iou"), + "yu": ("^", "v"), + "yuan": ("^", "van"), + "yue": ("^", "ve"), + "yun": ("^", "vn"), + "za": ("z", "a"), + "zai": ("z", "ai"), + "zan": ("z", "an"), + "zang": ("z", "ang"), + "zao": ("z", "ao"), + "ze": ("z", "e"), + "zei": ("z", "ei"), + "zen": ("z", "en"), + "zeng": ("z", "eng"), + "zha": ("zh", "a"), + "zhai": ("zh", "ai"), + "zhan": ("zh", "an"), + "zhang": ("zh", "ang"), + "zhao": ("zh", "ao"), + "zhe": ("zh", "e"), + "zhei": ("zh", "ei"), + "zhen": ("zh", "en"), + "zheng": ("zh", "eng"), + "zhi": ("zh", "iii"), + "zhong": ("zh", "ong"), + "zhou": ("zh", "ou"), + "zhu": ("zh", "u"), + "zhua": ("zh", "ua"), + "zhuai": ("zh", "uai"), + "zhuan": ("zh", "uan"), + "zhuang": ("zh", "uang"), + "zhui": ("zh", "uei"), + "zhun": ("zh", "uen"), + "zhuo": ("zh", "uo"), + "zi": ("z", "ii"), + "zong": ("z", "ong"), + "zou": ("z", "ou"), + "zu": ("z", "u"), + "zuan": ("z", "uan"), + "zui": ("z", "uei"), + "zun": ("z", "uen"), + "zuo": ("z", "uo"), +} diff --git a/bert/vocab.txt b/bert/vocab.txt new file mode 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+##鳕 +##鳖 +##鳗 +##鳝 +##鳞 +##鳥 +##鳩 +##鳳 +##鳴 +##鳶 +##鴉 +##鴕 +##鴛 +##鴦 +##鴨 +##鴻 +##鴿 +##鵑 +##鵜 +##鵝 +##鵡 +##鵬 +##鵰 +##鵲 +##鶘 +##鶩 +##鶯 +##鶴 +##鷗 +##鷲 +##鷹 +##鷺 +##鸚 +##鸞 +##鸟 +##鸠 +##鸡 +##鸢 +##鸣 +##鸥 +##鸦 +##鸨 +##鸪 +##鸭 +##鸯 +##鸳 +##鸵 +##鸽 +##鸾 +##鸿 +##鹂 +##鹃 +##鹄 +##鹅 +##鹈 +##鹉 +##鹊 +##鹌 +##鹏 +##鹑 +##鹕 +##鹘 +##鹜 +##鹞 +##鹤 +##鹦 +##鹧 +##鹫 +##鹭 +##鹰 +##鹳 +##鹵 +##鹹 +##鹼 +##鹽 +##鹿 +##麂 +##麋 +##麒 +##麓 +##麗 +##麝 +##麟 +##麥 +##麦 +##麩 +##麴 +##麵 +##麸 +##麺 +##麻 +##麼 +##麽 +##麾 +##黃 +##黄 +##黍 +##黎 +##黏 +##黑 +##黒 +##黔 +##默 +##黛 +##黜 +##黝 +##點 +##黠 +##黨 +##黯 +##黴 +##鼋 +##鼎 +##鼐 +##鼓 +##鼠 +##鼬 +##鼹 +##鼻 +##鼾 +##齁 +##齊 +##齋 +##齐 +##齒 +##齡 +##齢 +##齣 +##齦 +##齿 +##龄 +##龅 +##龈 +##龊 +##龋 +##龌 +##龍 +##龐 +##龔 +##龕 +##龙 +##龚 +##龛 +##龜 +##龟 +##︰ +##︱ +##︶ +##︿ +##﹁ +##﹂ +##﹍ +##﹏ +##﹐ +##﹑ +##﹒ +##﹔ +##﹕ +##﹖ +##﹗ +##﹙ +##﹚ +##﹝ +##﹞ +##﹡ +##﹣ +##！ +##＂ +##＃ +##＄ +##％ +##＆ +##＇ +##（ +##） +##＊ +##， +##－ +##． +##／ +##： +##； +##＜ +##？ +##＠ +##［ +##＼ +##］ +##＾ +##＿ +##｀ +##ｆ +##ｈ +##ｊ +##ｕ +##ｗ +##ｚ +##｛ +##｝ +##｡ +##｢ +##｣ +##､ +##･ +##ｯ +##ｰ +##ｲ +##ｸ +##ｼ +##ｽ +##ﾄ +##ﾉ +##ﾌ +##ﾗ +##ﾙ +##ﾝ +##ﾞ +##ﾟ +##￣ +##￥ +##👍 +##🔥 +##😂 +##😎 diff --git a/config.py b/config.py index 8ac6ff04f52c7e9a6cdf7e881e18426b76f281dc..e96df824dc9a82f80d9dc8ead10abab9bd14ec4d 100644 --- a/config.py +++ b/config.py @@ -46,6 +46,7 @@ MODEL_LIST = [ [ABS_PATH + "/Model/Nene_Nanami_Rong_Tang/1374_epochs.pth", ABS_PATH + "/Model/Nene_Nanami_Rong_Tang/config.json"], [ABS_PATH + "/Model/vctk/pretrained_vctk.pth", ABS_PATH + "/Model/vctk/vctk_base.json"], [ABS_PATH + "/Model/paimon/paimon6k_390000.pth", ABS_PATH + "/Model/paimon/paimon6k.json"], + [ABS_PATH + "/Model/vits_chinese/vits_bert_model.pth", ABS_PATH + "/Model/vits_chinese/bert_vits.json"], [ABS_PATH + "/Model/Bishojo_Mangekyo/generator_mangekyo.pth", ABS_PATH + "/Model/Bishojo_Mangekyo/config_mangekyo.json"], [ABS_PATH + "/Model/Cantonese/model.pth", ABS_PATH + "/Model/Cantonese/config.json"], [ABS_PATH + "/Model/shanghainese/2796_epochs.pth", ABS_PATH + "/Model/shanghainese/config.json"], diff --git a/models.py b/models.py index 7a1fd9815e76633918fe0ae85e700a69a99c349b..7175abb8c940abb4822191bfcc5e660c75c6bb44 100644 --- a/models.py +++ b/models.py @@ -13,242 +13,251 @@ from commons import init_weights class StochasticDurationPredictor(nn.Module): - def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0): - super().__init__() - filter_channels = in_channels # it needs to be removed from future version. - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.n_flows = n_flows - self.gin_channels = gin_channels - - self.log_flow = modules.Log() - self.flows = nn.ModuleList() - self.flows.append(modules.ElementwiseAffine(2)) - for i in range(n_flows): - self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)) - self.flows.append(modules.Flip()) - - self.post_pre = nn.Conv1d(1, filter_channels, 1) - self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1) - self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout) - self.post_flows = nn.ModuleList() - self.post_flows.append(modules.ElementwiseAffine(2)) - for i in range(4): - self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)) - self.post_flows.append(modules.Flip()) - - self.pre = nn.Conv1d(in_channels, filter_channels, 1) - self.proj = nn.Conv1d(filter_channels, filter_channels, 1) - self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout) - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, filter_channels, 1) - - def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0): - x = torch.detach(x) - x = self.pre(x) - if g is not None: - g = torch.detach(g) - x = x + self.cond(g) - x = self.convs(x, x_mask) - x = self.proj(x) * x_mask - - if not reverse: - flows = self.flows - assert w is not None - - logdet_tot_q = 0 - h_w = self.post_pre(w) - h_w = self.post_convs(h_w, x_mask) - h_w = self.post_proj(h_w) * x_mask - e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask - z_q = e_q - for flow in self.post_flows: - z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w)) - logdet_tot_q += logdet_q - z_u, z1 = torch.split(z_q, [1, 1], 1) - u = torch.sigmoid(z_u) * x_mask - z0 = (w - u) * x_mask - logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2]) - logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q - - logdet_tot = 0 - z0, logdet = self.log_flow(z0, x_mask) - logdet_tot += logdet - z = torch.cat([z0, z1], 1) - for flow in flows: - z, logdet = flow(z, x_mask, g=x, reverse=reverse) - logdet_tot = logdet_tot + logdet - nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot - return nll + logq # [b] - else: - flows = list(reversed(self.flows)) - flows = flows[:-2] + [flows[-1]] # remove a useless vflow - z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale - for flow in flows: - z = flow(z, x_mask, g=x, reverse=reverse) - z0, z1 = torch.split(z, [1, 1], 1) - logw = z0 - return logw + def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0): + super().__init__() + filter_channels = in_channels # it needs to be removed from future version. + self.in_channels = in_channels + self.filter_channels = filter_channels + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.n_flows = n_flows + self.gin_channels = gin_channels + + self.log_flow = modules.Log() + self.flows = nn.ModuleList() + self.flows.append(modules.ElementwiseAffine(2)) + for i in range(n_flows): + self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)) + self.flows.append(modules.Flip()) + + self.post_pre = nn.Conv1d(1, filter_channels, 1) + self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1) + self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout) + self.post_flows = nn.ModuleList() + self.post_flows.append(modules.ElementwiseAffine(2)) + for i in range(4): + self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)) + self.post_flows.append(modules.Flip()) + + self.pre = nn.Conv1d(in_channels, filter_channels, 1) + self.proj = nn.Conv1d(filter_channels, filter_channels, 1) + self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout) + if gin_channels != 0: + self.cond = nn.Conv1d(gin_channels, filter_channels, 1) + + def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0): + x = torch.detach(x) + x = self.pre(x) + if g is not None: + g = torch.detach(g) + x = x + self.cond(g) + x = self.convs(x, x_mask) + x = self.proj(x) * x_mask + + if not reverse: + flows = self.flows + assert w is not None + + logdet_tot_q = 0 + h_w = self.post_pre(w) + h_w = self.post_convs(h_w, x_mask) + h_w = self.post_proj(h_w) * x_mask + e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask + z_q = e_q + for flow in self.post_flows: + z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w)) + logdet_tot_q += logdet_q + z_u, z1 = torch.split(z_q, [1, 1], 1) + u = torch.sigmoid(z_u) * x_mask + z0 = (w - u) * x_mask + logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2]) + logq = torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q ** 2)) * x_mask, [1, 2]) - logdet_tot_q + + logdet_tot = 0 + z0, logdet = self.log_flow(z0, x_mask) + logdet_tot += logdet + z = torch.cat([z0, z1], 1) + for flow in flows: + z, logdet = flow(z, x_mask, g=x, reverse=reverse) + logdet_tot = logdet_tot + logdet + nll = torch.sum(0.5 * (math.log(2 * math.pi) + (z ** 2)) * x_mask, [1, 2]) - logdet_tot + return nll + logq # [b] + else: + flows = list(reversed(self.flows)) + flows = flows[:-2] + [flows[-1]] # remove a useless vflow + z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale + for flow in flows: + z = flow(z, x_mask, g=x, reverse=reverse) + z0, z1 = torch.split(z, [1, 1], 1) + logw = z0 + return logw class DurationPredictor(nn.Module): - def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0): - super().__init__() - - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.gin_channels = gin_channels - - self.drop = nn.Dropout(p_dropout) - self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2) - self.norm_1 = modules.LayerNorm(filter_channels) - self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2) - self.norm_2 = modules.LayerNorm(filter_channels) - self.proj = nn.Conv1d(filter_channels, 1, 1) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, in_channels, 1) - - def forward(self, x, x_mask, g=None): - x = torch.detach(x) - if g is not None: - g = torch.detach(g) - x = x + self.cond(g) - x = self.conv_1(x * x_mask) - x = torch.relu(x) - x = self.norm_1(x) - x = self.drop(x) - x = self.conv_2(x * x_mask) - x = torch.relu(x) - x = self.norm_2(x) - x = self.drop(x) - x = self.proj(x * x_mask) - return x * x_mask + def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0): + super().__init__() + + self.in_channels = in_channels + self.filter_channels = filter_channels + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.gin_channels = gin_channels + + self.drop = nn.Dropout(p_dropout) + self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2) + self.norm_1 = modules.LayerNorm(filter_channels) + self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2) + self.norm_2 = modules.LayerNorm(filter_channels) + self.proj = nn.Conv1d(filter_channels, 1, 1) + + if gin_channels != 0: + self.cond = nn.Conv1d(gin_channels, in_channels, 1) + + def forward(self, x, x_mask, g=None): + x = torch.detach(x) + if g is not None: + g = torch.detach(g) + x = x + self.cond(g) + x = self.conv_1(x * x_mask) + x = torch.relu(x) + x = self.norm_1(x) + x = self.drop(x) + x = self.conv_2(x * x_mask) + x = torch.relu(x) + x = self.norm_2(x) + x = self.drop(x) + x = self.proj(x * x_mask) + return x * x_mask class TextEncoder(nn.Module): - def __init__(self, - n_vocab, - out_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - emotion_embedding): - super().__init__() - self.n_vocab = n_vocab - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.emotion_embedding = emotion_embedding - - if self.n_vocab!=0: - self.emb = nn.Embedding(n_vocab, hidden_channels) - if emotion_embedding: - self.emo_proj = nn.Linear(1024, hidden_channels) - nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5) - - self.encoder = attentions.Encoder( - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout) - self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, x, x_lengths, emotion_embedding=None): - if self.n_vocab!=0: - x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h] - if emotion_embedding is not None: - x = x + self.emo_proj(emotion_embedding.unsqueeze(1)) - x = torch.transpose(x, 1, -1) # [b, h, t] - x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) - - x = self.encoder(x * x_mask, x_mask) - stats = self.proj(x) * x_mask - - m, logs = torch.split(stats, self.out_channels, dim=1) - return x, m, logs, x_mask + def __init__(self, + n_vocab, + out_channels, + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout, + emotion_embedding, + bert_embedding): + super().__init__() + self.n_vocab = n_vocab + self.out_channels = out_channels + self.hidden_channels = hidden_channels + self.filter_channels = filter_channels + self.n_heads = n_heads + self.n_layers = n_layers + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.emotion_embedding = emotion_embedding + + if self.n_vocab != 0: + self.emb = nn.Embedding(n_vocab, hidden_channels) + if emotion_embedding: + self.emo_proj = nn.Linear(1024, hidden_channels) + if bert_embedding: + self.emb_bert = nn.Linear(256, hidden_channels) + nn.init.normal_(self.emb.weight, 0.0, hidden_channels ** -0.5) + + self.encoder = attentions.Encoder( + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout) + self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) + + def forward(self, x, x_lengths, emotion_embedding=None, bert=None): + if self.n_vocab != 0: + x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h] + if emotion_embedding is not None: + x = x + self.emo_proj(emotion_embedding.unsqueeze(1)) + + if bert is not None: + x = x + self.emb_bert(bert) + x = torch.transpose(x, 1, -1) # [b, h, t] + x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) + + x = self.encoder(x * x_mask, x_mask) + stats = self.proj(x) * x_mask + + m, logs = torch.split(stats, self.out_channels, dim=1) + return x, m, logs, x_mask class ResidualCouplingBlock(nn.Module): - def __init__(self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - n_flows=4, - gin_channels=0): - super().__init__() - self.channels = channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.n_flows = n_flows - self.gin_channels = gin_channels - - self.flows = nn.ModuleList() - for i in range(n_flows): - self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True)) - self.flows.append(modules.Flip()) - - def forward(self, x, x_mask, g=None, reverse=False): - if not reverse: - for flow in self.flows: - x, _ = flow(x, x_mask, g=g, reverse=reverse) - else: - for flow in reversed(self.flows): - x = flow(x, x_mask, g=g, reverse=reverse) - return x + def __init__(self, + channels, + hidden_channels, + kernel_size, + dilation_rate, + n_layers, + n_flows=4, + gin_channels=0): + super().__init__() + self.channels = channels + self.hidden_channels = hidden_channels + self.kernel_size = kernel_size + self.dilation_rate = dilation_rate + self.n_layers = n_layers + self.n_flows = n_flows + self.gin_channels = gin_channels + + self.flows = nn.ModuleList() + for i in range(n_flows): + self.flows.append( + modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, + gin_channels=gin_channels, mean_only=True)) + self.flows.append(modules.Flip()) + + def forward(self, x, x_mask, g=None, reverse=False): + if not reverse: + for flow in self.flows: + x, _ = flow(x, x_mask, g=g, reverse=reverse) + else: + for flow in reversed(self.flows): + x = flow(x, x_mask, g=g, reverse=reverse) + return x class PosteriorEncoder(nn.Module): - def __init__(self, - in_channels, - out_channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=0): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.gin_channels = gin_channels - - self.pre = nn.Conv1d(in_channels, hidden_channels, 1) - self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels) - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, x, x_lengths, g=None): - x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) - x = self.pre(x) * x_mask - x = self.enc(x, x_mask, g=g) - stats = self.proj(x) * x_mask - m, logs = torch.split(stats, self.out_channels, dim=1) - z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask - return z, m, logs, x_mask + def __init__(self, + in_channels, + out_channels, + hidden_channels, + kernel_size, + dilation_rate, + n_layers, + gin_channels=0): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.hidden_channels = hidden_channels + self.kernel_size = kernel_size + self.dilation_rate = dilation_rate + self.n_layers = n_layers + self.gin_channels = gin_channels + + self.pre = nn.Conv1d(in_channels, hidden_channels, 1) + self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels) + self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) + + def forward(self, x, x_lengths, g=None): + x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) + x = self.pre(x) * x_mask + x = self.enc(x, x_mask, g=g) + stats = self.proj(x) * x_mask + m, logs = torch.split(stats, self.out_channels, dim=1) + z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask + return z, m, logs, x_mask class Generator(torch.nn.Module): - def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0): + def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, + upsample_initial_channel, upsample_kernel_sizes, gin_channels=0): super(Generator, self).__init__() self.num_kernels = len(resblock_kernel_sizes) self.num_upsamples = len(upsample_rates) @@ -258,12 +267,12 @@ class Generator(torch.nn.Module): self.ups = nn.ModuleList() for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): self.ups.append(weight_norm( - ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)), - k, u, padding=(k-u)//2))) + ConvTranspose1d(upsample_initial_channel // (2 ** i), upsample_initial_channel // (2 ** (i + 1)), + k, u, padding=(k - u) // 2))) self.resblocks = nn.ModuleList() for i in range(len(self.ups)): - ch = upsample_initial_channel//(2**(i+1)) + ch = upsample_initial_channel // (2 ** (i + 1)) for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)): self.resblocks.append(resblock(ch, k, d)) @@ -276,7 +285,7 @@ class Generator(torch.nn.Module): def forward(self, x, g=None): x = self.conv_pre(x) if g is not None: - x = x + self.cond(g) + x = x + self.cond(g) for i in range(self.num_upsamples): x = F.leaky_relu(x, modules.LRELU_SLOPE) @@ -284,9 +293,9 @@ class Generator(torch.nn.Module): xs = None for j in range(self.num_kernels): if xs is None: - xs = self.resblocks[i*self.num_kernels+j](x) + xs = self.resblocks[i * self.num_kernels + j](x) else: - xs += self.resblocks[i*self.num_kernels+j](x) + xs += self.resblocks[i * self.num_kernels + j](x) x = xs / self.num_kernels x = F.leaky_relu(x) x = self.conv_post(x) @@ -296,109 +305,115 @@ class Generator(torch.nn.Module): class SynthesizerTrn(nn.Module): - """ - Synthesizer for Training - """ - - def __init__(self, - n_vocab, - spec_channels, - segment_size, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - n_speakers=0, - gin_channels=0, - use_sdp=True, - emotion_embedding=False, - **kwargs): - - super().__init__() - self.n_vocab = n_vocab - self.spec_channels = spec_channels - self.inter_channels = inter_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.resblock = resblock - self.resblock_kernel_sizes = resblock_kernel_sizes - self.resblock_dilation_sizes = resblock_dilation_sizes - self.upsample_rates = upsample_rates - self.upsample_initial_channel = upsample_initial_channel - self.upsample_kernel_sizes = upsample_kernel_sizes - self.segment_size = segment_size - self.n_speakers = n_speakers - self.gin_channels = gin_channels - - self.use_sdp = use_sdp - - self.enc_p = TextEncoder(n_vocab, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - emotion_embedding) - self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels) - self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels) - self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels) - - if use_sdp: - self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels) - else: - self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels) - - if n_speakers >= 1: - self.emb_g = nn.Embedding(n_speakers, gin_channels) - - def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None, emotion_embedding=None): - x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, emotion_embedding) - if self.n_speakers > 0: - g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] - else: - g = None - - if self.use_sdp: - logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) - else: - logw = self.dp(x, x_mask, g=g) - w = torch.exp(logw) * x_mask * length_scale - w_ceil = torch.ceil(w) - y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() - y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype) - attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) - attn = commons.generate_path(w_ceil, attn_mask) - - m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t'] - logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t'] - - z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale - z = self.flow(z_p, y_mask, g=g, reverse=True) - o = self.dec((z * y_mask)[:,:,:max_len], g=g) - return o, attn, y_mask, (z, z_p, m_p, logs_p) - - def voice_conversion(self, y, y_lengths, sid_src, sid_tgt): - assert self.n_speakers > 0, "n_speakers have to be larger than 0." - g_src = self.emb_g(sid_src).unsqueeze(-1) - g_tgt = self.emb_g(sid_tgt).unsqueeze(-1) - z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src) - z_p = self.flow(z, y_mask, g=g_src) - z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True) - o_hat = self.dec(z_hat * y_mask, g=g_tgt) - return o_hat, y_mask, (z, z_p, z_hat) - + """ + Synthesizer for Training + """ + + def __init__(self, + n_vocab, + spec_channels, + segment_size, + inter_channels, + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout, + resblock, + resblock_kernel_sizes, + resblock_dilation_sizes, + upsample_rates, + upsample_initial_channel, + upsample_kernel_sizes, + n_speakers=0, + gin_channels=0, + use_sdp=True, + emotion_embedding=False, + bert_embedding=False, + **kwargs): + + super().__init__() + self.n_vocab = n_vocab + self.spec_channels = spec_channels + self.inter_channels = inter_channels + self.hidden_channels = hidden_channels + self.filter_channels = filter_channels + self.n_heads = n_heads + self.n_layers = n_layers + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.resblock = resblock + self.resblock_kernel_sizes = resblock_kernel_sizes + self.resblock_dilation_sizes = resblock_dilation_sizes + self.upsample_rates = upsample_rates + self.upsample_initial_channel = upsample_initial_channel + self.upsample_kernel_sizes = upsample_kernel_sizes + self.segment_size = segment_size + self.n_speakers = n_speakers + self.gin_channels = gin_channels + self.use_sdp = use_sdp + self.emotion_embedding = emotion_embedding + self.bert_embedding = bert_embedding + + self.enc_p = TextEncoder(n_vocab, + inter_channels, + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout, + emotion_embedding, + bert_embedding) + self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, + upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels) + self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, + gin_channels=gin_channels) + self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels) + + if self.use_sdp: + self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels) + else: + self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels) + + if n_speakers >= 1: + self.emb_g = nn.Embedding(n_speakers, gin_channels) + + def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None, + emotion_embedding=None, bert=None): + x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, emotion_embedding, bert) + if self.n_speakers > 0: + g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] + else: + g = None + + if self.use_sdp: + logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) + else: + logw = self.dp(x, x_mask, g=g) + w = torch.exp(logw) * x_mask * length_scale + w_ceil = torch.ceil(w) + y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() + y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype) + attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) + attn = commons.generate_path(w_ceil, attn_mask) + + m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t'] + logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, + 2) # [b, t', t], [b, t, d] -> [b, d, t'] + + z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale + z = self.flow(z_p, y_mask, g=g, reverse=True) + o = self.dec((z * y_mask)[:, :, :max_len], g=g) + return o, attn, y_mask, (z, z_p, m_p, logs_p) + + def voice_conversion(self, y, y_lengths, sid_src, sid_tgt): + assert self.n_speakers > 0, "n_speakers have to be larger than 0." + g_src = self.emb_g(sid_src).unsqueeze(-1) + g_tgt = self.emb_g(sid_tgt).unsqueeze(-1) + z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src) + z_p = self.flow(z, y_mask, g=g_src) + z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True) + o_hat = self.dec(z_hat * y_mask, g=g_tgt) + return o_hat, y_mask, (z, z_p, z_hat) diff --git a/requirements.txt b/requirements.txt index 37b1478b0c9784466c19be78343cfcf4b453d6e4..b0e5a23f7b683f645aaca3ea3a9416c1621cf2d8 100644 --- a/requirements.txt +++ b/requirements.txt @@ -26,4 +26,5 @@ flask_apscheduler fasttext fastlid langid -phonemizer==3.2.1 \ No newline at end of file +phonemizer==3.2.1 +transformers \ No newline at end of file diff --git a/static/css/bootstrap.min.css b/static/css/bootstrap.min.css new file mode 100644 index 0000000000000000000000000000000000000000..83a71b1f50721c12da8e13b8d476cad8ec471e92 --- /dev/null +++ b/static/css/bootstrap.min.css @@ -0,0 +1,7 @@ +/*! + * Bootstrap v4.6.2 (https://getbootstrap.com/) + * Copyright 2011-2022 The Bootstrap Authors + * Copyright 2011-2022 Twitter, Inc. + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) + 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Ut={placement:"bottom",positionFixed:!1,eventsEnabled:!0,removeOnDestroy:!1,onCreate:function(){},onUpdate:function(){},modifiers:{shift:{order:100,enabled:!0,fn:function(t){var e=t.placement,n=e.split("-")[0],i=e.split("-")[1];if(i){var o=t.offsets,r=o.reference,a=o.popper,s=-1!==["bottom","top"].indexOf(n),l=s?"left":"top",u=s?"width":"height",f={start:dt({},l,r[l]),end:dt({},l,r[l]+r[u]-a[u])};t.offsets.popper=ct({},a,f[i])}return t}},offset:{order:200,enabled:!0,fn:function(t,e){var n,i=e.offset,o=t.placement,r=t.offsets,a=r.popper,s=r.reference,l=o.split("-")[0];return n=Rt(+i)?[+i,0]:function(t,e,n,i){var o=[0,0],r=-1!==["right","left"].indexOf(i),a=t.split(/(\+|\-)/).map((function(t){return t.trim()})),s=a.indexOf(Nt(a,(function(t){return-1!==t.search(/,|\s/)})));a[s]&&-1===a[s].indexOf(",")&&console.warn("Offsets separated by white space(s) are deprecated, use a comma (,) instead.");var l=/\s*,\s*|\s+/,u=-1!==s?[a.slice(0,s).concat([a[s].split(l)[0]]),[a[s].split(l)[1]].concat(a.slice(s+1))]:[a];return u=u.map((function(t,i){var o=(1===i?!r:r)?"height":"width",a=!1;return t.reduce((function(t,e){return""===t[t.length-1]&&-1!==["+","-"].indexOf(e)?(t[t.length-1]=e,a=!0,t):a?(t[t.length-1]+=e,a=!1,t):t.concat(e)}),[]).map((function(t){return function(t,e,n,i){var o=t.match(/((?:\-|\+)?\d*\.?\d*)(.*)/),r=+o[1],a=o[2];return r?0===a.indexOf("%")?ht("%p"===a?n:i)[e]/100*r:"vh"===a||"vw"===a?("vh"===a?Math.max(document.documentElement.clientHeight,window.innerHeight||0):Math.max(document.documentElement.clientWidth,window.innerWidth||0))/100*r:r:t}(t,o,e,n)}))})),u.forEach((function(t,e){t.forEach((function(n,i){Rt(n)&&(o[e]+=n*("-"===t[i-1]?-1:1))}))})),o}(i,a,s,l),"left"===l?(a.top+=n[0],a.left-=n[1]):"right"===l?(a.top+=n[0],a.left+=n[1]):"top"===l?(a.left+=n[0],a.top-=n[1]):"bottom"===l&&(a.left+=n[0],a.top+=n[1]),t.popper=a,t},offset:0},preventOverflow:{order:300,enabled:!0,fn:function(t,e){var n=e.boundariesElement||et(t.instance.popper);t.instance.reference===n&&(n=et(n));var i=It("transform"),o=t.instance.popper.style,r=o.top,a=o.left,s=o[i];o.top="",o.left="",o[i]="";var l=bt(t.instance.popper,t.instance.reference,e.padding,n,t.positionFixed);o.top=r,o.left=a,o[i]=s,e.boundaries=l;var u=e.priority,f=t.offsets.popper,d={primary:function(t){var n=f[t];return f[t]l[t]&&!e.escapeWithReference&&(i=Math.min(f[n],l[t]-("right"===t?f.width:f.height))),dt({},n,i)}};return u.forEach((function(t){var e=-1!==["left","top"].indexOf(t)?"primary":"secondary";f=ct({},f,d[e](t))})),t.offsets.popper=f,t},priority:["left","right","top","bottom"],padding:5,boundariesElement:"scrollParent"},keepTogether:{order:400,enabled:!0,fn:function(t){var e=t.offsets,n=e.popper,i=e.reference,o=t.placement.split("-")[0],r=Math.floor,a=-1!==["top","bottom"].indexOf(o),s=a?"right":"bottom",l=a?"left":"top",u=a?"width":"height";return n[s]r(i[s])&&(t.offsets.popper[l]=r(i[s])),t}},arrow:{order:500,enabled:!0,fn:function(t,e){var n;if(!Mt(t.instance.modifiers,"arrow","keepTogether"))return t;var i=e.element;if("string"==typeof i){if(!(i=t.instance.popper.querySelector(i)))return t}else if(!t.instance.popper.contains(i))return console.warn("WARNING: `arrow.element` must be child of its popper element!"),t;var o=t.placement.split("-")[0],r=t.offsets,a=r.popper,s=r.reference,l=-1!==["left","right"].indexOf(o),u=l?"height":"width",f=l?"Top":"Left",d=f.toLowerCase(),c=l?"left":"top",h=l?"bottom":"right",p=Tt(i)[u];s[h]-pa[h]&&(t.offsets.popper[d]+=s[d]+p-a[h]),t.offsets.popper=ht(t.offsets.popper);var m=s[d]+s[u]/2-p/2,g=K(t.instance.popper),_=parseFloat(g["margin"+f]),v=parseFloat(g["border"+f+"Width"]),b=m-t.offsets.popper[d]-_-v;return b=Math.max(Math.min(a[u]-p,b),0),t.arrowElement=i,t.offsets.arrow=(dt(n={},d,Math.round(b)),dt(n,c,""),n),t},element:"[x-arrow]"},flip:{order:600,enabled:!0,fn:function(t,e){if(kt(t.instance.modifiers,"inner"))return t;if(t.flipped&&t.placement===t.originalPlacement)return t;var n=bt(t.instance.popper,t.instance.reference,e.padding,e.boundariesElement,t.positionFixed),i=t.placement.split("-")[0],o=Ct(i),r=t.placement.split("-")[1]||"",a=[];switch(e.behavior){case"flip":a=[i,o];break;case"clockwise":a=Wt(i);break;case"counterclockwise":a=Wt(i,!0);break;default:a=e.behavior}return a.forEach((function(s,l){if(i!==s||a.length===l+1)return t;i=t.placement.split("-")[0],o=Ct(i);var u=t.offsets.popper,f=t.offsets.reference,d=Math.floor,c="left"===i&&d(u.right)>d(f.left)||"right"===i&&d(u.left)d(f.top)||"bottom"===i&&d(u.top)d(n.right),m=d(u.top)d(n.bottom),_="left"===i&&h||"right"===i&&p||"top"===i&&m||"bottom"===i&&g,v=-1!==["top","bottom"].indexOf(i),b=!!e.flipVariations&&(v&&"start"===r&&h||v&&"end"===r&&p||!v&&"start"===r&&m||!v&&"end"===r&&g),y=!!e.flipVariationsByContent&&(v&&"start"===r&&p||v&&"end"===r&&h||!v&&"start"===r&&g||!v&&"end"===r&&m),E=b||y;(c||_||E)&&(t.flipped=!0,(c||_)&&(i=a[l+1]),E&&(r=function(t){return"end"===t?"start":"start"===t?"end":t}(r)),t.placement=i+(r?"-"+r:""),t.offsets.popper=ct({},t.offsets.popper,St(t.instance.popper,t.offsets.reference,t.placement)),t=Dt(t.instance.modifiers,t,"flip"))})),t},behavior:"flip",padding:5,boundariesElement:"viewport",flipVariations:!1,flipVariationsByContent:!1},inner:{order:700,enabled:!1,fn:function(t){var e=t.placement,n=e.split("-")[0],i=t.offsets,o=i.popper,r=i.reference,a=-1!==["left","right"].indexOf(n),s=-1===["top","left"].indexOf(n);return o[a?"left":"top"]=r[n]-(s?o[a?"width":"height"]:0),t.placement=Ct(e),t.offsets.popper=ht(o),t}},hide:{order:800,enabled:!0,fn:function(t){if(!Mt(t.instance.modifiers,"hide","preventOverflow"))return t;var e=t.offsets.reference,n=Nt(t.instance.modifiers,(function(t){return"preventOverflow"===t.name})).boundaries;if(e.bottomn.right||e.top>n.bottom||e.right2&&void 0!==arguments[2]?arguments[2]:{};ut(this,t),this.scheduleUpdate=function(){return requestAnimationFrame(i.update)},this.update=Y(this.update.bind(this)),this.options=ct({},t.Defaults,o),this.state={isDestroyed:!1,isCreated:!1,scrollParents:[]},this.reference=e&&e.jquery?e[0]:e,this.popper=n&&n.jquery?n[0]:n,this.options.modifiers={},Object.keys(ct({},t.Defaults.modifiers,o.modifiers)).forEach((function(e){i.options.modifiers[e]=ct({},t.Defaults.modifiers[e]||{},o.modifiers?o.modifiers[e]:{})})),this.modifiers=Object.keys(this.options.modifiers).map((function(t){return ct({name:t},i.options.modifiers[t])})).sort((function(t,e){return t.order-e.order})),this.modifiers.forEach((function(t){t.enabled&&z(t.onLoad)&&t.onLoad(i.reference,i.popper,i.options,t,i.state)})),this.update();var r=this.options.eventsEnabled;r&&this.enableEventListeners(),this.state.eventsEnabled=r}return ft(t,[{key:"update",value:function(){return At.call(this)}},{key:"destroy",value:function(){return Ot.call(this)}},{key:"enableEventListeners",value:function(){return Pt.call(this)}},{key:"disableEventListeners",value:function(){return Ft.call(this)}}]),t}();Vt.Utils=("undefined"!=typeof window?window:global).PopperUtils,Vt.placements=qt,Vt.Defaults=Ut;var Yt=Vt,zt="dropdown",Kt="bs.dropdown",Xt=i.default.fn[zt],Gt=new RegExp("38|40|27"),$t="disabled",Jt="show",Zt="dropdown-menu-right",te="hide.bs.dropdown",ee="hidden.bs.dropdown",ne="click.bs.dropdown.data-api",ie="keydown.bs.dropdown.data-api",oe='[data-toggle="dropdown"]',re=".dropdown-menu",ae={offset:0,flip:!0,boundary:"scrollParent",reference:"toggle",display:"dynamic",popperConfig:null},se={offset:"(number|string|function)",flip:"boolean",boundary:"(string|element)",reference:"(string|element)",display:"string",popperConfig:"(null|object)"},le=function(){function t(t,e){this._element=t,this._popper=null,this._config=this._getConfig(e),this._menu=this._getMenuElement(),this._inNavbar=this._detectNavbar(),this._addEventListeners()}var e=t.prototype;return e.toggle=function(){if(!this._element.disabled&&!i.default(this._element).hasClass($t)){var e=i.default(this._menu).hasClass(Jt);t._clearMenus(),e||this.show(!0)}},e.show=function(e){if(void 0===e&&(e=!1),!(this._element.disabled||i.default(this._element).hasClass($t)||i.default(this._menu).hasClass(Jt))){var n={relatedTarget:this._element},o=i.default.Event("show.bs.dropdown",n),r=t._getParentFromElement(this._element);if(i.default(r).trigger(o),!o.isDefaultPrevented()){if(!this._inNavbar&&e){if("undefined"==typeof Yt)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");var a=this._element;"parent"===this._config.reference?a=r:u.isElement(this._config.reference)&&(a=this._config.reference,"undefined"!=typeof this._config.reference.jquery&&(a=this._config.reference[0])),"scrollParent"!==this._config.boundary&&i.default(r).addClass("position-static"),this._popper=new Yt(a,this._menu,this._getPopperConfig())}"ontouchstart"in document.documentElement&&0===i.default(r).closest(".navbar-nav").length&&i.default(document.body).children().on("mouseover",null,i.default.noop),this._element.focus(),this._element.setAttribute("aria-expanded",!0),i.default(this._menu).toggleClass(Jt),i.default(r).toggleClass(Jt).trigger(i.default.Event("shown.bs.dropdown",n))}}},e.hide=function(){if(!this._element.disabled&&!i.default(this._element).hasClass($t)&&i.default(this._menu).hasClass(Jt)){var e={relatedTarget:this._element},n=i.default.Event(te,e),o=t._getParentFromElement(this._element);i.default(o).trigger(n),n.isDefaultPrevented()||(this._popper&&this._popper.destroy(),i.default(this._menu).toggleClass(Jt),i.default(o).toggleClass(Jt).trigger(i.default.Event(ee,e)))}},e.dispose=function(){i.default.removeData(this._element,Kt),i.default(this._element).off(".bs.dropdown"),this._element=null,this._menu=null,null!==this._popper&&(this._popper.destroy(),this._popper=null)},e.update=function(){this._inNavbar=this._detectNavbar(),null!==this._popper&&this._popper.scheduleUpdate()},e._addEventListeners=function(){var t=this;i.default(this._element).on("click.bs.dropdown",(function(e){e.preventDefault(),e.stopPropagation(),t.toggle()}))},e._getConfig=function(t){return t=a({},this.constructor.Default,i.default(this._element).data(),t),u.typeCheckConfig(zt,t,this.constructor.DefaultType),t},e._getMenuElement=function(){if(!this._menu){var e=t._getParentFromElement(this._element);e&&(this._menu=e.querySelector(re))}return this._menu},e._getPlacement=function(){var t=i.default(this._element.parentNode),e="bottom-start";return t.hasClass("dropup")?e=i.default(this._menu).hasClass(Zt)?"top-end":"top-start":t.hasClass("dropright")?e="right-start":t.hasClass("dropleft")?e="left-start":i.default(this._menu).hasClass(Zt)&&(e="bottom-end"),e},e._detectNavbar=function(){return i.default(this._element).closest(".navbar").length>0},e._getOffset=function(){var t=this,e={};return"function"==typeof this._config.offset?e.fn=function(e){return e.offsets=a({},e.offsets,t._config.offset(e.offsets,t._element)),e}:e.offset=this._config.offset,e},e._getPopperConfig=function(){var t={placement:this._getPlacement(),modifiers:{offset:this._getOffset(),flip:{enabled:this._config.flip},preventOverflow:{boundariesElement:this._config.boundary}}};return"static"===this._config.display&&(t.modifiers.applyStyle={enabled:!1}),a({},t,this._config.popperConfig)},t._jQueryInterface=function(e){return this.each((function(){var n=i.default(this).data(Kt);if(n||(n=new t(this,"object"==typeof e?e:null),i.default(this).data(Kt,n)),"string"==typeof e){if("undefined"==typeof n[e])throw new TypeError('No method named "'+e+'"');n[e]()}}))},t._clearMenus=function(e){if(!e||3!==e.which&&("keyup"!==e.type||9===e.which))for(var n=[].slice.call(document.querySelectorAll(oe)),o=0,r=n.length;o0&&a--,40===e.which&&adocument.documentElement.clientHeight;n||(this._element.style.overflowY="hidden"),this._element.classList.add(pe);var o=u.getTransitionDurationFromElement(this._dialog);i.default(this._element).off(u.TRANSITION_END),i.default(this._element).one(u.TRANSITION_END,(function(){t._element.classList.remove(pe),n||i.default(t._element).one(u.TRANSITION_END,(function(){t._element.style.overflowY=""})).emulateTransitionEnd(t._element,o)})).emulateTransitionEnd(o),this._element.focus()}},e._showElement=function(t){var e=this,n=i.default(this._element).hasClass(ce),o=this._dialog?this._dialog.querySelector(".modal-body"):null;this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.appendChild(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),i.default(this._dialog).hasClass("modal-dialog-scrollable")&&o?o.scrollTop=0:this._element.scrollTop=0,n&&u.reflow(this._element),i.default(this._element).addClass(he),this._config.focus&&this._enforceFocus();var r=i.default.Event("shown.bs.modal",{relatedTarget:t}),a=function(){e._config.focus&&e._element.focus(),e._isTransitioning=!1,i.default(e._element).trigger(r)};if(n){var s=u.getTransitionDurationFromElement(this._dialog);i.default(this._dialog).one(u.TRANSITION_END,a).emulateTransitionEnd(s)}else a()},e._enforceFocus=function(){var t=this;i.default(document).off(_e).on(_e,(function(e){document!==e.target&&t._element!==e.target&&0===i.default(t._element).has(e.target).length&&t._element.focus()}))},e._setEscapeEvent=function(){var t=this;this._isShown?i.default(this._element).on(ye,(function(e){t._config.keyboard&&27===e.which?(e.preventDefault(),t.hide()):t._config.keyboard||27!==e.which||t._triggerBackdropTransition()})):this._isShown||i.default(this._element).off(ye)},e._setResizeEvent=function(){var t=this;this._isShown?i.default(window).on(ve,(function(e){return t.handleUpdate(e)})):i.default(window).off(ve)},e._hideModal=function(){var t=this;this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._showBackdrop((function(){i.default(document.body).removeClass(de),t._resetAdjustments(),t._resetScrollbar(),i.default(t._element).trigger(me)}))},e._removeBackdrop=function(){this._backdrop&&(i.default(this._backdrop).remove(),this._backdrop=null)},e._showBackdrop=function(t){var e=this,n=i.default(this._element).hasClass(ce)?ce:"";if(this._isShown&&this._config.backdrop){if(this._backdrop=document.createElement("div"),this._backdrop.className="modal-backdrop",n&&this._backdrop.classList.add(n),i.default(this._backdrop).appendTo(document.body),i.default(this._element).on(be,(function(t){e._ignoreBackdropClick?e._ignoreBackdropClick=!1:t.target===t.currentTarget&&("static"===e._config.backdrop?e._triggerBackdropTransition():e.hide())})),n&&u.reflow(this._backdrop),i.default(this._backdrop).addClass(he),!t)return;if(!n)return void t();var o=u.getTransitionDurationFromElement(this._backdrop);i.default(this._backdrop).one(u.TRANSITION_END,t).emulateTransitionEnd(o)}else if(!this._isShown&&this._backdrop){i.default(this._backdrop).removeClass(he);var r=function(){e._removeBackdrop(),t&&t()};if(i.default(this._element).hasClass(ce)){var a=u.getTransitionDurationFromElement(this._backdrop);i.default(this._backdrop).one(u.TRANSITION_END,r).emulateTransitionEnd(a)}else r()}else t&&t()},e._adjustDialog=function(){var t=this._element.scrollHeight>document.documentElement.clientHeight;!this._isBodyOverflowing&&t&&(this._element.style.paddingLeft=this._scrollbarWidth+"px"),this._isBodyOverflowing&&!t&&(this._element.style.paddingRight=this._scrollbarWidth+"px")},e._resetAdjustments=function(){this._element.style.paddingLeft="",this._element.style.paddingRight=""},e._checkScrollbar=function(){var t=document.body.getBoundingClientRect();this._isBodyOverflowing=Math.round(t.left+t.right)
',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:0,container:!1,fallbackPlacement:"flip",boundary:"scrollParent",customClass:"",sanitize:!0,sanitizeFn:null,whiteList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},We={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(number|string|function)",container:"(string|element|boolean)",fallbackPlacement:"(string|array)",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",whiteList:"object",popperConfig:"(null|object)"},Ue={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},Ve=function(){function t(t,e){if("undefined"==typeof Yt)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this.element=t,this.config=this._getConfig(e),this.tip=null,this._setListeners()}var e=t.prototype;return e.enable=function(){this._isEnabled=!0},e.disable=function(){this._isEnabled=!1},e.toggleEnabled=function(){this._isEnabled=!this._isEnabled},e.toggle=function(t){if(this._isEnabled)if(t){var e=this.constructor.DATA_KEY,n=i.default(t.currentTarget).data(e);n||(n=new this.constructor(t.currentTarget,this._getDelegateConfig()),i.default(t.currentTarget).data(e,n)),n._activeTrigger.click=!n._activeTrigger.click,n._isWithActiveTrigger()?n._enter(null,n):n._leave(null,n)}else{if(i.default(this.getTipElement()).hasClass(Fe))return void 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