File size: 34,026 Bytes
3c7a160
 
5905fd5
3c7a160
5905fd5
3c7a160
 
5905fd5
d631c8d
5905fd5
 
 
 
 
3c7a160
2f92c5d
5905fd5
 
 
2eccd3d
 
 
 
 
 
 
 
 
 
2d122b6
 
2eccd3d
3c7a160
 
 
 
 
 
 
 
815c4e4
 
5905fd5
 
3c7a160
5905fd5
 
 
 
3c7a160
5905fd5
e70c011
5905fd5
e70c011
5905fd5
 
 
 
 
 
 
 
3c7a160
 
 
 
b2a5005
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3c7a160
 
b2a5005
 
 
 
 
 
3c7a160
 
 
b2a5005
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3c7a160
b2a5005
3c7a160
b2a5005
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
815c4e4
 
b2a5005
815c4e4
b2a5005
 
c4b28b2
b2a5005
 
 
 
 
 
d631c8d
b2a5005
d631c8d
b2a5005
d631c8d
b2a5005
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3c7a160
d631c8d
 
815c4e4
 
e70c011
815c4e4
 
2eccd3d
815c4e4
 
d631c8d
 
 
 
 
 
 
a943d2c
 
d631c8d
a943d2c
d631c8d
a943d2c
d631c8d
 
a943d2c
d631c8d
 
 
e70c011
 
 
 
 
2eccd3d
 
e70c011
 
 
 
2eccd3d
e70c011
 
 
 
 
 
 
 
b2a5005
e70c011
 
2eccd3d
e70c011
 
2eccd3d
e70c011
 
2eccd3d
e70c011
815c4e4
2fd0f46
 
 
815c4e4
 
 
e70c011
815c4e4
 
 
 
 
5905fd5
b2a5005
3c7a160
 
 
 
 
5905fd5
e70c011
3c7a160
 
 
 
 
 
 
5905fd5
 
3c7a160
 
 
 
 
 
 
5905fd5
 
3c7a160
 
 
e70c011
3c7a160
 
815c4e4
3c7a160
 
 
 
 
 
 
 
 
 
 
e70c011
b2a5005
3c7a160
 
 
815c4e4
 
 
 
 
e70c011
b2a5005
 
3c7a160
 
 
 
2fd0f46
b2a5005
2fd0f46
 
 
b2a5005
 
 
 
 
 
 
 
 
 
 
 
3c7a160
2f92c5d
 
 
 
 
3c7a160
 
 
 
 
 
 
 
 
 
 
 
 
e70c011
3c7a160
2fd0f46
 
3c7a160
c283d94
 
 
a943d2c
3c7a160
2fd0f46
c283d94
 
 
 
 
516fd45
2fd0f46
9a035cf
3c7a160
 
5905fd5
3c7a160
 
 
 
2f92c5d
 
 
3c7a160
d8137a5
 
 
 
 
 
 
 
e70c011
c283d94
 
2f92c5d
3c7a160
d631c8d
d8137a5
d631c8d
2fd0f46
d631c8d
 
2f92c5d
 
 
 
d631c8d
3c7a160
 
 
815c4e4
 
3c7a160
 
 
 
2f92c5d
815c4e4
 
5905fd5
3c7a160
 
 
 
d631c8d
 
3c7a160
 
b2a5005
 
 
 
 
815c4e4
b2a5005
815c4e4
b2a5005
e70c011
3c7a160
c283d94
2f92c5d
3c7a160
d631c8d
 
3c7a160
 
815c4e4
516fd45
815c4e4
 
3c7a160
d8137a5
 
 
 
 
 
3c7a160
e70c011
3c7a160
815c4e4
d631c8d
 
32524cc
 
 
 
b2a5005
d631c8d
 
3c7a160
2eccd3d
295263b
 
 
d631c8d
 
 
5905fd5
3c7a160
 
b2a5005
 
 
3c7a160
 
 
 
 
 
aa95d38
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
import gradio as gr
import numpy as np
import soundfile as sf
from datetime import datetime
from time import time as ttime
from my_utils import load_audio
from transformers import pipeline
from text.cleaner import clean_text
from polyglot.detect import  Detector
from feature_extractor import cnhubert
from timeit import default_timer as timer
from text import cleaned_text_to_sequence
from module.models  import  SynthesizerTrn
from module.mel_processing import spectrogram_torch
from transformers.pipelines.audio_utils import ffmpeg_read
import os,re,sys,LangSegment,librosa,pdb,torch,pytz,random
from transformers import AutoModelForMaskedLM, AutoTokenizer
from AR.models.t2s_lightning_module import Text2SemanticLightningModule


import logging
logging.getLogger("markdown_it").setLevel(logging.ERROR)
logging.getLogger("urllib3").setLevel(logging.ERROR)
logging.getLogger("httpcore").setLevel(logging.ERROR)
logging.getLogger("httpx").setLevel(logging.ERROR)
logging.getLogger("asyncio").setLevel(logging.ERROR)
logging.getLogger("charset_normalizer").setLevel(logging.ERROR)
logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
logging.getLogger("multipart").setLevel(logging.WARNING)
from download import *
download()

if "_CUDA_VISIBLE_DEVICES" in os.environ:
    os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
tz = pytz.timezone('Asia/Singapore')
device = "cuda" if torch.cuda.is_available() else "cpu"

def abs_path(dir):
    global_dir = os.path.dirname(os.path.abspath(sys.argv[0]))
    return(os.path.join(global_dir, dir))
gpt_path = abs_path("MODELS/22/22.ckpt")
sovits_path=abs_path("MODELS/22/22.pth")
cnhubert_base_path = os.environ.get("cnhubert_base_path", "pretrained_models/chinese-hubert-base")
bert_path = os.environ.get("bert_path", "pretrained_models/chinese-roberta-wwm-ext-large")

if not os.path.exists(cnhubert_base_path):
    cnhubert_base_path = "TencentGameMate/chinese-hubert-base"
if not os.path.exists(bert_path):
    bert_path = "hfl/chinese-roberta-wwm-ext-large"
cnhubert.cnhubert_base_path = cnhubert_base_path

whisper_path = os.environ.get("whisper_path", "pretrained_models/whisper-tiny")
if not os.path.exists(whisper_path):
    whisper_path = "openai/whisper-tiny"

pipe = pipeline(
    task="automatic-speech-recognition",
    model=whisper_path,
    chunk_length_s=30,
    device=device,)


is_half = eval(
    os.environ.get("is_half", "True" if torch.cuda.is_available() else "False")
)

tokenizer = AutoTokenizer.from_pretrained(bert_path)
bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
if is_half == True:
    bert_model = bert_model.half().to(device)
else:
    bert_model = bert_model.to(device)


def get_bert_feature(text, word2ph):
    with torch.no_grad():
        inputs = tokenizer(text, return_tensors="pt")
        for i in inputs:
            inputs[i] = inputs[i].to(device)
        res = bert_model(**inputs, output_hidden_states=True)
        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
    assert len(word2ph) == len(text)
    phone_level_feature = []
    for i in range(len(word2ph)):
        repeat_feature = res[i].repeat(word2ph[i], 1)
        phone_level_feature.append(repeat_feature)
    phone_level_feature = torch.cat(phone_level_feature, dim=0)
    return phone_level_feature.T


class DictToAttrRecursive(dict):
    def __init__(self, input_dict):
        super().__init__(input_dict)
        for key, value in input_dict.items():
            if isinstance(value, dict):
                value = DictToAttrRecursive(value)
            self[key] = value
            setattr(self, key, value)

    def __getattr__(self, item):
        try:
            return self[item]
        except KeyError:
            raise AttributeError(f"Attribute {item} not found")

    def __setattr__(self, key, value):
        if isinstance(value, dict):
            value = DictToAttrRecursive(value)
        super(DictToAttrRecursive, self).__setitem__(key, value)
        super().__setattr__(key, value)

    def __delattr__(self, item):
        try:
            del self[item]
        except KeyError:
            raise AttributeError(f"Attribute {item} not found")


ssl_model = cnhubert.get_model()
if is_half == True:
    ssl_model = ssl_model.half().to(device)
else:
    ssl_model = ssl_model.to(device)


def change_sovits_weights(sovits_path):
    global vq_model, hps
    dict_s2 = torch.load(sovits_path, map_location="cpu")
    hps = dict_s2["config"]
    hps = DictToAttrRecursive(hps)
    hps.model.semantic_frame_rate = "25hz"
    vq_model = SynthesizerTrn(
        hps.data.filter_length // 2 + 1,
        hps.train.segment_size // hps.data.hop_length,
        n_speakers=hps.data.n_speakers,
        **hps.model
    )
    if ("pretrained" not in sovits_path):
        del vq_model.enc_q
    if is_half == True:
        vq_model = vq_model.half().to(device)
    else:
        vq_model = vq_model.to(device)
    vq_model.eval()
    print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
    with open("./sweight.txt", "w", encoding="utf-8") as f:
        f.write(sovits_path)


change_sovits_weights(sovits_path)


def change_gpt_weights(gpt_path):
    global hz, max_sec, t2s_model, config
    hz = 50
    dict_s1 = torch.load(gpt_path, map_location="cpu")
    config = dict_s1["config"]
    max_sec = config["data"]["max_sec"]
    t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
    t2s_model.load_state_dict(dict_s1["weight"])
    if is_half == True:
        t2s_model = t2s_model.half()
    t2s_model = t2s_model.to(device)
    t2s_model.eval()
    total = sum([param.nelement() for param in t2s_model.parameters()])
    print("Number of parameter: %.2fM" % (total / 1e6))
    with open("./gweight.txt", "w", encoding="utf-8") as f: f.write(gpt_path)


change_gpt_weights(gpt_path)


def get_spepc(hps, filename):
    audio = load_audio(filename, int(hps.data.sampling_rate))
    audio = torch.FloatTensor(audio)
    audio_norm = audio
    audio_norm = audio_norm.unsqueeze(0)
    spec = spectrogram_torch(
        audio_norm,
        hps.data.filter_length,
        hps.data.sampling_rate,
        hps.data.hop_length,
        hps.data.win_length,
        center=False,
    )
    return spec


dict_language = {
    ("中文1"): "all_zh",#全部按中文识别
    ("English"): "en",#全部按英文识别#######不变
    ("日文1"): "all_ja",#全部按日文识别
    ("中文"): "zh",#按中英混合识别####不变
    ("日本語"): "ja",#按日英混合识别####不变
    ("混合"): "auto",#多语种启动切分识别语种
}


def splite_en_inf(sentence, language):
    pattern = re.compile(r'[a-zA-Z ]+')
    textlist = []
    langlist = []
    pos = 0
    for match in pattern.finditer(sentence):
        start, end = match.span()
        if start > pos:
            textlist.append(sentence[pos:start])
            langlist.append(language)
        textlist.append(sentence[start:end])
        langlist.append("en")
        pos = end
    if pos < len(sentence):
        textlist.append(sentence[pos:])
        langlist.append(language)
    # Merge punctuation into previous word
    for i in range(len(textlist)-1, 0, -1):
        if re.match(r'^[\W_]+$', textlist[i]):
            textlist[i-1] += textlist[i]
            del textlist[i]
            del langlist[i]
    # Merge consecutive words with the same language tag
    i = 0
    while i < len(langlist) - 1:
        if langlist[i] == langlist[i+1]:
            textlist[i] += textlist[i+1]
            del textlist[i+1]
            del langlist[i+1]
        else:
            i += 1

    return textlist, langlist


def clean_text_inf(text, language):
    formattext = ""
    language = language.replace("all_","")
    for tmp in LangSegment.getTexts(text):
        if language == "ja":
            if tmp["lang"] == language or tmp["lang"] == "zh":
                formattext += tmp["text"] + " "
            continue
        if tmp["lang"] == language:
            formattext += tmp["text"] + " "
    while "  " in formattext:
        formattext = formattext.replace("  ", " ")
    phones, word2ph, norm_text = clean_text(formattext, language)
    phones = cleaned_text_to_sequence(phones)
    return phones, word2ph, norm_text

dtype=torch.float16 if is_half == True else torch.float32
def get_bert_inf(phones, word2ph, norm_text, language):
    language=language.replace("all_","")
    if language == "zh":
        bert = get_bert_feature(norm_text, word2ph).to(device)#.to(dtype)
    else:
        bert = torch.zeros(
            (1024, len(phones)),
            dtype=torch.float16 if is_half == True else torch.float32,
        ).to(device)

    return bert


def nonen_clean_text_inf(text, language):
    if(language!="auto"):
        textlist, langlist = splite_en_inf(text, language)
    else:
        textlist=[]
        langlist=[]
        for tmp in LangSegment.getTexts(text):
            langlist.append(tmp["lang"])
            textlist.append(tmp["text"])
    print(textlist)
    print(langlist)
    phones_list = []
    word2ph_list = []
    norm_text_list = []
    for i in range(len(textlist)):
        lang = langlist[i]
        phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
        phones_list.append(phones)
        if lang == "zh":
            word2ph_list.append(word2ph)
        norm_text_list.append(norm_text)
    print(word2ph_list)
    phones = sum(phones_list, [])
    word2ph = sum(word2ph_list, [])
    norm_text = ' '.join(norm_text_list)

    return phones, word2ph, norm_text


def nonen_get_bert_inf(text, language):
    if(language!="auto"):
        textlist, langlist = splite_en_inf(text, language)
    else:
        textlist=[]
        langlist=[]
        for tmp in LangSegment.getTexts(text):
            langlist.append(tmp["lang"])
            textlist.append(tmp["text"])
    print(textlist)
    print(langlist)
    bert_list = []
    for i in range(len(textlist)):
        lang = langlist[i]
        phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
        bert = get_bert_inf(phones, word2ph, norm_text, lang)
        bert_list.append(bert)
    bert = torch.cat(bert_list, dim=1)

    return bert


splits = {",", "。", "?", "!", ",", ".", "?", "!", "~", ":", ":", "—", "…", }


def get_first(text):
    pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
    text = re.split(pattern, text)[0].strip()
    return text


def get_cleaned_text_final(text,language):
    if language in {"en","all_zh","all_ja"}:
        phones, word2ph, norm_text = clean_text_inf(text, language)
    elif language in {"zh", "ja","auto"}:
        phones, word2ph, norm_text = nonen_clean_text_inf(text, language)
    return phones, word2ph, norm_text

def get_bert_final(phones, word2ph, text,language,device):
    if language == "en":
        bert = get_bert_inf(phones, word2ph, text, language)
    elif language in {"zh", "ja","auto"}:
        bert = nonen_get_bert_inf(text, language)
    elif language == "all_zh":
        bert = get_bert_feature(text, word2ph).to(device)
    else:
        bert = torch.zeros((1024, len(phones))).to(device)
    return bert

def merge_short_text_in_array(texts, threshold):
    if (len(texts)) < 2:
        return texts
    result = []
    text = ""
    for ele in texts:
        text += ele
        if len(text) >= threshold:
            result.append(text)
            text = ""
    if (len(text) > 0):
        if len(result) == 0:
            result.append(text)
        else:
            result[len(result) - 1] += text
    return result


def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=("Do not split"), volume_scale=1.0):
    if not duration(ref_wav_path):
        return None
    if  text == '':
        wprint("Please enter text to generate/请输入生成文字")
        return None
    t0 = ttime()
    startTime=timer()
    text=trim_text(text,text_language)
    change_sovits_weights(sovits_path)
    tprint(f'🏕️LOADED SoVITS Model: {sovits_path}')
    change_gpt_weights(gpt_path)
    tprint(f'🏕️LOADED GPT Model: {gpt_path}')

    prompt_language = dict_language[prompt_language]
    try:
        text_language = dict_language[text_language]
    except KeyError as e:
        wprint(f"Unsupported language type: {e}")
        return None
        
    prompt_text = prompt_text.strip("\n")
    if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
    text = text.strip("\n")
    if (text[0] not in splits and len(get_first(text)) < 4): text = "。" + text if text_language != "en" else "." + text
    #print(("实际输入的参考文本:"), prompt_text)
    #print(("📝实际输入的目标文本:"), text)
    zero_wav = np.zeros(
        int(hps.data.sampling_rate * 0.3),
        dtype=np.float16 if is_half == True else np.float32,
    )
    with torch.no_grad():
        wav16k, sr = librosa.load(ref_wav_path, sr=16000)
        if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
            errinfo='参考音频在3~10秒范围外,请更换!'
            raise OSError((errinfo))
        wav16k = torch.from_numpy(wav16k)
        zero_wav_torch = torch.from_numpy(zero_wav)
        if is_half == True:
            wav16k = wav16k.half().to(device)
            zero_wav_torch = zero_wav_torch.half().to(device)
        else:
            wav16k = wav16k.to(device)
            zero_wav_torch = zero_wav_torch.to(device)
        wav16k = torch.cat([wav16k, zero_wav_torch])
        ssl_content = ssl_model.model(wav16k.unsqueeze(0))[
            "last_hidden_state"
        ].transpose(
            1, 2
        )  # .float()
        codes = vq_model.extract_latent(ssl_content)
        prompt_semantic = codes[0, 0]
    t1 = ttime()

    phones1, word2ph1, norm_text1=get_cleaned_text_final(prompt_text, prompt_language)

    if (how_to_cut == ("Split into groups of 4 sentences")):
        text = cut1(text)
    elif (how_to_cut == ("Split every 50 characters")):
        text = cut2(text)
    elif (how_to_cut == ("Split at CN/JP periods (。)")):
        text = cut3(text)
    elif (how_to_cut == ("Split at English periods (.)")):
        text = cut4(text)
    elif (how_to_cut == ("Split at punctuation marks")):
        text = cut5(text)
    while "\n\n" in text:
        text = text.replace("\n\n", "\n")
    print(f"🧨实际输入的目标文本(切句后):{text}\n")
    texts = text.split("\n")
    texts = merge_short_text_in_array(texts, 5)
    audio_opt = []
    bert1=get_bert_final(phones1, word2ph1, norm_text1,prompt_language,device).to(dtype)

    for text in texts:
        if (len(text.strip()) == 0):
            continue
        if (text[-1] not in splits): text += "。" if text_language != "en" else "."
        print(("\n🎈实际输入的目标文本(每句):"), text)
        phones2, word2ph2, norm_text2 = get_cleaned_text_final(text, text_language)
        try:
            bert2 = get_bert_final(phones2, word2ph2, norm_text2, text_language, device).to(dtype)
        except RuntimeError as e:
            wprint(f"The input text does not match the language/输入文本与语言不匹配: {e}")
            return None
        bert = torch.cat([bert1, bert2], 1)

        all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
        bert = bert.to(device).unsqueeze(0)
        all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
        prompt = prompt_semantic.unsqueeze(0).to(device)
        t2 = ttime()
        with torch.no_grad():
            # pred_semantic = t2s_model.model.infer(
            pred_semantic, idx = t2s_model.model.infer_panel(
                all_phoneme_ids,
                all_phoneme_len,
                prompt,
                bert,
                # prompt_phone_len=ph_offset,
                top_k=config["inference"]["top_k"],
                early_stop_num=hz * max_sec,
            )
        t3 = ttime()
        # print(pred_semantic.shape,idx)
        pred_semantic = pred_semantic[:, -idx:].unsqueeze(
            0
        )  # .unsqueeze(0)#mq要多unsqueeze一次
        refer = get_spepc(hps, ref_wav_path)  # .to(device)
        if is_half == True:
            refer = refer.half().to(device)
        else:
            refer = refer.to(device)
        # audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
        try:
          audio = (
            vq_model.decode(
                pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer
            )
                .detach()
                .cpu()
                .numpy()[0, 0]
        ) 
        except RuntimeError as e:
            wprint(f"The input text does not match the language/输入文本与语言不匹配: {e}")
            return None

        max_audio=np.abs(audio).max()
        if max_audio>1:audio/=max_audio
        audio_opt.append(audio)
        audio_opt.append(zero_wav)
        t4 = ttime()
    print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
    #yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(np.int16)
    audio_data = (np.concatenate(audio_opt, 0) * 32768).astype(np.int16)
    
    audio_data = (audio_data.astype(np.float32) * volume_scale).astype(np.int16)
    output_wav = "output_audio.wav"  
    sf.write(output_wav, audio_data, hps.data.sampling_rate)
    endTime=timer()
    tprint(f'🆗TTS COMPLETE,{round(endTime-startTime,4)}s')
    return output_wav

def split(todo_text):
    todo_text = todo_text.replace("……", "。").replace("——", ",")
    if todo_text[-1] not in splits:
        todo_text += "。"
    i_split_head = i_split_tail = 0
    len_text = len(todo_text)
    todo_texts = []
    while 1:
        if i_split_head >= len_text:
            break  
        if todo_text[i_split_head] in splits:
            i_split_head += 1
            todo_texts.append(todo_text[i_split_tail:i_split_head])
            i_split_tail = i_split_head
        else:
            i_split_head += 1
    return todo_texts


def cut1(inp):
    inp = inp.strip("\n")
    inps = split(inp)
    split_idx = list(range(0, len(inps), 4))
    split_idx[-1] = None
    if len(split_idx) > 1:
        opts = []
        for idx in range(len(split_idx) - 1):
            opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]]))
    else:
        opts = [inp]
    return "\n".join(opts)


def cut2(inp):
    inp = inp.strip("\n")
    inps = split(inp)
    if len(inps) < 2:
        return inp
    opts = []
    summ = 0
    tmp_str = ""
    for i in range(len(inps)):
        summ += len(inps[i])
        tmp_str += inps[i]
        if summ > 50:
            summ = 0
            opts.append(tmp_str)
            tmp_str = ""
    if tmp_str != "":
        opts.append(tmp_str)
    # print(opts)
    if len(opts) > 1 and len(opts[-1]) < 50:  
        opts[-2] = opts[-2] + opts[-1]
        opts = opts[:-1]
    return "\n".join(opts)


def cut3(inp):
    inp = inp.strip("\n")
    return "\n".join(["%s" % item for item in inp.strip("。").split("。")])


def cut4(inp):
    inp = inp.strip("\n")
    return "\n".join(["%s" % item for item in inp.strip(".").split(".")])


# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
def cut5(inp):
    # if not re.search(r'[^\w\s]', inp[-1]):
    # inp += '。'
    inp = inp.strip("\n")
    punds = r'[,.;?!、,。?!;:…]'
    items = re.split(f'({punds})', inp)
    mergeitems = ["".join(group) for group in zip(items[::2], items[1::2])]
    if len(items)%2 == 1:
        mergeitems.append(items[-1])
    opt = "\n".join(mergeitems)
    return opt



def custom_sort_key(s):
    # 使用正则表达式提取字符串中的数字部分和非数字部分
    parts = re.split('(\d+)', s)
    # 将数字部分转换为整数,非数字部分保持不变
    parts = [int(part) if part.isdigit() else part for part in parts]
    return parts

#==========custom functions============

def tprint(text):
    now=datetime.now(tz).strftime('%H:%M:%S')
    print(f'UTC+8 - {now} - {text}')

def wprint(text):
    tprint(text)
    gr.Warning(text)

def lang_detector(text):
    min_chars = 5
    if len(text) < min_chars:
        return "Input text too short/输入文本太短"
    try:
        detector = Detector(text).language
        lang_info = str(detector)
        code = re.search(r"name: (\w+)", lang_info).group(1)
        if code == 'Japanese':
            return "日本語"
        elif code == 'Chinese':
            return "中文"
        elif code == 'English':
            return 'English'
        else:
            return code
    except Exception as e:
        return f"ERROR:{str(e)}"
        
def trim_text(text,language): 
    limit_cj = 120 #character
    limit_en = 60 #words  
    search_limit_cj = limit_cj+30
    search_limit_en = limit_en +30
    text = text.replace('\n', '').strip()
    
    if language =='English':
        words = text.split()
        if len(words) <= limit_en:
            return text
        # English
        for i in range(limit_en, -1, -1):
            if any(punct in words[i] for punct in splits):
                return ' '.join(words[:i+1])
        for i in range(limit_en, min(len(words), search_limit_en)):
            if any(punct in words[i] for punct in splits):
                return ' '.join(words[:i+1])
        return ' '.join(words[:limit_en])
        
    else:#中文日文
        if len(text) <= limit_cj:
            return text
        for i in range(limit_cj, -1, -1):  
            if text[i] in splits:
                return text[:i+1]
        for i in range(limit_cj, min(len(text), search_limit_cj)):  
            if text[i] in splits:
                return text[:i+1]
        return text[:limit_cj]   

def duration(audio_file_path):
    if not audio_file_path:
        wprint("Failed to obtain uploaded audio/未找到音频文件")
        return False
    try:
        audio_duration = librosa.get_duration(filename=audio_file_path)
        if not 3 < audio_duration < 10:
            wprint("The audio length must be between 3~10 seconds/音频时长须在3~10秒之间")
            return False
        return True
    except FileNotFoundError:
        return False

def update_model(choice):
    global gpt_path, sovits_path  
    model_info = models[choice]
    gpt_path = abs_path(model_info["gpt_weight"])
    sovits_path = abs_path(model_info["sovits_weight"])
    model_name = choice
    tone_info = model_info["tones"]["tone1"] 
    tone_sample_path = abs_path(tone_info["sample"])
    tprint(f'✅SELECT MODEL:{choice}')
    # 返回默认tone“tone1”
    return (
        tone_info["example_voice_wav"],   
        tone_info["example_voice_wav_words"],   
        model_info["default_language"],   
        model_info["default_language"],
        model_name,
        "tone1"  ,
        tone_sample_path
    )

def update_tone(model_choice, tone_choice):
    model_info = models[model_choice]  
    tone_info = model_info["tones"][tone_choice]  
    example_voice_wav = abs_path(tone_info["example_voice_wav"])  
    example_voice_wav_words = tone_info["example_voice_wav_words"]  
    tone_sample_path = abs_path(tone_info["sample"])
    return example_voice_wav, example_voice_wav_words,tone_sample_path

def transcribe(voice):
    time1=timer()
    tprint('⚡Start Clone - transcribe')
    task="transcribe"
    if voice is None:
        wprint("No audio file submitted! Please upload or record an audio file before submitting your request.")
    R = pipe(voice, batch_size=8, generate_kwargs={"task": task}, return_timestamps=True,return_language=True)
    text=R['text']
    lang=R['chunks'][0]['language']
    if lang=='english':
      language='English'
    elif lang =='chinese':
      language='中文'
    elif lang=='japanese':
      language = '日本語'

    time2=timer()
    tprint(f'transcribe COMPLETE,{round(time2-time1,4)}s')
    tprint(f'\nTRANSCRIBE RESULT:\n 🔣Language:{language} \n 🔣Text:{text}' )
    return  text,language  

def clone_voice(user_voice,user_text,user_lang):
    if not duration(user_voice):
        return None
    if  user_text == '':
        wprint("Please enter text to generate/请输入生成文字")
        return None
    user_text=trim_text(user_text,user_lang)
    time1=timer()
    global gpt_path, sovits_path
    gpt_path = abs_path("pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt")
    #tprint(f'Model loaded:{gpt_path}')
    sovits_path = abs_path("pretrained_models/s2G488k.pth")
    #tprint(f'Model loaded:{sovits_path}')
    try:
        prompt_text, prompt_language = transcribe(user_voice)
    except UnboundLocalError as e:
        wprint(f"The language in the audio cannot be recognized :{str(e)}")
        return None
    
    output_wav = get_tts_wav(
    user_voice,
    prompt_text,
    prompt_language,
    user_text,
    user_lang,
    how_to_cut="Do not split",
    volume_scale=1.0)
    time2=timer()
    tprint(f'🆗CLONE COMPLETE,{round(time2-time1,4)}s')
    return output_wav

with open('dummy') as f:
    dummy_txt = f.read().strip().splitlines()

def dice():
    return random.choice(dummy_txt), '🎲'

from info import models
models_by_language = {
    "English": [],
    "中文": [],
    "日本語": []
}
for model_name, model_info in models.items():
    language = model_info["default_language"]
    models_by_language[language].append((model_name, model_info))

##########GRADIO###########

with gr.Blocks(theme='Kasien/ali_theme_custom') as app:
    gr.HTML('''
  <h1 style="font-size: 25px;">TEXT TO SPEECH</h1>
  <h1 style="font-size: 20px;">Support English/Chinese/Japanese</h1>
  <p style="margin-bottom: 10px; font-size: 100%">
   If you like this space, please click the ❤️ at the top of the page..如喜欢,请点一下页面顶部的❤️<br>
  </p>''')

    gr.Markdown("""* This space is based on the text-to-speech generation solution [GPT-SoVITS](https://github.com/RVC-Boss/GPT-SoVITS) . 
    You can visit the repo's github homepage to learn training and inference.<br>
    本空间基于文字转语音生成方案 [GPT-SoVITS](https://github.com/RVC-Boss/GPT-SoVITS). 你可以前往项目的github主页学习如何推理和训练。 
    * ⚠️Generating voice is very slow due to using HuggingFace's free CPU in this space. 
    For faster generation, click the Colab icon below to use this space in Colab,
    which will significantly improve the speed.<br>
    由于本空间使用huggingface的免费CPU进行推理,因此速度很慢,如想快速生成,请点击下方的Colab图标,
    前往Colab使用已获得更快的生成速度。
    <br>Colabの使用を強くお勧めします。より速い生成速度が得られます。 
    *  each model can speak three languages.<br>每个模型都能说三种语言<br>各モデルは3つの言語を話すことができます。""")   
    gr.HTML('''<a href="https://colab.research.google.com/drive/1fTuPZ4tZsAjS-TrhQWMCb7KRdnU8aF6j" target="_blank"><img src="https://camo.githubusercontent.com/dd83d4a334eab7ada034c13747d9e2237182826d32e3fda6629740b6e02f18d8/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f436f6c61622d4639414230303f7374796c653d666f722d7468652d6261646765266c6f676f3d676f6f676c65636f6c616226636f6c6f723d353235323532" alt="colab"></a>
''')

    default_voice_wav, default_voice_wav_words, default_language, _, default_model_name, _, default_tone_sample_path = update_model("Trump")
    english_models = [name for name, _ in models_by_language["English"]]
    chinese_models = [name for name, _ in models_by_language["中文"]]
    japanese_models = [name for name, _ in models_by_language["日本語"]]
    with gr.Row():
        english_choice = gr.Radio(english_models, label="EN",value="Trump",scale=3)
        chinese_choice = gr.Radio(chinese_models, label="ZH",scale=2)
        japanese_choice = gr.Radio(japanese_models, label="JA",scale=4)

    plsh='''
      Support【English/中文/日本語】,Input text here / 在这輸入文字 /ここにテキストを入力する。
     
      If you don't know what to input, you can click the dice on the right, and random text will appear.
      如果你不知道输入什么,可以点击右边的骰子,会出现随机文本。
      入力するものがわからない場合は、右側のサイコロをクリックすると、ランダムなテキストが表示されます。
  
    '''
    limit='Max 70 words. Excess will be ignored./单次最多处理120字左右,多余的会被忽略'

    gr.HTML('''
    <b>Input Text/输入文字</b>''')
    with gr.Row():
        with gr.Column(scale=2): 
            model_name = gr.Textbox(label="Seleted Model/已选模型", value=default_model_name, interactive=False,scale=1,) 
            text_language = gr.Textbox(
            label="Language for input text/生成语言",
            info='Automatic detection of input language type.',scale=1,interactive=False
            ) 
        text = gr.Textbox(label="INPUT TEXT", lines=5,placeholder=plsh,info=limit,scale=10,min_width=0)
        ddice= gr.Button('🎲', variant='tool',min_width=0,scale=0)

        ddice.click(dice, outputs=[text, ddice])
        text.change( lang_detector, text, text_language)


    with gr.Row():
        with gr.Column(scale=2):    
            tone_select = gr.Radio(
            label="Select Tone/选择语气",
            choices=["tone1","tone2","tone3"],
            value="tone1",
            info='Tone influences the emotional expression ',scale=1)
        tone_sample=gr.Audio(label="🔊Preview tone/试听语气 ", scale=8)


    with gr.Accordion(label="prpt voice", open=False,visible=False):
        with gr.Row(visible=True):
            inp_ref = gr.Audio(label="Reference audio", type="filepath", value=default_voice_wav, scale=3)
            prompt_text = gr.Textbox(label="Reference text", value=default_voice_wav_words, scale=3)
            prompt_language = gr.Dropdown(label="Language of the reference audio", choices=["中文", "English", "日本語"], value=default_language, scale=1,interactive=False)
            dummy = gr.Radio(choices=["中文","English","日本語"],visible=False)
     
    
    with gr.Accordion(label="Additional generation options/附加生成选项", open=False):
        how_to_cut = gr.Dropdown(
                label=("How to split?"),
                choices=[("Do not split"), ("Split into groups of 4 sentences"), ("Split every 50 characters"), 
                         ("Split at CN/JP periods (。)"), ("Split at English periods (.)"), ("Split at punctuation marks"), ],
                value=("Split into groups of 4 sentences"),
                interactive=True,
            info='A suitable splitting method can achieve better generation results'
            )
        volume = gr.Slider(minimum=0.5, maximum=2, value=1, step=0.01, label='Volume/音量')
        
    
    gr.HTML('''
    <b>Generate Voice/生成</b>''')
    with gr.Row():
        main_button = gr.Button("✨Generate Voice", variant="primary", scale=2)
        output = gr.Audio(label="💾Download it by clicking ⬇️", scale=6)
        #info = gr.Textbox(label="INFO", visible=True, readonly=True, scale=1)

    gr.HTML('''
    Generation is slower, please be patient and wait/合成比较慢,请耐心等待<br>
    If it generated silence, please try again./如果生成了空白声音,请重试
    <br><br><br><br>
    <h1 style="font-size: 25px;">Clone custom Voice/克隆自定义声音</h1>
    <p style="margin-bottom: 10px; font-size: 100%">
    需要3~10秒语音,克隆后的声音和原音相似度80%以上<br>
    Requires 3-10 seconds of voice input. The cloned voice will have a similarity of 80% or above compared to the original.<br>
    3~10秒の音声入力が必要です。クローンされた音声は、オリジナルと80%以上の類似性があります。

    
    </p>''')
    
    with gr.Row():
        user_voice = gr.Audio(type="filepath", label="(3~10s)Upload or Record audio/上传或录制声音",scale=3)
        with gr.Column(scale=7): 
            user_lang = gr.Textbox(label="Language/生成语言",info='Automatic detection of input language type.',interactive=False)
            with gr.Row():
                user_text= gr.Textbox(label="Text for generation/输入想要生成语音的文字", lines=5,placeholder=plsh,info=limit)
                dddice= gr.Button('🎲', variant='tool',min_width=0,scale=0)
       
    dddice.click(dice, outputs=[user_text, dddice])
    user_text.change( lang_detector, user_text, user_lang)

    user_button = gr.Button("✨Clone Voice", variant="primary")
    user_output = gr.Audio(label="💾Download it by clicking ⬇️")

    gr.HTML('''<div align=center><img id="visitor-badge" alt="visitor badge" src="https://visitor-badge.laobi.icu/badge?page_id=Ailyth/DLMP9" /></div>''')
    
    english_choice.change(update_model, inputs=[english_choice], outputs=[inp_ref, prompt_text, prompt_language,dummy,model_name, tone_select, tone_sample])
    chinese_choice.change(update_model, inputs=[chinese_choice], outputs=[inp_ref, prompt_text, prompt_language, dummy,model_name, tone_select, tone_sample])
    japanese_choice.change(update_model, inputs=[japanese_choice], outputs=[inp_ref, prompt_text, prompt_language,dummy,model_name, tone_select, tone_sample])
    tone_select.change(update_tone, inputs=[model_name, tone_select], outputs=[inp_ref, prompt_text, tone_sample])
    
    main_button.click(
    get_tts_wav,
    inputs=[inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut,volume],
    outputs=[output])

    user_button.click(
    clone_voice,
    inputs=[user_voice,user_text,user_lang],
    outputs=[user_output])

app.launch(share=True, show_api=False).queue(api_open=False)