-
Notifications
You must be signed in to change notification settings - Fork 3.2k
/
TTS.py
932 lines (804 loc) · 41.2 KB
/
TTS.py
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
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
from copy import deepcopy
import math
import os, sys
import random
import traceback
from tqdm import tqdm
now_dir = os.getcwd()
sys.path.append(now_dir)
import ffmpeg
import os
from typing import Generator, List, Union
import numpy as np
import torch
import torch.nn.functional as F
import yaml
from transformers import AutoModelForMaskedLM, AutoTokenizer
from AR.models.t2s_lightning_module import Text2SemanticLightningModule
from feature_extractor.cnhubert import CNHubert
from module.models import SynthesizerTrn
import librosa
from time import time as ttime
from tools.i18n.i18n import I18nAuto
from my_utils import load_audio
from module.mel_processing import spectrogram_torch
from TTS_infer_pack.text_segmentation_method import splits
from TTS_infer_pack.TextPreprocessor import TextPreprocessor
i18n = I18nAuto()
# configs/tts_infer.yaml
"""
default:
device: cpu
is_half: false
bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt
vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth
custom:
device: cuda
is_half: true
bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt
vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth
"""
def set_seed(seed:int):
seed = int(seed)
seed = seed if seed != -1 else random.randrange(1 << 32)
print(f"Set seed to {seed}")
os.environ['PYTHONHASHSEED'] = str(seed)
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
try:
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# torch.backends.cudnn.deterministic = True
# torch.backends.cudnn.benchmark = False
# torch.backends.cudnn.enabled = True
# 开启后会影响精度
torch.backends.cuda.matmul.allow_tf32 = False
torch.backends.cudnn.allow_tf32 = False
except:
pass
return seed
class TTS_Config:
default_configs={
"device": "cpu",
"is_half": False,
"t2s_weights_path": "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt",
"vits_weights_path": "GPT_SoVITS/pretrained_models/s2G488k.pth",
"cnhuhbert_base_path": "GPT_SoVITS/pretrained_models/chinese-hubert-base",
"bert_base_path": "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large",
}
configs:dict = None
def __init__(self, configs: Union[dict, str]=None):
# 设置默认配置文件路径
configs_base_path:str = "GPT_SoVITS/configs/"
os.makedirs(configs_base_path, exist_ok=True)
self.configs_path:str = os.path.join(configs_base_path, "tts_infer.yaml")
if configs in ["", None]:
if not os.path.exists(self.configs_path):
self.save_configs()
print(f"Create default config file at {self.configs_path}")
configs:dict = {"default": deepcopy(self.default_configs)}
if isinstance(configs, str):
self.configs_path = configs
configs:dict = self._load_configs(self.configs_path)
assert isinstance(configs, dict)
default_configs:dict = configs.get("default", None)
if default_configs is not None:
self.default_configs = default_configs
self.configs:dict = configs.get("custom", deepcopy(self.default_configs))
self.device = self.configs.get("device", torch.device("cpu"))
self.is_half = self.configs.get("is_half", False)
self.t2s_weights_path = self.configs.get("t2s_weights_path", None)
self.vits_weights_path = self.configs.get("vits_weights_path", None)
self.bert_base_path = self.configs.get("bert_base_path", None)
self.cnhuhbert_base_path = self.configs.get("cnhuhbert_base_path", None)
if (self.t2s_weights_path in [None, ""]) or (not os.path.exists(self.t2s_weights_path)):
self.t2s_weights_path = self.default_configs['t2s_weights_path']
print(f"fall back to default t2s_weights_path: {self.t2s_weights_path}")
if (self.vits_weights_path in [None, ""]) or (not os.path.exists(self.vits_weights_path)):
self.vits_weights_path = self.default_configs['vits_weights_path']
print(f"fall back to default vits_weights_path: {self.vits_weights_path}")
if (self.bert_base_path in [None, ""]) or (not os.path.exists(self.bert_base_path)):
self.bert_base_path = self.default_configs['bert_base_path']
print(f"fall back to default bert_base_path: {self.bert_base_path}")
if (self.cnhuhbert_base_path in [None, ""]) or (not os.path.exists(self.cnhuhbert_base_path)):
self.cnhuhbert_base_path = self.default_configs['cnhuhbert_base_path']
print(f"fall back to default cnhuhbert_base_path: {self.cnhuhbert_base_path}")
self.update_configs()
self.max_sec = None
self.hz:int = 50
self.semantic_frame_rate:str = "25hz"
self.segment_size:int = 20480
self.filter_length:int = 2048
self.sampling_rate:int = 32000
self.hop_length:int = 640
self.win_length:int = 2048
self.n_speakers:int = 300
self.languages:list = ["auto", "en", "zh", "ja", "all_zh", "all_ja"]
def _load_configs(self, configs_path: str)->dict:
with open(configs_path, 'r') as f:
configs = yaml.load(f, Loader=yaml.FullLoader)
return configs
def save_configs(self, configs_path:str=None)->None:
configs={
"default":self.default_configs,
}
if self.configs is not None:
configs["custom"] = self.update_configs()
if configs_path is None:
configs_path = self.configs_path
with open(configs_path, 'w') as f:
yaml.dump(configs, f)
def update_configs(self):
self.config = {
"device" : str(self.device),
"is_half" : self.is_half,
"t2s_weights_path" : self.t2s_weights_path,
"vits_weights_path" : self.vits_weights_path,
"bert_base_path" : self.bert_base_path,
"cnhuhbert_base_path": self.cnhuhbert_base_path,
}
return self.config
def __str__(self):
self.configs = self.update_configs()
string = "TTS Config".center(100, '-') + '\n'
for k, v in self.configs.items():
string += f"{str(k).ljust(20)}: {str(v)}\n"
string += "-" * 100 + '\n'
return string
def __repr__(self):
return self.__str__()
class TTS:
def __init__(self, configs: Union[dict, str, TTS_Config]):
if isinstance(configs, TTS_Config):
self.configs = configs
else:
self.configs:TTS_Config = TTS_Config(configs)
self.t2s_model:Text2SemanticLightningModule = None
self.vits_model:SynthesizerTrn = None
self.bert_tokenizer:AutoTokenizer = None
self.bert_model:AutoModelForMaskedLM = None
self.cnhuhbert_model:CNHubert = None
self._init_models()
self.text_preprocessor:TextPreprocessor = \
TextPreprocessor(self.bert_model,
self.bert_tokenizer,
self.configs.device)
self.prompt_cache:dict = {
"ref_audio_path" : None,
"prompt_semantic": None,
"refer_spec" : None,
"prompt_text" : None,
"prompt_lang" : None,
"phones" : None,
"bert_features" : None,
"norm_text" : None,
}
self.stop_flag:bool = False
self.precision:torch.dtype = torch.float16 if self.configs.is_half else torch.float32
def _init_models(self,):
self.init_t2s_weights(self.configs.t2s_weights_path)
self.init_vits_weights(self.configs.vits_weights_path)
self.init_bert_weights(self.configs.bert_base_path)
self.init_cnhuhbert_weights(self.configs.cnhuhbert_base_path)
# self.enable_half_precision(self.configs.is_half)
def init_cnhuhbert_weights(self, base_path: str):
print(f"Loading CNHuBERT weights from {base_path}")
self.cnhuhbert_model = CNHubert(base_path)
self.cnhuhbert_model=self.cnhuhbert_model.eval()
self.cnhuhbert_model = self.cnhuhbert_model.to(self.configs.device)
if self.configs.is_half and str(self.configs.device)!="cpu":
self.cnhuhbert_model = self.cnhuhbert_model.half()
def init_bert_weights(self, base_path: str):
print(f"Loading BERT weights from {base_path}")
self.bert_tokenizer = AutoTokenizer.from_pretrained(base_path)
self.bert_model = AutoModelForMaskedLM.from_pretrained(base_path)
self.bert_model=self.bert_model.eval()
self.bert_model = self.bert_model.to(self.configs.device)
if self.configs.is_half and str(self.configs.device)!="cpu":
self.bert_model = self.bert_model.half()
def init_vits_weights(self, weights_path: str):
print(f"Loading VITS weights from {weights_path}")
self.configs.vits_weights_path = weights_path
self.configs.save_configs()
dict_s2 = torch.load(weights_path, map_location=self.configs.device)
hps = dict_s2["config"]
self.configs.filter_length = hps["data"]["filter_length"]
self.configs.segment_size = hps["train"]["segment_size"]
self.configs.sampling_rate = hps["data"]["sampling_rate"]
self.configs.hop_length = hps["data"]["hop_length"]
self.configs.win_length = hps["data"]["win_length"]
self.configs.n_speakers = hps["data"]["n_speakers"]
self.configs.semantic_frame_rate = "25hz"
kwargs = hps["model"]
vits_model = SynthesizerTrn(
self.configs.filter_length // 2 + 1,
self.configs.segment_size // self.configs.hop_length,
n_speakers=self.configs.n_speakers,
**kwargs
)
# if ("pretrained" not in weights_path):
if hasattr(vits_model, "enc_q"):
del vits_model.enc_q
vits_model = vits_model.to(self.configs.device)
vits_model = vits_model.eval()
vits_model.load_state_dict(dict_s2["weight"], strict=False)
self.vits_model = vits_model
if self.configs.is_half and str(self.configs.device)!="cpu":
self.vits_model = self.vits_model.half()
def init_t2s_weights(self, weights_path: str):
print(f"Loading Text2Semantic weights from {weights_path}")
self.configs.t2s_weights_path = weights_path
self.configs.save_configs()
self.configs.hz = 50
dict_s1 = torch.load(weights_path, map_location=self.configs.device)
config = dict_s1["config"]
self.configs.max_sec = config["data"]["max_sec"]
t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
t2s_model.load_state_dict(dict_s1["weight"])
t2s_model = t2s_model.to(self.configs.device)
t2s_model = t2s_model.eval()
self.t2s_model = t2s_model
if self.configs.is_half and str(self.configs.device)!="cpu":
self.t2s_model = self.t2s_model.half()
def enable_half_precision(self, enable: bool = True):
'''
To enable half precision for the TTS model.
Args:
enable: bool, whether to enable half precision.
'''
if str(self.configs.device) == "cpu" and enable:
print("Half precision is not supported on CPU.")
return
self.configs.is_half = enable
self.precision = torch.float16 if enable else torch.float32
self.configs.save_configs()
if enable:
if self.t2s_model is not None:
self.t2s_model =self.t2s_model.half()
if self.vits_model is not None:
self.vits_model = self.vits_model.half()
if self.bert_model is not None:
self.bert_model =self.bert_model.half()
if self.cnhuhbert_model is not None:
self.cnhuhbert_model = self.cnhuhbert_model.half()
else:
if self.t2s_model is not None:
self.t2s_model = self.t2s_model.float()
if self.vits_model is not None:
self.vits_model = self.vits_model.float()
if self.bert_model is not None:
self.bert_model = self.bert_model.float()
if self.cnhuhbert_model is not None:
self.cnhuhbert_model = self.cnhuhbert_model.float()
def set_device(self, device: torch.device):
'''
To set the device for all models.
Args:
device: torch.device, the device to use for all models.
'''
self.configs.device = device
self.configs.save_configs()
if self.t2s_model is not None:
self.t2s_model = self.t2s_model.to(device)
if self.vits_model is not None:
self.vits_model = self.vits_model.to(device)
if self.bert_model is not None:
self.bert_model = self.bert_model.to(device)
if self.cnhuhbert_model is not None:
self.cnhuhbert_model = self.cnhuhbert_model.to(device)
def set_ref_audio(self, ref_audio_path:str):
'''
To set the reference audio for the TTS model,
including the prompt_semantic and refer_spepc.
Args:
ref_audio_path: str, the path of the reference audio.
'''
self._set_prompt_semantic(ref_audio_path)
self._set_ref_spec(ref_audio_path)
def _set_ref_spec(self, ref_audio_path):
audio = load_audio(ref_audio_path, int(self.configs.sampling_rate))
audio = torch.FloatTensor(audio)
audio_norm = audio
audio_norm = audio_norm.unsqueeze(0)
spec = spectrogram_torch(
audio_norm,
self.configs.filter_length,
self.configs.sampling_rate,
self.configs.hop_length,
self.configs.win_length,
center=False,
)
spec = spec.to(self.configs.device)
if self.configs.is_half:
spec = spec.half()
# self.refer_spec = spec
self.prompt_cache["refer_spec"] = spec
def _set_prompt_semantic(self, ref_wav_path:str):
zero_wav = np.zeros(
int(self.configs.sampling_rate * 0.3),
dtype=np.float16 if self.configs.is_half 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):
raise OSError(i18n("参考音频在3~10秒范围外,请更换!"))
wav16k = torch.from_numpy(wav16k)
zero_wav_torch = torch.from_numpy(zero_wav)
wav16k = wav16k.to(self.configs.device)
zero_wav_torch = zero_wav_torch.to(self.configs.device)
if self.configs.is_half:
wav16k = wav16k.half()
zero_wav_torch = zero_wav_torch.half()
wav16k = torch.cat([wav16k, zero_wav_torch])
hubert_feature = self.cnhuhbert_model.model(wav16k.unsqueeze(0))[
"last_hidden_state"
].transpose(
1, 2
) # .float()
codes = self.vits_model.extract_latent(hubert_feature)
prompt_semantic = codes[0, 0].to(self.configs.device)
self.prompt_cache["prompt_semantic"] = prompt_semantic
def batch_sequences(self, sequences: List[torch.Tensor], axis: int = 0, pad_value: int = 0, max_length:int=None):
seq = sequences[0]
ndim = seq.dim()
if axis < 0:
axis += ndim
dtype:torch.dtype = seq.dtype
pad_value = torch.tensor(pad_value, dtype=dtype)
seq_lengths = [seq.shape[axis] for seq in sequences]
if max_length is None:
max_length = max(seq_lengths)
else:
max_length = max(seq_lengths) if max_length < max(seq_lengths) else max_length
padded_sequences = []
for seq, length in zip(sequences, seq_lengths):
padding = [0] * axis + [0, max_length - length] + [0] * (ndim - axis - 1)
padded_seq = torch.nn.functional.pad(seq, padding, value=pad_value)
padded_sequences.append(padded_seq)
batch = torch.stack(padded_sequences)
return batch
def to_batch(self, data:list,
prompt_data:dict=None,
batch_size:int=5,
threshold:float=0.75,
split_bucket:bool=True,
device:torch.device=torch.device("cpu"),
precision:torch.dtype=torch.float32,
):
_data:list = []
index_and_len_list = []
for idx, item in enumerate(data):
norm_text_len = len(item["norm_text"])
index_and_len_list.append([idx, norm_text_len])
batch_index_list = []
if split_bucket:
index_and_len_list.sort(key=lambda x: x[1])
index_and_len_list = np.array(index_and_len_list, dtype=np.int64)
batch_index_list_len = 0
pos = 0
while pos <index_and_len_list.shape[0]:
# batch_index_list.append(index_and_len_list[pos:min(pos+batch_size,len(index_and_len_list))])
pos_end = min(pos+batch_size,index_and_len_list.shape[0])
while pos < pos_end:
batch=index_and_len_list[pos:pos_end, 1].astype(np.float32)
score=batch[(pos_end-pos)//2]/(batch.mean()+1e-8)
if (score>=threshold) or (pos_end-pos==1):
batch_index=index_and_len_list[pos:pos_end, 0].tolist()
batch_index_list_len += len(batch_index)
batch_index_list.append(batch_index)
pos = pos_end
break
pos_end=pos_end-1
assert batch_index_list_len == len(data)
else:
for i in range(len(data)):
if i%batch_size == 0:
batch_index_list.append([])
batch_index_list[-1].append(i)
for batch_idx, index_list in enumerate(batch_index_list):
item_list = [data[idx] for idx in index_list]
phones_list = []
phones_len_list = []
# bert_features_list = []
all_phones_list = []
all_phones_len_list = []
all_bert_features_list = []
norm_text_batch = []
bert_max_len = 0
phones_max_len = 0
for item in item_list:
if prompt_data is not None:
all_bert_features = torch.cat([prompt_data["bert_features"], item["bert_features"]], 1)\
.to(dtype=precision, device=device)
all_phones = torch.LongTensor(prompt_data["phones"]+item["phones"]).to(device)
phones = torch.LongTensor(item["phones"]).to(device)
# norm_text = prompt_data["norm_text"]+item["norm_text"]
else:
all_bert_features = item["bert_features"]\
.to(dtype=precision, device=device)
phones = torch.LongTensor(item["phones"]).to(device)
all_phones = phones
# norm_text = item["norm_text"]
bert_max_len = max(bert_max_len, all_bert_features.shape[-1])
phones_max_len = max(phones_max_len, phones.shape[-1])
phones_list.append(phones)
phones_len_list.append(phones.shape[-1])
all_phones_list.append(all_phones)
all_phones_len_list.append(all_phones.shape[-1])
all_bert_features_list.append(all_bert_features)
norm_text_batch.append(item["norm_text"])
phones_batch = phones_list
all_phones_batch = all_phones_list
all_bert_features_batch = all_bert_features_list
max_len = max(bert_max_len, phones_max_len)
# phones_batch = self.batch_sequences(phones_list, axis=0, pad_value=0, max_length=max_len)
#### 直接对phones和bert_features进行pad。(padding策略会影响T2S模型生成的结果,但不直接影响复读概率。影响复读概率的主要因素是mask的策略)
# all_phones_batch = self.batch_sequences(all_phones_list, axis=0, pad_value=0, max_length=max_len)
# all_bert_features_batch = all_bert_features_list
# all_bert_features_batch = torch.zeros((len(all_bert_features_list), 1024, max_len), dtype=precision, device=device)
# for idx, item in enumerate(all_bert_features_list):
# all_bert_features_batch[idx, :, : item.shape[-1]] = item
# #### 先对phones进行embedding、对bert_features进行project,再pad到相同长度,(padding策略会影响T2S模型生成的结果,但不直接影响复读概率。影响复读概率的主要因素是mask的策略)
# all_phones_list = [self.t2s_model.model.ar_text_embedding(item.to(self.t2s_model.device)) for item in all_phones_list]
# all_phones_list = [F.pad(item,(0,0,0,max_len-item.shape[0]),value=0) for item in all_phones_list]
# all_phones_batch = torch.stack(all_phones_list, dim=0)
# all_bert_features_list = [self.t2s_model.model.bert_proj(item.to(self.t2s_model.device).transpose(0, 1)) for item in all_bert_features_list]
# all_bert_features_list = [F.pad(item,(0,0,0,max_len-item.shape[0]), value=0) for item in all_bert_features_list]
# all_bert_features_batch = torch.stack(all_bert_features_list, dim=0)
batch = {
"phones": phones_batch,
"phones_len": torch.LongTensor(phones_len_list).to(device),
"all_phones": all_phones_batch,
"all_phones_len": torch.LongTensor(all_phones_len_list).to(device),
"all_bert_features": all_bert_features_batch,
"norm_text": norm_text_batch,
"max_len": max_len,
}
_data.append(batch)
return _data, batch_index_list
def recovery_order(self, data:list, batch_index_list:list)->list:
'''
Recovery the order of the audio according to the batch_index_list.
Args:
data (List[list(np.ndarray)]): the out of order audio .
batch_index_list (List[list[int]]): the batch index list.
Returns:
list (List[np.ndarray]): the data in the original order.
'''
length = len(sum(batch_index_list, []))
_data = [None]*length
for i, index_list in enumerate(batch_index_list):
for j, index in enumerate(index_list):
_data[index] = data[i][j]
return _data
def stop(self,):
'''
Stop the inference process.
'''
self.stop_flag = True
@torch.no_grad()
def run(self, inputs:dict):
"""
Text to speech inference.
Args:
inputs (dict):
{
"text": "", # str.(required) text to be synthesized
"text_lang: "", # str.(required) language of the text to be synthesized
"ref_audio_path": "", # str.(required) reference audio path
"prompt_text": "", # str.(optional) prompt text for the reference audio
"prompt_lang": "", # str.(required) language of the prompt text for the reference audio
"top_k": 5, # int. top k sampling
"top_p": 1, # float. top p sampling
"temperature": 1, # float. temperature for sampling
"text_split_method": "cut0", # str. text split method, see text_segmentation_method.py for details.
"batch_size": 1, # int. batch size for inference
"batch_threshold": 0.75, # float. threshold for batch splitting.
"split_bucket: True, # bool. whether to split the batch into multiple buckets.
"return_fragment": False, # bool. step by step return the audio fragment.
"speed_factor":1.0, # float. control the speed of the synthesized audio.
"fragment_interval":0.3, # float. to control the interval of the audio fragment.
"seed": -1, # int. random seed for reproducibility.
"parallel_infer": True, # bool. whether to use parallel inference.
"repetition_penalty": 1.35 # float. repetition penalty for T2S model.
}
returns:
tuple[int, np.ndarray]: sampling rate and audio data.
"""
########## variables initialization ###########
self.stop_flag:bool = False
text:str = inputs.get("text", "")
text_lang:str = inputs.get("text_lang", "")
ref_audio_path:str = inputs.get("ref_audio_path", "")
prompt_text:str = inputs.get("prompt_text", "")
prompt_lang:str = inputs.get("prompt_lang", "")
top_k:int = inputs.get("top_k", 5)
top_p:float = inputs.get("top_p", 1)
temperature:float = inputs.get("temperature", 1)
text_split_method:str = inputs.get("text_split_method", "cut0")
batch_size = inputs.get("batch_size", 1)
batch_threshold = inputs.get("batch_threshold", 0.75)
speed_factor = inputs.get("speed_factor", 1.0)
split_bucket = inputs.get("split_bucket", True)
return_fragment = inputs.get("return_fragment", False)
fragment_interval = inputs.get("fragment_interval", 0.3)
seed = inputs.get("seed", -1)
seed = -1 if seed in ["", None] else seed
actual_seed = set_seed(seed)
parallel_infer = inputs.get("parallel_infer", True)
repetition_penalty = inputs.get("repetition_penalty", 1.35)
if parallel_infer:
print(i18n("并行推理模式已开启"))
self.t2s_model.model.infer_panel = self.t2s_model.model.infer_panel_batch_infer_with_flash_attn
else:
print(i18n("并行推理模式已关闭"))
self.t2s_model.model.infer_panel = self.t2s_model.model.infer_panel_0307
if return_fragment:
print(i18n("分段返回模式已开启"))
if split_bucket:
split_bucket = False
print(i18n("分段返回模式不支持分桶处理,已自动关闭分桶处理"))
if split_bucket:
print(i18n("分桶处理模式已开启"))
if fragment_interval<0.01:
fragment_interval = 0.01
print(i18n("分段间隔过小,已自动设置为0.01"))
no_prompt_text = False
if prompt_text in [None, ""]:
no_prompt_text = True
assert text_lang in self.configs.languages
if not no_prompt_text:
assert prompt_lang in self.configs.languages
if ref_audio_path in [None, ""] and \
((self.prompt_cache["prompt_semantic"] is None) or (self.prompt_cache["refer_spec"] is None)):
raise ValueError("ref_audio_path cannot be empty, when the reference audio is not set using set_ref_audio()")
###### setting reference audio and prompt text preprocessing ########
t0 = ttime()
if (ref_audio_path is not None) and (ref_audio_path != self.prompt_cache["ref_audio_path"]):
self.set_ref_audio(ref_audio_path)
if not no_prompt_text:
prompt_text = prompt_text.strip("\n")
if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_lang != "en" else "."
print(i18n("实际输入的参考文本:"), prompt_text)
if self.prompt_cache["prompt_text"] != prompt_text:
self.prompt_cache["prompt_text"] = prompt_text
self.prompt_cache["prompt_lang"] = prompt_lang
phones, bert_features, norm_text = \
self.text_preprocessor.segment_and_extract_feature_for_text(
prompt_text,
prompt_lang)
self.prompt_cache["phones"] = phones
self.prompt_cache["bert_features"] = bert_features
self.prompt_cache["norm_text"] = norm_text
###### text preprocessing ########
t1 = ttime()
data:list = None
if not return_fragment:
data = self.text_preprocessor.preprocess(text, text_lang, text_split_method)
if len(data) == 0:
yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
dtype=np.int16)
return
batch_index_list:list = None
data, batch_index_list = self.to_batch(data,
prompt_data=self.prompt_cache if not no_prompt_text else None,
batch_size=batch_size,
threshold=batch_threshold,
split_bucket=split_bucket,
device=self.configs.device,
precision=self.precision
)
else:
print(i18n("############ 切分文本 ############"))
texts = self.text_preprocessor.pre_seg_text(text, text_lang, text_split_method)
data = []
for i in range(len(texts)):
if i%batch_size == 0:
data.append([])
data[-1].append(texts[i])
def make_batch(batch_texts):
batch_data = []
print(i18n("############ 提取文本Bert特征 ############"))
for text in tqdm(batch_texts):
phones, bert_features, norm_text = self.text_preprocessor.segment_and_extract_feature_for_text(text, text_lang)
if phones is None:
continue
res={
"phones": phones,
"bert_features": bert_features,
"norm_text": norm_text,
}
batch_data.append(res)
if len(batch_data) == 0:
return None
batch, _ = self.to_batch(batch_data,
prompt_data=self.prompt_cache if not no_prompt_text else None,
batch_size=batch_size,
threshold=batch_threshold,
split_bucket=False,
device=self.configs.device,
precision=self.precision
)
return batch[0]
t2 = ttime()
try:
print("############ 推理 ############")
###### inference ######
t_34 = 0.0
t_45 = 0.0
audio = []
for item in data:
t3 = ttime()
if return_fragment:
item = make_batch(item)
if item is None:
continue
batch_phones:List[torch.LongTensor] = item["phones"]
# batch_phones:torch.LongTensor = item["phones"]
batch_phones_len:torch.LongTensor = item["phones_len"]
all_phoneme_ids:torch.LongTensor = item["all_phones"]
all_phoneme_lens:torch.LongTensor = item["all_phones_len"]
all_bert_features:torch.LongTensor = item["all_bert_features"]
norm_text:str = item["norm_text"]
max_len = item["max_len"]
print(i18n("前端处理后的文本(每句):"), norm_text)
if no_prompt_text :
prompt = None
else:
prompt = self.prompt_cache["prompt_semantic"].expand(len(all_phoneme_ids), -1).to(self.configs.device)
pred_semantic_list, idx_list = self.t2s_model.model.infer_panel(
all_phoneme_ids,
all_phoneme_lens,
prompt,
all_bert_features,
# prompt_phone_len=ph_offset,
top_k=top_k,
top_p=top_p,
temperature=temperature,
early_stop_num=self.configs.hz * self.configs.max_sec,
max_len=max_len,
repetition_penalty=repetition_penalty,
)
t4 = ttime()
t_34 += t4 - t3
refer_audio_spec:torch.Tensor = self.prompt_cache["refer_spec"]\
.to(dtype=self.precision, device=self.configs.device)
batch_audio_fragment = []
# 这里要记得加 torch.no_grad() 不然速度慢一大截
# with torch.no_grad():
# ## vits并行推理 method 1
# pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)]
# pred_semantic_len = torch.LongTensor([item.shape[0] for item in pred_semantic_list]).to(self.configs.device)
# pred_semantic = self.batch_sequences(pred_semantic_list, axis=0, pad_value=0).unsqueeze(0)
# max_len = 0
# for i in range(0, len(batch_phones)):
# max_len = max(max_len, batch_phones[i].shape[-1])
# batch_phones = self.batch_sequences(batch_phones, axis=0, pad_value=0, max_length=max_len)
# batch_phones = batch_phones.to(self.configs.device)
# batch_audio_fragment = (self.vits_model.batched_decode(
# pred_semantic, pred_semantic_len, batch_phones, batch_phones_len,refer_audio_spec
# ))
# ## vits并行推理 method 2
pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)]
upsample_rate = math.prod(self.vits_model.upsample_rates)
audio_frag_idx = [pred_semantic_list[i].shape[0]*2*upsample_rate for i in range(0, len(pred_semantic_list))]
audio_frag_end_idx = [ sum(audio_frag_idx[:i+1]) for i in range(0, len(audio_frag_idx))]
all_pred_semantic = torch.cat(pred_semantic_list).unsqueeze(0).unsqueeze(0).to(self.configs.device)
_batch_phones = torch.cat(batch_phones).unsqueeze(0).to(self.configs.device)
_batch_audio_fragment = (self.vits_model.decode(
all_pred_semantic, _batch_phones, refer_audio_spec
).detach()[0, 0, :])
audio_frag_end_idx.insert(0, 0)
batch_audio_fragment= [_batch_audio_fragment[audio_frag_end_idx[i-1]:audio_frag_end_idx[i]] for i in range(1, len(audio_frag_end_idx))]
# ## vits串行推理
# for i, idx in enumerate(idx_list):
# phones = batch_phones[i].unsqueeze(0).to(self.configs.device)
# _pred_semantic = (pred_semantic_list[i][-idx:].unsqueeze(0).unsqueeze(0)) # .unsqueeze(0)#mq要多unsqueeze一次
# audio_fragment =(self.vits_model.decode(
# _pred_semantic, phones, refer_audio_spec
# ).detach()[0, 0, :])
# batch_audio_fragment.append(
# audio_fragment
# ) ###试试重建不带上prompt部分
t5 = ttime()
t_45 += t5 - t4
if return_fragment:
print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t4 - t3, t5 - t4))
yield self.audio_postprocess([batch_audio_fragment],
self.configs.sampling_rate,
None,
speed_factor,
False,
fragment_interval
)
else:
audio.append(batch_audio_fragment)
if self.stop_flag:
yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
dtype=np.int16)
return
if not return_fragment:
print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t_34, t_45))
yield self.audio_postprocess(audio,
self.configs.sampling_rate,
batch_index_list,
speed_factor,
split_bucket,
fragment_interval
)
except Exception as e:
traceback.print_exc()
# 必须返回一个空音频, 否则会导致显存不释放。
yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
dtype=np.int16)
# 重置模型, 否则会导致显存释放不完全。
del self.t2s_model
del self.vits_model
self.t2s_model = None
self.vits_model = None
self.init_t2s_weights(self.configs.t2s_weights_path)
self.init_vits_weights(self.configs.vits_weights_path)
raise e
finally:
self.empty_cache()
def empty_cache(self):
try:
if "cuda" in str(self.configs.device):
torch.cuda.empty_cache()
elif str(self.configs.device) == "mps":
torch.mps.empty_cache()
except:
pass
def audio_postprocess(self,
audio:List[torch.Tensor],
sr:int,
batch_index_list:list=None,
speed_factor:float=1.0,
split_bucket:bool=True,
fragment_interval:float=0.3
)->tuple[int, np.ndarray]:
zero_wav = torch.zeros(
int(self.configs.sampling_rate * fragment_interval),
dtype=self.precision,
device=self.configs.device
)
for i, batch in enumerate(audio):
for j, audio_fragment in enumerate(batch):
max_audio=torch.abs(audio_fragment).max()#简单防止16bit爆音
if max_audio>1: audio_fragment/=max_audio
audio_fragment:torch.Tensor = torch.cat([audio_fragment, zero_wav], dim=0)
audio[i][j] = audio_fragment.cpu().numpy()
if split_bucket:
audio = self.recovery_order(audio, batch_index_list)
else:
# audio = [item for batch in audio for item in batch]
audio = sum(audio, [])
audio = np.concatenate(audio, 0)
audio = (audio * 32768).astype(np.int16)
try:
if speed_factor != 1.0:
audio = speed_change(audio, speed=speed_factor, sr=int(sr))
except Exception as e:
print(f"Failed to change speed of audio: \n{e}")
return sr, audio
def speed_change(input_audio:np.ndarray, speed:float, sr:int):
# 将 NumPy 数组转换为原始 PCM 流
raw_audio = input_audio.astype(np.int16).tobytes()
# 设置 ffmpeg 输入流
input_stream = ffmpeg.input('pipe:', format='s16le', acodec='pcm_s16le', ar=str(sr), ac=1)
# 变速处理
output_stream = input_stream.filter('atempo', speed)
# 输出流到管道
out, _ = (
output_stream.output('pipe:', format='s16le', acodec='pcm_s16le')
.run(input=raw_audio, capture_stdout=True, capture_stderr=True)
)
# 将管道输出解码为 NumPy 数组
processed_audio = np.frombuffer(out, np.int16)
return processed_audio