File size: 69,054 Bytes
1a5d9a0
b6ac700
 
1e744c4
b6ac700
 
 
8077be2
b6ac700
8d120bf
b6ac700
 
 
 
 
 
 
a1da02d
18bb72f
b6ac700
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1e744c4
b6ac700
 
 
1e744c4
 
 
 
4c650d7
3ab1530
8077be2
1e744c4
4c650d7
b6ac700
 
 
 
 
 
 
 
ed2c604
b6ac700
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
18bb72f
 
 
 
b6ac700
 
 
 
 
 
 
 
 
 
 
 
 
18bb72f
 
 
3cd7d59
 
18bb72f
 
 
 
6fc9a01
 
18bb72f
 
1e744c4
 
b6ac700
1e744c4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6fc9a01
1e744c4
 
 
 
 
 
 
 
 
b6ac700
92bc446
1e744c4
 
 
 
 
 
 
 
28514b1
 
1e744c4
b6ac700
92bc446
1e744c4
 
 
b6ac700
 
 
1e744c4
92bc446
 
1e744c4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
92bc446
b6ac700
 
 
 
 
 
 
 
 
1e744c4
b6ac700
 
 
 
 
 
 
 
 
92bc446
b6ac700
 
92bc446
b6ac700
 
 
 
 
 
 
 
 
 
92bc446
 
 
b6ac700
 
1e744c4
 
 
 
77b92a2
b0efdc6
 
b6ac700
 
 
 
1e744c4
b0efdc6
 
 
 
 
 
 
 
1e744c4
 
b0efdc6
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b6ac700
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9428712
b6ac700
 
 
 
9428712
b6ac700
 
 
28514b1
 
1e744c4
28514b1
b6ac700
1e744c4
b6ac700
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1e744c4
b6ac700
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
aa33666
 
 
 
 
 
 
 
 
 
 
 
 
b6ac700
 
 
 
 
 
 
 
 
 
 
 
92bc446
b6ac700
92bc446
b6ac700
9b6dda4
 
b6ac700
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1e744c4
b6ac700
 
 
 
8077be2
b6ac700
 
 
1e744c4
8077be2
92bc446
 
 
 
8077be2
 
1e744c4
8077be2
 
1e744c4
 
8077be2
9b6dda4
8077be2
1e744c4
8077be2
92bc446
 
9b6dda4
92bc446
8077be2
 
 
1e744c4
8077be2
 
 
b6ac700
 
 
1e744c4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3ab1530
b6ac700
 
 
 
 
1e744c4
 
 
 
b6ac700
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1e744c4
 
 
 
b6ac700
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
18bb72f
 
 
 
b6ac700
 
1e744c4
 
 
 
 
 
 
 
 
 
b6ac700
 
05a2178
b6ac700
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1e744c4
 
 
b6ac700
1e744c4
b6ac700
 
 
1e744c4
b6ac700
 
1e744c4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8077be2
1e744c4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b6ac700
1e744c4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b6ac700
a1da02d
 
 
 
 
 
1e744c4
 
 
 
 
 
6fc9a01
1a5d9a0
b6ac700
77b92a2
 
1a5d9a0
b6ac700
1a5d9a0
b6ac700
1e744c4
 
 
 
 
 
1a5d9a0
 
1e744c4
8077be2
 
1e744c4
 
 
 
 
 
 
 
 
 
 
 
 
8077be2
1e744c4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1a5d9a0
1e744c4
 
 
 
 
 
 
 
 
 
 
 
 
1a5d9a0
1e744c4
 
1a5d9a0
1e744c4
 
 
 
1a5d9a0
 
1e744c4
8077be2
 
1e744c4
 
 
 
 
 
 
 
 
 
 
 
 
8077be2
1e744c4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1a5d9a0
1e744c4
 
 
 
 
 
 
 
 
 
 
 
 
b6ac700
1e744c4
b6ac700
 
 
 
 
 
 
 
4c650d7
b6ac700
 
 
 
 
 
1e744c4
b6ac700
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1e744c4
4c650d7
 
1e744c4
4c650d7
 
9428712
 
 
b0efdc6
 
4c650d7
 
1e744c4
3cd7d59
 
 
 
 
 
 
 
b6ac700
 
 
 
 
77b92a2
 
 
 
 
 
 
 
 
8077be2
b6ac700
 
 
6250a98
b6ac700
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
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
from datetime import datetime
import json
import math
from typing import Iterator, Union, List
import argparse

from io import StringIO
import time
import os
import pathlib
import tempfile
import zipfile
import numpy as np

import torch

from src.config import VAD_INITIAL_PROMPT_MODE_VALUES, ApplicationConfig, VadInitialPromptMode
from src.diarization.diarization import Diarization
from src.diarization.diarizationContainer import DiarizationContainer
from src.hooks.progressListener import ProgressListener
from src.hooks.subTaskProgressListener import SubTaskProgressListener
from src.hooks.whisperProgressHook import create_progress_listener_handle
from src.modelCache import ModelCache
from src.prompts.jsonPromptStrategy import JsonPromptStrategy
from src.prompts.prependPromptStrategy import PrependPromptStrategy
from src.source import get_audio_source_collection
from src.vadParallel import ParallelContext, ParallelTranscription

# External programs
import ffmpeg

# UI
import gradio as gr

from src.download import ExceededMaximumDuration, download_url
from src.utils import optional_int, slugify, str2bool, write_srt, write_srt_original, write_vtt
from src.vad import AbstractTranscription, NonSpeechStrategy, PeriodicTranscriptionConfig, TranscriptionConfig, VadPeriodicTranscription, VadSileroTranscription
from src.whisper.abstractWhisperContainer import AbstractWhisperContainer
from src.whisper.whisperFactory import create_whisper_container
from src.translation.translationModel import TranslationModel
from src.translation.translationLangs import (TranslationLang,
                                              _TO_LANG_CODE_WHISPER, get_lang_whisper_names, get_lang_from_whisper_name, get_lang_from_whisper_code, 
                                              get_lang_nllb_names, get_lang_from_nllb_name, get_lang_m2m100_names, get_lang_from_m2m100_name)
import shutil
import zhconv
import tqdm
import traceback

# Configure more application defaults in config.json5

# Gradio seems to truncate files without keeping the extension, so we need to truncate the file prefix ourself 
MAX_FILE_PREFIX_LENGTH = 17

# Limit auto_parallel to a certain number of CPUs (specify vad_cpu_cores to get a higher number)
MAX_AUTO_CPU_CORES = 8

WHISPER_MODELS = ["tiny", "base", "small", "medium", "large", "large-v1", "large-v2", "large-v3"]

class VadOptions:
    def __init__(self, vad: str = None, vadMergeWindow: float = 5, vadMaxMergeSize: float = 150, vadPadding: float = 1, vadPromptWindow: float = 1, 
                                        vadInitialPromptMode: Union[VadInitialPromptMode, str] = VadInitialPromptMode.PREPREND_FIRST_SEGMENT):
        self.vad = vad
        self.vadMergeWindow = vadMergeWindow
        self.vadMaxMergeSize = vadMaxMergeSize
        self.vadPadding = vadPadding
        self.vadPromptWindow = vadPromptWindow
        self.vadInitialPromptMode = vadInitialPromptMode if isinstance(vadInitialPromptMode, VadInitialPromptMode) \
                                        else VadInitialPromptMode.from_string(vadInitialPromptMode)

class WhisperTranscriber:
    def __init__(self, input_audio_max_duration: float = None, vad_process_timeout: float = None, 
                 vad_cpu_cores: int = 1, delete_uploaded_files: bool = False, output_dir: str = None, 
                 app_config: ApplicationConfig = None):
        self.model_cache = ModelCache()
        self.parallel_device_list = None
        self.gpu_parallel_context = None
        self.cpu_parallel_context = None
        self.vad_process_timeout = vad_process_timeout
        self.vad_cpu_cores = vad_cpu_cores

        self.vad_model = None
        self.inputAudioMaxDuration = input_audio_max_duration
        self.deleteUploadedFiles = delete_uploaded_files
        self.output_dir = output_dir

        # Support for diarization
        self.diarization: DiarizationContainer = None
        # Dictionary with parameters to pass to diarization.run - if None, diarization is not enabled
        self.diarization_kwargs = None
        self.app_config = app_config

    def set_parallel_devices(self, vad_parallel_devices: str):
        self.parallel_device_list = [ device.strip() for device in vad_parallel_devices.split(",") ] if vad_parallel_devices else None

    def set_auto_parallel(self, auto_parallel: bool):
        if auto_parallel:
            if torch.cuda.is_available():
                self.parallel_device_list = [ str(gpu_id) for gpu_id in range(torch.cuda.device_count())]

            self.vad_cpu_cores = min(os.cpu_count(), MAX_AUTO_CPU_CORES)
            print("[Auto parallel] Using GPU devices " + str(self.parallel_device_list) + " and " + str(self.vad_cpu_cores) + " CPU cores for VAD/transcription.")

    def set_diarization(self, auth_token: str, enable_daemon_process: bool = True, **kwargs):
        if self.diarization is None:
            self.diarization = DiarizationContainer(auth_token=auth_token, enable_daemon_process=enable_daemon_process, 
                                                    auto_cleanup_timeout_seconds=self.app_config.diarization_process_timeout, 
                                                    cache=self.model_cache)
        # Set parameters
        self.diarization_kwargs = kwargs

    def unset_diarization(self):
        if self.diarization is not None:
            self.diarization.cleanup()
        self.diarization_kwargs = None

    # Entry function for the simple tab, Queue mode disabled: progress bars will not be shown
    def transcribe_webui_simple(self, data: dict): return self.transcribe_webui_simple_progress(data)
    
    # Entry function for the simple tab progress, Progress tracking requires queuing to be enabled
    def transcribe_webui_simple_progress(self, data: dict, progress=gr.Progress()):
        dataDict = {}
        for key, value in data.items():
            dataDict.update({key.elem_id: value})
            
        return self.transcribe_webui(dataDict, progress=progress)

    # Entry function for the full tab, Queue mode disabled: progress bars will not be shown
    def transcribe_webui_full(self, data: dict): return self.transcribe_webui_full_progress(data)

    # Entry function for the full tab with progress, Progress tracking requires queuing to be enabled
    def transcribe_webui_full_progress(self, data: dict, progress=gr.Progress()):
        dataDict = {}
        for key, value in data.items():
            dataDict.update({key.elem_id: value})
            
        return self.transcribe_webui(dataDict, progress=progress)
    
    def transcribe_webui(self, decodeOptions: dict, progress: gr.Progress = None):
        """
        Transcribe an audio file using Whisper
        https://github.com/openai/whisper/blob/main/whisper/transcribe.py#L37
        Parameters
        ----------
        model: Whisper
            The Whisper model instance

        temperature: Union[float, Tuple[float, ...]]
            Temperature for sampling. It can be a tuple of temperatures, which will be successively used
            upon failures according to either `compression_ratio_threshold` or `logprob_threshold`.

        compression_ratio_threshold: float
            If the gzip compression ratio is above this value, treat as failed

        logprob_threshold: float
            If the average log probability over sampled tokens is below this value, treat as failed

        no_speech_threshold: float
            If the no_speech probability is higher than this value AND the average log probability
            over sampled tokens is below `logprob_threshold`, consider the segment as silent

        condition_on_previous_text: bool
            if True, the previous output of the model is provided as a prompt for the next window;
            disabling may make the text inconsistent across windows, but the model becomes less prone to
            getting stuck in a failure loop, such as repetition looping or timestamps going out of sync.

        word_timestamps: bool
            Extract word-level timestamps using the cross-attention pattern and dynamic time warping,
            and include the timestamps for each word in each segment.

        prepend_punctuations: str
            If word_timestamps is True, merge these punctuation symbols with the next word

        append_punctuations: str
            If word_timestamps is True, merge these punctuation symbols with the previous word

        initial_prompt: Optional[str]
            Optional text to provide as a prompt for the first window. This can be used to provide, or
            "prompt-engineer" a context for transcription, e.g. custom vocabularies or proper nouns
            to make it more likely to predict those word correctly.

        decode_options: dict
            Keyword arguments to construct `DecodingOptions` instances
            https://github.com/openai/whisper/blob/main/whisper/decoding.py#L81
            
            task: str = "transcribe"
                whether to perform X->X "transcribe" or X->English "translate"

            language: Optional[str] = None
                language that the audio is in; uses detected language if None

            temperature: float = 0.0
            sample_len: Optional[int] = None  # maximum number of tokens to sample
            best_of: Optional[int] = None  # number of independent sample trajectories, if t > 0
            beam_size: Optional[int] = None  # number of beams in beam search, if t == 0
            patience: Optional[float] = None  # patience in beam search (arxiv:2204.05424)
                sampling-related options

            length_penalty: Optional[float] = None
                "alpha" in Google NMT, or None for length norm, when ranking generations
                to select which to return among the beams or best-of-N samples

            prompt: Optional[Union[str, List[int]]] = None  # for the previous context
            prefix: Optional[Union[str, List[int]]] = None  # to prefix the current context
                text or tokens to feed as the prompt or the prefix; for more info:
                https://github.com/openai/whisper/discussions/117#discussioncomment-3727051

            suppress_tokens: Optional[Union[str, Iterable[int]]] = "-1"
            suppress_blank: bool = True  # this will suppress blank outputs
                list of tokens ids (or comma-separated token ids) to suppress
                "-1" will suppress a set of symbols as defined in `tokenizer.non_speech_tokens()`

            without_timestamps: bool = False  # use <|notimestamps|> to sample text tokens only
            max_initial_timestamp: Optional[float] = 1.0
                timestamp sampling options

            fp16: bool = True  # use fp16 for most of the calculation
                implementation details
        repetition_penalty: float
            The parameter for repetition penalty. Between 1.0 and infinity. 1.0 means no penalty. Default to 1.0.
        no_repeat_ngram_size: int
            The model ensures that a sequence of words of no_repeat_ngram_size isn’t repeated in the output sequence. If specified, it must be a positive integer greater than 1.
        """
        try:
            whisperModelName: str = decodeOptions.pop("whisperModelName")
            whisperLangName:  str = decodeOptions.pop("whisperLangName")

            translateInput:   str = decodeOptions.pop("translateInput")
            m2m100ModelName:  str = decodeOptions.pop("m2m100ModelName")
            m2m100LangName:   str = decodeOptions.pop("m2m100LangName")
            nllbModelName:    str = decodeOptions.pop("nllbModelName")
            nllbLangName:     str = decodeOptions.pop("nllbLangName")
            mt5ModelName:     str = decodeOptions.pop("mt5ModelName")
            mt5LangName:      str = decodeOptions.pop("mt5LangName")
            
            translationBatchSize:         int = decodeOptions.pop("translationBatchSize")
            translationNoRepeatNgramSize: int = decodeOptions.pop("translationNoRepeatNgramSize")
            translationNumBeams:          int = decodeOptions.pop("translationNumBeams")
            
            sourceInput:    str  = decodeOptions.pop("sourceInput")
            urlData:        str  = decodeOptions.pop("urlData")
            multipleFiles:  List = decodeOptions.pop("multipleFiles")
            microphoneData: str  = decodeOptions.pop("microphoneData")
            task:           str  = decodeOptions.pop("task")
            
            vad:                 str   = decodeOptions.pop("vad")
            vadMergeWindow:      float = decodeOptions.pop("vadMergeWindow")
            vadMaxMergeSize:     float = decodeOptions.pop("vadMaxMergeSize")
            vadPadding:          float = decodeOptions.pop("vadPadding", self.app_config.vad_padding)
            vadPromptWindow:     float = decodeOptions.pop("vadPromptWindow", self.app_config.vad_prompt_window)
            vadInitialPromptMode: str  = decodeOptions.pop("vadInitialPromptMode", self.app_config.vad_initial_prompt_mode)
            
            diarization:              bool = decodeOptions.pop("diarization", False)
            diarization_speakers:     int  = decodeOptions.pop("diarization_speakers", 2)
            diarization_min_speakers: int  = decodeOptions.pop("diarization_min_speakers", 1)
            diarization_max_speakers: int  = decodeOptions.pop("diarization_max_speakers", 8)
            highlight_words:          bool = decodeOptions.pop("highlight_words", False)
            
            temperature: float = decodeOptions.pop("temperature", None)
            temperature_increment_on_fallback: float = decodeOptions.pop("temperature_increment_on_fallback", None)
            
            whisperRepetitionPenalty: float = decodeOptions.get("repetition_penalty", None)
            whisperNoRepeatNgramSize: int = decodeOptions.get("no_repeat_ngram_size", None)
            if whisperRepetitionPenalty is not None and whisperRepetitionPenalty <= 1.0:
                decodeOptions.pop("repetition_penalty")
            if whisperNoRepeatNgramSize is not None and whisperNoRepeatNgramSize <= 1:
                decodeOptions.pop("no_repeat_ngram_size")

            # word_timestamps                   = options.get("word_timestamps", False)
            # condition_on_previous_text        = options.get("condition_on_previous_text", False)

            # prepend_punctuations              = options.get("prepend_punctuations", None)
            # append_punctuations               = options.get("append_punctuations", None)
            # initial_prompt                    = options.get("initial_prompt", None)
            # best_of                           = options.get("best_of", None)
            # beam_size                         = options.get("beam_size", None)
            # patience                          = options.get("patience", None)
            # length_penalty                    = options.get("length_penalty", None)
            # suppress_tokens                   = options.get("suppress_tokens", None)
            # compression_ratio_threshold       = options.get("compression_ratio_threshold", None)
            # logprob_threshold                 = options.get("logprob_threshold", None)

            vadOptions = VadOptions(vad, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow, vadInitialPromptMode)

            if diarization:
                if diarization_speakers is not None and diarization_speakers < 1:
                    self.set_diarization(auth_token=self.app_config.auth_token, min_speakers=diarization_min_speakers, max_speakers=diarization_max_speakers)
                else:
                    self.set_diarization(auth_token=self.app_config.auth_token, num_speakers=diarization_speakers, min_speakers=diarization_min_speakers, max_speakers=diarization_max_speakers)
            else:
                self.unset_diarization()
                
            # Handle temperature_increment_on_fallback
            if temperature is not None:
                if temperature_increment_on_fallback is not None:
                    temperature = tuple(np.arange(temperature, 1.0 + 1e-6, temperature_increment_on_fallback))
                else:
                    temperature = [temperature]
                decodeOptions["temperature"] = temperature

            progress(0, desc="init audio sources")
            
            if sourceInput == "urlData":
                sources = self.__get_source(urlData, None, None)
            elif sourceInput == "multipleFiles":
                sources = self.__get_source(None, multipleFiles, None)
            elif sourceInput == "microphoneData":
                sources = self.__get_source(None, None, microphoneData)
                
            if (len(sources) == 0):
                raise Exception("init audio sources failed...")
            
            try:
                progress(0, desc="init whisper model")
                whisperLang: TranslationLang = get_lang_from_whisper_name(whisperLangName)
                whisperLangCode = whisperLang.whisper.code if whisperLang is not None and whisperLang.whisper is not None else None
                selectedModel = whisperModelName if whisperModelName is not None else "base"

                model = create_whisper_container(whisper_implementation=self.app_config.whisper_implementation, 
                                                 model_name=selectedModel, compute_type=self.app_config.compute_type, 
                                                 cache=self.model_cache, models=self.app_config.models["whisper"])
                
                progress(0, desc="init translate model")
                translationLang = None
                translationModel = None
                if translateInput == "m2m100" and m2m100LangName is not None and len(m2m100LangName) > 0:
                    selectedModelName = m2m100ModelName if m2m100ModelName is not None and len(m2m100ModelName) > 0 else "m2m100_418M/facebook"
                    selectedModel = next((modelConfig for modelConfig in self.app_config.models["m2m100"] if modelConfig.name == selectedModelName), None)
                    translationLang = get_lang_from_m2m100_name(m2m100LangName)
                elif translateInput == "nllb" and nllbLangName is not None and len(nllbLangName) > 0:
                    selectedModelName = nllbModelName if nllbModelName is not None and len(nllbModelName) > 0 else "nllb-200-distilled-600M/facebook"
                    selectedModel = next((modelConfig for modelConfig in self.app_config.models["nllb"] if modelConfig.name == selectedModelName), None)
                    translationLang = get_lang_from_nllb_name(nllbLangName)
                elif translateInput == "mt5" and mt5LangName is not None and len(mt5LangName) > 0:
                    selectedModelName = mt5ModelName if mt5ModelName is not None and len(mt5ModelName) > 0 else "mt5-zh-ja-en-trimmed/K024"
                    selectedModel = next((modelConfig for modelConfig in self.app_config.models["mt5"] if modelConfig.name == selectedModelName), None)
                    translationLang = get_lang_from_m2m100_name(mt5LangName)

                if translationLang is not None:
                    translationModel = TranslationModel(modelConfig=selectedModel, whisperLang=whisperLang, translationLang=translationLang, batchSize=translationBatchSize, noRepeatNgramSize=translationNoRepeatNgramSize, numBeams=translationNumBeams)

                progress(0, desc="init transcribe")
                # Result
                download = []
                zip_file_lookup = {}
                text = ""
                vtt = ""

                # Write result
                downloadDirectory = tempfile.mkdtemp()
                source_index = 0
                extra_tasks_count = 1 if translationLang is not None else 0

                outputDirectory = self.output_dir if self.output_dir is not None else downloadDirectory

                # Progress
                total_duration = sum([source.get_audio_duration() for source in sources])
                current_progress = 0

                # A listener that will report progress to Gradio
                root_progress_listener = self._create_progress_listener(progress)
                sub_task_total = 1/(len(sources)+extra_tasks_count*len(sources))

                # Execute whisper
                for idx, source in enumerate(sources):
                    source_prefix = ""
                    source_audio_duration = source.get_audio_duration()

                    if (len(sources) > 1):
                        # Prefix (minimum 2 digits)
                        source_index += 1
                        source_prefix = str(source_index).zfill(2) + "_"
                        print("Transcribing ", source.source_path)

                    scaled_progress_listener = SubTaskProgressListener(root_progress_listener, 
                                                   base_task_total=1,
                                                   sub_task_start=idx*1/len(sources),
                                                   sub_task_total=sub_task_total)

                    # Transcribe
                    result = self.transcribe_file(model, source.source_path, whisperLangCode, task, vadOptions, scaled_progress_listener, **decodeOptions)
                    if whisperLang is None and result["language"] is not None and len(result["language"]) > 0:
                        whisperLang = get_lang_from_whisper_code(result["language"])
                        translationModel.whisperLang = whisperLang
                        
                    short_name, suffix = source.get_short_name_suffix(max_length=self.app_config.input_max_file_name_length)
                    filePrefix = slugify(source_prefix + short_name, allow_unicode=True)

                    # Update progress
                    current_progress += source_audio_duration

                    source_download, source_text, source_vtt = self.write_result(result, whisperLang, translationModel, filePrefix + suffix.replace(".", "_"), outputDirectory, highlight_words, scaled_progress_listener)

                    if self.app_config.merge_subtitle_with_sources and self.app_config.output_dir is not None:
                        print("\nmerge subtitle(srt) with source file [" + source.source_name + "]\n")
                        outRsult = ""
                        try:
                            srt_path = source_download[0]
                            save_path = os.path.join(self.app_config.output_dir, filePrefix)
                            # save_without_ext, ext = os.path.splitext(save_path)
                            source_lang = "." + whisperLang.whisper.code if whisperLang is not None and whisperLang.whisper is not None else ""
                            translate_lang = "." + translationLang.nllb.code if translationLang is not None else ""
                            output_with_srt = save_path + source_lang + translate_lang + suffix
        
                            #ffmpeg -i "input.mp4" -i "input.srt" -c copy -c:s mov_text output.mp4
                            input_file = ffmpeg.input(source.source_path)
                            input_srt = ffmpeg.input(srt_path)
                            out = ffmpeg.output(input_file, input_srt, output_with_srt, vcodec='copy', acodec='copy', scodec='mov_text')
                            outRsult = out.run(overwrite_output=True)
                        except Exception as e:
                            # Ignore error - it's just a cleanup
                            print("Error merge subtitle with source file: \n" + source.source_path + ", \n" + str(e), outRsult)
                    elif self.app_config.save_downloaded_files and self.app_config.output_dir is not None and urlData:
                        print("Saving downloaded file [" + source.source_name + "]")
                        try:
                            save_path = os.path.join(self.app_config.output_dir, filePrefix)
                            shutil.copy(source.source_path, save_path + suffix)
                        except Exception as e:
                            # Ignore error - it's just a cleanup
                            print("Error saving downloaded file: \n" + source.source_path + ", \n" + str(e))

                    if len(sources) > 1:
                        # Add new line separators
                        if (len(source_text) > 0):
                            source_text += os.linesep + os.linesep
                        if (len(source_vtt) > 0):
                            source_vtt += os.linesep + os.linesep

                        # Append file name to source text too
                        source_text = source.get_full_name() + ":" + os.linesep + source_text
                        source_vtt = source.get_full_name() + ":" + os.linesep + source_vtt

                    # Add to result
                    download.extend(source_download)
                    text += source_text
                    vtt += source_vtt

                    if (len(sources) > 1):
                        # Zip files support at least 260 characters, but we'll play it safe and use 200
                        zipFilePrefix = slugify(source_prefix + source.get_short_name(max_length=200), allow_unicode=True)

                        # File names in ZIP file can be longer
                        for source_download_file in source_download:
                            # Get file postfix (after last -)
                            filePostfix = os.path.basename(source_download_file).split("-")[-1]
                            zip_file_name = zipFilePrefix + "-" + filePostfix
                            zip_file_lookup[source_download_file] = zip_file_name

                # Create zip file from all sources
                if len(sources) > 1:
                    downloadAllPath = os.path.join(downloadDirectory, "All_Output-" + datetime.now().strftime("%Y%m%d-%H%M%S") + ".zip")

                    with zipfile.ZipFile(downloadAllPath, 'w', zipfile.ZIP_DEFLATED) as zip:
                        for download_file in download:
                            # Get file name from lookup
                            zip_file_name = zip_file_lookup.get(download_file, os.path.basename(download_file))
                            zip.write(download_file, arcname=zip_file_name)

                    download.insert(0, downloadAllPath)

                return download, text, vtt

            finally:
                # Cleanup source
                if self.deleteUploadedFiles:
                    for source in sources:
                        print("Deleting temporary source file: " + source.source_path)
                        try:
                            os.remove(source.source_path)
                        except Exception as e:
                            # Ignore error - it's just a cleanup
                            print("Error deleting temporary source file: \n" + source.source_path + ", \n" + str(e))
        
        except ExceededMaximumDuration as e:
            return [], ("[ERROR]: Maximum remote video length is " + str(e.maxDuration) + "s, file was " + str(e.videoDuration) + "s"), "[ERROR]"
        except Exception as e:
            print(traceback.format_exc())
            return [], ("Error occurred during transcribe: " + str(e)), traceback.format_exc()
        

    def transcribe_file(self, model: AbstractWhisperContainer, audio_path: str, languageCode: str, task: str = None, 
                        vadOptions: VadOptions = VadOptions(), 
                        progressListener: ProgressListener = None, **decodeOptions: dict):
        
        initial_prompt = decodeOptions.pop('initial_prompt', None)

        if progressListener is None:
            # Default progress listener
            progressListener = ProgressListener()

        if ('task' in decodeOptions):
            task = decodeOptions.pop('task')

        initial_prompt_mode = vadOptions.vadInitialPromptMode

        # Set default initial prompt mode
        if (initial_prompt_mode is None):
            initial_prompt_mode = VadInitialPromptMode.PREPREND_FIRST_SEGMENT

        if (initial_prompt_mode == VadInitialPromptMode.PREPEND_ALL_SEGMENTS or 
            initial_prompt_mode == VadInitialPromptMode.PREPREND_FIRST_SEGMENT):
            # Prepend initial prompt
            prompt_strategy = PrependPromptStrategy(initial_prompt, initial_prompt_mode)
        elif (vadOptions.vadInitialPromptMode == VadInitialPromptMode.JSON_PROMPT_MODE):
            # Use a JSON format to specify the prompt for each segment
            prompt_strategy = JsonPromptStrategy(initial_prompt)
        else:
            raise ValueError("Invalid vadInitialPromptMode: " + initial_prompt_mode)

        # Callable for processing an audio file
        whisperCallable = model.create_callback(languageCode, task, prompt_strategy=prompt_strategy, **decodeOptions)

        # The results
        if (vadOptions.vad == 'silero-vad'):
            # Silero VAD where non-speech gaps are transcribed
            process_gaps = self._create_silero_config(NonSpeechStrategy.CREATE_SEGMENT, vadOptions)
            result = self.process_vad(audio_path, whisperCallable, self.vad_model, process_gaps, progressListener=progressListener)
        elif (vadOptions.vad == 'silero-vad-skip-gaps'):
            # Silero VAD where non-speech gaps are simply ignored
            skip_gaps = self._create_silero_config(NonSpeechStrategy.SKIP, vadOptions)
            result = self.process_vad(audio_path, whisperCallable, self.vad_model, skip_gaps, progressListener=progressListener)
        elif (vadOptions.vad == 'silero-vad-expand-into-gaps'):
            # Use Silero VAD where speech-segments are expanded into non-speech gaps
            expand_gaps = self._create_silero_config(NonSpeechStrategy.EXPAND_SEGMENT, vadOptions)
            result = self.process_vad(audio_path, whisperCallable, self.vad_model, expand_gaps, progressListener=progressListener)
        elif (vadOptions.vad == 'periodic-vad'):
            # Very simple VAD - mark every 5 minutes as speech. This makes it less likely that Whisper enters an infinite loop, but
            # it may create a break in the middle of a sentence, causing some artifacts.
            periodic_vad = VadPeriodicTranscription()
            period_config = PeriodicTranscriptionConfig(periodic_duration=vadOptions.vadMaxMergeSize, max_prompt_window=vadOptions.vadPromptWindow)
            result = self.process_vad(audio_path, whisperCallable, periodic_vad, period_config, progressListener=progressListener)

        else:
            if (self._has_parallel_devices()):
                # Use a simple period transcription instead, as we need to use the parallel context
                periodic_vad = VadPeriodicTranscription()
                period_config = PeriodicTranscriptionConfig(periodic_duration=math.inf, max_prompt_window=1)

                result = self.process_vad(audio_path, whisperCallable, periodic_vad, period_config, progressListener=progressListener)
            else:
                # Default VAD
                result = whisperCallable.invoke(audio_path, 0, None, None, progress_listener=progressListener)
        
        # Diarization
        if self.diarization and self.diarization_kwargs:
            print("Diarizing ", audio_path)
            diarization_result = list(self.diarization.run(audio_path, **self.diarization_kwargs))

            # Print result
            print("Diarization result: ")
            for entry in diarization_result:
                print(f"  start={entry.start:.1f}s stop={entry.end:.1f}s speaker_{entry.speaker}")

            # Add speakers to result
            result = self.diarization.mark_speakers(diarization_result, result)

        return result

    def _create_progress_listener(self, progress: gr.Progress):
        if (progress is None):
            # Dummy progress listener
            return ProgressListener()
        
        class ForwardingProgressListener(ProgressListener):
            def __init__(self, progress: gr.Progress):
                self.progress = progress

            def on_progress(self, current: Union[int, float], total: Union[int, float], desc: str = None):
                # From 0 to 1
                self.progress(current / total, desc=desc)

            def on_finished(self, desc: str = None):
                self.progress(1, desc=desc)

        return ForwardingProgressListener(progress)

    def process_vad(self, audio_path, whisperCallable, vadModel: AbstractTranscription, vadConfig: TranscriptionConfig, 
                    progressListener: ProgressListener = None):
        if (not self._has_parallel_devices()):
            # No parallel devices, so just run the VAD and Whisper in sequence
            return vadModel.transcribe(audio_path, whisperCallable, vadConfig, progressListener=progressListener)

        gpu_devices = self.parallel_device_list

        if (gpu_devices is None or len(gpu_devices) == 0):
            # No GPU devices specified, pass the current environment variable to the first GPU process. This may be NULL.
            gpu_devices = [os.environ.get("CUDA_VISIBLE_DEVICES", None)]

        # Create parallel context if needed
        if (self.gpu_parallel_context is None):
            # Create a context wih processes and automatically clear the pool after 1 hour of inactivity
            self.gpu_parallel_context = ParallelContext(num_processes=len(gpu_devices), auto_cleanup_timeout_seconds=self.vad_process_timeout)
        # We also need a CPU context for the VAD
        if (self.cpu_parallel_context is None):
            self.cpu_parallel_context = ParallelContext(num_processes=self.vad_cpu_cores, auto_cleanup_timeout_seconds=self.vad_process_timeout)

        parallel_vad = ParallelTranscription()
        return parallel_vad.transcribe_parallel(transcription=vadModel, audio=audio_path, whisperCallable=whisperCallable,  
                                                config=vadConfig, cpu_device_count=self.vad_cpu_cores, gpu_devices=gpu_devices, 
                                                cpu_parallel_context=self.cpu_parallel_context, gpu_parallel_context=self.gpu_parallel_context, 
                                                progress_listener=progressListener) 

    def _has_parallel_devices(self):
        return (self.parallel_device_list is not None and len(self.parallel_device_list) > 0) or self.vad_cpu_cores > 1

    def _concat_prompt(self, prompt1, prompt2):
        if (prompt1 is None):
            return prompt2
        elif (prompt2 is None):
            return prompt1
        else:
            return prompt1 + " " + prompt2

    def _create_silero_config(self, non_speech_strategy: NonSpeechStrategy, vadOptions: VadOptions):
        # Use Silero VAD 
        if (self.vad_model is None):
            self.vad_model = VadSileroTranscription()

        config = TranscriptionConfig(non_speech_strategy = non_speech_strategy, 
                max_silent_period=vadOptions.vadMergeWindow, max_merge_size=vadOptions.vadMaxMergeSize, 
                segment_padding_left=vadOptions.vadPadding, segment_padding_right=vadOptions.vadPadding, 
                max_prompt_window=vadOptions.vadPromptWindow)

        return config

    def write_result(self, result: dict, whisperLang: TranslationLang, translationModel: TranslationModel, source_name: str, output_dir: str, highlight_words: bool = False, progressListener: ProgressListener = None):
        if not os.path.exists(output_dir):
            os.makedirs(output_dir)

        text = result["text"]
        segments = result["segments"]
        language = result["language"]
        languageMaxLineWidth = self.__get_max_line_width(language)

        if translationModel is not None and translationModel.translationLang is not None:
            try:
                segments_progress_listener = SubTaskProgressListener(progressListener, 
                                               base_task_total=progressListener.sub_task_total, 
                                               sub_task_start=1, 
                                               sub_task_total=1)
                pbar = tqdm.tqdm(total=len(segments))
                perf_start_time = time.perf_counter()
                translationModel.load_model()
                for idx, segment in enumerate(segments):
                    seg_text = segment["text"]
                    segment["original"] = seg_text
                    segment["text"] = translationModel.translation(seg_text)
                    pbar.update(1)
                    segments_progress_listener.on_progress(idx+1, len(segments), desc=f"Process segments: {idx}/{len(segments)}")

                translationModel.release_vram()
                perf_end_time = time.perf_counter()
                # Call the finished callback
                if segments_progress_listener is not None:
                    segments_progress_listener.on_finished(desc=f"Process segments: {idx}/{len(segments)}")

                print("\n\nprocess segments took {} seconds.\n\n".format(perf_end_time - perf_start_time))
            except Exception as e:
                # Ignore error - it's just a cleanup
                print(traceback.format_exc())
                print("Error process segments: " + str(e))

        print("Max line width " + str(languageMaxLineWidth) + " for language:" + language)
        vtt = self.__get_subs(result["segments"], "vtt", languageMaxLineWidth, highlight_words=highlight_words)
        srt = self.__get_subs(result["segments"], "srt", languageMaxLineWidth, highlight_words=highlight_words)
        json_result = json.dumps(result, indent=4, ensure_ascii=False)
        srt_original = None
        srt_bilingual = None
        if translationModel is not None and translationModel.translationLang is not None:
            srt_original  = self.__get_subs(result["segments"], "srt_original", languageMaxLineWidth, highlight_words=highlight_words)
            srt_bilingual = self.__get_subs(result["segments"], "srt_bilingual", languageMaxLineWidth, highlight_words=highlight_words)

        whisperLangZho: bool = whisperLang is not None and whisperLang.nllb is not None and whisperLang.nllb.code in ["zho_Hant", "zho_Hans", "yue_Hant"]
        translationZho: bool = translationModel is not None and translationModel.translationLang is not None and translationModel.translationLang.nllb is not None and translationModel.translationLang.nllb.code in ["zho_Hant", "zho_Hans", "yue_Hant"]
        if whisperLangZho or translationZho:
            locale = None
            if whisperLangZho:
                if whisperLang.nllb.code == "zho_Hant":
                    locale = "zh-tw"
                elif whisperLang.nllb.code == "zho_Hans":
                    locale = "zh-cn"
                elif whisperLang.nllb.code == "yue_Hant":
                    locale = "zh-hk"
            if translationZho:
                if translationModel.translationLang.nllb.code == "zho_Hant":
                    locale = "zh-tw"
                elif translationModel.translationLang.nllb.code == "zho_Hans":
                    locale = "zh-cn"
                elif translationModel.translationLang.nllb.code == "yue_Hant":
                    locale = "zh-hk"
            if locale is not None:
                vtt = zhconv.convert(vtt, locale)
                srt = zhconv.convert(srt, locale)
                text = zhconv.convert(text, locale)
                json_result = zhconv.convert(json_result, locale)
                if translationModel is not None and translationModel.translationLang is not None:
                    if srt_original is not None and len(srt_original) > 0:
                        srt_original = zhconv.convert(srt_original, locale)
                    if srt_bilingual is not None and len(srt_bilingual) > 0:
                        srt_bilingual = zhconv.convert(srt_bilingual, locale)

        output_files = []
        output_files.append(self.__create_file(srt, output_dir, source_name + "-subs.srt"));
        output_files.append(self.__create_file(vtt, output_dir, source_name + "-subs.vtt"));
        output_files.append(self.__create_file(text, output_dir, source_name + "-transcript.txt"));
        output_files.append(self.__create_file(json_result, output_dir, source_name + "-result.json"));
        if srt_original is not None and len(srt_original) > 0:
            output_files.append(self.__create_file(srt_original, output_dir, source_name + "-original.srt"));
        if srt_bilingual is not None and len(srt_bilingual) > 0:
            output_files.append(self.__create_file(srt_bilingual, output_dir, source_name + "-bilingual.srt"));

        return output_files, text, vtt

    def clear_cache(self):
        self.model_cache.clear()
        self.vad_model = None

    def __get_source(self, urlData, multipleFiles, microphoneData):
        return get_audio_source_collection(urlData, multipleFiles, microphoneData, self.inputAudioMaxDuration)

    def __get_max_line_width(self, language: str) -> int:
        if (language and language.lower() in ["japanese", "ja", "chinese", "zh"]):
            # Chinese characters and kana are wider, so limit line length to 40 characters
            return 40
        else:
            # TODO: Add more languages
            # 80 latin characters should fit on a 1080p/720p screen
            return 80

    def __get_subs(self, segments: Iterator[dict], format: str, maxLineWidth: int, highlight_words: bool = False) -> str:
        segmentStream = StringIO()

        if format == 'vtt':
            write_vtt(segments, file=segmentStream, maxLineWidth=maxLineWidth, highlight_words=highlight_words)
        elif format == 'srt':
            write_srt(segments, file=segmentStream, maxLineWidth=maxLineWidth, highlight_words=highlight_words)
        elif format == 'srt_original':
            write_srt_original(segments, file=segmentStream, maxLineWidth=maxLineWidth, highlight_words=highlight_words)
        elif format == 'srt_bilingual':
            write_srt_original(segments, file=segmentStream, maxLineWidth=maxLineWidth, highlight_words=highlight_words, bilingual=True)
        else:
            raise Exception("Unknown format " + format)

        segmentStream.seek(0)
        return segmentStream.read()

    def __create_file(self, text: str, directory: str, fileName: str) -> str:
        # Write the text to a file
        with open(os.path.join(directory, fileName), 'w+', encoding="utf-8") as file:
            file.write(text)

        return file.name

    def close(self):
        print("Closing parallel contexts")
        self.clear_cache()

        if (self.gpu_parallel_context is not None):
            self.gpu_parallel_context.close()
        if (self.cpu_parallel_context is not None):
            self.cpu_parallel_context.close()

        # Cleanup diarization
        if (self.diarization is not None):
            self.diarization.cleanup()
            self.diarization = None

def create_ui(app_config: ApplicationConfig):
    optionsMd: str = None
    readmeMd: str = None
    try:
        with open("docs\options.md", "r", encoding="utf-8") as optionsFile:
            optionsMd = optionsFile.read()
        with open("README.md", "r", encoding="utf-8") as readmeFile:
            readmeMd = readmeFile.read()
    except Exception as e:
        print("Error occurred during read options.md file: ", str(e))

    ui = WhisperTranscriber(app_config.input_audio_max_duration, app_config.vad_process_timeout, app_config.vad_cpu_cores, 
                            app_config.delete_uploaded_files, app_config.output_dir, app_config)

    # Specify a list of devices to use for parallel processing
    ui.set_parallel_devices(app_config.vad_parallel_devices)
    ui.set_auto_parallel(app_config.auto_parallel)

    is_whisper = False

    if app_config.whisper_implementation == "whisper":
        implementation_name = "Whisper"
        is_whisper = True
    elif app_config.whisper_implementation in ["faster-whisper", "faster_whisper"]:
        implementation_name = "Faster Whisper"
    else:
        # Try to convert from camel-case to title-case
        implementation_name = app_config.whisper_implementation.title().replace("_", " ").replace("-", " ")

    uiDescription = implementation_name + " is a general-purpose speech recognition model. It is trained on a large dataset of diverse " 
    uiDescription += " audio and is also a multi-task model that can perform multilingual speech recognition "
    uiDescription += " as well as speech translation and language identification. "

    uiDescription += "\n\n\n\nFor longer audio files (>10 minutes) not in English, it is recommended that you select Silero VAD (Voice Activity Detector) in the VAD option."

    # Recommend faster-whisper
    if is_whisper:
        uiDescription += "\n\n\n\nFor faster inference on GPU, try [faster-whisper](https://huggingface.co/spaces/aadnk/faster-whisper-webui)."

    if app_config.input_audio_max_duration > 0:
        uiDescription += "\n\n" + "Max audio file length: " + str(app_config.input_audio_max_duration) + " s"

    uiArticle = "Read the [documentation here](https://gitlab.com/aadnk/whisper-webui/-/blob/main/docs/options.md)."
    uiArticle += "\n\nWhisper's Task 'translate' only implements the functionality of translating other languages into English. "
    uiArticle += "OpenAI does not guarantee translations between arbitrary languages. In such cases, you can choose to use the NLLB Model to implement the translation task. "
    uiArticle += "However, it's important to note that the NLLB Model runs slowly, and the completion time may be twice as long as usual. "
    uiArticle += "\n\nThe larger the parameters of the NLLB model, the better its performance is expected to be. "
    uiArticle += "However, it also requires higher computational resources, making it slower to operate. "
    uiArticle += "On the other hand, the version converted from ct2 (CTranslate2) requires lower resources and operates at a faster speed."
    uiArticle += "\n\nCurrently, enabling word-level timestamps cannot be used in conjunction with NLLB Model translation "
    uiArticle += "because Word Timestamps will split the source text, and after translation, it becomes a non-word-level string. "
    uiArticle += "\n\nThe 'mt5-zh-ja-en-trimmed' model is finetuned from Google's 'mt5-base' model. "
    uiArticle += "This model has a relatively good translation speed, but it only supports three languages: Chinese, Japanese, and English. "

    whisper_models = app_config.get_model_names("whisper")
    nllb_models = app_config.get_model_names("nllb")
    m2m100_models = app_config.get_model_names("m2m100")
    mt5_models = app_config.get_model_names("mt5")
    
    common_whisper_inputs = lambda : {
        gr.Dropdown(label="Whisper - Model (for audio)", choices=whisper_models, value=app_config.default_model_name, elem_id="whisperModelName"),
        gr.Dropdown(label="Whisper - Language", choices=sorted(get_lang_whisper_names()), value=app_config.language, elem_id="whisperLangName"),
    }
    common_m2m100_inputs = lambda : {
        gr.Dropdown(label="M2M100 - Model (for translate)", choices=m2m100_models, elem_id="m2m100ModelName"),
        gr.Dropdown(label="M2M100 - Language", choices=sorted(get_lang_m2m100_names()), elem_id="m2m100LangName"),
    }
    common_nllb_inputs = lambda : {
        gr.Dropdown(label="NLLB - Model (for translate)", choices=nllb_models, elem_id="nllbModelName"),
        gr.Dropdown(label="NLLB - Language", choices=sorted(get_lang_nllb_names()), elem_id="nllbLangName"),
    }
    common_mt5_inputs = lambda : {
        gr.Dropdown(label="MT5 - Model (for translate)", choices=mt5_models, elem_id="mt5ModelName"),
        gr.Dropdown(label="MT5 - Language", choices=sorted(get_lang_m2m100_names(["en", "ja", "zh"])), elem_id="mt5LangName"),
    }
    
    common_translation_inputs = lambda : {
        gr.Number(label="Translation - Batch Size", precision=0, value=app_config.translation_batch_size, elem_id="translationBatchSize"),
        gr.Number(label="Translation - No Repeat Ngram Size", precision=0, value=app_config.translation_no_repeat_ngram_size, elem_id="translationNoRepeatNgramSize"),
        gr.Number(label="Translation - Num Beams", precision=0, value=app_config.translation_num_beams, elem_id="translationNumBeams")
    }

    common_vad_inputs = lambda : {
        gr.Dropdown(choices=["none", "silero-vad", "silero-vad-skip-gaps", "silero-vad-expand-into-gaps", "periodic-vad"], value=app_config.default_vad, label="VAD", elem_id="vad"),
        gr.Number(label="VAD - Merge Window (s)", precision=0, value=app_config.vad_merge_window, elem_id="vadMergeWindow"),
        gr.Number(label="VAD - Max Merge Size (s)", precision=0, value=app_config.vad_max_merge_size, elem_id="vadMaxMergeSize"),
    }
    
    common_word_timestamps_inputs = lambda : {
        gr.Checkbox(label="Word Timestamps", value=app_config.word_timestamps, elem_id="word_timestamps"),
        gr.Checkbox(label="Word Timestamps - Highlight Words", value=app_config.highlight_words, elem_id="highlight_words"),
    }

    has_diarization_libs = Diarization.has_libraries()

    if not has_diarization_libs:
        print("Diarization libraries not found - disabling diarization")
        app_config.diarization = False

    common_diarization_inputs = lambda : {
        gr.Checkbox(label="Diarization", value=app_config.diarization, interactive=has_diarization_libs, elem_id="diarization"),
        gr.Number(label="Diarization - Speakers", precision=0, value=app_config.diarization_speakers, interactive=has_diarization_libs, elem_id="diarization_speakers"),
        gr.Number(label="Diarization - Min Speakers", precision=0, value=app_config.diarization_min_speakers, interactive=has_diarization_libs, elem_id="diarization_min_speakers"),
        gr.Number(label="Diarization - Max Speakers", precision=0, value=app_config.diarization_max_speakers, interactive=has_diarization_libs, elem_id="diarization_max_speakers")
    }
    
    common_output = lambda : [
        gr.File(label="Download"),
        gr.Text(label="Transcription", autoscroll=False),
        gr.Text(label="Segments", autoscroll=False),
    ]

    is_queue_mode = app_config.queue_concurrency_count is not None and app_config.queue_concurrency_count > 0

    simpleInputDict = {}
    
    with gr.Blocks() as simpleTranscribe:
        simpleTranslateInput = gr.State(value="m2m100", elem_id = "translateInput")
        simpleSourceInput = gr.State(value="urlData", elem_id = "sourceInput")
        gr.Markdown(uiDescription)
        with gr.Row():
            with gr.Column():
                simpleSubmit = gr.Button("Submit", variant="primary")
                with gr.Column():
                    with gr.Row():
                        simpleInputDict = common_whisper_inputs()
                    with gr.Tab(label="M2M100") as simpleM2M100Tab:
                        with gr.Row():
                            simpleInputDict.update(common_m2m100_inputs())
                    with gr.Tab(label="NLLB") as simpleNllbTab:
                        with gr.Row():
                            simpleInputDict.update(common_nllb_inputs())
                    with gr.Tab(label="MT5") as simpleMT5Tab:
                        with gr.Row():
                            simpleInputDict.update(common_mt5_inputs())
                    simpleM2M100Tab.select(fn=lambda: "m2m100", inputs = [], outputs= [simpleTranslateInput] )
                    simpleNllbTab.select(fn=lambda: "nllb", inputs = [], outputs= [simpleTranslateInput] )
                    simpleMT5Tab.select(fn=lambda: "mt5", inputs = [], outputs= [simpleTranslateInput] )
                with gr.Column():
                    with gr.Tab(label="URL") as simpleUrlTab:
                        simpleInputDict.update({gr.Text(label="URL (YouTube, etc.)", elem_id = "urlData")})
                    with gr.Tab(label="Upload") as simpleUploadTab:
                        simpleInputDict.update({gr.File(label="Upload Files", file_count="multiple", elem_id = "multipleFiles")})
                    with gr.Tab(label="Microphone") as simpleMicTab:
                        simpleInputDict.update({gr.Audio(source="microphone", type="filepath", label="Microphone Input", elem_id = "microphoneData")})
                    simpleUrlTab.select(fn=lambda: "urlData", inputs = [], outputs= [simpleSourceInput] )
                    simpleUploadTab.select(fn=lambda: "multipleFiles", inputs = [], outputs= [simpleSourceInput] )
                    simpleMicTab.select(fn=lambda: "microphoneData", inputs = [], outputs= [simpleSourceInput] )
                    simpleInputDict.update({gr.Dropdown(choices=["transcribe", "translate"], label="Task", value=app_config.task, elem_id = "task")})
                    with gr.Accordion("VAD options", open=False):
                        simpleInputDict.update(common_vad_inputs())
                    with gr.Accordion("Word Timestamps options", open=False):
                        simpleInputDict.update(common_word_timestamps_inputs())
                    with gr.Accordion("Diarization options", open=False):
                        simpleInputDict.update(common_diarization_inputs())
                    with gr.Accordion("Translation options", open=False):
                        simpleInputDict.update(common_translation_inputs())
            with gr.Column():
                simpleOutput = common_output()
        with gr.Accordion("Article"):
            gr.Markdown(uiArticle)
        if optionsMd is not None:
            with gr.Accordion("docs/options.md", open=False):    
                gr.Markdown(optionsMd)
        if readmeMd is not None:
            with gr.Accordion("README.md", open=False):    
                gr.Markdown(readmeMd)
        
        simpleInputDict.update({simpleTranslateInput, simpleSourceInput})
        simpleSubmit.click(fn=ui.transcribe_webui_simple_progress if is_queue_mode else ui.transcribe_webui_simple,
                    inputs=simpleInputDict, outputs=simpleOutput)

    fullInputDict = {}
    fullDescription = uiDescription + "\n\n\n\n" + "Be careful when changing some of the options in the full interface - this can cause the model to crash."

    with gr.Blocks() as fullTranscribe:
        fullTranslateInput = gr.State(value="m2m100", elem_id = "translateInput")
        fullSourceInput = gr.State(value="urlData", elem_id = "sourceInput")
        gr.Markdown(fullDescription)
        with gr.Row():
            with gr.Column():
                fullSubmit = gr.Button("Submit", variant="primary")
                with gr.Column():
                    with gr.Row():
                        fullInputDict = common_whisper_inputs()
                    with gr.Tab(label="M2M100") as fullM2M100Tab:
                        with gr.Row():
                            fullInputDict.update(common_m2m100_inputs())
                    with gr.Tab(label="NLLB") as fullNllbTab:
                        with gr.Row():
                            fullInputDict.update(common_nllb_inputs())
                    with gr.Tab(label="MT5") as fullMT5Tab:
                        with gr.Row():
                            fullInputDict.update(common_mt5_inputs())
                    fullM2M100Tab.select(fn=lambda: "m2m100", inputs = [], outputs= [fullTranslateInput] )
                    fullNllbTab.select(fn=lambda: "nllb", inputs = [], outputs= [fullTranslateInput] )
                    fullMT5Tab.select(fn=lambda: "mt5", inputs = [], outputs= [fullTranslateInput] )
                with gr.Column():
                    with gr.Tab(label="URL") as fullUrlTab:
                        fullInputDict.update({gr.Text(label="URL (YouTube, etc.)", elem_id = "urlData")})
                    with gr.Tab(label="Upload") as fullUploadTab:
                        fullInputDict.update({gr.File(label="Upload Files", file_count="multiple", elem_id = "multipleFiles")})
                    with gr.Tab(label="Microphone") as fullMicTab:
                        fullInputDict.update({gr.Audio(source="microphone", type="filepath", label="Microphone Input", elem_id = "microphoneData")})
                    fullUrlTab.select(fn=lambda: "urlData", inputs = [], outputs= [fullSourceInput] )
                    fullUploadTab.select(fn=lambda: "multipleFiles", inputs = [], outputs= [fullSourceInput] )
                    fullMicTab.select(fn=lambda: "microphoneData", inputs = [], outputs= [fullSourceInput] )
                    fullInputDict.update({gr.Dropdown(choices=["transcribe", "translate"], label="Task", value=app_config.task, elem_id = "task")})
                    with gr.Accordion("VAD options", open=False):
                        fullInputDict.update(common_vad_inputs())
                        fullInputDict.update({
                            gr.Number(label="VAD - Padding (s)", precision=None, value=app_config.vad_padding, elem_id = "vadPadding"),
                            gr.Number(label="VAD - Prompt Window (s)", precision=None, value=app_config.vad_prompt_window, elem_id = "vadPromptWindow"),
                            gr.Dropdown(choices=VAD_INITIAL_PROMPT_MODE_VALUES, label="VAD - Initial Prompt Mode", value=app_config.vad_initial_prompt_mode, elem_id = "vadInitialPromptMode")})
                    with gr.Accordion("Word Timestamps options", open=False):
                        fullInputDict.update(common_word_timestamps_inputs())
                        fullInputDict.update({
                            gr.Text(label="Word Timestamps - Prepend Punctuations", value=app_config.prepend_punctuations, elem_id = "prepend_punctuations"),
                            gr.Text(label="Word Timestamps - Append Punctuations", value=app_config.append_punctuations, elem_id = "append_punctuations")})
                    with gr.Accordion("Whisper Advanced options", open=False):
                        fullInputDict.update({
                            gr.TextArea(label="Initial Prompt", elem_id = "initial_prompt"),
                            gr.Number(label="Temperature", value=app_config.temperature, elem_id = "temperature"),
                            gr.Number(label="Best Of - Non-zero temperature", value=app_config.best_of, precision=0, elem_id = "best_of"),
                            gr.Number(label="Beam Size - Zero temperature", value=app_config.beam_size, precision=0, elem_id = "beam_size"),
                            gr.Number(label="Patience - Zero temperature", value=app_config.patience, elem_id = "patience"),
                            gr.Number(label="Length Penalty - Any temperature", value=app_config.length_penalty, elem_id = "length_penalty"),
                            gr.Text(label="Suppress Tokens - Comma-separated list of token IDs", value=app_config.suppress_tokens, elem_id = "suppress_tokens"),
                            gr.Checkbox(label="Condition on previous text", value=app_config.condition_on_previous_text, elem_id = "condition_on_previous_text"),
                            gr.Checkbox(label="FP16", value=app_config.fp16, elem_id = "fp16"),
                            gr.Number(label="Temperature increment on fallback", value=app_config.temperature_increment_on_fallback, elem_id = "temperature_increment_on_fallback"),
                            gr.Number(label="Compression ratio threshold", value=app_config.compression_ratio_threshold, elem_id = "compression_ratio_threshold"),
                            gr.Number(label="Logprob threshold", value=app_config.logprob_threshold, elem_id = "logprob_threshold"),
                            gr.Number(label="No speech threshold", value=app_config.no_speech_threshold, elem_id = "no_speech_threshold"),
                            })
                        if app_config.whisper_implementation == "faster-whisper":
                            fullInputDict.update({
                                gr.Number(label="Repetition Penalty", value=app_config.repetition_penalty, elem_id = "repetition_penalty"),
                                gr.Number(label="No Repeat Ngram Size", value=app_config.no_repeat_ngram_size, precision=0, elem_id = "no_repeat_ngram_size")
                            })
                    with gr.Accordion("Diarization options", open=False):
                        fullInputDict.update(common_diarization_inputs())
                    with gr.Accordion("Translation options", open=False):
                        fullInputDict.update(common_translation_inputs())
            with gr.Column():
                fullOutput = common_output()
        with gr.Accordion("Article"):
            gr.Markdown(uiArticle)
        if optionsMd is not None:
            with gr.Accordion("docs/options.md", open=False):    
                gr.Markdown(optionsMd)
        if readmeMd is not None:
            with gr.Accordion("README.md", open=False):    
                gr.Markdown(readmeMd)
        
        fullInputDict.update({fullTranslateInput, fullSourceInput})
        fullSubmit.click(fn=ui.transcribe_webui_full_progress if is_queue_mode else ui.transcribe_webui_full,
                    inputs=fullInputDict, outputs=fullOutput)

    demo = gr.TabbedInterface([simpleTranscribe, fullTranscribe], tab_names=["Simple", "Full"])

    # Queue up the demo
    if is_queue_mode:
        demo.queue(concurrency_count=app_config.queue_concurrency_count)
        print("Queue mode enabled (concurrency count: " + str(app_config.queue_concurrency_count) + ")")
    else:
        print("Queue mode disabled - progress bars will not be shown.")
   
    demo.launch(inbrowser=app_config.autolaunch, share=app_config.share, server_name=app_config.server_name, server_port=app_config.server_port)
    
    # Clean up
    ui.close()

if __name__ == '__main__':
    default_app_config = ApplicationConfig.create_default()
    whisper_models = default_app_config.get_model_names("whisper")

    # Environment variable overrides
    default_whisper_implementation = os.environ.get("WHISPER_IMPLEMENTATION", default_app_config.whisper_implementation)

    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument("--input_audio_max_duration", type=int, default=default_app_config.input_audio_max_duration, \
                        help="Maximum audio file length in seconds, or -1 for no limit.") # 600
    parser.add_argument("--share", type=bool, default=default_app_config.share, \
                        help="True to share the app on HuggingFace.") # False
    parser.add_argument("--server_name", type=str, default=default_app_config.server_name, \
                        help="The host or IP to bind to. If None, bind to localhost.") # None
    parser.add_argument("--server_port", type=int, default=default_app_config.server_port, \
                        help="The port to bind to.") # 7860
    parser.add_argument("--queue_concurrency_count", type=int, default=default_app_config.queue_concurrency_count, \
                        help="The number of concurrent requests to process.") # 1
    parser.add_argument("--default_model_name", type=str, choices=whisper_models, default=default_app_config.default_model_name, \
                        help="The default model name.") # medium
    parser.add_argument("--default_vad", type=str, default=default_app_config.default_vad, \
                        help="The default VAD.") # silero-vad
    parser.add_argument("--vad_initial_prompt_mode", type=str, default=default_app_config.vad_initial_prompt_mode, choices=VAD_INITIAL_PROMPT_MODE_VALUES, \
                        help="Whether or not to prepend the initial prompt to each VAD segment (prepend_all_segments), or just the first segment (prepend_first_segment)") # prepend_first_segment
    parser.add_argument("--vad_parallel_devices", type=str, default=default_app_config.vad_parallel_devices, \
                        help="A commma delimited list of CUDA devices to use for parallel processing. If None, disable parallel processing.") # ""
    parser.add_argument("--vad_cpu_cores", type=int, default=default_app_config.vad_cpu_cores, \
                        help="The number of CPU cores to use for VAD pre-processing.") # 1
    parser.add_argument("--vad_process_timeout", type=float, default=default_app_config.vad_process_timeout, \
                        help="The number of seconds before inactivate processes are terminated. Use 0 to close processes immediately, or None for no timeout.") # 1800
    parser.add_argument("--auto_parallel", type=bool, default=default_app_config.auto_parallel, \
                        help="True to use all available GPUs and CPU cores for processing. Use vad_cpu_cores/vad_parallel_devices to specify the number of CPU cores/GPUs to use.") # False
    parser.add_argument("--output_dir", "-o", type=str, default=default_app_config.output_dir, \
                        help="directory to save the outputs")
    parser.add_argument("--whisper_implementation", type=str, default=default_whisper_implementation, choices=["whisper", "faster-whisper"],\
                        help="the Whisper implementation to use")
    parser.add_argument("--compute_type", type=str, default=default_app_config.compute_type, choices=["default", "auto", "int8", "int8_float16", "int16", "float16", "float32"], \
                        help="the compute type to use for inference")
    parser.add_argument("--threads", type=optional_int, default=0, 
                        help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS")
    
    parser.add_argument("--vad_max_merge_size", type=int, default=default_app_config.vad_max_merge_size, \
                        help="The number of VAD - Max Merge Size (s).") # 30
    parser.add_argument("--language", type=str, default=None, choices=sorted(get_lang_whisper_names()) + sorted([k.title() for k in _TO_LANG_CODE_WHISPER.keys()]),
                        help="language spoken in the audio, specify None to perform language detection")
    parser.add_argument("--save_downloaded_files", action='store_true', \
                        help="True to move downloaded files to outputs directory. This argument will take effect only after output_dir is set.")
    parser.add_argument("--merge_subtitle_with_sources", action='store_true', \
                        help="True to merge subtitle(srt) with sources and move the sources files to the outputs directory. This argument will take effect only after output_dir is set.")
    parser.add_argument("--input_max_file_name_length", type=int, default=100, \
                        help="Maximum length of a file name.")
    parser.add_argument("--autolaunch", action='store_true', \
                        help="open the webui URL in the system's default browser upon launch")
    
    parser.add_argument('--auth_token', type=str, default=default_app_config.auth_token, help='HuggingFace API Token (optional)')
    parser.add_argument("--diarization", type=str2bool, default=default_app_config.diarization, \
                        help="whether to perform speaker diarization")
    parser.add_argument("--diarization_num_speakers", type=int, default=default_app_config.diarization_speakers, help="Number of speakers")
    parser.add_argument("--diarization_min_speakers", type=int, default=default_app_config.diarization_min_speakers, help="Minimum number of speakers")
    parser.add_argument("--diarization_max_speakers", type=int, default=default_app_config.diarization_max_speakers, help="Maximum number of speakers")
    parser.add_argument("--diarization_process_timeout", type=int, default=default_app_config.diarization_process_timeout, \
                        help="Number of seconds before inactivate diarization processes are terminated. Use 0 to close processes immediately, or None for no timeout.")

    args = parser.parse_args().__dict__

    updated_config = default_app_config.update(**args)

    # updated_config.whisper_implementation = "faster-whisper"
    # updated_config.input_audio_max_duration = -1
    # updated_config.default_model_name = "large-v2"
    # updated_config.output_dir = "output"
    # updated_config.vad_max_merge_size = 90
    # updated_config.merge_subtitle_with_sources = False
    # updated_config.autolaunch = True
    # updated_config.auto_parallel = False
    # updated_config.save_downloaded_files = True

    if (threads := args.pop("threads")) > 0:
        torch.set_num_threads(threads)

    print("Using whisper implementation: " + updated_config.whisper_implementation)
    create_ui(app_config=updated_config)