Update vits_chinese

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"]
 }

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"]
+}

Model/vits_chinese/vits_bert_model.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:be0dcf53ffcd49d51fd9a710338a9ff7eed60d0c26ccbb03ebd5a9175f20dc39
+size 141822877

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)

bert/__init__.py

@@ -0,0 +1,2 @@
+""" from https://github.com/PlayVoice/vits_chinese """
+from .ProsodyModel import TTSProsody

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
+}

bert/prosody_model.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3accec7a0d5cbfccaa8a42b96374a91d442a69801c6a01402baae3bf06b8c015
+size 409941419

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"),
+}

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"],

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)

requirements.txt

@@ -26,4 +26,5 @@ flask_apscheduler
 fasttext
 fastlid
 langid
-phonemizer==3.2.1
+phonemizer==3.2.1
+transformers

static/js/bootstrap.bundle.min.js

@@ -0,0 +1,7 @@
+/*!
+  * Bootstrap v4.6.2 (https://getbootstrap.com/)
+  * Copyright 2011-2022 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
+  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
+  */
+!function(t,e){"object"==typeof exports&&"undefined"!=typeof module?e(exports,require("jquery")):"function"==typeof define&&define.amd?define(["exports","jquery"],e):e((t="undefined"!=typeof globalThis?globalThis:t||self).bootstrap={},t.jQuery)}(this,(function(t,e){"use strict";function n(t){return t&&"object"==typeof t&&"default"in t?t:{default:t}}var i=n(e);function o(t,e){for(var n=0;n<e.length;n++){var i=e[n];i.enumerable=i.enumerable||!1,i.configurable=!0,"value"in i&&(i.writable=!0),Object.defineProperty(t,i.key,i)}}function r(t,e,n){return e&&o(t.prototype,e),n&&o(t,n),Object.defineProperty(t,"prototype",{writable:!1}),t}function a(){return a=Object.assign?Object.assign.bind():function(t){for(var e=1;e<arguments.length;e++){var n=arguments[e];for(var i in n)Object.prototype.hasOwnProperty.call(n,i)&&(t[i]=n[i])}return t},a.apply(this,arguments)}function s(t,e){return s=Object.setPrototypeOf?Object.setPrototypeOf.bind():function(t,e){return t.__proto__=e,t},s(t,e)}var 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this.state.isDestroyed=!0,kt(this.modifiers,"applyStyle")&&(this.popper.removeAttribute("x-placement"),this.popper.style.position="",this.popper.style.top="",this.popper.style.left="",this.popper.style.right="",this.popper.style.bottom="",this.popper.style.willChange="",this.popper.style[It("transform")]=""),this.disableEventListeners(),this.options.removeOnDestroy&&this.popper.parentNode.removeChild(this.popper),this}function xt(t){var e=t.ownerDocument;return e?e.defaultView:window}function jt(t,e,n,i){var o="BODY"===t.nodeName,r=o?t.ownerDocument.defaultView:t;r.addEventListener(e,n,{passive:!0}),o||jt(G(r.parentNode),e,n,i),i.push(r)}function Lt(t,e,n,i){n.updateBound=i,xt(t).addEventListener("resize",n.updateBound,{passive:!0});var o=G(t);return jt(o,"scroll",n.updateBound,n.scrollParents),n.scrollElement=o,n.eventsEnabled=!0,n}function Pt(){this.state.eventsEnabled||(this.state=Lt(this.reference,this.options,this.state,this.scheduleUpdate))}function Ft(){var t,e;this.state.eventsEnabled&&(cancelAnimationFrame(this.scheduleUpdate),this.state=(t=this.reference,e=this.state,xt(t).removeEventListener("resize",e.updateBound),e.scrollParents.forEach((function(t){t.removeEventListener("scroll",e.updateBound)})),e.updateBound=null,e.scrollParents=[],e.scrollElement=null,e.eventsEnabled=!1,e))}function Rt(t){return""!==t&&!isNaN(parseFloat(t))&&isFinite(t)}function Bt(t,e){Object.keys(e).forEach((function(n){var i="";-1!==["width","height","top","right","bottom","left"].indexOf(n)&&Rt(e[n])&&(i="px"),t.style[n]=e[n]+i}))}var Ht=U&&/Firefox/i.test(navigator.userAgent);function Mt(t,e,n){var i=Nt(t,(function(t){return t.name===e})),o=!!i&&t.some((function(t){return t.name===n&&t.enabled&&t.order<i.order}));if(!o){var r="`"+e+"`",a="`"+n+"`";console.warn(a+" modifier is required by "+r+" modifier in order to work, be sure to include it before "+r+"!")}return o}var qt=["auto-start","auto","auto-end","top-start","top","top-end","right-start","right","right-end","bottom-end","bottom","bottom-start","left-end","left","left-start"],Qt=qt.slice(3);function Wt(t){var e=arguments.length>1&&void 0!==arguments[1]&&arguments[1],n=Qt.indexOf(t),i=Qt.slice(n+1).concat(Qt.slice(0,n));return e?i.reverse():i}var 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 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f[t]<l[t]&&!e.escapeWithReference&&(n=Math.max(f[t],l[t])),dt({},t,n)},secondary:function(t){var n="right"===t?"left":"top",i=f[n];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[l])&&(t.offsets.popper[l]=r(i[l])-n[u]),n[l]>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]-p<a[d]&&(t.offsets.popper[d]-=a[d]-(s[h]-p)),s[d]+p>a[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.right)||"top"===i&&d(u.bottom)>d(f.top)||"bottom"===i&&d(u.top)<d(f.bottom),h=d(u.left)<d(n.left),p=d(u.right)>d(n.right),m=d(u.top)<d(n.top),g=d(u.bottom)>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.bottom<n.top||e.left>n.right||e.top>n.bottom||e.right<n.left){if(!0===t.hide)return t;t.hide=!0,t.attributes["x-out-of-boundaries"]=""}else{if(!1===t.hide)return t;t.hide=!1,t.attributes["x-out-of-boundaries"]=!1}return t}},computeStyle:{order:850,enabled:!0,fn:function(t,e){var n=e.x,i=e.y,o=t.offsets.popper,r=Nt(t.instance.modifiers,(function(t){return"applyStyle"===t.name})).gpuAcceleration;void 0!==r&&console.warn("WARNING: `gpuAcceleration` option moved to `computeStyle` modifier and will not be supported in future versions of 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t.attributes=ct({},v,t.attributes),t.styles=ct({},d,t.styles),t.arrowStyles=ct({},t.offsets.arrow,t.arrowStyles),t},gpuAcceleration:!0,x:"bottom",y:"right"},applyStyle:{order:900,enabled:!0,fn:function(t){var e,n;return Bt(t.instance.popper,t.styles),e=t.instance.popper,n=t.attributes,Object.keys(n).forEach((function(t){!1!==n[t]?e.setAttribute(t,n[t]):e.removeAttribute(t)})),t.arrowElement&&Object.keys(t.arrowStyles).length&&Bt(t.arrowElement,t.arrowStyles),t},onLoad:function(t,e,n,i,o){var r=wt(o,e,t,n.positionFixed),a=Et(n.placement,r,e,t,n.modifiers.flip.boundariesElement,n.modifiers.flip.padding);return e.setAttribute("x-placement",a),Bt(e,{position:n.positionFixed?"fixed":"absolute"}),n},gpuAcceleration:void 0}}},Vt=function(){function t(e,n){var i=this,o=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};ut(this,t),this.scheduleUpdate=function(){return 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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 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static/js/jquery.slim.min.js

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templates/index.html

@@ -5,7 +5,7 @@
 		<meta name="viewport" content="width=device-width, initial-scale=1.0" />
 		<title>vits-simple-api</title>
 
-		<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@4.6.2/dist/css/bootstrap.min.css" />
+		<link rel="stylesheet" href="/static/css/bootstrap.min.css" />
 	</head>
 	<body>
 		<main style="margin: 0 auto; width: 1024px">
@@ -43,7 +43,7 @@
 						<select class="form-control" id="inputId" oninput="updateLink()">
 							<option value="164"></option>
 							{% for speaker in speakers["VITS"] %}
-							{% if speaker["id"] == 164 %}
+							{% if speaker["name"] == "雷电将军（雷神）" %}
 								<option value="{{speaker["id"]}}" selected>{{speaker["id"]}} | {{speaker["name"]}} | {{speaker["lang"]}}</option>
 							{% else %}
 								<option value="{{speaker["id"]}}">{{speaker["id"]}} | {{speaker["name"]}} | {{speaker["lang"]}}</option>
@@ -142,10 +142,14 @@
 				paimon:
 				<a href="https://github.com/zixiiu/Digital_Life_Server" rel="noreferrer" target="_blank">zixiiu/Digital_Life_Server</a>
 			</p>
+			<p>
+				vits_chinese:
+				<a href="https://github.com/PlayVoice/vits_chinese" rel="noreferrer" target="_blank">PlayVoice/vits_chinese</a>
+			</p>
 		</main>
 
-		<script src="https://cdn.jsdelivr.net/npm/jquery@3.5.1/dist/jquery.slim.min.js"></script>
-		<script src="https://cdn.jsdelivr.net/npm/bootstrap@4.6.2/dist/js/bootstrap.bundle.min.js"></script>
+		<script src="/static/js/jquery.slim.min.js"></script>
+		<script src="/static/js/bootstrap.bundle.min.js"></script>
 
 		<script>
 			function getProtocol(){

utils/merge.py

@@ -24,6 +24,7 @@ lang_dict = {
     "chinese_dialect_cleaners": ["zh", "ja", "sh", "gd", "en", "SZ", "WX", "CZ", "HZ", "SX", "NB", "JJ", "YX", "JD",
                                  "ZR", "PH", "TX", "JS", "HN", "LP", "XS", "FY", "RA", "CX", "SM", "TT", "WZ", "SC",
                                  "YB"],
+    "bert_chinese_cleaners":["zh"],
 }
 
 
@@ -107,7 +108,7 @@ def merge_model(merging_model):
     new_id = 0
     for obj_id, i in enumerate(vits_list):
         obj = vits(model=i[0], config=i[1], model_type="vits")
-        lang = lang_dict.get(obj.get_cleaner(), obj.get_cleaner())
+        lang = lang_dict.get(obj.get_cleaner(), ["unknown"])
 
         for id, name in enumerate(obj.get_speakers()):
             vits_obj.append([int(id), obj, obj_id])
@@ -127,7 +128,7 @@ def merge_model(merging_model):
     new_id = 0
     for obj_id, i in enumerate(hubert_vits_list):
         obj = vits(model=i[0], config=i[1], model_=hubert, model_type="hubert")
-        lang = lang_dict.get(obj.get_cleaner(), obj.get_cleaner())
+        lang = lang_dict.get(obj.get_cleaner(), ["unknown"])
 
         for id, name in enumerate(obj.get_speakers()):
             hubert_vits_obj.append([int(id), obj, obj_id])
@@ -146,7 +147,7 @@ def merge_model(merging_model):
     new_id = 0
     for obj_id, i in enumerate(w2v2_vits_list):
         obj = vits(model=i[0], config=i[1], model_=emotion_reference, model_type="w2v2")
-        lang = lang_dict.get(obj.get_cleaner(), obj.get_cleaner())
+        lang = lang_dict.get(obj.get_cleaner(), ["unknown"])
 
         for id, name in enumerate(obj.get_speakers()):
             w2v2_vits_obj.append([int(id), obj, obj_id])

utils/utils.py

@@ -40,7 +40,7 @@ class HParams():
 
 def load_checkpoint(checkpoint_path, model):
     checkpoint_dict = load(checkpoint_path, map_location='cpu')
-    iteration = checkpoint_dict['iteration']
+    iteration = checkpoint_dict.get('iteration', None)
     saved_state_dict = checkpoint_dict['model']
     if hasattr(model, 'module'):
         state_dict = model.module.state_dict()
@@ -51,14 +51,16 @@ def load_checkpoint(checkpoint_path, model):
         try:
             new_state_dict[k] = saved_state_dict[k]
         except:
-            logging.info("%s is not in the checkpoint" % k)
+            logging.info(f"{k} is not in the checkpoint")
             new_state_dict[k] = v
     if hasattr(model, 'module'):
         model.module.load_state_dict(new_state_dict)
     else:
         model.load_state_dict(new_state_dict)
-    logging.info("Loaded checkpoint '{}' (iteration {})".format(
-        checkpoint_path, iteration))
+    if iteration:
+        logging.info(f"Loaded checkpoint '{checkpoint_path}' (iteration {iteration})")
+    else:
+        logging.info(f"Loaded checkpoint '{checkpoint_path}'")
     return
 
 

vits_pinyin.py

@@ -0,0 +1,100 @@
+import re
+
+import pypinyin
+from pypinyin import Style
+from pypinyin.contrib.neutral_tone import NeutralToneWith5Mixin
+from pypinyin.converter import DefaultConverter
+from pypinyin.core import Pinyin
+
+import numpy as np
+
+from bert.prosody_tool import pinyin_dict
+from bert import TTSProsody
+
+
+class MyConverter(NeutralToneWith5Mixin, DefaultConverter):
+    pass
+
+
+def is_chinese(uchar):
+    if uchar >= u'\u4e00' and uchar <= u'\u9fa5':
+        return True
+    else:
+        return False
+
+
+def clean_chinese(text: str):
+    text = text.strip()
+    text_clean = []
+    for char in text:
+        if (is_chinese(char)):
+            text_clean.append(char)
+        else:
+            if len(text_clean) > 1 and is_chinese(text_clean[-1]):
+                text_clean.append(',')
+    text_clean = ''.join(text_clean).strip(',')
+    return text_clean
+
+
+class VITS_PinYin:
+    def __init__(self, bert_path, device):
+        self.pinyin_parser = Pinyin(MyConverter())
+        self.prosody = TTSProsody(bert_path, device)
+
+    def chinese_to_phonemes(self, text):
+        # 考虑使用g2pw的chinese bert替换原始的pypinyin,目前测试下来运行速度太慢。
+        # 将标准中文文本符号替换成 bert 符号库中的单符号,以保证bert的效果.
+        text = text.replace("——", "...")\
+            .replace("—", "...")\
+            .replace("……", "...")\
+            .replace("…", "...")\
+            .replace('“', '"')\
+            .replace('”', '"')\
+            .replace("\n", "")
+        tokens = self.prosody.char_model.tokenizer.tokenize(text)
+        text = ''.join(tokens)
+        assert not tokens.count("[UNK]")
+        pinyins = np.reshape(pypinyin.pinyin(text, style=pypinyin.TONE3), (-1))
+        try:
+            phone_index = 0
+            phone_items = []
+            phone_items.append('sil')
+            count_phone = []
+            count_phone.append(1)
+            temp = ""
+
+            len_pys = len(tokens)
+            for word in tokens:
+                if is_chinese(word):
+                    count_phone.append(2)
+                    if (phone_index >= len_pys):
+                        print(
+                            f"!!!![{text}]plz check ur text whether includes MULTIBYTE symbol.\
+                                (请检查你的文本中是否包含多字节符号)")
+                    pinyin = pinyins[phone_index]
+                    phone_index = phone_index + 1
+                    if not pinyin[-1].isdigit():
+                        pinyin += "5"
+                    if pinyin[:-1] in pinyin_dict:
+                        tone = pinyin[-1]
+                        a = pinyin[:-1]
+                        a1, a2 = pinyin_dict[a]
+                        phone_items += [a1, a2 + tone]
+                else:
+                    temp += word
+                    if temp == pinyins[phone_index]:
+                        temp = ""
+                        phone_index += 1
+                    count_phone.append(1)
+                    phone_items.append('sp')
+
+            count_phone.append(1)
+            phone_items.append('sil')
+            phone_items_str = ' '.join(phone_items)
+        except IndexError as e:
+            print('except:', e)
+
+        text = f'[PAD]{text}[PAD]'
+        char_embeds = self.prosody.get_char_embeds(text)
+        char_embeds = self.prosody.expand_for_phone(char_embeds, count_phone)
+        return phone_items_str, char_embeds

voice.py

@@ -26,18 +26,22 @@ class vits:
     def __init__(self, model, config, model_=None, model_type=None):
         self.model_type = model_type
         self.hps_ms = utils.get_hparams_from_file(config)
-        self.n_speakers = self.hps_ms.data.n_speakers if 'n_speakers' in self.hps_ms.data.keys() else 0
-        self.n_symbols = len(self.hps_ms.symbols) if 'symbols' in self.hps_ms.keys() else 0
-        self.speakers = self.hps_ms.speakers if 'speakers' in self.hps_ms.keys() else ['0']
-        self.use_f0 = self.hps_ms.data.use_f0 if 'use_f0' in self.hps_ms.data.keys() else False
-        self.emotion_embedding = self.hps_ms.data.emotion_embedding if 'emotion_embedding' in self.hps_ms.data.keys() else False
+        self.n_speakers = getattr(self.hps_ms.data, 'n_speakers', 0)
+        self.n_symbols = len(getattr(self.hps_ms, 'symbols', []))
+        self.speakers = getattr(self.hps_ms, 'speakers', ['0'])
+        self.use_f0 = getattr(self.hps_ms.data, 'use_f0', False)
+        self.emotion_embedding = getattr(self.hps_ms.data, 'emotion_embedding',
+                                         getattr(self.hps_ms.model, 'emotion_embedding', False))
+        self.bert_embedding = getattr(self.hps_ms.data, 'bert_embedding',
+                                      getattr(self.hps_ms.model, 'bert_embedding', False))
+        self.hps_ms.model.emotion_embedding = self.emotion_embedding
+        self.hps_ms.model.bert_embedding = self.bert_embedding
 
         self.net_g_ms = SynthesizerTrn(
             self.n_symbols,
             self.hps_ms.data.filter_length // 2 + 1,
             self.hps_ms.train.segment_size // self.hps_ms.data.hop_length,
             n_speakers=self.n_speakers,
-            emotion_embedding=self.emotion_embedding,
             **self.hps_ms.model)
         _ = self.net_g_ms.eval()
 
@@ -56,7 +60,13 @@ class vits:
         if cleaned:
             text_norm = text_to_sequence(text, hps.symbols, [])
         else:
-            text_norm = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
+            if self.bert_embedding:
+                text_norm, char_embed = text_to_sequence(text, hps.symbols, hps.data.text_cleaners,
+                                                         bert_embedding=self.bert_embedding)
+                text_norm = LongTensor(text_norm)
+                return text_norm, char_embed
+            else:
+                text_norm = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
         if hps.data.add_blank:
             text_norm = commons.intersperse(text_norm, 0)
         text_norm = LongTensor(text_norm)
@@ -69,18 +79,22 @@ class vits:
         return self.speakers
 
     def infer(self, params):
-        emotion = params.get("emotion", None)
-        emotion = emotion.to(device) if emotion != None else None
-
         with no_grad():
-            x_tst = params.get("stn_tst").unsqueeze(0)
-            x_tst_lengths = LongTensor([params.get("stn_tst").size(0)])
-
-            audio = self.net_g_ms.infer(x_tst.to(device), x_tst_lengths.to(device), sid=params.get("sid").to(device),
+            x_tst = params.get("stn_tst").unsqueeze(0).to(device)
+            x_tst_lengths = LongTensor([params.get("stn_tst").size(0)]).to(device)
+            x_tst_prosody = torch.FloatTensor(params.get("char_embeds")).unsqueeze(0).to(
+                device) if self.bert_embedding else None
+            sid = params.get("sid").to(device) if not self.bert_embedding else None
+            emotion = params.get("emotion").to(device) if self.emotion_embedding else None
+
+            audio = self.net_g_ms.infer(x=x_tst,
+                                        x_lengths=x_tst_lengths,
+                                        sid=sid,
                                         noise_scale=params.get("noise_scale"),
                                         noise_scale_w=params.get("noise_scale_w"),
                                         length_scale=params.get("length_scale"),
-                                        emotion_embedding=emotion)[0][0, 0].data.float().cpu().numpy()
+                                        emotion_embedding=emotion,
+                                        bert=x_tst_prosody)[0][0, 0].data.float().cpu().numpy()
 
         torch.cuda.empty_cache()
 
@@ -89,9 +103,14 @@ class vits:
     def get_infer_param(self, length_scale, noise_scale, noise_scale_w, text=None, speaker_id=None, audio_path=None,
                         emotion=None, cleaned=False, f0_scale=1):
         emo = None
+        char_embeds = None
         if self.model_type != "hubert":
-            stn_tst = self.get_cleaned_text(text, self.hps_ms, cleaned=cleaned)
-            sid = LongTensor([speaker_id])
+            if self.bert_embedding:
+                stn_tst, char_embeds = self.get_cleaned_text(text, self.hps_ms, cleaned=cleaned)
+                sid = None
+            else:
+                stn_tst = self.get_cleaned_text(text, self.hps_ms, cleaned=cleaned)
+                sid = LongTensor([speaker_id])
 
         if self.model_type == "w2v2":
             # if emotion_reference.endswith('.npy'):
@@ -133,7 +152,7 @@ class vits:
             sid = LongTensor([speaker_id])
         params = {"length_scale": length_scale, "noise_scale": noise_scale,
                   "noise_scale_w": noise_scale_w, "stn_tst": stn_tst,
-                  "sid": sid, "emotion": emo}
+                  "sid": sid, "emotion": emo, "char_embeds": char_embeds}
 
         return params