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  1. checkpoint-1200/added_tokens.json +1611 -0
  2. checkpoint-1200/config.json +44 -0
  3. checkpoint-1200/generation_config.json +7 -0
  4. checkpoint-1200/merges.txt +0 -0
  5. checkpoint-1200/model.safetensors +3 -0
  6. checkpoint-1200/normalizer.json +1742 -0
  7. checkpoint-1200/optimizer.pt +3 -0
  8. checkpoint-1200/preprocessor_config.json +14 -0
  9. checkpoint-1200/processor_config.json +11 -0
  10. checkpoint-1200/rng_state_0.pth +3 -0
  11. checkpoint-1200/rng_state_1.pth +3 -0
  12. checkpoint-1200/rng_state_2.pth +3 -0
  13. checkpoint-1200/rng_state_3.pth +3 -0
  14. checkpoint-1200/rng_state_4.pth +3 -0
  15. checkpoint-1200/rng_state_5.pth +3 -0
  16. checkpoint-1200/rng_state_6.pth +3 -0
  17. checkpoint-1200/rng_state_7.pth +3 -0
  18. checkpoint-1200/scheduler.pt +3 -0
  19. checkpoint-1200/special_tokens_map.json +28 -0
  20. checkpoint-1200/tokenizer.json +0 -0
  21. checkpoint-1200/tokenizer_config.json +159 -0
  22. checkpoint-1200/trainer_state.json +169 -0
  23. checkpoint-1200/training_args.bin +3 -0
  24. checkpoint-1200/ultravox_config.py +178 -0
  25. checkpoint-1200/ultravox_model.py +786 -0
  26. checkpoint-1200/ultravox_processing.py +359 -0
  27. checkpoint-1200/vocab.json +0 -0
  28. checkpoint-2400/added_tokens.json +1611 -0
  29. checkpoint-2400/config.json +44 -0
  30. checkpoint-2400/generation_config.json +7 -0
  31. checkpoint-2400/merges.txt +0 -0
  32. checkpoint-2400/model.safetensors +3 -0
  33. checkpoint-2400/normalizer.json +1742 -0
  34. checkpoint-2400/optimizer.pt +3 -0
  35. checkpoint-2400/preprocessor_config.json +14 -0
  36. checkpoint-2400/processor_config.json +11 -0
  37. checkpoint-2400/rng_state_0.pth +3 -0
  38. checkpoint-2400/rng_state_1.pth +3 -0
  39. checkpoint-2400/rng_state_2.pth +3 -0
  40. checkpoint-2400/rng_state_3.pth +3 -0
  41. checkpoint-2400/rng_state_4.pth +3 -0
  42. checkpoint-2400/rng_state_5.pth +3 -0
  43. checkpoint-2400/rng_state_6.pth +3 -0
  44. checkpoint-2400/rng_state_7.pth +3 -0
  45. checkpoint-2400/scheduler.pt +3 -0
  46. checkpoint-2400/special_tokens_map.json +28 -0
  47. checkpoint-2400/tokenizer.json +0 -0
  48. checkpoint-2400/tokenizer_config.json +159 -0
  49. checkpoint-2400/trainer_state.json +298 -0
  50. checkpoint-2400/training_args.bin +3 -0
checkpoint-1200/added_tokens.json ADDED
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+ "analyse": "analyze",
61
+ "analysed": "analyzed",
62
+ "analyses": "analyzes",
63
+ "analysing": "analyzing",
64
+ "anglicise": "anglicize",
65
+ "anglicised": "anglicized",
66
+ "anglicises": "anglicizes",
67
+ "anglicising": "anglicizing",
68
+ "annualised": "annualized",
69
+ "antagonise": "antagonize",
70
+ "antagonised": "antagonized",
71
+ "antagonises": "antagonizes",
72
+ "antagonising": "antagonizing",
73
+ "apologise": "apologize",
74
+ "apologised": "apologized",
75
+ "apologises": "apologizes",
76
+ "apologising": "apologizing",
77
+ "appal": "appall",
78
+ "appals": "appalls",
79
+ "appetiser": "appetizer",
80
+ "appetisers": "appetizers",
81
+ "appetising": "appetizing",
82
+ "appetisingly": "appetizingly",
83
+ "arbour": "arbor",
84
+ "arbours": "arbors",
85
+ "archaeologically": "archeologically",
86
+ "archaeologist": "archeologist",
87
+ "archaeologists": "archeologists",
88
+ "archaeology": "archeology</span>",
89
+ "archeological": "archaeological",
90
+ "ardour": "ardor",
91
+ "armour": "armor",
92
+ "armoured": "armored",
93
+ "armourer": "armorer",
94
+ "armourers": "armorers",
95
+ "armouries": "armories",
96
+ "armoury": "armory",
97
+ "artefact": "artifact",
98
+ "artefacts": "artifacts",
99
+ "authorise": "authorize",
100
+ "authorised": "authorized",
101
+ "authorises": "authorizes",
102
+ "authorising": "authorizing",
103
+ "axe": "ax",
104
+ "backpedalled": "backpedaled",
105
+ "backpedalling": "backpedaling",
106
+ "bannister": "banister",
107
+ "bannisters": "banisters",
108
+ "baptise": "baptize",
109
+ "baptised": "baptized",
110
+ "baptises": "baptizes",
111
+ "baptising": "baptizing",
112
+ "bastardise": "bastardize",
113
+ "bastardised": "bastardized",
114
+ "bastardises": "bastardizes",
115
+ "bastardising": "bastardizing",
116
+ "battleax": "battleaxe",
117
+ "baulk": "balk",
118
+ "baulked": "balked",
119
+ "baulking": "balking",
120
+ "baulks": "balks",
121
+ "bedevilled": "bedeviled",
122
+ "bedevilling": "bedeviling",
123
+ "behaviour": "behavior",
124
+ "behavioural": "behavioral",
125
+ "behaviourism": "behaviorism",
126
+ "behaviourist": "behaviorist",
127
+ "behaviourists": "behaviorists",
128
+ "behaviours": "behaviors",
129
+ "behove": "behoove",
130
+ "behoved": "behooved",
131
+ "behoves": "behooves",
132
+ "bejewelled": "bejeweled",
133
+ "belabour": "belabor",
134
+ "belaboured": "belabored",
135
+ "belabouring": "belaboring",
136
+ "belabours": "belabors",
137
+ "bevelled": "beveled",
138
+ "bevvies": "bevies",
139
+ "bevvy": "bevy",
140
+ "biassed": "biased",
141
+ "biassing": "biasing",
142
+ "bingeing": "binging",
143
+ "bougainvillaea": "bougainvillea",
144
+ "bougainvillaeas": "bougainvilleas",
145
+ "bowdlerise": "bowdlerize",
146
+ "bowdlerised": "bowdlerized",
147
+ "bowdlerises": "bowdlerizes",
148
+ "bowdlerising": "bowdlerizing",
149
+ "breathalyse": "breathalyze",
150
+ "breathalysed": "breathalyzed",
151
+ "breathalyser": "breathalyzer",
152
+ "breathalysers": "breathalyzers",
153
+ "breathalyses": "breathalyzes",
154
+ "breathalysing": "breathalyzing",
155
+ "brutalise": "brutalize",
156
+ "brutalised": "brutalized",
157
+ "brutalises": "brutalizes",
158
+ "brutalising": "brutalizing",
159
+ "busses": "buses",
160
+ "bussing": "busing",
161
+ "caesarean": "cesarean",
162
+ "caesareans": "cesareans",
163
+ "calibre": "caliber",
164
+ "calibres": "calibers",
165
+ "calliper": "caliper",
166
+ "callipers": "calipers",
167
+ "callisthenics": "calisthenics",
168
+ "canalise": "canalize",
169
+ "canalised": "canalized",
170
+ "canalises": "canalizes",
171
+ "canalising": "canalizing",
172
+ "cancelation": "cancellation",
173
+ "cancelations": "cancellations",
174
+ "cancelled": "canceled",
175
+ "cancelling": "canceling",
176
+ "candour": "candor",
177
+ "cannibalise": "cannibalize",
178
+ "cannibalised": "cannibalized",
179
+ "cannibalises": "cannibalizes",
180
+ "cannibalising": "cannibalizing",
181
+ "canonise": "canonize",
182
+ "canonised": "canonized",
183
+ "canonises": "canonizes",
184
+ "canonising": "canonizing",
185
+ "capitalise": "capitalize",
186
+ "capitalised": "capitalized",
187
+ "capitalises": "capitalizes",
188
+ "capitalising": "capitalizing",
189
+ "caramelise": "caramelize",
190
+ "caramelised": "caramelized",
191
+ "caramelises": "caramelizes",
192
+ "caramelising": "caramelizing",
193
+ "carbonise": "carbonize",
194
+ "carbonised": "carbonized",
195
+ "carbonises": "carbonizes",
196
+ "carbonising": "carbonizing",
197
+ "carolled": "caroled",
198
+ "carolling": "caroling",
199
+ "catalogue": "catalog",
200
+ "catalogued": "cataloged",
201
+ "catalogues": "catalogs",
202
+ "cataloguing": "cataloging",
203
+ "catalyse": "catalyze",
204
+ "catalysed": "catalyzed",
205
+ "catalyses": "catalyzes",
206
+ "catalysing": "catalyzing",
207
+ "categorise": "categorize",
208
+ "categorised": "categorized",
209
+ "categorises": "categorizes",
210
+ "categorising": "categorizing",
211
+ "cauterise": "cauterize",
212
+ "cauterised": "cauterized",
213
+ "cauterises": "cauterizes",
214
+ "cauterising": "cauterizing",
215
+ "cavilled": "caviled",
216
+ "cavilling": "caviling",
217
+ "centigramme": "centigram",
218
+ "centigrammes": "centigrams",
219
+ "centilitre": "centiliter",
220
+ "centilitres": "centiliters",
221
+ "centimetre": "centimeter",
222
+ "centimetres": "centimeters",
223
+ "centralise": "centralize",
224
+ "centralised": "centralized",
225
+ "centralises": "centralizes",
226
+ "centralising": "centralizing",
227
+ "centre": "center",
228
+ "centred": "centered",
229
+ "centrefold": "centerfold",
230
+ "centrefolds": "centerfolds",
231
+ "centrepiece": "centerpiece",
232
+ "centrepieces": "centerpieces",
233
+ "centres": "centers",
234
+ "channelled": "channeled",
235
+ "channelling": "channeling",
236
+ "characterise": "characterize",
237
+ "characterised": "characterized",
238
+ "characterises": "characterizes",
239
+ "characterising": "characterizing",
240
+ "cheque": "check",
241
+ "chequebook": "checkbook",
242
+ "chequebooks": "checkbooks",
243
+ "chequered": "checkered",
244
+ "cheques": "checks",
245
+ "chilli": "chili",
246
+ "chimaera": "chimera",
247
+ "chimaeras": "chimeras",
248
+ "chiselled": "chiseled",
249
+ "chiselling": "chiseling",
250
+ "circularise": "circularize",
251
+ "circularised": "circularized",
252
+ "circularises": "circularizes",
253
+ "circularising": "circularizing",
254
+ "civilise": "civilize",
255
+ "civilised": "civilized",
256
+ "civilises": "civilizes",
257
+ "civilising": "civilizing",
258
+ "clamour": "clamor",
259
+ "clamoured": "clamored",
260
+ "clamouring": "clamoring",
261
+ "clamours": "clamors",
262
+ "clangour": "clangor",
263
+ "clarinettist": "clarinetist",
264
+ "clarinettists": "clarinetists",
265
+ "collectivise": "collectivize",
266
+ "collectivised": "collectivized",
267
+ "collectivises": "collectivizes",
268
+ "collectivising": "collectivizing",
269
+ "colonisation": "colonization",
270
+ "colonise": "colonize",
271
+ "colonised": "colonized",
272
+ "coloniser": "colonizer",
273
+ "colonisers": "colonizers",
274
+ "colonises": "colonizes",
275
+ "colonising": "colonizing",
276
+ "colour": "color",
277
+ "colourant": "colorant",
278
+ "colourants": "colorants",
279
+ "coloured": "colored",
280
+ "coloureds": "coloreds",
281
+ "colourful": "colorful",
282
+ "colourfully": "colorfully",
283
+ "colouring": "coloring",
284
+ "colourize": "colorize",
285
+ "colourized": "colorized",
286
+ "colourizes": "colorizes",
287
+ "colourizing": "colorizing",
288
+ "colourless": "colorless",
289
+ "colours": "colors",
290
+ "commercialise": "commercialize",
291
+ "commercialised": "commercialized",
292
+ "commercialises": "commercializes",
293
+ "commercialising": "commercializing",
294
+ "compartmentalise": "compartmentalize",
295
+ "compartmentalised": "compartmentalized",
296
+ "compartmentalises": "compartmentalizes",
297
+ "compartmentalising": "compartmentalizing",
298
+ "computerise": "computerize",
299
+ "computerised": "computerized",
300
+ "computerises": "computerizes",
301
+ "computerising": "computerizing",
302
+ "conceptualise": "conceptualize",
303
+ "conceptualised": "conceptualized",
304
+ "conceptualises": "conceptualizes",
305
+ "conceptualising": "conceptualizing",
306
+ "connexion": "connection",
307
+ "connexions": "connections",
308
+ "contextualise": "contextualize",
309
+ "contextualised": "contextualized",
310
+ "contextualises": "contextualizes",
311
+ "contextualising": "contextualizing",
312
+ "cosier": "cozier",
313
+ "cosies": "cozies",
314
+ "cosiest": "coziest",
315
+ "cosily": "cozily",
316
+ "cosiness": "coziness",
317
+ "cosy": "cozy",
318
+ "councillor": "councilor",
319
+ "councillors": "councilors",
320
+ "counselled": "counseled",
321
+ "counselling": "counseling",
322
+ "counsellor": "counselor",
323
+ "counsellors": "counselors",
324
+ "crenelated": "crenellated",
325
+ "criminalise": "criminalize",
326
+ "criminalised": "criminalized",
327
+ "criminalises": "criminalizes",
328
+ "criminalising": "criminalizing",
329
+ "criticise": "criticize",
330
+ "criticised": "criticized",
331
+ "criticises": "criticizes",
332
+ "criticising": "criticizing",
333
+ "crueller": "crueler",
334
+ "cruellest": "cruelest",
335
+ "crystallisation": "crystallization",
336
+ "crystallise": "crystallize",
337
+ "crystallised": "crystallized",
338
+ "crystallises": "crystallizes",
339
+ "crystallising": "crystallizing",
340
+ "cudgelled": "cudgeled",
341
+ "cudgelling": "cudgeling",
342
+ "customise": "customize",
343
+ "customised": "customized",
344
+ "customises": "customizes",
345
+ "customising": "customizing",
346
+ "cypher": "cipher",
347
+ "cyphers": "ciphers",
348
+ "decentralisation": "decentralization",
349
+ "decentralise": "decentralize",
350
+ "decentralised": "decentralized",
351
+ "decentralises": "decentralizes",
352
+ "decentralising": "decentralizing",
353
+ "decriminalisation": "decriminalization",
354
+ "decriminalise": "decriminalize",
355
+ "decriminalised": "decriminalized",
356
+ "decriminalises": "decriminalizes",
357
+ "decriminalising": "decriminalizing",
358
+ "defence": "defense",
359
+ "defenceless": "defenseless",
360
+ "defences": "defenses",
361
+ "dehumanisation": "dehumanization",
362
+ "dehumanise": "dehumanize",
363
+ "dehumanised": "dehumanized",
364
+ "dehumanises": "dehumanizes",
365
+ "dehumanising": "dehumanizing",
366
+ "demeanour": "demeanor",
367
+ "demilitarisation": "demilitarization",
368
+ "demilitarise": "demilitarize",
369
+ "demilitarised": "demilitarized",
370
+ "demilitarises": "demilitarizes",
371
+ "demilitarising": "demilitarizing",
372
+ "demobilisation": "demobilization",
373
+ "demobilise": "demobilize",
374
+ "demobilised": "demobilized",
375
+ "demobilises": "demobilizes",
376
+ "demobilising": "demobilizing",
377
+ "democratisation": "democratization",
378
+ "democratise": "democratize",
379
+ "democratised": "democratized",
380
+ "democratises": "democratizes",
381
+ "democratising": "democratizing",
382
+ "demonise": "demonize",
383
+ "demonised": "demonized",
384
+ "demonises": "demonizes",
385
+ "demonising": "demonizing",
386
+ "demoralisation": "demoralization",
387
+ "demoralise": "demoralize",
388
+ "demoralised": "demoralized",
389
+ "demoralises": "demoralizes",
390
+ "demoralising": "demoralizing",
391
+ "denationalisation": "denationalization",
392
+ "denationalise": "denationalize",
393
+ "denationalised": "denationalized",
394
+ "denationalises": "denationalizes",
395
+ "denationalising": "denationalizing",
396
+ "deodorise": "deodorize",
397
+ "deodorised": "deodorized",
398
+ "deodorises": "deodorizes",
399
+ "deodorising": "deodorizing",
400
+ "depersonalise": "depersonalize",
401
+ "depersonalised": "depersonalized",
402
+ "depersonalises": "depersonalizes",
403
+ "depersonalising": "depersonalizing",
404
+ "deputise": "deputize",
405
+ "deputised": "deputized",
406
+ "deputises": "deputizes",
407
+ "deputising": "deputizing",
408
+ "desensitisation": "desensitization",
409
+ "desensitise": "desensitize",
410
+ "desensitised": "desensitized",
411
+ "desensitises": "desensitizes",
412
+ "desensitising": "desensitizing",
413
+ "destabilisation": "destabilization",
414
+ "destabilise": "destabilize",
415
+ "destabilised": "destabilized",
416
+ "destabilises": "destabilizes",
417
+ "destabilising": "destabilizing",
418
+ "dialled": "dialed",
419
+ "dialling": "dialing",
420
+ "dialogue": "dialog",
421
+ "dialogues": "dialogs",
422
+ "diarrhoea": "diarrhea",
423
+ "digitise": "digitize",
424
+ "digitised": "digitized",
425
+ "digitises": "digitizes",
426
+ "digitising": "digitizing",
427
+ "disc": "disk",
428
+ "discolour": "discolor",
429
+ "discoloured": "discolored",
430
+ "discolouring": "discoloring",
431
+ "discolours": "discolors",
432
+ "discs": "disks",
433
+ "disembowelled": "disemboweled",
434
+ "disembowelling": "disemboweling",
435
+ "disfavour": "disfavor",
436
+ "dishevelled": "disheveled",
437
+ "dishonour": "dishonor",
438
+ "dishonourable": "dishonorable",
439
+ "dishonourably": "dishonorably",
440
+ "dishonoured": "dishonored",
441
+ "dishonouring": "dishonoring",
442
+ "dishonours": "dishonors",
443
+ "disorganisation": "disorganization",
444
+ "disorganised": "disorganized",
445
+ "distil": "distill",
446
+ "distils": "distills",
447
+ "dramatisation": "dramatization",
448
+ "dramatisations": "dramatizations",
449
+ "dramatise": "dramatize",
450
+ "dramatised": "dramatized",
451
+ "dramatises": "dramatizes",
452
+ "dramatising": "dramatizing",
453
+ "draught": "draft",
454
+ "draughtboard": "draftboard",
455
+ "draughtboards": "draftboards",
456
+ "draughtier": "draftier",
457
+ "draughtiest": "draftiest",
458
+ "draughts": "drafts",
459
+ "draughtsman": "draftsman",
460
+ "draughtsmanship": "draftsmanship",
461
+ "draughtsmen": "draftsmen",
462
+ "draughtswoman": "draftswoman",
463
+ "draughtswomen": "draftswomen",
464
+ "draughty": "drafty",
465
+ "drivelled": "driveled",
466
+ "drivelling": "driveling",
467
+ "duelled": "dueled",
468
+ "duelling": "dueling",
469
+ "economise": "economize",
470
+ "economised": "economized",
471
+ "economises": "economizes",
472
+ "economising": "economizing",
473
+ "editorialise": "editorialize",
474
+ "editorialised": "editorialized",
475
+ "editorialises": "editorializes",
476
+ "editorialising": "editorializing",
477
+ "edoema": "edema",
478
+ "empathise": "empathize",
479
+ "empathised": "empathized",
480
+ "empathises": "empathizes",
481
+ "empathising": "empathizing",
482
+ "emphasise": "emphasize",
483
+ "emphasised": "emphasized",
484
+ "emphasises": "emphasizes",
485
+ "emphasising": "emphasizing",
486
+ "enamelled": "enameled",
487
+ "enamelling": "enameling",
488
+ "enamoured": "enamored",
489
+ "encyclopaedia": "encyclopedia",
490
+ "encyclopaedias": "encyclopedias",
491
+ "encyclopaedic": "encyclopedic",
492
+ "endeavour": "endeavor",
493
+ "endeavoured": "endeavored",
494
+ "endeavouring": "endeavoring",
495
+ "endeavours": "endeavors",
496
+ "energise": "energize",
497
+ "energised": "energized",
498
+ "energises": "energizes",
499
+ "energising": "energizing",
500
+ "enrol": "enroll",
501
+ "enrols": "enrolls",
502
+ "enthral": "enthrall",
503
+ "enthrals": "enthralls",
504
+ "epaulette": "epaulet",
505
+ "epaulettes": "epaulets",
506
+ "epicentre": "epicenter",
507
+ "epicentres": "epicenters",
508
+ "epilogue": "epilog",
509
+ "epilogues": "epilogs",
510
+ "epitomise": "epitomize",
511
+ "epitomised": "epitomized",
512
+ "epitomises": "epitomizes",
513
+ "epitomising": "epitomizing",
514
+ "equalisation": "equalization",
515
+ "equalise": "equalize",
516
+ "equalised": "equalized",
517
+ "equaliser": "equalizer",
518
+ "equalisers": "equalizers",
519
+ "equalises": "equalizes",
520
+ "equalising": "equalizing",
521
+ "eulogise": "eulogize",
522
+ "eulogised": "eulogized",
523
+ "eulogises": "eulogizes",
524
+ "eulogising": "eulogizing",
525
+ "evangelise": "evangelize",
526
+ "evangelised": "evangelized",
527
+ "evangelises": "evangelizes",
528
+ "evangelising": "evangelizing",
529
+ "exorcise": "exorcize",
530
+ "exorcised": "exorcized",
531
+ "exorcises": "exorcizes",
532
+ "exorcising": "exorcizing",
533
+ "extemporisation": "extemporization",
534
+ "extemporise": "extemporize",
535
+ "extemporised": "extemporized",
536
+ "extemporises": "extemporizes",
537
+ "extemporising": "extemporizing",
538
+ "externalisation": "externalization",
539
+ "externalisations": "externalizations",
540
+ "externalise": "externalize",
541
+ "externalised": "externalized",
542
+ "externalises": "externalizes",
543
+ "externalising": "externalizing",
544
+ "factorise": "factorize",
545
+ "factorised": "factorized",
546
+ "factorises": "factorizes",
547
+ "factorising": "factorizing",
548
+ "faecal": "fecal",
549
+ "faeces": "feces",
550
+ "familiarisation": "familiarization",
551
+ "familiarise": "familiarize",
552
+ "familiarised": "familiarized",
553
+ "familiarises": "familiarizes",
554
+ "familiarising": "familiarizing",
555
+ "fantasise": "fantasize",
556
+ "fantasised": "fantasized",
557
+ "fantasises": "fantasizes",
558
+ "fantasising": "fantasizing",
559
+ "favour": "favor",
560
+ "favourable": "favorable",
561
+ "favourably": "favorably",
562
+ "favoured": "favored",
563
+ "favouring": "favoring",
564
+ "favourite": "favorite",
565
+ "favourites": "favorites",
566
+ "favouritism": "favoritism",
567
+ "favours": "favors",
568
+ "feminise": "feminize",
569
+ "feminised": "feminized",
570
+ "feminises": "feminizes",
571
+ "feminising": "feminizing",
572
+ "fertilisation": "fertilization",
573
+ "fertilise": "fertilize",
574
+ "fertilised": "fertilized",
575
+ "fertiliser": "fertilizer",
576
+ "fertilisers": "fertilizers",
577
+ "fertilises": "fertilizes",
578
+ "fertilising": "fertilizing",
579
+ "fervour": "fervor",
580
+ "fibre": "fiber",
581
+ "fibreglass": "fiberglass",
582
+ "fibres": "fibers",
583
+ "fictionalisation": "fictionalization",
584
+ "fictionalisations": "fictionalizations",
585
+ "fictionalise": "fictionalize",
586
+ "fictionalised": "fictionalized",
587
+ "fictionalises": "fictionalizes",
588
+ "fictionalising": "fictionalizing",
589
+ "fillet": "filet",
590
+ "filleted": "fileted",
591
+ "filleting": "fileting",
592
+ "fillets": "filets",
593
+ "finalisation": "finalization",
594
+ "finalise": "finalize",
595
+ "finalised": "finalized",
596
+ "finalises": "finalizes",
597
+ "finalising": "finalizing",
598
+ "flautist": "flutist",
599
+ "flautists": "flutists",
600
+ "flavour": "flavor",
601
+ "flavoured": "flavored",
602
+ "flavouring": "flavoring",
603
+ "flavourings": "flavorings",
604
+ "flavourless": "flavorless",
605
+ "flavours": "flavors",
606
+ "flavoursome": "flavorsome",
607
+ "flyer / flier": "flier / flyer",
608
+ "foetal": "fetal",
609
+ "foetid": "fetid",
610
+ "foetus": "fetus",
611
+ "foetuses": "fetuses",
612
+ "formalisation": "formalization",
613
+ "formalise": "formalize",
614
+ "formalised": "formalized",
615
+ "formalises": "formalizes",
616
+ "formalising": "formalizing",
617
+ "fossilisation": "fossilization",
618
+ "fossilise": "fossilize",
619
+ "fossilised": "fossilized",
620
+ "fossilises": "fossilizes",
621
+ "fossilising": "fossilizing",
622
+ "fraternisation": "fraternization",
623
+ "fraternise": "fraternize",
624
+ "fraternised": "fraternized",
625
+ "fraternises": "fraternizes",
626
+ "fraternising": "fraternizing",
627
+ "fulfil": "fulfill",
628
+ "fulfilment": "fulfillment",
629
+ "fulfils": "fulfills",
630
+ "funnelled": "funneled",
631
+ "funnelling": "funneling",
632
+ "gage": "gauge",
633
+ "gaged": "gauged",
634
+ "gages": "gauges",
635
+ "gaging": "gauging",
636
+ "galvanise": "galvanize",
637
+ "galvanised": "galvanized",
638
+ "galvanises": "galvanizes",
639
+ "galvanising": "galvanizing",
640
+ "gambolled": "gamboled",
641
+ "gambolling": "gamboling",
642
+ "gaol": "jail",
643
+ "gaolbird": "jailbird",
644
+ "gaolbirds": "jailbirds",
645
+ "gaolbreak": "jailbreak",
646
+ "gaolbreaks": "jailbreaks",
647
+ "gaoled": "jailed",
648
+ "gaoler": "jailer",
649
+ "gaolers": "jailers",
650
+ "gaoling": "jailing",
651
+ "gaols": "jails",
652
+ "gasses": "gases",
653
+ "generalisation": "generalization",
654
+ "generalisations": "generalizations",
655
+ "generalise": "generalize",
656
+ "generalised": "generalized",
657
+ "generalises": "generalizes",
658
+ "generalising": "generalizing",
659
+ "ghettoise": "ghettoize",
660
+ "ghettoised": "ghettoized",
661
+ "ghettoises": "ghettoizes",
662
+ "ghettoising": "ghettoizing",
663
+ "gipsies": "gypsies",
664
+ "glamor": "glamour",
665
+ "glamorise": "glamorize",
666
+ "glamorised": "glamorized",
667
+ "glamorises": "glamorizes",
668
+ "glamorising": "glamorizing",
669
+ "globalisation": "globalization",
670
+ "globalise": "globalize",
671
+ "globalised": "globalized",
672
+ "globalises": "globalizes",
673
+ "globalising": "globalizing",
674
+ "glueing": "gluing",
675
+ "goitre": "goiter",
676
+ "goitres": "goiters",
677
+ "gonorrhoea": "gonorrhea",
678
+ "gramme": "gram",
679
+ "grammes": "grams",
680
+ "gravelled": "graveled",
681
+ "grey": "gray",
682
+ "greyed": "grayed",
683
+ "greying": "graying",
684
+ "greyish": "grayish",
685
+ "greyness": "grayness",
686
+ "greys": "grays",
687
+ "grovelled": "groveled",
688
+ "grovelling": "groveling",
689
+ "groyne": "groin",
690
+ "groynes": "groins",
691
+ "gruelling": "grueling",
692
+ "gruellingly": "gruelingly",
693
+ "gryphon": "griffin",
694
+ "gryphons": "griffins",
695
+ "gynaecological": "gynecological",
696
+ "gynaecologist": "gynecologist",
697
+ "gynaecologists": "gynecologists",
698
+ "gynaecology": "gynecology",
699
+ "haematological": "hematological",
700
+ "haematologist": "hematologist",
701
+ "haematologists": "hematologists",
702
+ "haematology": "hematology",
703
+ "haemoglobin": "hemoglobin",
704
+ "haemophilia": "hemophilia",
705
+ "haemophiliac": "hemophiliac",
706
+ "haemophiliacs": "hemophiliacs",
707
+ "haemorrhage": "hemorrhage",
708
+ "haemorrhaged": "hemorrhaged",
709
+ "haemorrhages": "hemorrhages",
710
+ "haemorrhaging": "hemorrhaging",
711
+ "haemorrhoids": "hemorrhoids",
712
+ "harbour": "harbor",
713
+ "harboured": "harbored",
714
+ "harbouring": "harboring",
715
+ "harbours": "harbors",
716
+ "harmonisation": "harmonization",
717
+ "harmonise": "harmonize",
718
+ "harmonised": "harmonized",
719
+ "harmonises": "harmonizes",
720
+ "harmonising": "harmonizing",
721
+ "homoeopath": "homeopath",
722
+ "homoeopathic": "homeopathic",
723
+ "homoeopaths": "homeopaths",
724
+ "homoeopathy": "homeopathy",
725
+ "homogenise": "homogenize",
726
+ "homogenised": "homogenized",
727
+ "homogenises": "homogenizes",
728
+ "homogenising": "homogenizing",
729
+ "honour": "honor",
730
+ "honourable": "honorable",
731
+ "honourably": "honorably",
732
+ "honoured": "honored",
733
+ "honouring": "honoring",
734
+ "honours": "honors",
735
+ "hospitalisation": "hospitalization",
736
+ "hospitalise": "hospitalize",
737
+ "hospitalised": "hospitalized",
738
+ "hospitalises": "hospitalizes",
739
+ "hospitalising": "hospitalizing",
740
+ "humanise": "humanize",
741
+ "humanised": "humanized",
742
+ "humanises": "humanizes",
743
+ "humanising": "humanizing",
744
+ "humour": "humor",
745
+ "humoured": "humored",
746
+ "humouring": "humoring",
747
+ "humourless": "humorless",
748
+ "humours": "humors",
749
+ "hybridise": "hybridize",
750
+ "hybridised": "hybridized",
751
+ "hybridises": "hybridizes",
752
+ "hybridising": "hybridizing",
753
+ "hypnotise": "hypnotize",
754
+ "hypnotised": "hypnotized",
755
+ "hypnotises": "hypnotizes",
756
+ "hypnotising": "hypnotizing",
757
+ "hypothesise": "hypothesize",
758
+ "hypothesised": "hypothesized",
759
+ "hypothesises": "hypothesizes",
760
+ "hypothesising": "hypothesizing",
761
+ "idealisation": "idealization",
762
+ "idealise": "idealize",
763
+ "idealised": "idealized",
764
+ "idealises": "idealizes",
765
+ "idealising": "idealizing",
766
+ "idolise": "idolize",
767
+ "idolised": "idolized",
768
+ "idolises": "idolizes",
769
+ "idolising": "idolizing",
770
+ "immobilisation": "immobilization",
771
+ "immobilise": "immobilize",
772
+ "immobilised": "immobilized",
773
+ "immobiliser": "immobilizer",
774
+ "immobilisers": "immobilizers",
775
+ "immobilises": "immobilizes",
776
+ "immobilising": "immobilizing",
777
+ "immortalise": "immortalize",
778
+ "immortalised": "immortalized",
779
+ "immortalises": "immortalizes",
780
+ "immortalising": "immortalizing",
781
+ "immunisation": "immunization",
782
+ "immunise": "immunize",
783
+ "immunised": "immunized",
784
+ "immunises": "immunizes",
785
+ "immunising": "immunizing",
786
+ "impanelled": "impaneled",
787
+ "impanelling": "impaneling",
788
+ "imperilled": "imperiled",
789
+ "imperilling": "imperiling",
790
+ "individualise": "individualize",
791
+ "individualised": "individualized",
792
+ "individualises": "individualizes",
793
+ "individualising": "individualizing",
794
+ "industrialise": "industrialize",
795
+ "industrialised": "industrialized",
796
+ "industrialises": "industrializes",
797
+ "industrialising": "industrializing",
798
+ "inflexion": "inflection",
799
+ "inflexions": "inflections",
800
+ "initialise": "initialize",
801
+ "initialised": "initialized",
802
+ "initialises": "initializes",
803
+ "initialising": "initializing",
804
+ "initialled": "initialed",
805
+ "initialling": "initialing",
806
+ "instal": "install",
807
+ "instalment": "installment",
808
+ "instalments": "installments",
809
+ "instals": "installs",
810
+ "instil": "instill",
811
+ "instils": "instills",
812
+ "institutionalisation": "institutionalization",
813
+ "institutionalise": "institutionalize",
814
+ "institutionalised": "institutionalized",
815
+ "institutionalises": "institutionalizes",
816
+ "institutionalising": "institutionalizing",
817
+ "intellectualise": "intellectualize",
818
+ "intellectualised": "intellectualized",
819
+ "intellectualises": "intellectualizes",
820
+ "intellectualising": "intellectualizing",
821
+ "internalisation": "internalization",
822
+ "internalise": "internalize",
823
+ "internalised": "internalized",
824
+ "internalises": "internalizes",
825
+ "internalising": "internalizing",
826
+ "internationalisation": "internationalization",
827
+ "internationalise": "internationalize",
828
+ "internationalised": "internationalized",
829
+ "internationalises": "internationalizes",
830
+ "internationalising": "internationalizing",
831
+ "ionisation": "ionization",
832
+ "ionise": "ionize",
833
+ "ionised": "ionized",
834
+ "ioniser": "ionizer",
835
+ "ionisers": "ionizers",
836
+ "ionises": "ionizes",
837
+ "ionising": "ionizing",
838
+ "italicise": "italicize",
839
+ "italicised": "italicized",
840
+ "italicises": "italicizes",
841
+ "italicising": "italicizing",
842
+ "itemise": "itemize",
843
+ "itemised": "itemized",
844
+ "itemises": "itemizes",
845
+ "itemising": "itemizing",
846
+ "jeopardise": "jeopardize",
847
+ "jeopardised": "jeopardized",
848
+ "jeopardises": "jeopardizes",
849
+ "jeopardising": "jeopardizing",
850
+ "jewelled": "jeweled",
851
+ "jeweller": "jeweler",
852
+ "jewellers": "jewelers",
853
+ "jewellery": "jewelry",
854
+ "judgement": "judgment",
855
+ "kilogramme": "kilogram",
856
+ "kilogrammes": "kilograms",
857
+ "kilometre": "kilometer",
858
+ "kilometres": "kilometers",
859
+ "labelled": "labeled",
860
+ "labelling": "labeling",
861
+ "labour": "labor",
862
+ "laboured": "labored",
863
+ "labourer": "laborer",
864
+ "labourers": "laborers",
865
+ "labouring": "laboring",
866
+ "labours": "labors",
867
+ "lacklustre": "lackluster",
868
+ "legalisation": "legalization",
869
+ "legalise": "legalize",
870
+ "legalised": "legalized",
871
+ "legalises": "legalizes",
872
+ "legalising": "legalizing",
873
+ "legitimise": "legitimize",
874
+ "legitimised": "legitimized",
875
+ "legitimises": "legitimizes",
876
+ "legitimising": "legitimizing",
877
+ "leukaemia": "leukemia",
878
+ "levelled": "leveled",
879
+ "leveller": "leveler",
880
+ "levellers": "levelers",
881
+ "levelling": "leveling",
882
+ "libelled": "libeled",
883
+ "libelling": "libeling",
884
+ "libellous": "libelous",
885
+ "liberalisation": "liberalization",
886
+ "liberalise": "liberalize",
887
+ "liberalised": "liberalized",
888
+ "liberalises": "liberalizes",
889
+ "liberalising": "liberalizing",
890
+ "licence": "license",
891
+ "licenced": "licensed",
892
+ "licences": "licenses",
893
+ "licencing": "licensing",
894
+ "likeable": "likable",
895
+ "lionisation": "lionization",
896
+ "lionise": "lionize",
897
+ "lionised": "lionized",
898
+ "lionises": "lionizes",
899
+ "lionising": "lionizing",
900
+ "liquidise": "liquidize",
901
+ "liquidised": "liquidized",
902
+ "liquidiser": "liquidizer",
903
+ "liquidisers": "liquidizers",
904
+ "liquidises": "liquidizes",
905
+ "liquidising": "liquidizing",
906
+ "litre": "liter",
907
+ "litres": "liters",
908
+ "localise": "localize",
909
+ "localised": "localized",
910
+ "localises": "localizes",
911
+ "localising": "localizing",
912
+ "louvre": "louver",
913
+ "louvred": "louvered",
914
+ "louvres": "louvers",
915
+ "lustre": "luster",
916
+ "magnetise": "magnetize",
917
+ "magnetised": "magnetized",
918
+ "magnetises": "magnetizes",
919
+ "magnetising": "magnetizing",
920
+ "manoeuvrability": "maneuverability",
921
+ "manoeuvrable": "maneuverable",
922
+ "manoeuvre": "maneuver",
923
+ "manoeuvred": "maneuvered",
924
+ "manoeuvres": "maneuvers",
925
+ "manoeuvring": "maneuvering",
926
+ "manoeuvrings": "maneuverings",
927
+ "marginalisation": "marginalization",
928
+ "marginalise": "marginalize",
929
+ "marginalised": "marginalized",
930
+ "marginalises": "marginalizes",
931
+ "marginalising": "marginalizing",
932
+ "marshalled": "marshaled",
933
+ "marshalling": "marshaling",
934
+ "marvelled": "marveled",
935
+ "marvelling": "marveling",
936
+ "marvellous": "marvelous",
937
+ "marvellously": "marvelously",
938
+ "materialisation": "materialization",
939
+ "materialise": "materialize",
940
+ "materialised": "materialized",
941
+ "materialises": "materializes",
942
+ "materialising": "materializing",
943
+ "maximisation": "maximization",
944
+ "maximise": "maximize",
945
+ "maximised": "maximized",
946
+ "maximises": "maximizes",
947
+ "maximising": "maximizing",
948
+ "meagre": "meager",
949
+ "mechanisation": "mechanization",
950
+ "mechanise": "mechanize",
951
+ "mechanised": "mechanized",
952
+ "mechanises": "mechanizes",
953
+ "mechanising": "mechanizing",
954
+ "mediaeval": "medieval",
955
+ "memorialise": "memorialize",
956
+ "memorialised": "memorialized",
957
+ "memorialises": "memorializes",
958
+ "memorialising": "memorializing",
959
+ "memorise": "memorize",
960
+ "memorised": "memorized",
961
+ "memorises": "memorizes",
962
+ "memorising": "memorizing",
963
+ "mesmerise": "mesmerize",
964
+ "mesmerised": "mesmerized",
965
+ "mesmerises": "mesmerizes",
966
+ "mesmerising": "mesmerizing",
967
+ "metabolise": "metabolize",
968
+ "metabolised": "metabolized",
969
+ "metabolises": "metabolizes",
970
+ "metabolising": "metabolizing",
971
+ "metre": "meter",
972
+ "metres": "meters",
973
+ "mhm": "hmm",
974
+ "micrometre": "micrometer",
975
+ "micrometres": "micrometers",
976
+ "militarise": "militarize",
977
+ "militarised": "militarized",
978
+ "militarises": "militarizes",
979
+ "militarising": "militarizing",
980
+ "milligramme": "milligram",
981
+ "milligrammes": "milligrams",
982
+ "millilitre": "milliliter",
983
+ "millilitres": "milliliters",
984
+ "millimetre": "millimeter",
985
+ "millimetres": "millimeters",
986
+ "miniaturisation": "miniaturization",
987
+ "miniaturise": "miniaturize",
988
+ "miniaturised": "miniaturized",
989
+ "miniaturises": "miniaturizes",
990
+ "miniaturising": "miniaturizing",
991
+ "minibusses": "minibuses",
992
+ "minimise": "minimize",
993
+ "minimised": "minimized",
994
+ "minimises": "minimizes",
995
+ "minimising": "minimizing",
996
+ "misbehaviour": "misbehavior",
997
+ "misdemeanour": "misdemeanor",
998
+ "misdemeanours": "misdemeanors",
999
+ "misspelt": "misspelled",
1000
+ "mitre": "miter",
1001
+ "mitres": "miters",
1002
+ "mm": "hmm",
1003
+ "mmm": "hmm",
1004
+ "mobilisation": "mobilization",
1005
+ "mobilise": "mobilize",
1006
+ "mobilised": "mobilized",
1007
+ "mobilises": "mobilizes",
1008
+ "mobilising": "mobilizing",
1009
+ "modelled": "modeled",
1010
+ "modeller": "modeler",
1011
+ "modellers": "modelers",
1012
+ "modelling": "modeling",
1013
+ "modernise": "modernize",
1014
+ "modernised": "modernized",
1015
+ "modernises": "modernizes",
1016
+ "modernising": "modernizing",
1017
+ "moisturise": "moisturize",
1018
+ "moisturised": "moisturized",
1019
+ "moisturiser": "moisturizer",
1020
+ "moisturisers": "moisturizers",
1021
+ "moisturises": "moisturizes",
1022
+ "moisturising": "moisturizing",
1023
+ "monologue": "monolog",
1024
+ "monologues": "monologs",
1025
+ "monopolisation": "monopolization",
1026
+ "monopolise": "monopolize",
1027
+ "monopolised": "monopolized",
1028
+ "monopolises": "monopolizes",
1029
+ "monopolising": "monopolizing",
1030
+ "moralise": "moralize",
1031
+ "moralised": "moralized",
1032
+ "moralises": "moralizes",
1033
+ "moralising": "moralizing",
1034
+ "motorised": "motorized",
1035
+ "mould": "mold",
1036
+ "moulded": "molded",
1037
+ "moulder": "molder",
1038
+ "mouldered": "moldered",
1039
+ "mouldering": "moldering",
1040
+ "moulders": "molders",
1041
+ "mouldier": "moldier",
1042
+ "mouldiest": "moldiest",
1043
+ "moulding": "molding",
1044
+ "mouldings": "moldings",
1045
+ "moulds": "molds",
1046
+ "mouldy": "moldy",
1047
+ "moult": "molt",
1048
+ "moulted": "molted",
1049
+ "moulting": "molting",
1050
+ "moults": "molts",
1051
+ "moustache": "mustache",
1052
+ "moustached": "mustached",
1053
+ "moustaches": "mustaches",
1054
+ "moustachioed": "mustachioed",
1055
+ "multicoloured": "multicolored",
1056
+ "nationalisation": "nationalization",
1057
+ "nationalisations": "nationalizations",
1058
+ "nationalise": "nationalize",
1059
+ "nationalised": "nationalized",
1060
+ "nationalises": "nationalizes",
1061
+ "nationalising": "nationalizing",
1062
+ "naturalisation": "naturalization",
1063
+ "naturalise": "naturalize",
1064
+ "naturalised": "naturalized",
1065
+ "naturalises": "naturalizes",
1066
+ "naturalising": "naturalizing",
1067
+ "neighbour": "neighbor",
1068
+ "neighbourhood": "neighborhood",
1069
+ "neighbourhoods": "neighborhoods",
1070
+ "neighbouring": "neighboring",
1071
+ "neighbourliness": "neighborliness",
1072
+ "neighbourly": "neighborly",
1073
+ "neighbours": "neighbors",
1074
+ "neutralisation": "neutralization",
1075
+ "neutralise": "neutralize",
1076
+ "neutralised": "neutralized",
1077
+ "neutralises": "neutralizes",
1078
+ "neutralising": "neutralizing",
1079
+ "normalisation": "normalization",
1080
+ "normalise": "normalize",
1081
+ "normalised": "normalized",
1082
+ "normalises": "normalizes",
1083
+ "normalising": "normalizing",
1084
+ "odour": "odor",
1085
+ "odourless": "odorless",
1086
+ "odours": "odors",
1087
+ "oesophagus": "esophagus",
1088
+ "oesophaguses": "esophaguses",
1089
+ "oestrogen": "estrogen",
1090
+ "offence": "offense",
1091
+ "offences": "offenses",
1092
+ "omelette": "omelet",
1093
+ "omelettes": "omelets",
1094
+ "optimise": "optimize",
1095
+ "optimised": "optimized",
1096
+ "optimises": "optimizes",
1097
+ "optimising": "optimizing",
1098
+ "organisation": "organization",
1099
+ "organisational": "organizational",
1100
+ "organisations": "organizations",
1101
+ "organise": "organize",
1102
+ "organised": "organized",
1103
+ "organiser": "organizer",
1104
+ "organisers": "organizers",
1105
+ "organises": "organizes",
1106
+ "organising": "organizing",
1107
+ "orthopaedic": "orthopedic",
1108
+ "orthopaedics": "orthopedics",
1109
+ "ostracise": "ostracize",
1110
+ "ostracised": "ostracized",
1111
+ "ostracises": "ostracizes",
1112
+ "ostracising": "ostracizing",
1113
+ "outmanoeuvre": "outmaneuver",
1114
+ "outmanoeuvred": "outmaneuvered",
1115
+ "outmanoeuvres": "outmaneuvers",
1116
+ "outmanoeuvring": "outmaneuvering",
1117
+ "overemphasise": "overemphasize",
1118
+ "overemphasised": "overemphasized",
1119
+ "overemphasises": "overemphasizes",
1120
+ "overemphasising": "overemphasizing",
1121
+ "oxidisation": "oxidization",
1122
+ "oxidise": "oxidize",
1123
+ "oxidised": "oxidized",
1124
+ "oxidises": "oxidizes",
1125
+ "oxidising": "oxidizing",
1126
+ "paederast": "pederast",
1127
+ "paederasts": "pederasts",
1128
+ "paediatric": "pediatric",
1129
+ "paediatrician": "pediatrician",
1130
+ "paediatricians": "pediatricians",
1131
+ "paediatrics": "pediatrics",
1132
+ "paedophile": "pedophile",
1133
+ "paedophiles": "pedophiles",
1134
+ "paedophilia": "pedophilia",
1135
+ "palaeolithic": "paleolithic",
1136
+ "palaeontologist": "paleontologist",
1137
+ "palaeontologists": "paleontologists",
1138
+ "palaeontology": "paleontology",
1139
+ "panelled": "paneled",
1140
+ "panelling": "paneling",
1141
+ "panellist": "panelist",
1142
+ "panellists": "panelists",
1143
+ "paralyse": "paralyze",
1144
+ "paralysed": "paralyzed",
1145
+ "paralyses": "paralyzes",
1146
+ "paralysing": "paralyzing",
1147
+ "parcelled": "parceled",
1148
+ "parcelling": "parceling",
1149
+ "parlour": "parlor",
1150
+ "parlours": "parlors",
1151
+ "particularise": "particularize",
1152
+ "particularised": "particularized",
1153
+ "particularises": "particularizes",
1154
+ "particularising": "particularizing",
1155
+ "passivisation": "passivization",
1156
+ "passivise": "passivize",
1157
+ "passivised": "passivized",
1158
+ "passivises": "passivizes",
1159
+ "passivising": "passivizing",
1160
+ "pasteurisation": "pasteurization",
1161
+ "pasteurise": "pasteurize",
1162
+ "pasteurised": "pasteurized",
1163
+ "pasteurises": "pasteurizes",
1164
+ "pasteurising": "pasteurizing",
1165
+ "patronise": "patronize",
1166
+ "patronised": "patronized",
1167
+ "patronises": "patronizes",
1168
+ "patronising": "patronizing",
1169
+ "patronisingly": "patronizingly",
1170
+ "pedalled": "pedaled",
1171
+ "pedalling": "pedaling",
1172
+ "pedestrianisation": "pedestrianization",
1173
+ "pedestrianise": "pedestrianize",
1174
+ "pedestrianised": "pedestrianized",
1175
+ "pedestrianises": "pedestrianizes",
1176
+ "pedestrianising": "pedestrianizing",
1177
+ "penalise": "penalize",
1178
+ "penalised": "penalized",
1179
+ "penalises": "penalizes",
1180
+ "penalising": "penalizing",
1181
+ "pencilled": "penciled",
1182
+ "pencilling": "penciling",
1183
+ "personalise": "personalize",
1184
+ "personalised": "personalized",
1185
+ "personalises": "personalizes",
1186
+ "personalising": "personalizing",
1187
+ "pharmacopoeia": "pharmacopeia",
1188
+ "pharmacopoeias": "pharmacopeias",
1189
+ "philosophise": "philosophize",
1190
+ "philosophised": "philosophized",
1191
+ "philosophises": "philosophizes",
1192
+ "philosophising": "philosophizing",
1193
+ "philtre": "filter",
1194
+ "philtres": "filters",
1195
+ "phoney": "phony",
1196
+ "plagiarise": "plagiarize",
1197
+ "plagiarised": "plagiarized",
1198
+ "plagiarises": "plagiarizes",
1199
+ "plagiarising": "plagiarizing",
1200
+ "plough": "plow",
1201
+ "ploughed": "plowed",
1202
+ "ploughing": "plowing",
1203
+ "ploughman": "plowman",
1204
+ "ploughmen": "plowmen",
1205
+ "ploughs": "plows",
1206
+ "ploughshare": "plowshare",
1207
+ "ploughshares": "plowshares",
1208
+ "polarisation": "polarization",
1209
+ "polarise": "polarize",
1210
+ "polarised": "polarized",
1211
+ "polarises": "polarizes",
1212
+ "polarising": "polarizing",
1213
+ "politicisation": "politicization",
1214
+ "politicise": "politicize",
1215
+ "politicised": "politicized",
1216
+ "politicises": "politicizes",
1217
+ "politicising": "politicizing",
1218
+ "popularisation": "popularization",
1219
+ "popularise": "popularize",
1220
+ "popularised": "popularized",
1221
+ "popularises": "popularizes",
1222
+ "popularising": "popularizing",
1223
+ "pouffe": "pouf",
1224
+ "pouffes": "poufs",
1225
+ "practise": "practice",
1226
+ "practised": "practiced",
1227
+ "practises": "practices",
1228
+ "practising": "practicing",
1229
+ "praesidium": "presidium",
1230
+ "praesidiums": "presidiums",
1231
+ "pressurisation": "pressurization",
1232
+ "pressurise": "pressurize",
1233
+ "pressurised": "pressurized",
1234
+ "pressurises": "pressurizes",
1235
+ "pressurising": "pressurizing",
1236
+ "pretence": "pretense",
1237
+ "pretences": "pretenses",
1238
+ "primaeval": "primeval",
1239
+ "prioritisation": "prioritization",
1240
+ "prioritise": "prioritize",
1241
+ "prioritised": "prioritized",
1242
+ "prioritises": "prioritizes",
1243
+ "prioritising": "prioritizing",
1244
+ "privatisation": "privatization",
1245
+ "privatisations": "privatizations",
1246
+ "privatise": "privatize",
1247
+ "privatised": "privatized",
1248
+ "privatises": "privatizes",
1249
+ "privatising": "privatizing",
1250
+ "professionalisation": "professionalization",
1251
+ "professionalise": "professionalize",
1252
+ "professionalised": "professionalized",
1253
+ "professionalises": "professionalizes",
1254
+ "professionalising": "professionalizing",
1255
+ "programme": "program",
1256
+ "programmes": "programs",
1257
+ "prologue": "prolog",
1258
+ "prologues": "prologs",
1259
+ "propagandise": "propagandize",
1260
+ "propagandised": "propagandized",
1261
+ "propagandises": "propagandizes",
1262
+ "propagandising": "propagandizing",
1263
+ "proselytise": "proselytize",
1264
+ "proselytised": "proselytized",
1265
+ "proselytiser": "proselytizer",
1266
+ "proselytisers": "proselytizers",
1267
+ "proselytises": "proselytizes",
1268
+ "proselytising": "proselytizing",
1269
+ "psychoanalyse": "psychoanalyze",
1270
+ "psychoanalysed": "psychoanalyzed",
1271
+ "psychoanalyses": "psychoanalyzes",
1272
+ "psychoanalysing": "psychoanalyzing",
1273
+ "publicise": "publicize",
1274
+ "publicised": "publicized",
1275
+ "publicises": "publicizes",
1276
+ "publicising": "publicizing",
1277
+ "pulverisation": "pulverization",
1278
+ "pulverise": "pulverize",
1279
+ "pulverised": "pulverized",
1280
+ "pulverises": "pulverizes",
1281
+ "pulverising": "pulverizing",
1282
+ "pummelled": "pummel",
1283
+ "pummelling": "pummeled",
1284
+ "pyjama": "pajama",
1285
+ "pyjamas": "pajamas",
1286
+ "pzazz": "pizzazz",
1287
+ "quarrelled": "quarreled",
1288
+ "quarrelling": "quarreling",
1289
+ "radicalise": "radicalize",
1290
+ "radicalised": "radicalized",
1291
+ "radicalises": "radicalizes",
1292
+ "radicalising": "radicalizing",
1293
+ "rancour": "rancor",
1294
+ "randomise": "randomize",
1295
+ "randomised": "randomized",
1296
+ "randomises": "randomizes",
1297
+ "randomising": "randomizing",
1298
+ "rationalisation": "rationalization",
1299
+ "rationalisations": "rationalizations",
1300
+ "rationalise": "rationalize",
1301
+ "rationalised": "rationalized",
1302
+ "rationalises": "rationalizes",
1303
+ "rationalising": "rationalizing",
1304
+ "ravelled": "raveled",
1305
+ "ravelling": "raveling",
1306
+ "realisable": "realizable",
1307
+ "realisation": "realization",
1308
+ "realisations": "realizations",
1309
+ "realise": "realize",
1310
+ "realised": "realized",
1311
+ "realises": "realizes",
1312
+ "realising": "realizing",
1313
+ "recognisable": "recognizable",
1314
+ "recognisably": "recognizably",
1315
+ "recognisance": "recognizance",
1316
+ "recognise": "recognize",
1317
+ "recognised": "recognized",
1318
+ "recognises": "recognizes",
1319
+ "recognising": "recognizing",
1320
+ "reconnoitre": "reconnoiter",
1321
+ "reconnoitred": "reconnoitered",
1322
+ "reconnoitres": "reconnoiters",
1323
+ "reconnoitring": "reconnoitering",
1324
+ "refuelled": "refueled",
1325
+ "refuelling": "refueling",
1326
+ "regularisation": "regularization",
1327
+ "regularise": "regularize",
1328
+ "regularised": "regularized",
1329
+ "regularises": "regularizes",
1330
+ "regularising": "regularizing",
1331
+ "remodelled": "remodeled",
1332
+ "remodelling": "remodeling",
1333
+ "remould": "remold",
1334
+ "remoulded": "remolded",
1335
+ "remoulding": "remolding",
1336
+ "remoulds": "remolds",
1337
+ "reorganisation": "reorganization",
1338
+ "reorganisations": "reorganizations",
1339
+ "reorganise": "reorganize",
1340
+ "reorganised": "reorganized",
1341
+ "reorganises": "reorganizes",
1342
+ "reorganising": "reorganizing",
1343
+ "revelled": "reveled",
1344
+ "reveller": "reveler",
1345
+ "revellers": "revelers",
1346
+ "revelling": "reveling",
1347
+ "revitalise": "revitalize",
1348
+ "revitalised": "revitalized",
1349
+ "revitalises": "revitalizes",
1350
+ "revitalising": "revitalizing",
1351
+ "revolutionise": "revolutionize",
1352
+ "revolutionised": "revolutionized",
1353
+ "revolutionises": "revolutionizes",
1354
+ "revolutionising": "revolutionizing",
1355
+ "rhapsodise": "rhapsodize",
1356
+ "rhapsodised": "rhapsodized",
1357
+ "rhapsodises": "rhapsodizes",
1358
+ "rhapsodising": "rhapsodizing",
1359
+ "rigour": "rigor",
1360
+ "rigours": "rigors",
1361
+ "ritualised": "ritualized",
1362
+ "rivalled": "rivaled",
1363
+ "rivalling": "rivaling",
1364
+ "romanticise": "romanticize",
1365
+ "romanticised": "romanticized",
1366
+ "romanticises": "romanticizes",
1367
+ "romanticising": "romanticizing",
1368
+ "rumour": "rumor",
1369
+ "rumoured": "rumored",
1370
+ "rumours": "rumors",
1371
+ "sabre": "saber",
1372
+ "sabres": "sabers",
1373
+ "saltpetre": "saltpeter",
1374
+ "sanitise": "sanitize",
1375
+ "sanitised": "sanitized",
1376
+ "sanitises": "sanitizes",
1377
+ "sanitising": "sanitizing",
1378
+ "satirise": "satirize",
1379
+ "satirised": "satirized",
1380
+ "satirises": "satirizes",
1381
+ "satirising": "satirizing",
1382
+ "saviour": "savior",
1383
+ "saviours": "saviors",
1384
+ "savour": "savor",
1385
+ "savoured": "savored",
1386
+ "savouries": "savories",
1387
+ "savouring": "savoring",
1388
+ "savours": "savors",
1389
+ "savoury": "savory",
1390
+ "scandalise": "scandalize",
1391
+ "scandalised": "scandalized",
1392
+ "scandalises": "scandalizes",
1393
+ "scandalising": "scandalizing",
1394
+ "sceptic": "skeptic",
1395
+ "sceptical": "skeptical",
1396
+ "sceptically": "skeptically",
1397
+ "scepticism": "skepticism",
1398
+ "sceptics": "skeptics",
1399
+ "sceptre": "scepter",
1400
+ "sceptres": "scepters",
1401
+ "scrutinise": "scrutinize",
1402
+ "scrutinised": "scrutinized",
1403
+ "scrutinises": "scrutinizes",
1404
+ "scrutinising": "scrutinizing",
1405
+ "secularisation": "secularization",
1406
+ "secularise": "secularize",
1407
+ "secularised": "secularized",
1408
+ "secularises": "secularizes",
1409
+ "secularising": "secularizing",
1410
+ "sensationalise": "sensationalize",
1411
+ "sensationalised": "sensationalized",
1412
+ "sensationalises": "sensationalizes",
1413
+ "sensationalising": "sensationalizing",
1414
+ "sensitise": "sensitize",
1415
+ "sensitised": "sensitized",
1416
+ "sensitises": "sensitizes",
1417
+ "sensitising": "sensitizing",
1418
+ "sentimentalise": "sentimentalize",
1419
+ "sentimentalised": "sentimentalized",
1420
+ "sentimentalises": "sentimentalizes",
1421
+ "sentimentalising": "sentimentalizing",
1422
+ "sepulchre": "sepulcher",
1423
+ "sepulchres": "sepulchers",
1424
+ "serialisation": "serialization",
1425
+ "serialisations": "serializations",
1426
+ "serialise": "serialize",
1427
+ "serialised": "serialized",
1428
+ "serialises": "serializes",
1429
+ "serialising": "serializing",
1430
+ "sermonise": "sermonize",
1431
+ "sermonised": "sermonized",
1432
+ "sermonises": "sermonizes",
1433
+ "sermonising": "sermonizing",
1434
+ "sheikh": "sheik",
1435
+ "shovelled": "shoveled",
1436
+ "shovelling": "shoveling",
1437
+ "shrivelled": "shriveled",
1438
+ "shrivelling": "shriveling",
1439
+ "signalise": "signalize",
1440
+ "signalised": "signalized",
1441
+ "signalises": "signalizes",
1442
+ "signalising": "signalizing",
1443
+ "signalled": "signaled",
1444
+ "signalling": "signaling",
1445
+ "smoulder": "smolder",
1446
+ "smouldered": "smoldered",
1447
+ "smouldering": "smoldering",
1448
+ "smoulders": "smolders",
1449
+ "snivelled": "sniveled",
1450
+ "snivelling": "sniveling",
1451
+ "snorkelled": "snorkeled",
1452
+ "snorkelling": "snorkeling",
1453
+ "snowplough": "snowplow",
1454
+ "snowploughs": "snowplow",
1455
+ "socialisation": "socialization",
1456
+ "socialise": "socialize",
1457
+ "socialised": "socialized",
1458
+ "socialises": "socializes",
1459
+ "socialising": "socializing",
1460
+ "sodomise": "sodomize",
1461
+ "sodomised": "sodomized",
1462
+ "sodomises": "sodomizes",
1463
+ "sodomising": "sodomizing",
1464
+ "solemnise": "solemnize",
1465
+ "solemnised": "solemnized",
1466
+ "solemnises": "solemnizes",
1467
+ "solemnising": "solemnizing",
1468
+ "sombre": "somber",
1469
+ "specialisation": "specialization",
1470
+ "specialisations": "specializations",
1471
+ "specialise": "specialize",
1472
+ "specialised": "specialized",
1473
+ "specialises": "specializes",
1474
+ "specialising": "specializing",
1475
+ "spectre": "specter",
1476
+ "spectres": "specters",
1477
+ "spiralled": "spiraled",
1478
+ "spiralling": "spiraling",
1479
+ "splendour": "splendor",
1480
+ "splendours": "splendors",
1481
+ "squirrelled": "squirreled",
1482
+ "squirrelling": "squirreling",
1483
+ "stabilisation": "stabilization",
1484
+ "stabilise": "stabilize",
1485
+ "stabilised": "stabilized",
1486
+ "stabiliser": "stabilizer",
1487
+ "stabilisers": "stabilizers",
1488
+ "stabilises": "stabilizes",
1489
+ "stabilising": "stabilizing",
1490
+ "standardisation": "standardization",
1491
+ "standardise": "standardize",
1492
+ "standardised": "standardized",
1493
+ "standardises": "standardizes",
1494
+ "standardising": "standardizing",
1495
+ "stencilled": "stenciled",
1496
+ "stencilling": "stenciling",
1497
+ "sterilisation": "sterilization",
1498
+ "sterilisations": "sterilizations",
1499
+ "sterilise": "sterilize",
1500
+ "sterilised": "sterilized",
1501
+ "steriliser": "sterilizer",
1502
+ "sterilisers": "sterilizers",
1503
+ "sterilises": "sterilizes",
1504
+ "sterilising": "sterilizing",
1505
+ "stigmatisation": "stigmatization",
1506
+ "stigmatise": "stigmatize",
1507
+ "stigmatised": "stigmatized",
1508
+ "stigmatises": "stigmatizes",
1509
+ "stigmatising": "stigmatizing",
1510
+ "storey": "story",
1511
+ "storeys": "stories",
1512
+ "subsidisation": "subsidization",
1513
+ "subsidise": "subsidize",
1514
+ "subsidised": "subsidized",
1515
+ "subsidiser": "subsidizer",
1516
+ "subsidisers": "subsidizers",
1517
+ "subsidises": "subsidizes",
1518
+ "subsidising": "subsidizing",
1519
+ "succour": "succor",
1520
+ "succoured": "succored",
1521
+ "succouring": "succoring",
1522
+ "succours": "succors",
1523
+ "sulphate": "sulfate",
1524
+ "sulphates": "sulfates",
1525
+ "sulphide": "sulfide",
1526
+ "sulphides": "sulfides",
1527
+ "sulphur": "sulfur",
1528
+ "sulphurous": "sulfurous",
1529
+ "summarise": "summarize",
1530
+ "summarised": "summarized",
1531
+ "summarises": "summarizes",
1532
+ "summarising": "summarizing",
1533
+ "swivelled": "swiveled",
1534
+ "swivelling": "swiveling",
1535
+ "symbolise": "symbolize",
1536
+ "symbolised": "symbolized",
1537
+ "symbolises": "symbolizes",
1538
+ "symbolising": "symbolizing",
1539
+ "sympathise": "sympathize",
1540
+ "sympathised": "sympathized",
1541
+ "sympathiser": "sympathizer",
1542
+ "sympathisers": "sympathizers",
1543
+ "sympathises": "sympathizes",
1544
+ "sympathising": "sympathizing",
1545
+ "synchronisation": "synchronization",
1546
+ "synchronise": "synchronize",
1547
+ "synchronised": "synchronized",
1548
+ "synchronises": "synchronizes",
1549
+ "synchronising": "synchronizing",
1550
+ "synthesise": "synthesize",
1551
+ "synthesised": "synthesized",
1552
+ "synthesiser": "synthesizer",
1553
+ "synthesisers": "synthesizers",
1554
+ "synthesises": "synthesizes",
1555
+ "synthesising": "synthesizing",
1556
+ "syphon": "siphon",
1557
+ "syphoned": "siphoned",
1558
+ "syphoning": "siphoning",
1559
+ "syphons": "siphons",
1560
+ "systematisation": "systematization",
1561
+ "systematise": "systematize",
1562
+ "systematised": "systematized",
1563
+ "systematises": "systematizes",
1564
+ "systematising": "systematizing",
1565
+ "tantalise": "tantalize",
1566
+ "tantalised": "tantalized",
1567
+ "tantalises": "tantalizes",
1568
+ "tantalising": "tantalizing",
1569
+ "tantalisingly": "tantalizingly",
1570
+ "tasselled": "tasseled",
1571
+ "technicolour": "technicolor",
1572
+ "temporise": "temporize",
1573
+ "temporised": "temporized",
1574
+ "temporises": "temporizes",
1575
+ "temporising": "temporizing",
1576
+ "tenderise": "tenderize",
1577
+ "tenderised": "tenderized",
1578
+ "tenderises": "tenderizes",
1579
+ "tenderising": "tenderizing",
1580
+ "terrorise": "terrorize",
1581
+ "terrorised": "terrorized",
1582
+ "terrorises": "terrorizes",
1583
+ "terrorising": "terrorizing",
1584
+ "theatre": "theater",
1585
+ "theatregoer": "theatergoer",
1586
+ "theatregoers": "theatergoers",
1587
+ "theatres": "theaters",
1588
+ "theorise": "theorize",
1589
+ "theorised": "theorized",
1590
+ "theorises": "theorizes",
1591
+ "theorising": "theorizing",
1592
+ "tonne": "ton",
1593
+ "tonnes": "tons",
1594
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+ "towelling": "toweling",
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+ "toxaemia": "toxemia",
1597
+ "tranquillise": "tranquilize",
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+ "tranquillisers": "tranquilizers",
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+ "tranquillity": "tranquility",
1604
+ "tranquillize": "tranquilize",
1605
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+ "tranquillizer": "tranquilizer",
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+ "tranquillizers": "tranquilizers",
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+ "traumatise": "traumatize",
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+ "traumatised": "traumatized",
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+ "traumatises": "traumatizes",
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+ "traumatising": "traumatizing",
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+ "travelled": "traveled",
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+ "traveller": "traveler",
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+ "travellers": "travelers",
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+ "travelling": "traveling",
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+ "travelog": "travelogue",
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+ "trialled": "trialed",
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+ "tricolour": "tricolor",
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+ "trivialise": "trivialize",
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1632
+ "tunnelled": "tunneled",
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1634
+ "tyrannise": "tyrannize",
1635
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+ "tyre": "tire",
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+ "urbanisation": "urbanization",
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+ },
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+ "epoch": 0.4997917534360683,
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142
+ "eval_peoplespeech-clean-transcription_runtime": 13.1079,
143
+ "eval_peoplespeech-clean-transcription_samples_per_second": 4.883,
144
+ "eval_peoplespeech-clean-transcription_steps_per_second": 0.076,
145
+ "step": 1200
146
+ }
147
+ ],
148
+ "logging_steps": 100,
149
+ "max_steps": 4800,
150
+ "num_input_tokens_seen": 0,
151
+ "num_train_epochs": 2,
152
+ "save_steps": 1200,
153
+ "stateful_callbacks": {
154
+ "TrainerControl": {
155
+ "args": {
156
+ "should_epoch_stop": false,
157
+ "should_evaluate": false,
158
+ "should_log": false,
159
+ "should_save": true,
160
+ "should_training_stop": false
161
+ },
162
+ "attributes": {}
163
+ }
164
+ },
165
+ "total_flos": 1.7954024695099556e+18,
166
+ "train_batch_size": 576,
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+ "trial_name": null,
168
+ "trial_params": null
169
+ }
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checkpoint-1200/ultravox_config.py ADDED
@@ -0,0 +1,178 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import dataclasses
2
+ from enum import Enum
3
+ from typing import Any, Dict, List, Optional
4
+
5
+ import transformers
6
+
7
+
8
+ @dataclasses.dataclass
9
+ class LoraConfigSimplified:
10
+ """
11
+ Low Rank Approximation (LoRA) configuration.
12
+
13
+ Used for language and audio models separately.
14
+ """
15
+
16
+ # The rank of the approximation
17
+ r: int = 0
18
+ lora_alpha: float = 8
19
+ target_modules: Optional[List[str]] = dataclasses.field(
20
+ default_factory=lambda: ["k_proj", "q_proj", "linear_k", "linear_q"]
21
+ )
22
+ # A list of module names regex patterns to unfreeze. Only used if r == 0.
23
+ unfreeze_layers: Optional[List[str]] = None
24
+
25
+
26
+ class LossFunction(str, Enum):
27
+ CrossEntropy = "ce"
28
+ KL_Divergence = "kl"
29
+
30
+
31
+ @dataclasses.dataclass
32
+ class LossConfig:
33
+ loss_function: LossFunction = LossFunction.CrossEntropy
34
+ kl_temperature: float = 2.0
35
+
36
+ @property
37
+ def requires_alt_fields(self):
38
+ return self.loss_function == LossFunction.KL_Divergence
39
+
40
+
41
+ class UltravoxConfig(transformers.PretrainedConfig):
42
+ r"""
43
+ This is the configuration class to store the configuration of a [`UltravoxForConditionalGeneration`]. It is used to instantiate an
44
+ Ultravox model according to the specified arguments, defining the model architecture.
45
+
46
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
47
+ documentation from [`PretrainedConfig`] for more information.
48
+
49
+ Args:
50
+ audio_config (`WhisperConfig`, *optional*):
51
+ Custom audio config or dict
52
+ text_config (`Union[AutoConfig, dict]`, *optional*):
53
+ The config object of the text backbone. Can be any of `LlamaConfig` or `MistralConfig`.
54
+ ignore_index (`int`, *optional*, defaults to -100):
55
+ The ignore index for the loss function.
56
+ audio_token_index (`int`, *optional*, defaults to 32000):
57
+ The audio token index to encode the audio prompt.
58
+ stack_factor (`int`, *optional*, defaults to 8):
59
+ Audio downsampling factor for the multimodal projector.
60
+ norm_init (`float`, *optional*, defaults to 0.4):
61
+ The initialization value for the layer normalization.
62
+ projector_act (`str`, *optional*, defaults to `"swiglu"`):
63
+ The activation function used by the multimodal projector.
64
+ text_model_lora_config (`LoraConfigSimplified`, *optional*):
65
+ The LoRA configuration for finetuning the text model.
66
+ audio_model_lora_config (`LoraConfigSimplified`, *optional*):
67
+ The LoRA configuration for finetuning the audio model.
68
+ audio_latency_block_size (`int`, *optional*, defaults to `None`):
69
+ The latency block size for simulating audio streaming.
70
+
71
+
72
+ Example:
73
+
74
+ ```python
75
+ >>> from transformers import UltravoxModel, WhisperConfig, UltravoxConfig, LlamaConfig
76
+
77
+ >>> # Initializing an audio encoder config
78
+ >>> audio_config = WhisperConfig()
79
+
80
+ >>> # Initializing a Llama config
81
+ >>> text_config = LlamaConfig()
82
+
83
+ >>> # Initializing a default configuration
84
+ >>> configuration = UltravoxConfig(audio_config, text_config)
85
+
86
+ >>> # Initializing a completely untrained model from the configuration
87
+ >>> model = UltravoxModel(configuration)
88
+
89
+ >>> # Accessing the model configuration
90
+ >>> configuration = model.config
91
+
92
+ >>> # Initialize a model from pretrained checkpoints and random projector weights
93
+ >>> config = UltravoxConfig(audio_model_id="openai/whisper-tiny", text_model_id="meta-llama/Llama-2-7b-chat-hf")
94
+ ```"""
95
+
96
+ model_type = "ultravox"
97
+ is_composition = False
98
+
99
+ def __init__(
100
+ self,
101
+ audio_config: Optional[Dict[str, Any]] = None,
102
+ text_config: Optional[Dict[str, Any]] = None,
103
+ audio_model_id: Optional[str] = None,
104
+ text_model_id: Optional[str] = None,
105
+ ignore_index: int = -100,
106
+ hidden_size: int = 4096,
107
+ stack_factor: int = 8,
108
+ norm_init: float = 0.4,
109
+ projector_act: str = "swiglu",
110
+ projector_ln_mid: bool = False, # defaults to False for compatibility with v0.4.1 and below
111
+ text_model_lora_config: Optional[LoraConfigSimplified] = None,
112
+ audio_model_lora_config: Optional[LoraConfigSimplified] = None,
113
+ audio_latency_block_size: Optional[int] = None,
114
+ **kwargs,
115
+ ):
116
+ self.ignore_index = ignore_index
117
+
118
+ self.audio_model_id = audio_model_id
119
+ self.text_model_id = text_model_id
120
+
121
+ self.hidden_size = hidden_size
122
+ self.stack_factor = stack_factor
123
+ self.norm_init = norm_init
124
+ self.projector_act = projector_act
125
+ self.projector_ln_mid = projector_ln_mid
126
+ if text_model_id is not None:
127
+ self.text_config: transformers.LlamaConfig = (
128
+ transformers.AutoConfig.from_pretrained(text_model_id)
129
+ )
130
+ else:
131
+ text_config = text_config or {}
132
+ self.text_config = transformers.CONFIG_MAPPING[
133
+ text_config.get("model_type", "llama")
134
+ ](**text_config)
135
+
136
+ if audio_model_id is not None:
137
+ self.audio_config: transformers.PretrainedConfig = (
138
+ transformers.AutoConfig.from_pretrained(audio_model_id)
139
+ )
140
+ else:
141
+ audio_config = audio_config or {}
142
+ self.audio_config = transformers.CONFIG_MAPPING[
143
+ audio_config.get("model_type", "whisper")
144
+ ](**audio_config)
145
+
146
+ self.text_model_lora_config = (
147
+ text_model_lora_config
148
+ if isinstance(text_model_lora_config, dict)
149
+ else dataclasses.asdict(text_model_lora_config or LoraConfigSimplified())
150
+ )
151
+ self.audio_model_lora_config = (
152
+ audio_model_lora_config
153
+ if isinstance(audio_model_lora_config, dict)
154
+ else dataclasses.asdict(audio_model_lora_config or LoraConfigSimplified())
155
+ )
156
+ self.audio_latency_block_size = audio_latency_block_size
157
+
158
+ self.vocab_size = self.text_config.vocab_size
159
+
160
+ self.initializer_range = self.text_config.initializer_range
161
+
162
+ super().__init__(**kwargs)
163
+
164
+ def to_diff_dict(self) -> Dict[str, Any]:
165
+ diff_dict = super().to_diff_dict()
166
+
167
+ # remove text_config and audio_config if text_model_id and audio_model_id are present
168
+ if self.text_model_id is not None:
169
+ diff_dict.pop("text_config", None)
170
+ elif "text_config" in diff_dict:
171
+ diff_dict["text_config"].pop("_attn_implementation_autoset", None)
172
+
173
+ if self.audio_model_id is not None:
174
+ diff_dict.pop("audio_config", None)
175
+ elif "audio_config" in diff_dict:
176
+ diff_dict["audio_config"].pop("_attn_implementation_autoset", None)
177
+
178
+ return diff_dict
checkpoint-1200/ultravox_model.py ADDED
@@ -0,0 +1,786 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import logging
2
+ import re
3
+ from typing import Any, Dict, Generator, Optional, Set, Tuple, Union
4
+
5
+ import peft
6
+ import torch
7
+ import torch.nn as nn
8
+ import torch.nn.functional as F
9
+ import transformers
10
+ import transformers.activations
11
+ import transformers.modeling_outputs
12
+ import transformers.models
13
+ from transformers.models.whisper import modeling_whisper as whisper
14
+
15
+ # We must use relative import in this directory to allow uploading to HF Hub
16
+ # Even "from . import X" pattern doesn't work (undocumented and unclear why)
17
+ from .ultravox_config import LossConfig
18
+ from .ultravox_config import LossFunction
19
+ from .ultravox_config import UltravoxConfig
20
+
21
+
22
+ class UltravoxModel(transformers.LlamaPreTrainedModel):
23
+ """
24
+ The Ultravox model which consists of an audio encoder and a language model.
25
+
26
+ Audio input is processed by the audio encoder, then every `stack_factor` frames are stacked together and
27
+ projected to the language model's embedding space using a few linear layers.
28
+ The text is embedded by the language model as usual and then the audio and text embeddings are merged together.
29
+
30
+ A special token `<|audio|>` is used to indicate the start of the audio embeddings in the merged embeddings.
31
+
32
+ Parameters:
33
+ config: Model configuration class with all the parameters of the model.
34
+ """
35
+
36
+ config_class = UltravoxConfig
37
+ config: UltravoxConfig # for type hinting
38
+ # Usually we load encoder and LLM weights from a pretrained model separately, so they are allowed to be missing
39
+ _keys_to_ignore_on_load_missing = ["audio_tower.*", "language_model.*"]
40
+ # Since we have kwargs in forward, we need to set this to False, otherwise grad_accum_steps will cause incorrect train loss to be reported
41
+ # see https://github.com/huggingface/transformers/issues/35856 and https://github.com/huggingface/trl/pull/2615/files
42
+ accepts_loss_kwargs = False
43
+
44
+ def __init__(self, config: UltravoxConfig):
45
+ super().__init__(config)
46
+ self._register_load_state_dict_pre_hook(self._pre_load_state_dict_hook)
47
+
48
+ self.keep_params: Set[str] = set()
49
+ self.vocab_size = config.vocab_size
50
+
51
+ self.audio_tower = self._create_audio_tower(config)
52
+ self.audio_tower_context_length: Optional[int] = None
53
+ self.audio_tower_context_length = self.audio_tower.max_context_length
54
+
55
+ self.multi_modal_projector = self._create_multi_modal_projector(config)
56
+ self.language_model = self._create_language_model(config)
57
+
58
+ # Determine no_split_modules dynamically to use with FSDP auto_wrap policy.
59
+ # FSDP throws an error if some of the layer types are not found in the model.
60
+ # This would be something like ["LlamaDecoderLayer"] as we don't split audio encoder layers.
61
+ self._no_split_modules = self.language_model._no_split_modules
62
+
63
+ self.loss_config = LossConfig()
64
+ self.post_init()
65
+
66
+ def get_input_embeddings(self):
67
+ return self.language_model.get_input_embeddings()
68
+
69
+ def set_input_embeddings(self, value):
70
+ self.language_model.set_input_embeddings(value)
71
+
72
+ def get_output_embeddings(self):
73
+ return self.language_model.get_output_embeddings()
74
+
75
+ def set_output_embeddings(self, new_embeddings):
76
+ self.language_model.set_output_embeddings(new_embeddings)
77
+
78
+ def set_decoder(self, decoder):
79
+ self.language_model.set_decoder(decoder)
80
+
81
+ def get_decoder(self):
82
+ return self.language_model.get_decoder()
83
+
84
+ def tie_weights(self):
85
+ return self.language_model.tie_weights()
86
+
87
+ def set_loss_config(self, loss_config: LossConfig):
88
+ self.loss_config = loss_config
89
+
90
+ def _setup_cache(
91
+ self, cache_cls, max_batch_size: int, max_cache_len: Optional[int] = None
92
+ ):
93
+ self.language_model._setup_cache(cache_cls, max_batch_size, max_cache_len)
94
+
95
+ def _reorder_cache(self, past_key_values, beam_idx):
96
+ return self.language_model._reorder_cache(past_key_values, beam_idx)
97
+
98
+ def resize_token_embeddings(
99
+ self,
100
+ new_num_tokens: Optional[int] = None,
101
+ pad_to_multiple_of: Optional[int] = None,
102
+ ) -> nn.Embedding:
103
+ model_embeds = self.language_model.resize_token_embeddings(
104
+ new_num_tokens, pad_to_multiple_of
105
+ )
106
+ # update vocab size
107
+ self.config.text_config.vocab_size = model_embeds.num_embeddings
108
+ self.config.vocab_size = model_embeds.num_embeddings
109
+ self.vocab_size = model_embeds.num_embeddings
110
+ return model_embeds
111
+
112
+ def _compute_kl_loss(
113
+ self,
114
+ lm_output: transformers.modeling_outputs.CausalLMOutputWithPast,
115
+ labels: Optional[torch.Tensor] = None,
116
+ past_key_values: Optional[Union[Tuple, transformers.cache_utils.Cache]] = None,
117
+ alt_input_ids: Optional[torch.Tensor] = None,
118
+ alt_attention_mask: Optional[torch.Tensor] = None,
119
+ alt_labels: Optional[torch.Tensor] = None,
120
+ **kwargs,
121
+ ):
122
+ # disable gradient computation for the teacher model
123
+ with torch.no_grad():
124
+ # compute the teacher (text-only) model's distribution
125
+ alt_inputs_embeds = self.get_input_embeddings().forward(alt_input_ids)
126
+ alt_lm_output = self.language_model.forward(
127
+ inputs_embeds=alt_inputs_embeds,
128
+ labels=alt_labels,
129
+ attention_mask=alt_attention_mask,
130
+ past_key_values=past_key_values,
131
+ **kwargs,
132
+ )
133
+ # compute the KL divergence loss between the two models
134
+ kl_loss = F.kl_div(
135
+ F.log_softmax(
136
+ lm_output.logits[labels != -100] / self.loss_config.kl_temperature,
137
+ dim=-1,
138
+ ),
139
+ F.softmax(
140
+ alt_lm_output.logits[alt_labels != -100]
141
+ / self.loss_config.kl_temperature,
142
+ dim=-1,
143
+ ),
144
+ reduction="batchmean",
145
+ )
146
+ return {"loss": kl_loss}
147
+
148
+ def _audio_iter(
149
+ self, audio_batch_size: torch.Tensor
150
+ ) -> Generator[Tuple[int, int], None, None]:
151
+ """
152
+ Iterate over the audio batch size and yield the batch index and audio index of each audio item.
153
+
154
+ Args:
155
+ audio_batch_size: A tensor of shape (B,) where B is the batch size.
156
+
157
+ Returns:
158
+ A generator that yields a tuple of (start index, length) for each audio item.
159
+ """
160
+ audio_index = 0
161
+ for i_b, batch_count in enumerate(audio_batch_size):
162
+ for _ in range(batch_count):
163
+ yield i_b, audio_index
164
+ audio_index += 1
165
+
166
+ def forward(
167
+ self,
168
+ input_ids: torch.Tensor,
169
+ audio_values: Optional[torch.FloatTensor] = None,
170
+ inputs_embeds: Optional[torch.FloatTensor] = None,
171
+ labels: Optional[torch.Tensor] = None,
172
+ attention_mask: Optional[torch.Tensor] = None,
173
+ audio_token_start_idx: Optional[torch.Tensor] = None,
174
+ audio_lens: Optional[torch.Tensor] = None,
175
+ audio_token_len: Optional[torch.Tensor] = None,
176
+ audio_batch_size: Optional[torch.Tensor] = None,
177
+ past_key_values: Optional[Union[Tuple, transformers.cache_utils.Cache]] = None,
178
+ # the alt_* fields are needed for KL divergence loss
179
+ alt_input_ids: Optional[torch.Tensor] = None,
180
+ alt_attention_mask: Optional[torch.Tensor] = None,
181
+ alt_labels: Optional[torch.Tensor] = None,
182
+ **kwargs,
183
+ ) -> Union[Tuple, transformers.modeling_outputs.CausalLMOutputWithPast]:
184
+ """
185
+ Forward pass for the Ultravox model.
186
+
187
+ `input_ids` are the tokenized text input. They are embedded by the language model as usual.
188
+ `audio_values` are processed by the audio encoder and then every `stack_factor` frames are stacked together and
189
+ projected to the language model's embedding space using a few linear layers.
190
+ The audio and text embeddings are merged together. A special token `<|audio|>` is used to indicate the start
191
+ of the audio embeddings in the merged embeddings.
192
+
193
+ Args:
194
+ input_ids: The tokenized text input.
195
+ audio_values: The processed audio values.
196
+ inputs_embeds: The embeddings for the input tokens.
197
+ labels: The tokenized text labels.
198
+ attention_mask: The attention mask for the input.
199
+ position_ids: The position ids for the input.
200
+ past_key_values: The past key value cache for the language model attention layers.
201
+ **kwargs: Additional keyword arguments. Passed directly to the language model.
202
+ """
203
+ if inputs_embeds is None:
204
+ # B x T -> B x T x D
205
+ inputs_embeds = self.get_input_embeddings().forward(input_ids)
206
+
207
+ if audio_values is not None and len(audio_values) > 0:
208
+ assert (
209
+ audio_token_start_idx is not None
210
+ and audio_token_len is not None
211
+ and audio_lens is not None
212
+ and audio_batch_size is not None
213
+ ), "audio_token_start_idx/audio_token_len/audio_lens must be provided if audio_values are provided."
214
+ assert (
215
+ len(audio_token_start_idx)
216
+ == len(audio_token_len)
217
+ == len(audio_lens)
218
+ == len(audio_values)
219
+ ), "audio_token_start_idx/audio_token_len/audio_lens/audio_values must have the same batch size."
220
+ assert len(audio_batch_size) == len(
221
+ inputs_embeds
222
+ ), "audio_batch_size and inputs_embeds must have the same batch size."
223
+
224
+ # B x A/3200 x (D=max-audio-length-in-batch)
225
+ audio_tower_output = self.audio_tower.forward(
226
+ audio_values.to(self.audio_tower.dtype),
227
+ audio_len=audio_lens,
228
+ ).last_hidden_state
229
+ audio_tower_output = audio_tower_output.to(inputs_embeds.dtype)
230
+ audio_embeds = self.multi_modal_projector.forward(audio_tower_output)
231
+
232
+ # combine audio and text embeddings
233
+ for i_b, i_a in self._audio_iter(audio_batch_size):
234
+ start_idx = audio_token_start_idx[i_a]
235
+ token_len = audio_token_len[i_a]
236
+ item_embedding = audio_embeds[i_a][:token_len]
237
+ inputs_embeds[i_b][start_idx : start_idx + token_len] = item_embedding
238
+
239
+ lm_output = self.language_model.forward(
240
+ inputs_embeds=inputs_embeds,
241
+ labels=labels,
242
+ attention_mask=attention_mask,
243
+ past_key_values=past_key_values,
244
+ **kwargs,
245
+ )
246
+ if self.training:
247
+ if self.loss_config.loss_function == LossFunction.CrossEntropy:
248
+ return lm_output
249
+ elif self.loss_config.loss_function == LossFunction.KL_Divergence:
250
+ return self._compute_kl_loss(
251
+ lm_output=lm_output,
252
+ labels=labels,
253
+ past_key_values=past_key_values,
254
+ alt_input_ids=alt_input_ids,
255
+ alt_attention_mask=alt_attention_mask,
256
+ alt_labels=alt_labels,
257
+ **kwargs,
258
+ )
259
+ else:
260
+ raise ValueError(
261
+ f"Unsupported loss function: {self.loss_config.loss_function}"
262
+ )
263
+ else:
264
+ return lm_output
265
+
266
+ def prepare_inputs_for_generation(
267
+ self,
268
+ input_ids: torch.Tensor,
269
+ audio_values: Optional[torch.FloatTensor] = None,
270
+ audio_token_start_idx: Optional[torch.Tensor] = None,
271
+ audio_token_len: Optional[torch.Tensor] = None,
272
+ audio_lens: Optional[torch.Tensor] = None,
273
+ audio_batch_size: Optional[torch.Tensor] = None,
274
+ past_key_values: Optional[Union[Tuple, transformers.cache_utils.Cache]] = None,
275
+ attention_mask: Optional[torch.Tensor] = None,
276
+ inputs_embeds: Optional[torch.Tensor] = None,
277
+ cache_position: Optional[torch.Tensor] = None,
278
+ **kwargs,
279
+ ) -> Dict[str, Any]:
280
+ model_input = self.language_model.prepare_inputs_for_generation(
281
+ input_ids=input_ids,
282
+ past_key_values=past_key_values,
283
+ attention_mask=attention_mask,
284
+ inputs_embeds=inputs_embeds,
285
+ cache_position=cache_position,
286
+ **kwargs,
287
+ )
288
+
289
+ # include audio information in model_input only when it is needed during prefilling
290
+ # audio_token_start_idx should always be relative to the current cache position
291
+ prefill_start_idx: torch.Tensor = (
292
+ torch.tensor(0) if cache_position is None else cache_position[0]
293
+ )
294
+ if (
295
+ audio_values is not None
296
+ and audio_token_start_idx is not None
297
+ and prefill_start_idx <= torch.max(audio_token_start_idx)
298
+ ):
299
+ model_input["audio_values"] = audio_values
300
+ model_input["audio_token_start_idx"] = (
301
+ audio_token_start_idx - prefill_start_idx
302
+ )
303
+ model_input["audio_token_len"] = audio_token_len
304
+ model_input["audio_batch_size"] = audio_batch_size
305
+ model_input["audio_lens"] = audio_lens
306
+
307
+ return model_input
308
+
309
+ @classmethod
310
+ def _create_multi_modal_projector(
311
+ cls, config: UltravoxConfig
312
+ ) -> "UltravoxProjector":
313
+ projector = UltravoxProjector(config)
314
+ projector.to(config.torch_dtype)
315
+ return projector
316
+
317
+ @classmethod
318
+ def _create_audio_tower(
319
+ cls, config: UltravoxConfig
320
+ ) -> Union[transformers.Wav2Vec2Model, "ModifiedWhisperEncoder"]:
321
+ if config.audio_model_id is not None:
322
+ if "whisper" in config.audio_model_id.lower():
323
+ audio_tower = ModifiedWhisperEncoder.from_pretrained(
324
+ config.audio_model_id, torch_dtype=config.torch_dtype
325
+ )
326
+ audio_tower.init_latency_mask(
327
+ config.audio_latency_block_size, dtype=config.torch_dtype
328
+ )
329
+ else:
330
+ assert config.audio_latency_block_size in (
331
+ None,
332
+ 0,
333
+ ), "only whisper audio tower supports audio latency masking, got non-zero value for 'audio_latency_block_size'"
334
+ audio_tower = transformers.AutoModel.from_pretrained(
335
+ config.audio_model_id, torch_dtype=config.torch_dtype
336
+ )
337
+ else:
338
+ if "whisper" in config.audio_config._name_or_path.lower():
339
+ audio_tower = ModifiedWhisperEncoder(config.audio_config)
340
+ audio_tower.init_latency_mask(
341
+ config.audio_latency_block_size, dtype=config.torch_dtype
342
+ )
343
+ else:
344
+ assert config.audio_latency_block_size in (
345
+ None,
346
+ 0,
347
+ ), "only whisper audio tower supports audio latency masking, got non-zero value for 'audio_latency_block_size'"
348
+ with transformers.modeling_utils.no_init_weights():
349
+ # we only ever use from_config if the weights are retrained, hence initializing is not
350
+ # required. This makes the model quite creation faster since init on CPU is quite slow.
351
+ audio_tower = transformers.AutoModel.from_config(
352
+ config.audio_config
353
+ )
354
+
355
+ if isinstance(
356
+ audio_tower,
357
+ (transformers.Wav2Vec2BertModel, transformers.WhisperModel),
358
+ ):
359
+ # For these models we only need the encoder part
360
+ # Wav2Vec2BertModel -> Wav2Vec2BertEncoder
361
+ # WhisperModel -> WhisperEncoder
362
+ audio_tower = audio_tower.encoder
363
+
364
+ audio_tower = apply_lora(audio_tower, config.audio_model_lora_config)
365
+ return audio_tower
366
+
367
+ @classmethod
368
+ def _create_language_model(
369
+ cls, config: UltravoxConfig
370
+ ) -> transformers.LlamaForCausalLM:
371
+ if config.text_model_id is not None:
372
+ language_model = transformers.AutoModelForCausalLM.from_pretrained(
373
+ config.text_model_id,
374
+ attn_implementation=config._attn_implementation,
375
+ torch_dtype=config.torch_dtype,
376
+ )
377
+ else:
378
+ with transformers.modeling_utils.no_init_weights():
379
+ # we only ever use from_config if the weights are retrained, hence initializing is not
380
+ # required. This makes the model quite creation faster since init on CPU is quite slow.
381
+ language_model = transformers.AutoModelForCausalLM.from_config(
382
+ config.text_config,
383
+ attn_implementation=config._attn_implementation,
384
+ torch_dtype=config.torch_dtype,
385
+ )
386
+
387
+ language_model = apply_lora(language_model, config.text_model_lora_config)
388
+ return language_model
389
+
390
+ def merge_and_unload(self):
391
+ if isinstance(self.language_model, peft.PeftModel):
392
+ self.language_model = self.language_model.merge_and_unload()
393
+ # no need to download base language model weights anymore, so we can remove the id
394
+ self.config.text_model_id = None
395
+ self.keep_params.update(
396
+ set(
397
+ [
398
+ f"language_model.{name}"
399
+ for name, _ in self.language_model.named_parameters()
400
+ ]
401
+ )
402
+ )
403
+
404
+ if isinstance(self.audio_tower, peft.PeftModel):
405
+ self.audio_tower = self.audio_tower.merge_and_unload()
406
+ # no need to download base audio model weights anymore, so we can remove the id
407
+ self.config.audio_model_id = None
408
+ self.keep_params.update(
409
+ set(
410
+ [
411
+ f"audio_tower.{name}"
412
+ for name, _ in self.audio_tower.named_parameters()
413
+ ]
414
+ )
415
+ )
416
+
417
+ for param in ["text_model_lora_config", "audio_model_lora_config"]:
418
+ if hasattr(self.config, param):
419
+ delattr(self.config, param)
420
+
421
+ def push_to_hub(self, *args, **kwargs):
422
+ self.merge_and_unload()
423
+ return super().push_to_hub(*args, **kwargs)
424
+
425
+ def diff_state_dict(
426
+ self, state_dict: Optional[Dict[str, Any]] = None
427
+ ) -> Dict[str, Any]:
428
+ if state_dict is None:
429
+ state_dict = super().state_dict()
430
+
431
+ trainable_params = {k for k, v in self.named_parameters() if v.requires_grad}
432
+ # normalize the keys to match the original model
433
+ # Example: audio_tower.base_model.model.layers.0._fsdp_wrapped_module.self_attn.k_proj.lora_B.default.weight
434
+ trainable_params = {
435
+ k.replace("_fsdp_wrapped_module.", "") for k in trainable_params
436
+ }
437
+
438
+ state_dict = {
439
+ k: v
440
+ for k, v in state_dict.items()
441
+ if k in self.keep_params or k in trainable_params
442
+ }
443
+
444
+ return state_dict
445
+
446
+ def save_pretrained(
447
+ self, *args, state_dict: Optional[Dict[str, Any]] = None, **kwargs
448
+ ):
449
+ state_dict = self.diff_state_dict(state_dict)
450
+
451
+ super().save_pretrained(*args, state_dict=state_dict, **kwargs)
452
+
453
+ def _pre_load_state_dict_hook(self, state_dict: Dict[str, Any], *args, **kwargs):
454
+ self.keep_params.update(set(state_dict.keys()))
455
+
456
+ def print_trainable_parameters(self):
457
+ """
458
+ Prints the number of trainable parameters in the model (reuses Peft model's method)
459
+ """
460
+ count_params = peft.peft_model.PeftModel.get_nb_trainable_parameters
461
+
462
+ trainable_params, all_param = count_params(self)
463
+
464
+ logging.info(
465
+ f"trainable params: {trainable_params:,d} || all params: {all_param:,d}"
466
+ f" || trainable%: {100 * trainable_params / all_param:.1f}%"
467
+ )
468
+
469
+ lm_trainable_params, lm_all_params = count_params(self.language_model)
470
+ audio_trainable_params, audio_all_params = count_params(self.audio_tower)
471
+
472
+ projector_trainable_params = (
473
+ trainable_params - lm_trainable_params - audio_trainable_params
474
+ )
475
+ projector_all_params = all_param - lm_all_params - audio_all_params
476
+
477
+ logging.info(
478
+ f"Trainable%: "
479
+ f" LLM: {100 * lm_trainable_params / lm_all_params:.1f}%"
480
+ f" || Audio Encoder: {100 * audio_trainable_params / audio_all_params:.1f}%"
481
+ f" || Projector: {100 * projector_trainable_params / projector_all_params:.1f}%"
482
+ )
483
+
484
+
485
+ # TODO: refactor common parts to a shared module
486
+ def is_cache_empty(
487
+ past_key_values: Optional[Union[Tuple, transformers.cache_utils.Cache]],
488
+ ) -> bool:
489
+ """
490
+ Check if the cache is empty.
491
+ """
492
+ if past_key_values is None:
493
+ return True
494
+ if isinstance(past_key_values, tuple):
495
+ return all(len(c) == 0 for c in past_key_values)
496
+ return past_key_values.get_seq_length() == 0
497
+
498
+
499
+ def apply_lora(model: torch.nn.Module, lora_config: dict) -> torch.nn.Module:
500
+ """
501
+ Applies LoRA finetuning to the model. If the `r` parameter is set to 0, the model is frozen instead.
502
+ """
503
+ unfreeze_layers = lora_config.pop("unfreeze_layers", None)
504
+ lora_config = peft.LoraConfig(**lora_config or {})
505
+
506
+ if lora_config.r == 0:
507
+ # freeze the model entirely, except for the specified layers
508
+ for name, param in model.named_parameters():
509
+ if not unfreeze_layers or not any(
510
+ re.match(layer, name) for layer in unfreeze_layers
511
+ ):
512
+ param.requires_grad = False
513
+ else:
514
+ logging.info(f"Unfreezing layer: {name} with #{param.numel()} params")
515
+ else:
516
+ model = peft.get_peft_model(model, lora_config)
517
+
518
+ return model
519
+
520
+
521
+ class StackAudioFrames(nn.Module):
522
+ """
523
+ Stack the audio embedding frames to reduce the sequence length by a factor
524
+ of `stack_factor`.
525
+ """
526
+
527
+ def __init__(self, stack_factor: int = 8):
528
+ super().__init__()
529
+ self.stack_factor = stack_factor
530
+
531
+ def forward(self, audio_embeds: torch.Tensor) -> torch.Tensor:
532
+ B, T, C = audio_embeds.shape
533
+ T_pad = (T + self.stack_factor - 1) // self.stack_factor * self.stack_factor
534
+ audio_embeds = F.pad(audio_embeds, (0, 0, 0, T_pad - T))
535
+ B, T, C = audio_embeds.shape
536
+ audio_embeds = audio_embeds.view(
537
+ B, T // self.stack_factor, C * self.stack_factor
538
+ )
539
+ return audio_embeds
540
+
541
+
542
+ class RMSNorm(transformers.models.llama.modeling_llama.LlamaRMSNorm):
543
+ def __init__(self, hidden_size: int, init: float = 1, eps: float = 1e-6):
544
+ super().__init__(hidden_size=hidden_size, eps=eps)
545
+ self.weight.data.fill_(init)
546
+
547
+
548
+ class SwiGLU(nn.Module):
549
+ def forward(self, x):
550
+ x, gate = x.chunk(2, dim=-1)
551
+ return F.silu(gate) * x
552
+
553
+
554
+ class UltravoxProjector(nn.Module):
555
+ def __init__(self, config: UltravoxConfig):
556
+ super().__init__()
557
+ self.hidden_dim = config.hidden_size
558
+ self._pad_and_stack = StackAudioFrames(config.stack_factor)
559
+ dim_in = config.audio_config.hidden_size * config.stack_factor
560
+ self.ln_pre = RMSNorm(dim_in, init=config.norm_init)
561
+ self.linear_1 = nn.Linear(dim_in, self.hidden_dim, bias=False)
562
+ dim_mid = self.hidden_dim
563
+ self.act = transformers.activations.get_activation(config.projector_act)
564
+ dim_mid = dim_mid // 2 if config.projector_act == "swiglu" else dim_mid
565
+ dim_out = config.text_config.hidden_size
566
+ self.linear_2 = nn.Linear(dim_mid, dim_out, bias=False)
567
+
568
+ # Ultravox v0.4.1 and below uses layer_norm after the second linear layer,
569
+ # while v0.5.0 and above uses layer_norm after the first linear layer.
570
+ if config.projector_ln_mid:
571
+ self.ln_mid: nn.Module = RMSNorm(dim_mid, init=config.norm_init)
572
+ self.ln_post: nn.Module = nn.Identity()
573
+ else:
574
+ self.ln_mid = nn.Identity()
575
+ self.ln_post = RMSNorm(dim_out, init=config.norm_init)
576
+
577
+ def forward(self, audio_features: torch.Tensor) -> torch.Tensor:
578
+ audio_features = self._pad_and_stack(audio_features)
579
+ audio_features = self.ln_pre(audio_features)
580
+ hidden_states = self.linear_1(audio_features)
581
+ hidden_states = self.act(hidden_states)
582
+ hidden_states = self.ln_mid(hidden_states)
583
+ hidden_states = self.linear_2(hidden_states)
584
+ hidden_states = self.ln_post(hidden_states)
585
+ return hidden_states
586
+
587
+
588
+ class ModifiedWhisperEncoder(
589
+ whisper.WhisperEncoder, transformers.modeling_utils.ModuleUtilsMixin
590
+ ):
591
+ """
592
+ Encoder portion of OpenAI's Whisper model.
593
+
594
+ This implementation is a slightly modified version of HF Transformers' Whisper Encoder, with only a few fixes:
595
+ 1. base_model_prefix updated to allow for doing `.from_pretrained` directly on the encoder
596
+ 2. allow less than 30 second of audio padding to be passed in:
597
+ - relaxed ValueError check for `input_features` length to be less than or equal to `expected_seq_length` instead of strictly equal
598
+ - embed_pos is now sliced to match the length of `inputs_embeds`
599
+
600
+ Original: https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/modeling_whisper.py
601
+ """
602
+
603
+ base_model_prefix = "model.encoder"
604
+ _no_split_modules = ["WhisperEncoderLayer"]
605
+
606
+ def __init__(self, config: transformers.WhisperConfig):
607
+ super().__init__(config)
608
+ self.config.is_decoder = False
609
+
610
+ @property
611
+ def max_context_length(self):
612
+ return (
613
+ self.config.max_source_positions
614
+ * self.conv1.stride[0]
615
+ * self.conv2.stride[0]
616
+ )
617
+
618
+ def init_latency_mask(self, audio_latency_block_size: int, dtype: torch.dtype):
619
+ if audio_latency_block_size is None:
620
+ self.audio_streaming_mask = None
621
+ return
622
+
623
+ # Use max_context_length directly in the calculation
624
+ max_seqlen = self.max_context_length
625
+ assert (
626
+ max_seqlen > 0
627
+ ), f"maximum sequence length must be positive, got {max_seqlen}"
628
+ assert (
629
+ max_seqlen % audio_latency_block_size == 0
630
+ ), f"audio_latency_block_size {audio_latency_block_size} must divide {max_seqlen} evenly."
631
+ # Given the block size, we calculate number of blocks.
632
+ audio_latency_nblocks = max_seqlen // audio_latency_block_size
633
+ audio_streaming_mask = (
634
+ torch.tril(
635
+ torch.ones(audio_latency_nblocks, audio_latency_nblocks),
636
+ diagonal=0,
637
+ )
638
+ .repeat_interleave(audio_latency_block_size, dim=0)
639
+ .repeat_interleave(audio_latency_block_size, dim=1)
640
+ )
641
+ audio_streaming_mask = (1.0 - audio_streaming_mask) * torch.finfo(dtype).min
642
+ audio_streaming_mask = audio_streaming_mask[None, None, :, :]
643
+ self.register_buffer(
644
+ "audio_streaming_mask", audio_streaming_mask, persistent=False
645
+ )
646
+
647
+ def forward(
648
+ self,
649
+ input_features,
650
+ audio_len=None,
651
+ head_mask=None,
652
+ output_attentions=None,
653
+ output_hidden_states=None,
654
+ return_dict=None,
655
+ ):
656
+ expected_seq_length = self.max_context_length
657
+ if input_features.shape[-1] > expected_seq_length:
658
+ raise ValueError(
659
+ f"Whisper expects the mel input features to be of length {expected_seq_length} or less, but found {input_features.shape[-1]}. Make sure to pad the input mel features to {expected_seq_length}."
660
+ )
661
+
662
+ output_attentions = (
663
+ output_attentions
664
+ if output_attentions is not None
665
+ else self.config.output_attentions
666
+ )
667
+ output_hidden_states = (
668
+ output_hidden_states
669
+ if output_hidden_states is not None
670
+ else self.config.output_hidden_states
671
+ )
672
+ return_dict = (
673
+ return_dict if return_dict is not None else self.config.use_return_dict
674
+ )
675
+ inputs_embeds = nn.functional.gelu(self.conv1(input_features))
676
+ inputs_embeds = nn.functional.gelu(self.conv2(inputs_embeds))
677
+
678
+ inputs_embeds = inputs_embeds.permute(0, 2, 1)
679
+ embed_pos = self.embed_positions.weight[: inputs_embeds.size(-2)]
680
+
681
+ hidden_states = inputs_embeds + embed_pos
682
+ hidden_states = nn.functional.dropout(
683
+ hidden_states, p=self.dropout, training=self.training
684
+ )
685
+
686
+ encoder_states = () if output_hidden_states else None
687
+ all_attentions = () if output_attentions else None
688
+
689
+ # Create attention mask based on audio lengths to mask out padding tokens
690
+ # For each sample in batch:
691
+ # - Convert raw audio length to feature length after convolutions
692
+ # - Create boolean mask that is True for valid positions and False for padding
693
+ # - Convert to extended attention mask format expected by transformer layers
694
+ # (1.0 for positions to attend to, large negative for positions to ignore)
695
+ # This masking ensures consistent behavior between training and inference
696
+ # by preventing the model from attending to padding tokens in both cases
697
+ attention_mask = None
698
+ if audio_len != None:
699
+ audio_feature_len = self._get_feat_extract_output_lengths(audio_len)
700
+ max_seq_len = hidden_states.shape[1]
701
+ attention_mask = torch.arange(max_seq_len, device=hidden_states.device)[
702
+ None, :
703
+ ].lt(audio_feature_len.view(-1, 1))
704
+ attention_mask = self.get_extended_attention_mask(
705
+ attention_mask,
706
+ None,
707
+ device=hidden_states.device,
708
+ dtype=hidden_states.dtype,
709
+ )
710
+
711
+ if self.audio_streaming_mask is not None:
712
+ seqlen = hidden_states.size(-2)
713
+ if attention_mask is not None:
714
+ attention_mask = torch.minimum(
715
+ self.audio_streaming_mask[:, :, :seqlen, :seqlen], attention_mask
716
+ ) # merge
717
+ else:
718
+ attention_mask = self.audio_streaming_mask[:, :, :seqlen, :seqlen]
719
+ attention_mask = attention_mask.to(hidden_states.dtype)
720
+
721
+ # check if head_mask has a correct number of layers specified if desired
722
+ if head_mask is not None:
723
+ assert head_mask.size()[0] == (
724
+ len(self.layers)
725
+ ), f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
726
+
727
+ for idx, encoder_layer in enumerate(self.layers):
728
+ if output_hidden_states:
729
+ encoder_states = encoder_states + (hidden_states,)
730
+ # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
731
+ to_drop = False
732
+ if self.training:
733
+ dropout_probability = torch.rand([])
734
+ if dropout_probability < self.layerdrop: # skip the layer
735
+ to_drop = True
736
+
737
+ if to_drop:
738
+ layer_outputs = (None, None)
739
+ else:
740
+ if self.gradient_checkpointing and self.training:
741
+ layer_outputs = self._gradient_checkpointing_func(
742
+ encoder_layer.__call__,
743
+ hidden_states,
744
+ attention_mask,
745
+ (head_mask[idx] if head_mask is not None else None),
746
+ output_attentions,
747
+ )
748
+ else:
749
+ layer_outputs = encoder_layer(
750
+ hidden_states,
751
+ attention_mask,
752
+ layer_head_mask=(
753
+ head_mask[idx] if head_mask is not None else None
754
+ ),
755
+ output_attentions=output_attentions,
756
+ )
757
+
758
+ hidden_states = layer_outputs[0]
759
+
760
+ if output_attentions:
761
+ all_attentions = all_attentions + (layer_outputs[1],)
762
+
763
+ hidden_states = self.layer_norm(hidden_states)
764
+ if output_hidden_states:
765
+ encoder_states = encoder_states + (hidden_states,)
766
+
767
+ if not return_dict:
768
+ return tuple(
769
+ v
770
+ for v in [hidden_states, encoder_states, all_attentions]
771
+ if v is not None
772
+ )
773
+ return transformers.modeling_outputs.BaseModelOutput(
774
+ last_hidden_state=hidden_states,
775
+ hidden_states=encoder_states,
776
+ attentions=all_attentions,
777
+ )
778
+
779
+
780
+ UltravoxConfig.register_for_auto_class()
781
+ UltravoxModel.register_for_auto_class()
782
+
783
+ transformers.AutoConfig.register("ultravox", UltravoxConfig)
784
+ transformers.AutoModel.register(UltravoxConfig, UltravoxModel)
785
+
786
+ transformers.activations.ACT2FN["swiglu"] = SwiGLU
checkpoint-1200/ultravox_processing.py ADDED
@@ -0,0 +1,359 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import dataclasses
2
+ from typing import Any, Dict, List, Optional, Union
3
+
4
+ import numpy as np
5
+ import torch
6
+ import torch.nn.functional as F
7
+ import transformers
8
+
9
+ from .ultravox_config import UltravoxConfig
10
+
11
+
12
+ @dataclasses.dataclass
13
+ class DataCollatorForSeq2SeqWithAudio(transformers.DataCollatorForSeq2Seq):
14
+ # when enabled, the alt_input_ids, alt_attention_mask, and alt_labels fields are used for computing the KL loss in UltravoxModel
15
+ include_alt_fields: bool = False
16
+
17
+ def __call__(self, features, *args, **kwargs):
18
+ audio_values = [x for f in features for x in f.pop("audio_values", [])]
19
+ audio_lens = [x for f in features for x in f.pop("audio_lens", [])]
20
+ audio_token_len = [x for f in features for x in f.pop("audio_token_len", [])]
21
+ audio_token_start_idx = [
22
+ x for f in features for x in f.pop("audio_token_start_idx", [])
23
+ ]
24
+
25
+ if self.include_alt_fields:
26
+ # these fields are hard-coded in the transformer data collator, so they need special handling before calling the super method
27
+ alt_features = [
28
+ {
29
+ "input_ids": f.pop("alt_input_ids"),
30
+ "attention_mask": f.pop("alt_attention_mask"),
31
+ "labels": f.pop("alt_labels"),
32
+ }
33
+ for f in features
34
+ ]
35
+
36
+ batch = super().__call__(features, *args, **kwargs)
37
+ if self.include_alt_fields:
38
+ alt_batch = super().__call__(alt_features, *args, **kwargs)
39
+ batch["alt_input_ids"] = alt_batch["input_ids"]
40
+ batch["alt_attention_mask"] = alt_batch["attention_mask"]
41
+ batch["alt_labels"] = alt_batch["labels"]
42
+
43
+ batch["audio_token_start_idx"] = torch.stack(audio_token_start_idx)
44
+ batch["audio_lens"] = torch.stack(audio_lens)
45
+ batch["audio_token_len"] = torch.stack(audio_token_len)
46
+
47
+ # Pad the last dimension of all audio_values to the same length, with 0s on the right.
48
+ if audio_values:
49
+ max_len = max([x.shape[-1] for x in audio_values])
50
+ batch["audio_values"] = torch.stack(
51
+ [F.pad(x, (0, max_len - x.shape[-1])) for x in audio_values]
52
+ )
53
+ if self.tokenizer.padding_side == "left":
54
+ input_ids_lens = torch.LongTensor(
55
+ [f["input_ids"].shape[-1] for f in features]
56
+ )
57
+ displacement = batch["input_ids"].shape[-1] - input_ids_lens
58
+ displacement = displacement.repeat_interleave(
59
+ batch["audio_batch_size"].squeeze(-1)
60
+ )
61
+ batch["audio_token_start_idx"] += displacement.to(
62
+ batch["audio_token_start_idx"].device
63
+ )
64
+ return batch
65
+
66
+
67
+ class UltravoxProcessor(transformers.ProcessorMixin):
68
+ """
69
+ Constructs an Ultravox processor which wraps an audio processor and a tokenizer into a single processor.
70
+
71
+ Args:
72
+ audio_processor: The audio processor for the audio encoder.
73
+ tokenizer: The tokenizer for the language model.
74
+ """
75
+
76
+ attributes = ["audio_processor", "tokenizer"]
77
+ audio_processor_class = ("WhisperProcessor",)
78
+ tokenizer_class = (
79
+ "PreTrainedTokenizer",
80
+ "PreTrainedTokenizerFast",
81
+ )
82
+
83
+ tokenizer: transformers.PreTrainedTokenizerBase
84
+ audio_processor: transformers.ProcessorMixin
85
+
86
+ def __init__(
87
+ self,
88
+ audio_processor=None,
89
+ tokenizer=None,
90
+ audio_padding: str = "longest",
91
+ encoder_ds_factor: int = 2,
92
+ stack_factor: int = 8,
93
+ audio_placeholder: str = "<|audio|>",
94
+ # Defaults to whisper encoder context size
95
+ audio_context_size: Optional[int] = 3000,
96
+ ):
97
+ """
98
+ Args:
99
+ audio_processor: The audio processor for the audio encoder.
100
+ tokenizer: The tokenizer for the language model.
101
+ audio_padding: The padding strategy for the audio encoder.
102
+ stack_factor: The factor by which the audio encoder output is stacked in the multimodal projector.
103
+ encoder_ds_factor: The downsampling factor of the audio encoder.
104
+ audio_placeholder: The placeholder for the audio in the text.
105
+ audio_context_size: The maximum number of frames that the audio encoder can handle.
106
+ """
107
+ self.audio_padding = audio_padding
108
+ self.encoder_ds_factor = encoder_ds_factor
109
+ self.stack_factor = stack_factor
110
+ self.audio_placeholder = audio_placeholder
111
+ self.audio_token_replacement = tokenizer.eos_token
112
+ self.audio_context_size = audio_context_size
113
+ assert (
114
+ self.audio_token_replacement is not None
115
+ ), "The tokenizer has no EOS token. Cannot recover."
116
+ if tokenizer.pad_token_id is None:
117
+ tokenizer.pad_token_id = tokenizer.eos_token_id
118
+
119
+ super().__init__(audio_processor=audio_processor, tokenizer=tokenizer)
120
+
121
+ @classmethod
122
+ def from_pretrained(cls, pretrained_model_name_or_path: str, **kwargs):
123
+ config: UltravoxConfig = transformers.AutoConfig.from_pretrained(
124
+ pretrained_model_name_or_path, **kwargs
125
+ )
126
+ audio_processor = transformers.AutoProcessor.from_pretrained(
127
+ config.audio_model_id
128
+ or config.audio_config._name_or_path
129
+ or "openai/whisper-tiny"
130
+ )
131
+
132
+ tokenizer = transformers.AutoTokenizer.from_pretrained(
133
+ pretrained_model_name_or_path, **kwargs
134
+ )
135
+ tokenizer.padding_side = "left"
136
+ tokenizer.pad_token = tokenizer.eos_token
137
+
138
+ return cls(
139
+ audio_processor=audio_processor,
140
+ tokenizer=tokenizer,
141
+ stack_factor=config.stack_factor,
142
+ )
143
+
144
+ def _chunk_and_pad_audio(
145
+ self, audio_values: torch.Tensor, audio_lens: torch.Tensor
146
+ ) -> Dict[str, Any]:
147
+ """
148
+ Processes the audio batch by chunking any items in the batch according to the audio_context_size,
149
+ padding the last chunk if needed, and returns a dictionary with updated audio data.
150
+
151
+ Args:
152
+ audio_values (torch.Tensor): A tensor of audio values (e.g., in B, D, T format).
153
+ audio_lens (torch.Tensor): A tensor of audio lengths.
154
+
155
+ Returns:
156
+ Dict[str, Any]: Dictionary with the following keys:
157
+ - "audio_values": The concatenated audio tensor after chunking and padding.
158
+ - "audio_lens": Tensor of lengths for each chunk.
159
+ - "audio_is_continuation": Tensor of booleans indicating if the chunk is a continuation of the previous chunk.
160
+ - "audio_batch_size": A Tensor with one integer representing the number of chunks.
161
+
162
+ """
163
+ chunked_audio_values: List[torch.Tensor] = []
164
+ chunked_audio_lens: List[int] = []
165
+ is_continuation_list: List[bool] = []
166
+ context_size = self.audio_context_size or audio_values.shape[-1]
167
+
168
+ for audio, audio_len in zip(audio_values, audio_lens):
169
+ for offset in range(0, audio_len, context_size):
170
+ is_continuation = offset > 0
171
+ chunk = audio[..., offset : offset + context_size]
172
+ if is_continuation and chunk.shape[-1] < context_size:
173
+ # N.B. We only need to pad continuation chunks. If none of the samples require chunking, the
174
+ # batch might not (need to) be padded all the way to the audio_context_size, in which case
175
+ # we've already included the padding above. On the other hand, if we have any continuation
176
+ # chunks we know that the batch needs to be padded to audio_context_size because that's what
177
+ # we're slicing to.
178
+ chunk = F.pad(chunk, (0, context_size - chunk.shape[-1]))
179
+ chunked_audio_values.append(torch.as_tensor(chunk))
180
+ chunked_audio_lens.append(min(audio_len - offset, context_size))
181
+ is_continuation_list.append(is_continuation)
182
+
183
+ return {
184
+ "audio_values": torch.stack(chunked_audio_values),
185
+ "audio_lens": torch.tensor(chunked_audio_lens),
186
+ "audio_is_continuation": torch.tensor(is_continuation_list),
187
+ "audio_batch_size": torch.tensor([len(chunked_audio_values)]),
188
+ }
189
+
190
+ def __call__(
191
+ self,
192
+ text: Optional[str] = None,
193
+ audio: Optional[Union[np.ndarray, torch.Tensor]] = None,
194
+ audios: Optional[
195
+ Union[
196
+ List[Union[np.ndarray, torch.Tensor]], Union[np.ndarray, torch.Tensor]
197
+ ]
198
+ ] = None,
199
+ sampling_rate: Optional[int] = None,
200
+ return_tensors: Optional[
201
+ Union[str, transformers.TensorType]
202
+ ] = transformers.TensorType.PYTORCH,
203
+ **kwargs,
204
+ ) -> transformers.BatchFeature:
205
+ """
206
+ Main method to prepare for the model one text sequence and audio. This method forwards the `text`
207
+ and `kwargs` arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizerFast.__call__`] if `text` is not `None` to encode
208
+ the text. To prepare the audio(s), this method forwards the `audio`, `sampling_rate` and `kwargs` arguments to
209
+ audio processor's [`~WhisperProcessor.__call__`] if `audio` is not `None`. Please refer to the docstring
210
+ of the above two methods for more information.
211
+
212
+ Args:
213
+ text (`str`, `List[str]`):
214
+ The sequence to be encoded. Sequence can be a string or (pretokenized string).
215
+ audio (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
216
+ The audio to be prepared. Audio can be a single-channel (1-dimensional) NumPy array or PyTorch tensor.
217
+ audios (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
218
+ A list or two dimensional array of audio to be prepared.
219
+ sampling_rate (`int`, *optional*, defaults to 16000):
220
+ Sampling rate of the input audio. We expect 16kHz audio. Don't change this value unless you know what
221
+ you are doing.
222
+ return_tensors (`str` or [`~utils.TensorType`], *optional*):
223
+ If set, will return tensors of a particular framework. Acceptable values are:
224
+
225
+ - `'tf'`: Return TensorFlow `tf.constant` objects.
226
+ - `'pt'`: Return PyTorch `torch.Tensor` objects.
227
+ - `'np'`: Return NumPy `np.ndarray` objects.
228
+ - `'jax'`: Return JAX `jnp.ndarray` objects.
229
+
230
+ Returns:
231
+ [`BatchFeature`]: A [`BatchFeature`] with the following fields:
232
+
233
+ - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
234
+ - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
235
+ `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
236
+ `None`).
237
+ - **audio_values** -- Processed audio values to be fed to a model. Returned when `audio` is not `None`.
238
+ - **audio_token_len** -- Predicted number of audio frames: this value is guaranteed to be a close upper bound.
239
+ Returned when `audio` is not `None`.
240
+ - **audio_token_start_idx** -- The index in the tokenized text where the audio starts. Returned when `audio` is not `None`.
241
+ """
242
+ # TODO: Add support for multiple text inputs.
243
+ if audio is not None and audios is not None:
244
+ raise ValueError("Only one of `audio` or `audios` should be provided.")
245
+ elif audio is not None:
246
+ audios = audio if isinstance(audio, list) or audio.ndim == 2 else [audio]
247
+ elif audios is None:
248
+ audios = []
249
+
250
+ data = {}
251
+ audio_is_continuation = []
252
+ if len(audios) > 0:
253
+ audios = [x.numpy() if isinstance(x, torch.Tensor) else x for x in audios]
254
+
255
+ # Pad out each audio to at least 2 hops (the minimum required by the processor).
256
+ hop_length = self.audio_processor.feature_extractor.hop_length
257
+ audios = [
258
+ (
259
+ np.pad(x, (0, 2 * hop_length - len(x)), mode="constant")
260
+ if len(x) < 2 * hop_length
261
+ else x
262
+ )
263
+ for x in audios
264
+ ]
265
+
266
+ # Main audio processing. The processor is model-specific.
267
+ x: transformers.BatchFeature = self.audio_processor(
268
+ audios,
269
+ sampling_rate=sampling_rate,
270
+ padding="longest",
271
+ pad_to_multiple_of=hop_length, # The attention mask effectively gets padded to the hop length, so pad the audio to be consistent.
272
+ truncation=False,
273
+ return_attention_mask=True,
274
+ **kwargs,
275
+ )
276
+
277
+ data.update(
278
+ self._chunk_and_pad_audio(
279
+ audio_values=torch.as_tensor(
280
+ x.input_features if "input_features" in x else x.input_values
281
+ ),
282
+ audio_lens=torch.as_tensor(x.attention_mask).sum(-1),
283
+ )
284
+ )
285
+
286
+ audio_is_continuation = data.pop("audio_is_continuation")
287
+ data["audio_token_len"] = torch.ceil(
288
+ data["audio_lens"] / (self.encoder_ds_factor * self.stack_factor)
289
+ ).to(dtype=torch.int)
290
+
291
+ if text is not None:
292
+ if not isinstance(text, str):
293
+ raise ValueError("Text must be a string. Batch mode not supported yet.")
294
+
295
+ # Special tokens like BOS should already have been added by the caller.
296
+ tokenized_parts = self.tokenizer(
297
+ text.split(
298
+ "<|audio|>" # The placeholder isn't part of the vocabulary, so split the text around it.
299
+ ),
300
+ add_special_tokens=False,
301
+ **kwargs,
302
+ )
303
+
304
+ audio_token_start_idx = []
305
+ replacement_token_id = self.tokenizer.get_vocab()[
306
+ self.audio_token_replacement
307
+ ]
308
+ placeholder_index = -1
309
+ split_input_ids = tokenized_parts["input_ids"]
310
+ input_ids: List[int] = []
311
+
312
+ for i, token_len in enumerate(data.get("audio_token_len", [])):
313
+ if not audio_is_continuation[i]:
314
+ placeholder_index += 1
315
+ if placeholder_index >= len(split_input_ids):
316
+ raise ValueError(
317
+ f"Text contains too few audio placeholders. (Expected {len(audios)} placeholders)"
318
+ )
319
+
320
+ input_ids.extend(split_input_ids[placeholder_index])
321
+
322
+ audio_token_start_idx.append(len(input_ids))
323
+
324
+ input_ids.extend([replacement_token_id] * token_len)
325
+
326
+ # Include any tokens after the last audio.
327
+ placeholder_index += 1
328
+ if placeholder_index != len(split_input_ids) - 1:
329
+ raise ValueError(
330
+ f"Text contains too many audio placeholders. (Expected {len(audios)} placeholders)"
331
+ )
332
+ input_ids.extend(split_input_ids[placeholder_index])
333
+
334
+ if "audio_token_len" in data:
335
+ data["audio_token_start_idx"] = torch.as_tensor(audio_token_start_idx)
336
+
337
+ data["input_ids"] = [input_ids]
338
+ data["attention_mask"] = [[1] * len(input_ids)]
339
+
340
+ # Ensure that there are no audio placeholders after the last audio.
341
+
342
+ return transformers.BatchFeature(data=data, tensor_type=return_tensors)
343
+
344
+ def batch_decode(self, *args, **kwargs):
345
+ return self.tokenizer.batch_decode(*args, **kwargs)
346
+
347
+ def decode(self, *args, **kwargs):
348
+ return self.tokenizer.decode(*args, **kwargs)
349
+
350
+ @property
351
+ def model_input_names(self):
352
+ tokenizer_input_names = self.tokenizer.model_input_names
353
+ audio_processor_input_names = self.audio_processor.model_input_names
354
+ return list(set(tokenizer_input_names + audio_processor_input_names))
355
+
356
+
357
+ UltravoxProcessor.register_for_auto_class()
358
+
359
+ transformers.AutoProcessor.register(UltravoxConfig, UltravoxProcessor)
checkpoint-1200/vocab.json ADDED
The diff for this file is too large to render. See raw diff
 
checkpoint-2400/added_tokens.json ADDED
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+ "behove": "behoove",
130
+ "behoved": "behooved",
131
+ "behoves": "behooves",
132
+ "bejewelled": "bejeweled",
133
+ "belabour": "belabor",
134
+ "belaboured": "belabored",
135
+ "belabouring": "belaboring",
136
+ "belabours": "belabors",
137
+ "bevelled": "beveled",
138
+ "bevvies": "bevies",
139
+ "bevvy": "bevy",
140
+ "biassed": "biased",
141
+ "biassing": "biasing",
142
+ "bingeing": "binging",
143
+ "bougainvillaea": "bougainvillea",
144
+ "bougainvillaeas": "bougainvilleas",
145
+ "bowdlerise": "bowdlerize",
146
+ "bowdlerised": "bowdlerized",
147
+ "bowdlerises": "bowdlerizes",
148
+ "bowdlerising": "bowdlerizing",
149
+ "breathalyse": "breathalyze",
150
+ "breathalysed": "breathalyzed",
151
+ "breathalyser": "breathalyzer",
152
+ "breathalysers": "breathalyzers",
153
+ "breathalyses": "breathalyzes",
154
+ "breathalysing": "breathalyzing",
155
+ "brutalise": "brutalize",
156
+ "brutalised": "brutalized",
157
+ "brutalises": "brutalizes",
158
+ "brutalising": "brutalizing",
159
+ "busses": "buses",
160
+ "bussing": "busing",
161
+ "caesarean": "cesarean",
162
+ "caesareans": "cesareans",
163
+ "calibre": "caliber",
164
+ "calibres": "calibers",
165
+ "calliper": "caliper",
166
+ "callipers": "calipers",
167
+ "callisthenics": "calisthenics",
168
+ "canalise": "canalize",
169
+ "canalised": "canalized",
170
+ "canalises": "canalizes",
171
+ "canalising": "canalizing",
172
+ "cancelation": "cancellation",
173
+ "cancelations": "cancellations",
174
+ "cancelled": "canceled",
175
+ "cancelling": "canceling",
176
+ "candour": "candor",
177
+ "cannibalise": "cannibalize",
178
+ "cannibalised": "cannibalized",
179
+ "cannibalises": "cannibalizes",
180
+ "cannibalising": "cannibalizing",
181
+ "canonise": "canonize",
182
+ "canonised": "canonized",
183
+ "canonises": "canonizes",
184
+ "canonising": "canonizing",
185
+ "capitalise": "capitalize",
186
+ "capitalised": "capitalized",
187
+ "capitalises": "capitalizes",
188
+ "capitalising": "capitalizing",
189
+ "caramelise": "caramelize",
190
+ "caramelised": "caramelized",
191
+ "caramelises": "caramelizes",
192
+ "caramelising": "caramelizing",
193
+ "carbonise": "carbonize",
194
+ "carbonised": "carbonized",
195
+ "carbonises": "carbonizes",
196
+ "carbonising": "carbonizing",
197
+ "carolled": "caroled",
198
+ "carolling": "caroling",
199
+ "catalogue": "catalog",
200
+ "catalogued": "cataloged",
201
+ "catalogues": "catalogs",
202
+ "cataloguing": "cataloging",
203
+ "catalyse": "catalyze",
204
+ "catalysed": "catalyzed",
205
+ "catalyses": "catalyzes",
206
+ "catalysing": "catalyzing",
207
+ "categorise": "categorize",
208
+ "categorised": "categorized",
209
+ "categorises": "categorizes",
210
+ "categorising": "categorizing",
211
+ "cauterise": "cauterize",
212
+ "cauterised": "cauterized",
213
+ "cauterises": "cauterizes",
214
+ "cauterising": "cauterizing",
215
+ "cavilled": "caviled",
216
+ "cavilling": "caviling",
217
+ "centigramme": "centigram",
218
+ "centigrammes": "centigrams",
219
+ "centilitre": "centiliter",
220
+ "centilitres": "centiliters",
221
+ "centimetre": "centimeter",
222
+ "centimetres": "centimeters",
223
+ "centralise": "centralize",
224
+ "centralised": "centralized",
225
+ "centralises": "centralizes",
226
+ "centralising": "centralizing",
227
+ "centre": "center",
228
+ "centred": "centered",
229
+ "centrefold": "centerfold",
230
+ "centrefolds": "centerfolds",
231
+ "centrepiece": "centerpiece",
232
+ "centrepieces": "centerpieces",
233
+ "centres": "centers",
234
+ "channelled": "channeled",
235
+ "channelling": "channeling",
236
+ "characterise": "characterize",
237
+ "characterised": "characterized",
238
+ "characterises": "characterizes",
239
+ "characterising": "characterizing",
240
+ "cheque": "check",
241
+ "chequebook": "checkbook",
242
+ "chequebooks": "checkbooks",
243
+ "chequered": "checkered",
244
+ "cheques": "checks",
245
+ "chilli": "chili",
246
+ "chimaera": "chimera",
247
+ "chimaeras": "chimeras",
248
+ "chiselled": "chiseled",
249
+ "chiselling": "chiseling",
250
+ "circularise": "circularize",
251
+ "circularised": "circularized",
252
+ "circularises": "circularizes",
253
+ "circularising": "circularizing",
254
+ "civilise": "civilize",
255
+ "civilised": "civilized",
256
+ "civilises": "civilizes",
257
+ "civilising": "civilizing",
258
+ "clamour": "clamor",
259
+ "clamoured": "clamored",
260
+ "clamouring": "clamoring",
261
+ "clamours": "clamors",
262
+ "clangour": "clangor",
263
+ "clarinettist": "clarinetist",
264
+ "clarinettists": "clarinetists",
265
+ "collectivise": "collectivize",
266
+ "collectivised": "collectivized",
267
+ "collectivises": "collectivizes",
268
+ "collectivising": "collectivizing",
269
+ "colonisation": "colonization",
270
+ "colonise": "colonize",
271
+ "colonised": "colonized",
272
+ "coloniser": "colonizer",
273
+ "colonisers": "colonizers",
274
+ "colonises": "colonizes",
275
+ "colonising": "colonizing",
276
+ "colour": "color",
277
+ "colourant": "colorant",
278
+ "colourants": "colorants",
279
+ "coloured": "colored",
280
+ "coloureds": "coloreds",
281
+ "colourful": "colorful",
282
+ "colourfully": "colorfully",
283
+ "colouring": "coloring",
284
+ "colourize": "colorize",
285
+ "colourized": "colorized",
286
+ "colourizes": "colorizes",
287
+ "colourizing": "colorizing",
288
+ "colourless": "colorless",
289
+ "colours": "colors",
290
+ "commercialise": "commercialize",
291
+ "commercialised": "commercialized",
292
+ "commercialises": "commercializes",
293
+ "commercialising": "commercializing",
294
+ "compartmentalise": "compartmentalize",
295
+ "compartmentalised": "compartmentalized",
296
+ "compartmentalises": "compartmentalizes",
297
+ "compartmentalising": "compartmentalizing",
298
+ "computerise": "computerize",
299
+ "computerised": "computerized",
300
+ "computerises": "computerizes",
301
+ "computerising": "computerizing",
302
+ "conceptualise": "conceptualize",
303
+ "conceptualised": "conceptualized",
304
+ "conceptualises": "conceptualizes",
305
+ "conceptualising": "conceptualizing",
306
+ "connexion": "connection",
307
+ "connexions": "connections",
308
+ "contextualise": "contextualize",
309
+ "contextualised": "contextualized",
310
+ "contextualises": "contextualizes",
311
+ "contextualising": "contextualizing",
312
+ "cosier": "cozier",
313
+ "cosies": "cozies",
314
+ "cosiest": "coziest",
315
+ "cosily": "cozily",
316
+ "cosiness": "coziness",
317
+ "cosy": "cozy",
318
+ "councillor": "councilor",
319
+ "councillors": "councilors",
320
+ "counselled": "counseled",
321
+ "counselling": "counseling",
322
+ "counsellor": "counselor",
323
+ "counsellors": "counselors",
324
+ "crenelated": "crenellated",
325
+ "criminalise": "criminalize",
326
+ "criminalised": "criminalized",
327
+ "criminalises": "criminalizes",
328
+ "criminalising": "criminalizing",
329
+ "criticise": "criticize",
330
+ "criticised": "criticized",
331
+ "criticises": "criticizes",
332
+ "criticising": "criticizing",
333
+ "crueller": "crueler",
334
+ "cruellest": "cruelest",
335
+ "crystallisation": "crystallization",
336
+ "crystallise": "crystallize",
337
+ "crystallised": "crystallized",
338
+ "crystallises": "crystallizes",
339
+ "crystallising": "crystallizing",
340
+ "cudgelled": "cudgeled",
341
+ "cudgelling": "cudgeling",
342
+ "customise": "customize",
343
+ "customised": "customized",
344
+ "customises": "customizes",
345
+ "customising": "customizing",
346
+ "cypher": "cipher",
347
+ "cyphers": "ciphers",
348
+ "decentralisation": "decentralization",
349
+ "decentralise": "decentralize",
350
+ "decentralised": "decentralized",
351
+ "decentralises": "decentralizes",
352
+ "decentralising": "decentralizing",
353
+ "decriminalisation": "decriminalization",
354
+ "decriminalise": "decriminalize",
355
+ "decriminalised": "decriminalized",
356
+ "decriminalises": "decriminalizes",
357
+ "decriminalising": "decriminalizing",
358
+ "defence": "defense",
359
+ "defenceless": "defenseless",
360
+ "defences": "defenses",
361
+ "dehumanisation": "dehumanization",
362
+ "dehumanise": "dehumanize",
363
+ "dehumanised": "dehumanized",
364
+ "dehumanises": "dehumanizes",
365
+ "dehumanising": "dehumanizing",
366
+ "demeanour": "demeanor",
367
+ "demilitarisation": "demilitarization",
368
+ "demilitarise": "demilitarize",
369
+ "demilitarised": "demilitarized",
370
+ "demilitarises": "demilitarizes",
371
+ "demilitarising": "demilitarizing",
372
+ "demobilisation": "demobilization",
373
+ "demobilise": "demobilize",
374
+ "demobilised": "demobilized",
375
+ "demobilises": "demobilizes",
376
+ "demobilising": "demobilizing",
377
+ "democratisation": "democratization",
378
+ "democratise": "democratize",
379
+ "democratised": "democratized",
380
+ "democratises": "democratizes",
381
+ "democratising": "democratizing",
382
+ "demonise": "demonize",
383
+ "demonised": "demonized",
384
+ "demonises": "demonizes",
385
+ "demonising": "demonizing",
386
+ "demoralisation": "demoralization",
387
+ "demoralise": "demoralize",
388
+ "demoralised": "demoralized",
389
+ "demoralises": "demoralizes",
390
+ "demoralising": "demoralizing",
391
+ "denationalisation": "denationalization",
392
+ "denationalise": "denationalize",
393
+ "denationalised": "denationalized",
394
+ "denationalises": "denationalizes",
395
+ "denationalising": "denationalizing",
396
+ "deodorise": "deodorize",
397
+ "deodorised": "deodorized",
398
+ "deodorises": "deodorizes",
399
+ "deodorising": "deodorizing",
400
+ "depersonalise": "depersonalize",
401
+ "depersonalised": "depersonalized",
402
+ "depersonalises": "depersonalizes",
403
+ "depersonalising": "depersonalizing",
404
+ "deputise": "deputize",
405
+ "deputised": "deputized",
406
+ "deputises": "deputizes",
407
+ "deputising": "deputizing",
408
+ "desensitisation": "desensitization",
409
+ "desensitise": "desensitize",
410
+ "desensitised": "desensitized",
411
+ "desensitises": "desensitizes",
412
+ "desensitising": "desensitizing",
413
+ "destabilisation": "destabilization",
414
+ "destabilise": "destabilize",
415
+ "destabilised": "destabilized",
416
+ "destabilises": "destabilizes",
417
+ "destabilising": "destabilizing",
418
+ "dialled": "dialed",
419
+ "dialling": "dialing",
420
+ "dialogue": "dialog",
421
+ "dialogues": "dialogs",
422
+ "diarrhoea": "diarrhea",
423
+ "digitise": "digitize",
424
+ "digitised": "digitized",
425
+ "digitises": "digitizes",
426
+ "digitising": "digitizing",
427
+ "disc": "disk",
428
+ "discolour": "discolor",
429
+ "discoloured": "discolored",
430
+ "discolouring": "discoloring",
431
+ "discolours": "discolors",
432
+ "discs": "disks",
433
+ "disembowelled": "disemboweled",
434
+ "disembowelling": "disemboweling",
435
+ "disfavour": "disfavor",
436
+ "dishevelled": "disheveled",
437
+ "dishonour": "dishonor",
438
+ "dishonourable": "dishonorable",
439
+ "dishonourably": "dishonorably",
440
+ "dishonoured": "dishonored",
441
+ "dishonouring": "dishonoring",
442
+ "dishonours": "dishonors",
443
+ "disorganisation": "disorganization",
444
+ "disorganised": "disorganized",
445
+ "distil": "distill",
446
+ "distils": "distills",
447
+ "dramatisation": "dramatization",
448
+ "dramatisations": "dramatizations",
449
+ "dramatise": "dramatize",
450
+ "dramatised": "dramatized",
451
+ "dramatises": "dramatizes",
452
+ "dramatising": "dramatizing",
453
+ "draught": "draft",
454
+ "draughtboard": "draftboard",
455
+ "draughtboards": "draftboards",
456
+ "draughtier": "draftier",
457
+ "draughtiest": "draftiest",
458
+ "draughts": "drafts",
459
+ "draughtsman": "draftsman",
460
+ "draughtsmanship": "draftsmanship",
461
+ "draughtsmen": "draftsmen",
462
+ "draughtswoman": "draftswoman",
463
+ "draughtswomen": "draftswomen",
464
+ "draughty": "drafty",
465
+ "drivelled": "driveled",
466
+ "drivelling": "driveling",
467
+ "duelled": "dueled",
468
+ "duelling": "dueling",
469
+ "economise": "economize",
470
+ "economised": "economized",
471
+ "economises": "economizes",
472
+ "economising": "economizing",
473
+ "editorialise": "editorialize",
474
+ "editorialised": "editorialized",
475
+ "editorialises": "editorializes",
476
+ "editorialising": "editorializing",
477
+ "edoema": "edema",
478
+ "empathise": "empathize",
479
+ "empathised": "empathized",
480
+ "empathises": "empathizes",
481
+ "empathising": "empathizing",
482
+ "emphasise": "emphasize",
483
+ "emphasised": "emphasized",
484
+ "emphasises": "emphasizes",
485
+ "emphasising": "emphasizing",
486
+ "enamelled": "enameled",
487
+ "enamelling": "enameling",
488
+ "enamoured": "enamored",
489
+ "encyclopaedia": "encyclopedia",
490
+ "encyclopaedias": "encyclopedias",
491
+ "encyclopaedic": "encyclopedic",
492
+ "endeavour": "endeavor",
493
+ "endeavoured": "endeavored",
494
+ "endeavouring": "endeavoring",
495
+ "endeavours": "endeavors",
496
+ "energise": "energize",
497
+ "energised": "energized",
498
+ "energises": "energizes",
499
+ "energising": "energizing",
500
+ "enrol": "enroll",
501
+ "enrols": "enrolls",
502
+ "enthral": "enthrall",
503
+ "enthrals": "enthralls",
504
+ "epaulette": "epaulet",
505
+ "epaulettes": "epaulets",
506
+ "epicentre": "epicenter",
507
+ "epicentres": "epicenters",
508
+ "epilogue": "epilog",
509
+ "epilogues": "epilogs",
510
+ "epitomise": "epitomize",
511
+ "epitomised": "epitomized",
512
+ "epitomises": "epitomizes",
513
+ "epitomising": "epitomizing",
514
+ "equalisation": "equalization",
515
+ "equalise": "equalize",
516
+ "equalised": "equalized",
517
+ "equaliser": "equalizer",
518
+ "equalisers": "equalizers",
519
+ "equalises": "equalizes",
520
+ "equalising": "equalizing",
521
+ "eulogise": "eulogize",
522
+ "eulogised": "eulogized",
523
+ "eulogises": "eulogizes",
524
+ "eulogising": "eulogizing",
525
+ "evangelise": "evangelize",
526
+ "evangelised": "evangelized",
527
+ "evangelises": "evangelizes",
528
+ "evangelising": "evangelizing",
529
+ "exorcise": "exorcize",
530
+ "exorcised": "exorcized",
531
+ "exorcises": "exorcizes",
532
+ "exorcising": "exorcizing",
533
+ "extemporisation": "extemporization",
534
+ "extemporise": "extemporize",
535
+ "extemporised": "extemporized",
536
+ "extemporises": "extemporizes",
537
+ "extemporising": "extemporizing",
538
+ "externalisation": "externalization",
539
+ "externalisations": "externalizations",
540
+ "externalise": "externalize",
541
+ "externalised": "externalized",
542
+ "externalises": "externalizes",
543
+ "externalising": "externalizing",
544
+ "factorise": "factorize",
545
+ "factorised": "factorized",
546
+ "factorises": "factorizes",
547
+ "factorising": "factorizing",
548
+ "faecal": "fecal",
549
+ "faeces": "feces",
550
+ "familiarisation": "familiarization",
551
+ "familiarise": "familiarize",
552
+ "familiarised": "familiarized",
553
+ "familiarises": "familiarizes",
554
+ "familiarising": "familiarizing",
555
+ "fantasise": "fantasize",
556
+ "fantasised": "fantasized",
557
+ "fantasises": "fantasizes",
558
+ "fantasising": "fantasizing",
559
+ "favour": "favor",
560
+ "favourable": "favorable",
561
+ "favourably": "favorably",
562
+ "favoured": "favored",
563
+ "favouring": "favoring",
564
+ "favourite": "favorite",
565
+ "favourites": "favorites",
566
+ "favouritism": "favoritism",
567
+ "favours": "favors",
568
+ "feminise": "feminize",
569
+ "feminised": "feminized",
570
+ "feminises": "feminizes",
571
+ "feminising": "feminizing",
572
+ "fertilisation": "fertilization",
573
+ "fertilise": "fertilize",
574
+ "fertilised": "fertilized",
575
+ "fertiliser": "fertilizer",
576
+ "fertilisers": "fertilizers",
577
+ "fertilises": "fertilizes",
578
+ "fertilising": "fertilizing",
579
+ "fervour": "fervor",
580
+ "fibre": "fiber",
581
+ "fibreglass": "fiberglass",
582
+ "fibres": "fibers",
583
+ "fictionalisation": "fictionalization",
584
+ "fictionalisations": "fictionalizations",
585
+ "fictionalise": "fictionalize",
586
+ "fictionalised": "fictionalized",
587
+ "fictionalises": "fictionalizes",
588
+ "fictionalising": "fictionalizing",
589
+ "fillet": "filet",
590
+ "filleted": "fileted",
591
+ "filleting": "fileting",
592
+ "fillets": "filets",
593
+ "finalisation": "finalization",
594
+ "finalise": "finalize",
595
+ "finalised": "finalized",
596
+ "finalises": "finalizes",
597
+ "finalising": "finalizing",
598
+ "flautist": "flutist",
599
+ "flautists": "flutists",
600
+ "flavour": "flavor",
601
+ "flavoured": "flavored",
602
+ "flavouring": "flavoring",
603
+ "flavourings": "flavorings",
604
+ "flavourless": "flavorless",
605
+ "flavours": "flavors",
606
+ "flavoursome": "flavorsome",
607
+ "flyer / flier": "flier / flyer",
608
+ "foetal": "fetal",
609
+ "foetid": "fetid",
610
+ "foetus": "fetus",
611
+ "foetuses": "fetuses",
612
+ "formalisation": "formalization",
613
+ "formalise": "formalize",
614
+ "formalised": "formalized",
615
+ "formalises": "formalizes",
616
+ "formalising": "formalizing",
617
+ "fossilisation": "fossilization",
618
+ "fossilise": "fossilize",
619
+ "fossilised": "fossilized",
620
+ "fossilises": "fossilizes",
621
+ "fossilising": "fossilizing",
622
+ "fraternisation": "fraternization",
623
+ "fraternise": "fraternize",
624
+ "fraternised": "fraternized",
625
+ "fraternises": "fraternizes",
626
+ "fraternising": "fraternizing",
627
+ "fulfil": "fulfill",
628
+ "fulfilment": "fulfillment",
629
+ "fulfils": "fulfills",
630
+ "funnelled": "funneled",
631
+ "funnelling": "funneling",
632
+ "gage": "gauge",
633
+ "gaged": "gauged",
634
+ "gages": "gauges",
635
+ "gaging": "gauging",
636
+ "galvanise": "galvanize",
637
+ "galvanised": "galvanized",
638
+ "galvanises": "galvanizes",
639
+ "galvanising": "galvanizing",
640
+ "gambolled": "gamboled",
641
+ "gambolling": "gamboling",
642
+ "gaol": "jail",
643
+ "gaolbird": "jailbird",
644
+ "gaolbirds": "jailbirds",
645
+ "gaolbreak": "jailbreak",
646
+ "gaolbreaks": "jailbreaks",
647
+ "gaoled": "jailed",
648
+ "gaoler": "jailer",
649
+ "gaolers": "jailers",
650
+ "gaoling": "jailing",
651
+ "gaols": "jails",
652
+ "gasses": "gases",
653
+ "generalisation": "generalization",
654
+ "generalisations": "generalizations",
655
+ "generalise": "generalize",
656
+ "generalised": "generalized",
657
+ "generalises": "generalizes",
658
+ "generalising": "generalizing",
659
+ "ghettoise": "ghettoize",
660
+ "ghettoised": "ghettoized",
661
+ "ghettoises": "ghettoizes",
662
+ "ghettoising": "ghettoizing",
663
+ "gipsies": "gypsies",
664
+ "glamor": "glamour",
665
+ "glamorise": "glamorize",
666
+ "glamorised": "glamorized",
667
+ "glamorises": "glamorizes",
668
+ "glamorising": "glamorizing",
669
+ "globalisation": "globalization",
670
+ "globalise": "globalize",
671
+ "globalised": "globalized",
672
+ "globalises": "globalizes",
673
+ "globalising": "globalizing",
674
+ "glueing": "gluing",
675
+ "goitre": "goiter",
676
+ "goitres": "goiters",
677
+ "gonorrhoea": "gonorrhea",
678
+ "gramme": "gram",
679
+ "grammes": "grams",
680
+ "gravelled": "graveled",
681
+ "grey": "gray",
682
+ "greyed": "grayed",
683
+ "greying": "graying",
684
+ "greyish": "grayish",
685
+ "greyness": "grayness",
686
+ "greys": "grays",
687
+ "grovelled": "groveled",
688
+ "grovelling": "groveling",
689
+ "groyne": "groin",
690
+ "groynes": "groins",
691
+ "gruelling": "grueling",
692
+ "gruellingly": "gruelingly",
693
+ "gryphon": "griffin",
694
+ "gryphons": "griffins",
695
+ "gynaecological": "gynecological",
696
+ "gynaecologist": "gynecologist",
697
+ "gynaecologists": "gynecologists",
698
+ "gynaecology": "gynecology",
699
+ "haematological": "hematological",
700
+ "haematologist": "hematologist",
701
+ "haematologists": "hematologists",
702
+ "haematology": "hematology",
703
+ "haemoglobin": "hemoglobin",
704
+ "haemophilia": "hemophilia",
705
+ "haemophiliac": "hemophiliac",
706
+ "haemophiliacs": "hemophiliacs",
707
+ "haemorrhage": "hemorrhage",
708
+ "haemorrhaged": "hemorrhaged",
709
+ "haemorrhages": "hemorrhages",
710
+ "haemorrhaging": "hemorrhaging",
711
+ "haemorrhoids": "hemorrhoids",
712
+ "harbour": "harbor",
713
+ "harboured": "harbored",
714
+ "harbouring": "harboring",
715
+ "harbours": "harbors",
716
+ "harmonisation": "harmonization",
717
+ "harmonise": "harmonize",
718
+ "harmonised": "harmonized",
719
+ "harmonises": "harmonizes",
720
+ "harmonising": "harmonizing",
721
+ "homoeopath": "homeopath",
722
+ "homoeopathic": "homeopathic",
723
+ "homoeopaths": "homeopaths",
724
+ "homoeopathy": "homeopathy",
725
+ "homogenise": "homogenize",
726
+ "homogenised": "homogenized",
727
+ "homogenises": "homogenizes",
728
+ "homogenising": "homogenizing",
729
+ "honour": "honor",
730
+ "honourable": "honorable",
731
+ "honourably": "honorably",
732
+ "honoured": "honored",
733
+ "honouring": "honoring",
734
+ "honours": "honors",
735
+ "hospitalisation": "hospitalization",
736
+ "hospitalise": "hospitalize",
737
+ "hospitalised": "hospitalized",
738
+ "hospitalises": "hospitalizes",
739
+ "hospitalising": "hospitalizing",
740
+ "humanise": "humanize",
741
+ "humanised": "humanized",
742
+ "humanises": "humanizes",
743
+ "humanising": "humanizing",
744
+ "humour": "humor",
745
+ "humoured": "humored",
746
+ "humouring": "humoring",
747
+ "humourless": "humorless",
748
+ "humours": "humors",
749
+ "hybridise": "hybridize",
750
+ "hybridised": "hybridized",
751
+ "hybridises": "hybridizes",
752
+ "hybridising": "hybridizing",
753
+ "hypnotise": "hypnotize",
754
+ "hypnotised": "hypnotized",
755
+ "hypnotises": "hypnotizes",
756
+ "hypnotising": "hypnotizing",
757
+ "hypothesise": "hypothesize",
758
+ "hypothesised": "hypothesized",
759
+ "hypothesises": "hypothesizes",
760
+ "hypothesising": "hypothesizing",
761
+ "idealisation": "idealization",
762
+ "idealise": "idealize",
763
+ "idealised": "idealized",
764
+ "idealises": "idealizes",
765
+ "idealising": "idealizing",
766
+ "idolise": "idolize",
767
+ "idolised": "idolized",
768
+ "idolises": "idolizes",
769
+ "idolising": "idolizing",
770
+ "immobilisation": "immobilization",
771
+ "immobilise": "immobilize",
772
+ "immobilised": "immobilized",
773
+ "immobiliser": "immobilizer",
774
+ "immobilisers": "immobilizers",
775
+ "immobilises": "immobilizes",
776
+ "immobilising": "immobilizing",
777
+ "immortalise": "immortalize",
778
+ "immortalised": "immortalized",
779
+ "immortalises": "immortalizes",
780
+ "immortalising": "immortalizing",
781
+ "immunisation": "immunization",
782
+ "immunise": "immunize",
783
+ "immunised": "immunized",
784
+ "immunises": "immunizes",
785
+ "immunising": "immunizing",
786
+ "impanelled": "impaneled",
787
+ "impanelling": "impaneling",
788
+ "imperilled": "imperiled",
789
+ "imperilling": "imperiling",
790
+ "individualise": "individualize",
791
+ "individualised": "individualized",
792
+ "individualises": "individualizes",
793
+ "individualising": "individualizing",
794
+ "industrialise": "industrialize",
795
+ "industrialised": "industrialized",
796
+ "industrialises": "industrializes",
797
+ "industrialising": "industrializing",
798
+ "inflexion": "inflection",
799
+ "inflexions": "inflections",
800
+ "initialise": "initialize",
801
+ "initialised": "initialized",
802
+ "initialises": "initializes",
803
+ "initialising": "initializing",
804
+ "initialled": "initialed",
805
+ "initialling": "initialing",
806
+ "instal": "install",
807
+ "instalment": "installment",
808
+ "instalments": "installments",
809
+ "instals": "installs",
810
+ "instil": "instill",
811
+ "instils": "instills",
812
+ "institutionalisation": "institutionalization",
813
+ "institutionalise": "institutionalize",
814
+ "institutionalised": "institutionalized",
815
+ "institutionalises": "institutionalizes",
816
+ "institutionalising": "institutionalizing",
817
+ "intellectualise": "intellectualize",
818
+ "intellectualised": "intellectualized",
819
+ "intellectualises": "intellectualizes",
820
+ "intellectualising": "intellectualizing",
821
+ "internalisation": "internalization",
822
+ "internalise": "internalize",
823
+ "internalised": "internalized",
824
+ "internalises": "internalizes",
825
+ "internalising": "internalizing",
826
+ "internationalisation": "internationalization",
827
+ "internationalise": "internationalize",
828
+ "internationalised": "internationalized",
829
+ "internationalises": "internationalizes",
830
+ "internationalising": "internationalizing",
831
+ "ionisation": "ionization",
832
+ "ionise": "ionize",
833
+ "ionised": "ionized",
834
+ "ioniser": "ionizer",
835
+ "ionisers": "ionizers",
836
+ "ionises": "ionizes",
837
+ "ionising": "ionizing",
838
+ "italicise": "italicize",
839
+ "italicised": "italicized",
840
+ "italicises": "italicizes",
841
+ "italicising": "italicizing",
842
+ "itemise": "itemize",
843
+ "itemised": "itemized",
844
+ "itemises": "itemizes",
845
+ "itemising": "itemizing",
846
+ "jeopardise": "jeopardize",
847
+ "jeopardised": "jeopardized",
848
+ "jeopardises": "jeopardizes",
849
+ "jeopardising": "jeopardizing",
850
+ "jewelled": "jeweled",
851
+ "jeweller": "jeweler",
852
+ "jewellers": "jewelers",
853
+ "jewellery": "jewelry",
854
+ "judgement": "judgment",
855
+ "kilogramme": "kilogram",
856
+ "kilogrammes": "kilograms",
857
+ "kilometre": "kilometer",
858
+ "kilometres": "kilometers",
859
+ "labelled": "labeled",
860
+ "labelling": "labeling",
861
+ "labour": "labor",
862
+ "laboured": "labored",
863
+ "labourer": "laborer",
864
+ "labourers": "laborers",
865
+ "labouring": "laboring",
866
+ "labours": "labors",
867
+ "lacklustre": "lackluster",
868
+ "legalisation": "legalization",
869
+ "legalise": "legalize",
870
+ "legalised": "legalized",
871
+ "legalises": "legalizes",
872
+ "legalising": "legalizing",
873
+ "legitimise": "legitimize",
874
+ "legitimised": "legitimized",
875
+ "legitimises": "legitimizes",
876
+ "legitimising": "legitimizing",
877
+ "leukaemia": "leukemia",
878
+ "levelled": "leveled",
879
+ "leveller": "leveler",
880
+ "levellers": "levelers",
881
+ "levelling": "leveling",
882
+ "libelled": "libeled",
883
+ "libelling": "libeling",
884
+ "libellous": "libelous",
885
+ "liberalisation": "liberalization",
886
+ "liberalise": "liberalize",
887
+ "liberalised": "liberalized",
888
+ "liberalises": "liberalizes",
889
+ "liberalising": "liberalizing",
890
+ "licence": "license",
891
+ "licenced": "licensed",
892
+ "licences": "licenses",
893
+ "licencing": "licensing",
894
+ "likeable": "likable",
895
+ "lionisation": "lionization",
896
+ "lionise": "lionize",
897
+ "lionised": "lionized",
898
+ "lionises": "lionizes",
899
+ "lionising": "lionizing",
900
+ "liquidise": "liquidize",
901
+ "liquidised": "liquidized",
902
+ "liquidiser": "liquidizer",
903
+ "liquidisers": "liquidizers",
904
+ "liquidises": "liquidizes",
905
+ "liquidising": "liquidizing",
906
+ "litre": "liter",
907
+ "litres": "liters",
908
+ "localise": "localize",
909
+ "localised": "localized",
910
+ "localises": "localizes",
911
+ "localising": "localizing",
912
+ "louvre": "louver",
913
+ "louvred": "louvered",
914
+ "louvres": "louvers",
915
+ "lustre": "luster",
916
+ "magnetise": "magnetize",
917
+ "magnetised": "magnetized",
918
+ "magnetises": "magnetizes",
919
+ "magnetising": "magnetizing",
920
+ "manoeuvrability": "maneuverability",
921
+ "manoeuvrable": "maneuverable",
922
+ "manoeuvre": "maneuver",
923
+ "manoeuvred": "maneuvered",
924
+ "manoeuvres": "maneuvers",
925
+ "manoeuvring": "maneuvering",
926
+ "manoeuvrings": "maneuverings",
927
+ "marginalisation": "marginalization",
928
+ "marginalise": "marginalize",
929
+ "marginalised": "marginalized",
930
+ "marginalises": "marginalizes",
931
+ "marginalising": "marginalizing",
932
+ "marshalled": "marshaled",
933
+ "marshalling": "marshaling",
934
+ "marvelled": "marveled",
935
+ "marvelling": "marveling",
936
+ "marvellous": "marvelous",
937
+ "marvellously": "marvelously",
938
+ "materialisation": "materialization",
939
+ "materialise": "materialize",
940
+ "materialised": "materialized",
941
+ "materialises": "materializes",
942
+ "materialising": "materializing",
943
+ "maximisation": "maximization",
944
+ "maximise": "maximize",
945
+ "maximised": "maximized",
946
+ "maximises": "maximizes",
947
+ "maximising": "maximizing",
948
+ "meagre": "meager",
949
+ "mechanisation": "mechanization",
950
+ "mechanise": "mechanize",
951
+ "mechanised": "mechanized",
952
+ "mechanises": "mechanizes",
953
+ "mechanising": "mechanizing",
954
+ "mediaeval": "medieval",
955
+ "memorialise": "memorialize",
956
+ "memorialised": "memorialized",
957
+ "memorialises": "memorializes",
958
+ "memorialising": "memorializing",
959
+ "memorise": "memorize",
960
+ "memorised": "memorized",
961
+ "memorises": "memorizes",
962
+ "memorising": "memorizing",
963
+ "mesmerise": "mesmerize",
964
+ "mesmerised": "mesmerized",
965
+ "mesmerises": "mesmerizes",
966
+ "mesmerising": "mesmerizing",
967
+ "metabolise": "metabolize",
968
+ "metabolised": "metabolized",
969
+ "metabolises": "metabolizes",
970
+ "metabolising": "metabolizing",
971
+ "metre": "meter",
972
+ "metres": "meters",
973
+ "mhm": "hmm",
974
+ "micrometre": "micrometer",
975
+ "micrometres": "micrometers",
976
+ "militarise": "militarize",
977
+ "militarised": "militarized",
978
+ "militarises": "militarizes",
979
+ "militarising": "militarizing",
980
+ "milligramme": "milligram",
981
+ "milligrammes": "milligrams",
982
+ "millilitre": "milliliter",
983
+ "millilitres": "milliliters",
984
+ "millimetre": "millimeter",
985
+ "millimetres": "millimeters",
986
+ "miniaturisation": "miniaturization",
987
+ "miniaturise": "miniaturize",
988
+ "miniaturised": "miniaturized",
989
+ "miniaturises": "miniaturizes",
990
+ "miniaturising": "miniaturizing",
991
+ "minibusses": "minibuses",
992
+ "minimise": "minimize",
993
+ "minimised": "minimized",
994
+ "minimises": "minimizes",
995
+ "minimising": "minimizing",
996
+ "misbehaviour": "misbehavior",
997
+ "misdemeanour": "misdemeanor",
998
+ "misdemeanours": "misdemeanors",
999
+ "misspelt": "misspelled",
1000
+ "mitre": "miter",
1001
+ "mitres": "miters",
1002
+ "mm": "hmm",
1003
+ "mmm": "hmm",
1004
+ "mobilisation": "mobilization",
1005
+ "mobilise": "mobilize",
1006
+ "mobilised": "mobilized",
1007
+ "mobilises": "mobilizes",
1008
+ "mobilising": "mobilizing",
1009
+ "modelled": "modeled",
1010
+ "modeller": "modeler",
1011
+ "modellers": "modelers",
1012
+ "modelling": "modeling",
1013
+ "modernise": "modernize",
1014
+ "modernised": "modernized",
1015
+ "modernises": "modernizes",
1016
+ "modernising": "modernizing",
1017
+ "moisturise": "moisturize",
1018
+ "moisturised": "moisturized",
1019
+ "moisturiser": "moisturizer",
1020
+ "moisturisers": "moisturizers",
1021
+ "moisturises": "moisturizes",
1022
+ "moisturising": "moisturizing",
1023
+ "monologue": "monolog",
1024
+ "monologues": "monologs",
1025
+ "monopolisation": "monopolization",
1026
+ "monopolise": "monopolize",
1027
+ "monopolised": "monopolized",
1028
+ "monopolises": "monopolizes",
1029
+ "monopolising": "monopolizing",
1030
+ "moralise": "moralize",
1031
+ "moralised": "moralized",
1032
+ "moralises": "moralizes",
1033
+ "moralising": "moralizing",
1034
+ "motorised": "motorized",
1035
+ "mould": "mold",
1036
+ "moulded": "molded",
1037
+ "moulder": "molder",
1038
+ "mouldered": "moldered",
1039
+ "mouldering": "moldering",
1040
+ "moulders": "molders",
1041
+ "mouldier": "moldier",
1042
+ "mouldiest": "moldiest",
1043
+ "moulding": "molding",
1044
+ "mouldings": "moldings",
1045
+ "moulds": "molds",
1046
+ "mouldy": "moldy",
1047
+ "moult": "molt",
1048
+ "moulted": "molted",
1049
+ "moulting": "molting",
1050
+ "moults": "molts",
1051
+ "moustache": "mustache",
1052
+ "moustached": "mustached",
1053
+ "moustaches": "mustaches",
1054
+ "moustachioed": "mustachioed",
1055
+ "multicoloured": "multicolored",
1056
+ "nationalisation": "nationalization",
1057
+ "nationalisations": "nationalizations",
1058
+ "nationalise": "nationalize",
1059
+ "nationalised": "nationalized",
1060
+ "nationalises": "nationalizes",
1061
+ "nationalising": "nationalizing",
1062
+ "naturalisation": "naturalization",
1063
+ "naturalise": "naturalize",
1064
+ "naturalised": "naturalized",
1065
+ "naturalises": "naturalizes",
1066
+ "naturalising": "naturalizing",
1067
+ "neighbour": "neighbor",
1068
+ "neighbourhood": "neighborhood",
1069
+ "neighbourhoods": "neighborhoods",
1070
+ "neighbouring": "neighboring",
1071
+ "neighbourliness": "neighborliness",
1072
+ "neighbourly": "neighborly",
1073
+ "neighbours": "neighbors",
1074
+ "neutralisation": "neutralization",
1075
+ "neutralise": "neutralize",
1076
+ "neutralised": "neutralized",
1077
+ "neutralises": "neutralizes",
1078
+ "neutralising": "neutralizing",
1079
+ "normalisation": "normalization",
1080
+ "normalise": "normalize",
1081
+ "normalised": "normalized",
1082
+ "normalises": "normalizes",
1083
+ "normalising": "normalizing",
1084
+ "odour": "odor",
1085
+ "odourless": "odorless",
1086
+ "odours": "odors",
1087
+ "oesophagus": "esophagus",
1088
+ "oesophaguses": "esophaguses",
1089
+ "oestrogen": "estrogen",
1090
+ "offence": "offense",
1091
+ "offences": "offenses",
1092
+ "omelette": "omelet",
1093
+ "omelettes": "omelets",
1094
+ "optimise": "optimize",
1095
+ "optimised": "optimized",
1096
+ "optimises": "optimizes",
1097
+ "optimising": "optimizing",
1098
+ "organisation": "organization",
1099
+ "organisational": "organizational",
1100
+ "organisations": "organizations",
1101
+ "organise": "organize",
1102
+ "organised": "organized",
1103
+ "organiser": "organizer",
1104
+ "organisers": "organizers",
1105
+ "organises": "organizes",
1106
+ "organising": "organizing",
1107
+ "orthopaedic": "orthopedic",
1108
+ "orthopaedics": "orthopedics",
1109
+ "ostracise": "ostracize",
1110
+ "ostracised": "ostracized",
1111
+ "ostracises": "ostracizes",
1112
+ "ostracising": "ostracizing",
1113
+ "outmanoeuvre": "outmaneuver",
1114
+ "outmanoeuvred": "outmaneuvered",
1115
+ "outmanoeuvres": "outmaneuvers",
1116
+ "outmanoeuvring": "outmaneuvering",
1117
+ "overemphasise": "overemphasize",
1118
+ "overemphasised": "overemphasized",
1119
+ "overemphasises": "overemphasizes",
1120
+ "overemphasising": "overemphasizing",
1121
+ "oxidisation": "oxidization",
1122
+ "oxidise": "oxidize",
1123
+ "oxidised": "oxidized",
1124
+ "oxidises": "oxidizes",
1125
+ "oxidising": "oxidizing",
1126
+ "paederast": "pederast",
1127
+ "paederasts": "pederasts",
1128
+ "paediatric": "pediatric",
1129
+ "paediatrician": "pediatrician",
1130
+ "paediatricians": "pediatricians",
1131
+ "paediatrics": "pediatrics",
1132
+ "paedophile": "pedophile",
1133
+ "paedophiles": "pedophiles",
1134
+ "paedophilia": "pedophilia",
1135
+ "palaeolithic": "paleolithic",
1136
+ "palaeontologist": "paleontologist",
1137
+ "palaeontologists": "paleontologists",
1138
+ "palaeontology": "paleontology",
1139
+ "panelled": "paneled",
1140
+ "panelling": "paneling",
1141
+ "panellist": "panelist",
1142
+ "panellists": "panelists",
1143
+ "paralyse": "paralyze",
1144
+ "paralysed": "paralyzed",
1145
+ "paralyses": "paralyzes",
1146
+ "paralysing": "paralyzing",
1147
+ "parcelled": "parceled",
1148
+ "parcelling": "parceling",
1149
+ "parlour": "parlor",
1150
+ "parlours": "parlors",
1151
+ "particularise": "particularize",
1152
+ "particularised": "particularized",
1153
+ "particularises": "particularizes",
1154
+ "particularising": "particularizing",
1155
+ "passivisation": "passivization",
1156
+ "passivise": "passivize",
1157
+ "passivised": "passivized",
1158
+ "passivises": "passivizes",
1159
+ "passivising": "passivizing",
1160
+ "pasteurisation": "pasteurization",
1161
+ "pasteurise": "pasteurize",
1162
+ "pasteurised": "pasteurized",
1163
+ "pasteurises": "pasteurizes",
1164
+ "pasteurising": "pasteurizing",
1165
+ "patronise": "patronize",
1166
+ "patronised": "patronized",
1167
+ "patronises": "patronizes",
1168
+ "patronising": "patronizing",
1169
+ "patronisingly": "patronizingly",
1170
+ "pedalled": "pedaled",
1171
+ "pedalling": "pedaling",
1172
+ "pedestrianisation": "pedestrianization",
1173
+ "pedestrianise": "pedestrianize",
1174
+ "pedestrianised": "pedestrianized",
1175
+ "pedestrianises": "pedestrianizes",
1176
+ "pedestrianising": "pedestrianizing",
1177
+ "penalise": "penalize",
1178
+ "penalised": "penalized",
1179
+ "penalises": "penalizes",
1180
+ "penalising": "penalizing",
1181
+ "pencilled": "penciled",
1182
+ "pencilling": "penciling",
1183
+ "personalise": "personalize",
1184
+ "personalised": "personalized",
1185
+ "personalises": "personalizes",
1186
+ "personalising": "personalizing",
1187
+ "pharmacopoeia": "pharmacopeia",
1188
+ "pharmacopoeias": "pharmacopeias",
1189
+ "philosophise": "philosophize",
1190
+ "philosophised": "philosophized",
1191
+ "philosophises": "philosophizes",
1192
+ "philosophising": "philosophizing",
1193
+ "philtre": "filter",
1194
+ "philtres": "filters",
1195
+ "phoney": "phony",
1196
+ "plagiarise": "plagiarize",
1197
+ "plagiarised": "plagiarized",
1198
+ "plagiarises": "plagiarizes",
1199
+ "plagiarising": "plagiarizing",
1200
+ "plough": "plow",
1201
+ "ploughed": "plowed",
1202
+ "ploughing": "plowing",
1203
+ "ploughman": "plowman",
1204
+ "ploughmen": "plowmen",
1205
+ "ploughs": "plows",
1206
+ "ploughshare": "plowshare",
1207
+ "ploughshares": "plowshares",
1208
+ "polarisation": "polarization",
1209
+ "polarise": "polarize",
1210
+ "polarised": "polarized",
1211
+ "polarises": "polarizes",
1212
+ "polarising": "polarizing",
1213
+ "politicisation": "politicization",
1214
+ "politicise": "politicize",
1215
+ "politicised": "politicized",
1216
+ "politicises": "politicizes",
1217
+ "politicising": "politicizing",
1218
+ "popularisation": "popularization",
1219
+ "popularise": "popularize",
1220
+ "popularised": "popularized",
1221
+ "popularises": "popularizes",
1222
+ "popularising": "popularizing",
1223
+ "pouffe": "pouf",
1224
+ "pouffes": "poufs",
1225
+ "practise": "practice",
1226
+ "practised": "practiced",
1227
+ "practises": "practices",
1228
+ "practising": "practicing",
1229
+ "praesidium": "presidium",
1230
+ "praesidiums": "presidiums",
1231
+ "pressurisation": "pressurization",
1232
+ "pressurise": "pressurize",
1233
+ "pressurised": "pressurized",
1234
+ "pressurises": "pressurizes",
1235
+ "pressurising": "pressurizing",
1236
+ "pretence": "pretense",
1237
+ "pretences": "pretenses",
1238
+ "primaeval": "primeval",
1239
+ "prioritisation": "prioritization",
1240
+ "prioritise": "prioritize",
1241
+ "prioritised": "prioritized",
1242
+ "prioritises": "prioritizes",
1243
+ "prioritising": "prioritizing",
1244
+ "privatisation": "privatization",
1245
+ "privatisations": "privatizations",
1246
+ "privatise": "privatize",
1247
+ "privatised": "privatized",
1248
+ "privatises": "privatizes",
1249
+ "privatising": "privatizing",
1250
+ "professionalisation": "professionalization",
1251
+ "professionalise": "professionalize",
1252
+ "professionalised": "professionalized",
1253
+ "professionalises": "professionalizes",
1254
+ "professionalising": "professionalizing",
1255
+ "programme": "program",
1256
+ "programmes": "programs",
1257
+ "prologue": "prolog",
1258
+ "prologues": "prologs",
1259
+ "propagandise": "propagandize",
1260
+ "propagandised": "propagandized",
1261
+ "propagandises": "propagandizes",
1262
+ "propagandising": "propagandizing",
1263
+ "proselytise": "proselytize",
1264
+ "proselytised": "proselytized",
1265
+ "proselytiser": "proselytizer",
1266
+ "proselytisers": "proselytizers",
1267
+ "proselytises": "proselytizes",
1268
+ "proselytising": "proselytizing",
1269
+ "psychoanalyse": "psychoanalyze",
1270
+ "psychoanalysed": "psychoanalyzed",
1271
+ "psychoanalyses": "psychoanalyzes",
1272
+ "psychoanalysing": "psychoanalyzing",
1273
+ "publicise": "publicize",
1274
+ "publicised": "publicized",
1275
+ "publicises": "publicizes",
1276
+ "publicising": "publicizing",
1277
+ "pulverisation": "pulverization",
1278
+ "pulverise": "pulverize",
1279
+ "pulverised": "pulverized",
1280
+ "pulverises": "pulverizes",
1281
+ "pulverising": "pulverizing",
1282
+ "pummelled": "pummel",
1283
+ "pummelling": "pummeled",
1284
+ "pyjama": "pajama",
1285
+ "pyjamas": "pajamas",
1286
+ "pzazz": "pizzazz",
1287
+ "quarrelled": "quarreled",
1288
+ "quarrelling": "quarreling",
1289
+ "radicalise": "radicalize",
1290
+ "radicalised": "radicalized",
1291
+ "radicalises": "radicalizes",
1292
+ "radicalising": "radicalizing",
1293
+ "rancour": "rancor",
1294
+ "randomise": "randomize",
1295
+ "randomised": "randomized",
1296
+ "randomises": "randomizes",
1297
+ "randomising": "randomizing",
1298
+ "rationalisation": "rationalization",
1299
+ "rationalisations": "rationalizations",
1300
+ "rationalise": "rationalize",
1301
+ "rationalised": "rationalized",
1302
+ "rationalises": "rationalizes",
1303
+ "rationalising": "rationalizing",
1304
+ "ravelled": "raveled",
1305
+ "ravelling": "raveling",
1306
+ "realisable": "realizable",
1307
+ "realisation": "realization",
1308
+ "realisations": "realizations",
1309
+ "realise": "realize",
1310
+ "realised": "realized",
1311
+ "realises": "realizes",
1312
+ "realising": "realizing",
1313
+ "recognisable": "recognizable",
1314
+ "recognisably": "recognizably",
1315
+ "recognisance": "recognizance",
1316
+ "recognise": "recognize",
1317
+ "recognised": "recognized",
1318
+ "recognises": "recognizes",
1319
+ "recognising": "recognizing",
1320
+ "reconnoitre": "reconnoiter",
1321
+ "reconnoitred": "reconnoitered",
1322
+ "reconnoitres": "reconnoiters",
1323
+ "reconnoitring": "reconnoitering",
1324
+ "refuelled": "refueled",
1325
+ "refuelling": "refueling",
1326
+ "regularisation": "regularization",
1327
+ "regularise": "regularize",
1328
+ "regularised": "regularized",
1329
+ "regularises": "regularizes",
1330
+ "regularising": "regularizing",
1331
+ "remodelled": "remodeled",
1332
+ "remodelling": "remodeling",
1333
+ "remould": "remold",
1334
+ "remoulded": "remolded",
1335
+ "remoulding": "remolding",
1336
+ "remoulds": "remolds",
1337
+ "reorganisation": "reorganization",
1338
+ "reorganisations": "reorganizations",
1339
+ "reorganise": "reorganize",
1340
+ "reorganised": "reorganized",
1341
+ "reorganises": "reorganizes",
1342
+ "reorganising": "reorganizing",
1343
+ "revelled": "reveled",
1344
+ "reveller": "reveler",
1345
+ "revellers": "revelers",
1346
+ "revelling": "reveling",
1347
+ "revitalise": "revitalize",
1348
+ "revitalised": "revitalized",
1349
+ "revitalises": "revitalizes",
1350
+ "revitalising": "revitalizing",
1351
+ "revolutionise": "revolutionize",
1352
+ "revolutionised": "revolutionized",
1353
+ "revolutionises": "revolutionizes",
1354
+ "revolutionising": "revolutionizing",
1355
+ "rhapsodise": "rhapsodize",
1356
+ "rhapsodised": "rhapsodized",
1357
+ "rhapsodises": "rhapsodizes",
1358
+ "rhapsodising": "rhapsodizing",
1359
+ "rigour": "rigor",
1360
+ "rigours": "rigors",
1361
+ "ritualised": "ritualized",
1362
+ "rivalled": "rivaled",
1363
+ "rivalling": "rivaling",
1364
+ "romanticise": "romanticize",
1365
+ "romanticised": "romanticized",
1366
+ "romanticises": "romanticizes",
1367
+ "romanticising": "romanticizing",
1368
+ "rumour": "rumor",
1369
+ "rumoured": "rumored",
1370
+ "rumours": "rumors",
1371
+ "sabre": "saber",
1372
+ "sabres": "sabers",
1373
+ "saltpetre": "saltpeter",
1374
+ "sanitise": "sanitize",
1375
+ "sanitised": "sanitized",
1376
+ "sanitises": "sanitizes",
1377
+ "sanitising": "sanitizing",
1378
+ "satirise": "satirize",
1379
+ "satirised": "satirized",
1380
+ "satirises": "satirizes",
1381
+ "satirising": "satirizing",
1382
+ "saviour": "savior",
1383
+ "saviours": "saviors",
1384
+ "savour": "savor",
1385
+ "savoured": "savored",
1386
+ "savouries": "savories",
1387
+ "savouring": "savoring",
1388
+ "savours": "savors",
1389
+ "savoury": "savory",
1390
+ "scandalise": "scandalize",
1391
+ "scandalised": "scandalized",
1392
+ "scandalises": "scandalizes",
1393
+ "scandalising": "scandalizing",
1394
+ "sceptic": "skeptic",
1395
+ "sceptical": "skeptical",
1396
+ "sceptically": "skeptically",
1397
+ "scepticism": "skepticism",
1398
+ "sceptics": "skeptics",
1399
+ "sceptre": "scepter",
1400
+ "sceptres": "scepters",
1401
+ "scrutinise": "scrutinize",
1402
+ "scrutinised": "scrutinized",
1403
+ "scrutinises": "scrutinizes",
1404
+ "scrutinising": "scrutinizing",
1405
+ "secularisation": "secularization",
1406
+ "secularise": "secularize",
1407
+ "secularised": "secularized",
1408
+ "secularises": "secularizes",
1409
+ "secularising": "secularizing",
1410
+ "sensationalise": "sensationalize",
1411
+ "sensationalised": "sensationalized",
1412
+ "sensationalises": "sensationalizes",
1413
+ "sensationalising": "sensationalizing",
1414
+ "sensitise": "sensitize",
1415
+ "sensitised": "sensitized",
1416
+ "sensitises": "sensitizes",
1417
+ "sensitising": "sensitizing",
1418
+ "sentimentalise": "sentimentalize",
1419
+ "sentimentalised": "sentimentalized",
1420
+ "sentimentalises": "sentimentalizes",
1421
+ "sentimentalising": "sentimentalizing",
1422
+ "sepulchre": "sepulcher",
1423
+ "sepulchres": "sepulchers",
1424
+ "serialisation": "serialization",
1425
+ "serialisations": "serializations",
1426
+ "serialise": "serialize",
1427
+ "serialised": "serialized",
1428
+ "serialises": "serializes",
1429
+ "serialising": "serializing",
1430
+ "sermonise": "sermonize",
1431
+ "sermonised": "sermonized",
1432
+ "sermonises": "sermonizes",
1433
+ "sermonising": "sermonizing",
1434
+ "sheikh": "sheik",
1435
+ "shovelled": "shoveled",
1436
+ "shovelling": "shoveling",
1437
+ "shrivelled": "shriveled",
1438
+ "shrivelling": "shriveling",
1439
+ "signalise": "signalize",
1440
+ "signalised": "signalized",
1441
+ "signalises": "signalizes",
1442
+ "signalising": "signalizing",
1443
+ "signalled": "signaled",
1444
+ "signalling": "signaling",
1445
+ "smoulder": "smolder",
1446
+ "smouldered": "smoldered",
1447
+ "smouldering": "smoldering",
1448
+ "smoulders": "smolders",
1449
+ "snivelled": "sniveled",
1450
+ "snivelling": "sniveling",
1451
+ "snorkelled": "snorkeled",
1452
+ "snorkelling": "snorkeling",
1453
+ "snowplough": "snowplow",
1454
+ "snowploughs": "snowplow",
1455
+ "socialisation": "socialization",
1456
+ "socialise": "socialize",
1457
+ "socialised": "socialized",
1458
+ "socialises": "socializes",
1459
+ "socialising": "socializing",
1460
+ "sodomise": "sodomize",
1461
+ "sodomised": "sodomized",
1462
+ "sodomises": "sodomizes",
1463
+ "sodomising": "sodomizing",
1464
+ "solemnise": "solemnize",
1465
+ "solemnised": "solemnized",
1466
+ "solemnises": "solemnizes",
1467
+ "solemnising": "solemnizing",
1468
+ "sombre": "somber",
1469
+ "specialisation": "specialization",
1470
+ "specialisations": "specializations",
1471
+ "specialise": "specialize",
1472
+ "specialised": "specialized",
1473
+ "specialises": "specializes",
1474
+ "specialising": "specializing",
1475
+ "spectre": "specter",
1476
+ "spectres": "specters",
1477
+ "spiralled": "spiraled",
1478
+ "spiralling": "spiraling",
1479
+ "splendour": "splendor",
1480
+ "splendours": "splendors",
1481
+ "squirrelled": "squirreled",
1482
+ "squirrelling": "squirreling",
1483
+ "stabilisation": "stabilization",
1484
+ "stabilise": "stabilize",
1485
+ "stabilised": "stabilized",
1486
+ "stabiliser": "stabilizer",
1487
+ "stabilisers": "stabilizers",
1488
+ "stabilises": "stabilizes",
1489
+ "stabilising": "stabilizing",
1490
+ "standardisation": "standardization",
1491
+ "standardise": "standardize",
1492
+ "standardised": "standardized",
1493
+ "standardises": "standardizes",
1494
+ "standardising": "standardizing",
1495
+ "stencilled": "stenciled",
1496
+ "stencilling": "stenciling",
1497
+ "sterilisation": "sterilization",
1498
+ "sterilisations": "sterilizations",
1499
+ "sterilise": "sterilize",
1500
+ "sterilised": "sterilized",
1501
+ "steriliser": "sterilizer",
1502
+ "sterilisers": "sterilizers",
1503
+ "sterilises": "sterilizes",
1504
+ "sterilising": "sterilizing",
1505
+ "stigmatisation": "stigmatization",
1506
+ "stigmatise": "stigmatize",
1507
+ "stigmatised": "stigmatized",
1508
+ "stigmatises": "stigmatizes",
1509
+ "stigmatising": "stigmatizing",
1510
+ "storey": "story",
1511
+ "storeys": "stories",
1512
+ "subsidisation": "subsidization",
1513
+ "subsidise": "subsidize",
1514
+ "subsidised": "subsidized",
1515
+ "subsidiser": "subsidizer",
1516
+ "subsidisers": "subsidizers",
1517
+ "subsidises": "subsidizes",
1518
+ "subsidising": "subsidizing",
1519
+ "succour": "succor",
1520
+ "succoured": "succored",
1521
+ "succouring": "succoring",
1522
+ "succours": "succors",
1523
+ "sulphate": "sulfate",
1524
+ "sulphates": "sulfates",
1525
+ "sulphide": "sulfide",
1526
+ "sulphides": "sulfides",
1527
+ "sulphur": "sulfur",
1528
+ "sulphurous": "sulfurous",
1529
+ "summarise": "summarize",
1530
+ "summarised": "summarized",
1531
+ "summarises": "summarizes",
1532
+ "summarising": "summarizing",
1533
+ "swivelled": "swiveled",
1534
+ "swivelling": "swiveling",
1535
+ "symbolise": "symbolize",
1536
+ "symbolised": "symbolized",
1537
+ "symbolises": "symbolizes",
1538
+ "symbolising": "symbolizing",
1539
+ "sympathise": "sympathize",
1540
+ "sympathised": "sympathized",
1541
+ "sympathiser": "sympathizer",
1542
+ "sympathisers": "sympathizers",
1543
+ "sympathises": "sympathizes",
1544
+ "sympathising": "sympathizing",
1545
+ "synchronisation": "synchronization",
1546
+ "synchronise": "synchronize",
1547
+ "synchronised": "synchronized",
1548
+ "synchronises": "synchronizes",
1549
+ "synchronising": "synchronizing",
1550
+ "synthesise": "synthesize",
1551
+ "synthesised": "synthesized",
1552
+ "synthesiser": "synthesizer",
1553
+ "synthesisers": "synthesizers",
1554
+ "synthesises": "synthesizes",
1555
+ "synthesising": "synthesizing",
1556
+ "syphon": "siphon",
1557
+ "syphoned": "siphoned",
1558
+ "syphoning": "siphoning",
1559
+ "syphons": "siphons",
1560
+ "systematisation": "systematization",
1561
+ "systematise": "systematize",
1562
+ "systematised": "systematized",
1563
+ "systematises": "systematizes",
1564
+ "systematising": "systematizing",
1565
+ "tantalise": "tantalize",
1566
+ "tantalised": "tantalized",
1567
+ "tantalises": "tantalizes",
1568
+ "tantalising": "tantalizing",
1569
+ "tantalisingly": "tantalizingly",
1570
+ "tasselled": "tasseled",
1571
+ "technicolour": "technicolor",
1572
+ "temporise": "temporize",
1573
+ "temporised": "temporized",
1574
+ "temporises": "temporizes",
1575
+ "temporising": "temporizing",
1576
+ "tenderise": "tenderize",
1577
+ "tenderised": "tenderized",
1578
+ "tenderises": "tenderizes",
1579
+ "tenderising": "tenderizing",
1580
+ "terrorise": "terrorize",
1581
+ "terrorised": "terrorized",
1582
+ "terrorises": "terrorizes",
1583
+ "terrorising": "terrorizing",
1584
+ "theatre": "theater",
1585
+ "theatregoer": "theatergoer",
1586
+ "theatregoers": "theatergoers",
1587
+ "theatres": "theaters",
1588
+ "theorise": "theorize",
1589
+ "theorised": "theorized",
1590
+ "theorises": "theorizes",
1591
+ "theorising": "theorizing",
1592
+ "tonne": "ton",
1593
+ "tonnes": "tons",
1594
+ "towelled": "toweled",
1595
+ "towelling": "toweling",
1596
+ "toxaemia": "toxemia",
1597
+ "tranquillise": "tranquilize",
1598
+ "tranquillised": "tranquilized",
1599
+ "tranquilliser": "tranquilizer",
1600
+ "tranquillisers": "tranquilizers",
1601
+ "tranquillises": "tranquilizes",
1602
+ "tranquillising": "tranquilizing",
1603
+ "tranquillity": "tranquility",
1604
+ "tranquillize": "tranquilize",
1605
+ "tranquillized": "tranquilized",
1606
+ "tranquillizer": "tranquilizer",
1607
+ "tranquillizers": "tranquilizers",
1608
+ "tranquillizes": "tranquilizes",
1609
+ "tranquillizing": "tranquilizing",
1610
+ "tranquilly": "tranquility",
1611
+ "transistorised": "transistorized",
1612
+ "traumatise": "traumatize",
1613
+ "traumatised": "traumatized",
1614
+ "traumatises": "traumatizes",
1615
+ "traumatising": "traumatizing",
1616
+ "travelled": "traveled",
1617
+ "traveller": "traveler",
1618
+ "travellers": "travelers",
1619
+ "travelling": "traveling",
1620
+ "travelog": "travelogue",
1621
+ "travelogs": "travelogues",
1622
+ "trialled": "trialed",
1623
+ "trialling": "trialing",
1624
+ "tricolour": "tricolor",
1625
+ "tricolours": "tricolors",
1626
+ "trivialise": "trivialize",
1627
+ "trivialised": "trivialized",
1628
+ "trivialises": "trivializes",
1629
+ "trivialising": "trivializing",
1630
+ "tumour": "tumor",
1631
+ "tumours": "tumors",
1632
+ "tunnelled": "tunneled",
1633
+ "tunnelling": "tunneling",
1634
+ "tyrannise": "tyrannize",
1635
+ "tyrannised": "tyrannized",
1636
+ "tyrannises": "tyrannizes",
1637
+ "tyrannising": "tyrannizing",
1638
+ "tyre": "tire",
1639
+ "tyres": "tires",
1640
+ "unauthorised": "unauthorized",
1641
+ "uncivilised": "uncivilized",
1642
+ "underutilised": "underutilized",
1643
+ "unequalled": "unequaled",
1644
+ "unfavourable": "unfavorable",
1645
+ "unfavourably": "unfavorably",
1646
+ "unionisation": "unionization",
1647
+ "unionise": "unionize",
1648
+ "unionised": "unionized",
1649
+ "unionises": "unionizes",
1650
+ "unionising": "unionizing",
1651
+ "unorganised": "unorganized",
1652
+ "unravelled": "unraveled",
1653
+ "unravelling": "unraveling",
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1656
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