Sherpa-CTC (en)

.gitattributes

@@ -37,3 +37,5 @@ en/sherpa-onnx-nemo-ctc-en-citrinet-512/test_wavs/0.wav filter=lfs diff=lfs merg
 en/sherpa-onnx-nemo-ctc-en-citrinet-512/test_wavs/1.wav filter=lfs diff=lfs merge=lfs -text
 en/sherpa-onnx-nemo-ctc-en-conformer-small/test_wavs/0.wav filter=lfs diff=lfs merge=lfs -text
 en/sherpa-onnx-nemo-ctc-en-conformer-small/test_wavs/1.wav filter=lfs diff=lfs merge=lfs -text
+en/sherpa-onnx-nemo-ctc-en-conformer-medium/test_wavs/0.wav filter=lfs diff=lfs merge=lfs -text
+en/sherpa-onnx-nemo-ctc-en-conformer-medium/test_wavs/1.wav filter=lfs diff=lfs merge=lfs -text

en/sherpa-onnx-nemo-ctc-en-conformer-medium/.gitattributes

@@ -0,0 +1,34 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

en/sherpa-onnx-nemo-ctc-en-conformer-medium/README.md

@@ -0,0 +1,21 @@
+---
+license: apache-2.0
+---
+
+# Introduction
+
+This repo contains torchscript model of `stt_en_conformer_ctc_medium` from NeMo.
+
+See https://registry.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_ctc_medium
+
+The following code is used to obtain `model.onnx` and `tokens.txt`:
+
+```python3
+m = nemo_asr.models.EncDecCTCModelBPE.from_pretrained('stt_en_conformer_ctc_medium')
+m.export('model.onnx')
+
+with open('tokens.txt', 'w') as f:
+  for i, s in enumerate(m.decoder.vocabulary):
+    f.write(f"{s} {i}\n")
+  f.write(f"<blk> {i+1}\n")
+```

en/sherpa-onnx-nemo-ctc-en-conformer-medium/add-model-metadata.py

@@ -0,0 +1,55 @@
+#!/usr/bin/env python3
+
+# Copyright (c)  2023  Xiaomi Corporation
+# Author: Fangjun Kuang
+
+from typing import Dict
+
+import numpy as np
+import onnx
+
+
+def get_vocab_size():
+    with open("tokens.txt") as f:
+        return len(f.readlines())
+
+
+def add_meta_data(filename: str, meta_data: Dict[str, str]):
+    """Add meta data to an ONNX model. It is changed in-place.
+
+    Args:
+      filename:
+        Filename of the ONNX model to be changed.
+      meta_data:
+        Key-value pairs.
+    """
+    model = onnx.load(filename)
+    for key, value in meta_data.items():
+        meta = model.metadata_props.add()
+        meta.key = key
+        meta.value = value
+
+    onnx.save(model, filename)
+    print(f"Updated {filename}")
+
+
+def main():
+    vocab_size = get_vocab_size()
+    # 8 for citrinet
+    # 4 for conformer ctc
+    subsampling_factor = 4
+
+    meta_data = {
+        "vocab_size": str(vocab_size),
+        "normalize_type": "per_feature",
+        "subsampling_factor": str(subsampling_factor),
+        "model_type": "EncDecCTCModelBPE",
+        "version": "1",
+        "model_author": "nemo",
+        "comment": "https://registry.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_ctc_small",
+    }
+    add_meta_data("model.onnx", meta_data)
+
+
+if __name__ == "__main__":
+    main()

en/sherpa-onnx-nemo-ctc-en-conformer-medium/model.int8.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c6a9ebcdd25bd7a4609ab64e694ce91b7e2503f4a8c77df6daca405b2734df6a
+size 67632742

en/sherpa-onnx-nemo-ctc-en-conformer-medium/model.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0b3f57c704dfb3e94716898f17649aef29939625494f35c59b86c7278db512ff
+size 159082187

en/sherpa-onnx-nemo-ctc-en-conformer-medium/quantize-model.py

@@ -0,0 +1,18 @@
+#!/usr/bin/env python3
+
+import onnx
+from onnxruntime.quantization import QuantType, quantize_dynamic
+
+
+def main():
+    onnx_model = onnx.load("model.onnx")
+    quantize_dynamic(
+        model_input="model.onnx",
+        model_output="model.int8.onnx",
+        per_channel=True,
+        weight_type=QuantType.QUInt8,
+    )
+
+
+if __name__ == "__main__":
+    main()

en/sherpa-onnx-nemo-ctc-en-conformer-medium/source.txt

@@ -0,0 +1 @@
+https://huggingface.co/csukuangfj/sherpa-onnx-nemo-ctc-en-conformer-medium

en/sherpa-onnx-nemo-ctc-en-conformer-medium/test.py

@@ -0,0 +1,93 @@
+#!/usr/bin/env python3
+
+# Copyright (c)  2023  Xiaomi Corporation
+# Author: Fangjun Kuang
+
+import kaldi_native_fbank as knf
+import itertools
+import librosa
+import numpy as np
+import onnxruntime as ort
+
+
+def compute_feat(filename):
+    sample_rate = 16000
+    samples, _ = librosa.load(filename, sr=sample_rate)
+    opts = knf.FbankOptions()
+    opts.frame_opts.dither = 0
+    opts.frame_opts.snip_edges = False
+    opts.frame_opts.samp_freq = sample_rate
+    opts.mel_opts.num_bins = 80
+
+    online_fbank = knf.OnlineFbank(opts)
+    online_fbank.accept_waveform(sample_rate, (samples * 32768).tolist())
+    online_fbank.input_finished()
+
+    features = np.stack(
+        [online_fbank.get_frame(i) for i in range(online_fbank.num_frames_ready)]
+    )
+    assert features.data.contiguous is True
+    assert features.dtype == np.float32, features.dtype
+    mean = features.mean(axis=0, keepdims=True)
+    stddev = features.std(axis=0, keepdims=True)
+    features = (features - mean) / (stddev + 1e-5)
+    return features
+
+
+def load_tokens():
+    ans = dict()
+    with open("tokens.txt", encoding="utf-8") as f:
+        for line in f:
+            sym, idx = line.strip().split()
+            ans[int(idx)] = sym
+    return ans
+
+
+def main():
+    filename = "./test_wavs/0.wav"
+    features = compute_feat(filename)  # (T, C)
+    features = np.expand_dims(features, axis=0)  # (N, T, C)
+    features = features.transpose(0, 2, 1)  # (N, C, T)
+    print(features.shape)  # (N, C, T), (1, 80, 663)
+    features_length = np.array([features.shape[2]], dtype=np.int64)
+    print(features_length)
+
+    sess = ort.InferenceSession("model.onnx")
+
+    for n in sess.get_inputs():
+        print(n.name, n.type, n.shape)
+
+    for n in sess.get_outputs():
+        print(n.name, n.type, n.shape)
+
+    inputs = {
+        sess.get_inputs()[0].name: features,
+        sess.get_inputs()[1].name: features_length,
+    }
+
+    outputs = sess.run([sess.get_outputs()[0].name], input_feed=inputs)
+    # outputs[0] contains log_probs
+
+    print(outputs[0].shape)  # (N, T, C), (1, 166, 1025)
+    print(outputs[0].dtype)  # float32
+    print(np.exp(outputs[0]).sum(axis=-1).reshape(-1)[:10])  # validate it is log_probs
+    indexes = outputs[0].argmax(axis=-1)
+    print(indexes.shape)
+    indexes = indexes.squeeze().tolist()
+    unique_indexes = [k for k, _ in itertools.groupby(indexes)]
+    print(indexes)
+    print(unique_indexes)
+
+    tokens = load_tokens()
+    text = "".join([tokens[i] for i in unique_indexes if i != len(tokens) - 1])
+    print(text)
+
+    """
+audio_signal tensor(float) ['audio_signal_dynamic_axes_1', 80, 'audio_signal_dynamic_axes_2']
+length tensor(int64) ['length_dynamic_axes_1']
+logprobs tensor(float) ['logprobs_dynamic_axes_1', 'logprobs_dynamic_axes_2', 1025]
+    """
+
+
+if __name__ == "__main__":
+    main()

en/sherpa-onnx-nemo-ctc-en-conformer-medium/test_wavs/0.wav

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6bc58a4efdf20daac252b6b1502632601a71efe0308f6757dc1eda34891a7e4f
+size 212044

en/sherpa-onnx-nemo-ctc-en-conformer-medium/test_wavs/1.wav

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5143a6ba93c4b274e2c4ac22deb75c2c48936c853f0519add1de828b6c79cc5a
+size 534924

en/sherpa-onnx-nemo-ctc-en-conformer-medium/test_wavs/trans.txt

@@ -0,0 +1,3 @@
+0.wav AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS
+1.wav GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONOURED BOSOM TO CONNECT HER PARENT FOR EVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN
+8k.wav YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION

en/sherpa-onnx-nemo-ctc-en-conformer-medium/tokens.txt

@@ -0,0 +1,1025 @@
+<unk> 0
+s 1
+▁the 2
+▁a 3
+t 4
+▁to 5
+▁and 6
+▁i 7
+▁of 8
+' 9
+ed 10
+▁in 11
+d 12
+ing 13
+n 14
+e 15
+▁it 16
+▁that 17
+▁you 18
+y 19
+er 20
+r 21
+▁for 22
+m 23
+▁is 24
+▁he 25
+re 26
+▁was 27
+▁be 28
+p 29
+ly 30
+▁so 31
+▁we 32
+a 33
+g 34
+o 35
+▁c 36
+b 37
+u 38
+▁on 39
+▁have 40
+▁but 41
+ll 42
+▁with 43
+▁re 44
+or 45
+▁s 46
+al 47
+▁do 48
+▁know 49
+ar 50
+▁they 51
+▁not 52
+▁as 53
+▁this 54
+in 55
+le 56
+▁e 57
+▁are 58
+▁like 59
+c 60
+▁uh 61
+ri 62
+▁me 63
+▁his 64
+▁at 65
+l 66
+es 67
+▁de 68
+▁yeah 69
+▁can 70
+k 71
+▁or 72
+▁my 73
+▁all 74
+▁had 75
+▁there 76
+▁will 77
+▁one 78
+il 79
+▁no 80
+▁what 81
+en 82
+ck 83
+▁b 84
+▁f 85
+ce 86
+ch 87
+i 88
+▁by 89
+▁she 90
+▁from 91
+▁an 92
+ic 93
+ur 94
+ve 95
+w 96
+ter 97
+la 98
+▁if 99
+▁just 100
+th 101
+li 102
+▁ 103
+▁her 104
+▁um 105
+on 106
+ation 107
+▁w 108
+▁would 109
+f 110
+te 111
+▁st 112
+▁go 113
+ir 114
+it 115
+▁out 116
+ro 117
+▁pa 118
+▁were 119
+▁g 120
+▁t 121
+ion 122
+▁think 123
+an 124
+▁right 125
+▁about 126
+se 127
+lo 128
+ent 129
+▁up 130
+ment 131
+ate 132
+▁when 133
+h 134
+ne 135
+▁don 136
+▁has 137
+▁also 138
+▁more 139
+▁see 140
+▁okay 141
+▁their 142
+▁your 143
+ge 144
+▁who 145
+▁well 146
+▁co 147
+▁which 148
+▁some 149
+▁se 150
+▁time 151
+▁ba 152
+▁said 153
+▁con 154
+ers 155
+▁ra 156
+us 157
+de 158
+ra 159
+▁him 160
+▁our 161
+▁been 162
+▁fa 163
+▁po 164
+▁pro 165
+et 166
+x 167
+▁la 168
+id 169
+ver 170
+▁oh 171
+▁ma 172
+v 173
+▁now 174
+age 175
+▁two 176
+ld 177
+▁mo 178
+▁how 179
+tion 180
+▁people 181
+ive 182
+▁other 183
+ng 184
+ity 185
+z 186
+ist 187
+▁very 188
+▁get 189
+▁any 190
+▁un 191
+▁ro 192
+is 193
+▁work 194
+▁mean 195
+▁them 196
+▁lo 197
+vi 198
+▁because 199
+ies 200
+ul 201
+as 202
+ad 203
+mp 204
+▁bo 205
+- 206
+▁then 207
+▁good 208
+el 209
+nd 210
+▁li 211
+▁man 212
+▁dis 213
+▁could 214
+▁ho 215
+at 216
+ol 217
+▁bu 218
+▁te 219
+▁ha 220
+est 221
+me 222
+▁say 223
+ru 224
+ke 225
+▁sp 226
+▁k 227
+able 228
+▁su 229
+▁sa 230
+▁di 231
+▁fi 232
+ance 233
+▁really 234
+▁over 235
+▁even 236
+ry 237
+▁us 238
+▁ca 239
+ow 240
+ho 241
+▁into 242
+ence 243
+mo 244
+▁mi 245
+one 246
+qu 247
+ut 248
+lu 249
+▁o 250
+ty 251
+▁after 252
+▁want 253
+▁new 254
+▁take 255
+▁p 256
+▁look 257
+▁pre 258
+sh 259
+▁day 260
+▁should 261
+▁th 262
+▁need 263
+▁cha 264
+co 265
+▁much 266
+▁where 267
+▁d 268
+ant 269
+▁fe 270
+▁da 271
+▁make 272
+om 273
+▁did 274
+▁le 275
+un 276
+▁only 277
+im 278
+▁these 279
+ff 280
+ti 281
+ish 282
+▁ex 283
+ted 284
+▁first 285
+he 286
+ig 287
+▁vi 288
+▁ri 289
+▁en 290
+▁com 291
+ated 292
+▁than 293
+ma 294
+▁way 295
+um 296
+ct 297
+end 298
+ight 299
+▁here 300
+▁ta 301
+▁car 302
+▁part 303
+▁come 304
+ia 305
+▁off 306
+▁sc 307
+▁ah 308
+am 309
+▁tra 310
+▁yes 311
+▁back 312
+ture 313
+ful 314
+▁pri 315
+ction 316
+ine 317
+▁three 318
+ard 319
+▁let 320
+pe 321
+▁little 322
+▁down 323
+mb 324
+▁si 325
+▁dr 326
+▁mr 327
+▁going 328
+▁comp 329
+po 330
+▁m 331
+▁sta 332
+▁gra 333
+day 334
+▁many 335
+ian 336
+ta 337
+▁long 338
+▁pi 339
+▁too 340
+▁app 341
+▁kind 342
+ous 343
+ci 344
+▁ga 345
+ten 346
+nt 347
+▁before 348
+▁may 349
+▁got 350
+man 351
+tic 352
+ition 353
+cu 354
+ugh 355
+tra 356
+▁n 357
+ward 358
+▁give 359
+▁every 360
+▁hi 361
+ting 362
+▁exp 363
+▁those 364
+▁hu 365
+ot 366
+▁something 367
+▁lot 368
+▁still 369
+▁ne 370
+na 371
+ise 372
+pp 373
+▁most 374
+▁gu 375
+▁state 376
+▁actually 377
+▁such 378
+▁bi 379
+▁never 380
+tain 381
+▁great 382
+▁through 383
+▁al 384
+no 385
+▁mar 386
+▁year 387
+ach 388
+les 389
+▁school 390
+ally 391
+ial 392
+ha 393
+▁old 394
+▁made 395
+ary 396
+▁ar 397
+▁years 398
+▁help 399
+▁per 400
+ving 401
+ical 402
+ther 403
+▁does 404
+ac 405
+ca 406
+▁must 407
+di 408
+▁own 409
+▁ru 410
+▁things 411
+▁hand 412
+▁thing 413
+▁high 414
+▁last 415
+go 416
+▁sh 417
+▁under 418
+▁four 419
+▁place 420
+ations 421
+▁sure 422
+mi 423
+nce 424
+▁am 425
+for 426
+ness 427
+▁name 428
+▁five 429
+ound 430
+▁op 431
+▁cons 432
+▁ph 433
+▁same 434
+row 435
+ven 436
+ph 437
+ite 438
+▁pe 439
+j 440
+▁sha 441
+▁friend 442
+▁wi 443
+▁call 444
+▁european 445
+▁h 446
+ect 447
+ress 448
+▁live 449
+port 450
+▁mhm 451
+▁house 452
+ie 453
+ni 454
+▁plan 455
+▁jo 456
+▁play 457
+side 458
+▁va 459
+min 460
+ious 461
+▁life 462
+▁du 463
+▁ti 464
+▁six 465
+▁men 466
+▁again 467
+▁thank 468
+▁talk 469
+par 470
+▁home 471
+op 472
+▁both 473
+▁why 474
+▁put 475
+▁another 476
+nc 477
+▁being 478
+mit 479
+▁came 480
+led 481
+▁fo 482
+▁end 483
+▁member 484
+ative 485
+▁thought 486
+▁tri 487
+iv 488
+our 489
+red 490
+▁went 491
+lic 492
+▁find 493
+▁pu 494
+land 495
+▁start 496
+▁far 497
+▁eu 498
+▁imp 499
+▁always 500
+▁ju 501
+▁wa 502
+▁person 503
+▁singapore 504
+ap 505
+▁show 506
+▁chi 507
+▁ten 508
+▁eight 509
+▁while 510
+▁point 511
+▁y 512
+▁ja 513
+▁ya 514
+ling 515
+ctor 516
+▁use 517
+▁acc 518
+▁world 519
+▁pay 520
+▁read 521
+va 522
+vo 523
+▁change 524
+▁u 525
+▁pl 526
+▁sw 527
+▁war 528
+▁might 529
+nk 530
+ments 531
+and 532
+▁different 533
+▁dec 534
+cent 535
+▁ste 536
+▁better 537
+▁fun 538
+▁month 539
+ship 540
+ton 541
+▁tell 542
+▁twenty 543
+▁commission 544
+▁exc 545
+▁miss 546
+if 547
+▁love 548
+▁money 549
+▁found 550
+▁hundred 551
+gg 552
+▁add 553
+▁real 554
+ities 555
+▁na 556
+▁pass 557
+▁didn 558
+▁v 559
+▁feel 560
+▁week 561
+▁win 562
+ible 563
+▁try 564
+▁upon 565
+ba 566
+▁interest 567
+▁inter 568
+son 569
+line 570
+▁ob 571
+▁boy 572
+▁big 573
+▁used 574
+▁seven 575
+▁away 576
+▁family 577
+less 578
+▁ki 579
+ber 580
+▁around 581
+▁turn 582
+▁anything 583
+▁care 584
+▁young 585
+▁guess 586
+▁happen 587
+▁course 588
+▁agree 589
+▁support 590
+▁conf 591
+ual 592
+▁number 593
+▁trans 594
+ating 595
+▁mister 596
+▁hard 597
+▁watch 598
+ft 599
+▁next 600
+▁sea 601
+▁open 602
+▁without 603
+duc 604
+gra 605
+ak 606
+▁cap 607
+▁cre 608
+hi 609
+▁government 610
+▁vo 611
+▁between 612
+▁each 613
+▁ve 614
+▁though 615
+▁country 616
+▁few 617
+▁once 618
+▁' 619
+▁head 620
+▁free 621
+▁mu 622
+▁maybe 623
+▁act 624
+▁night 625
+▁thousand 626
+▁face 627
+▁uhhuh 628
+▁keep 629
+▁nine 630
+▁close 631
+▁case 632
+▁che 633
+▁against 634
+▁done 635
+▁ever 636
+▁law 637
+▁believe 638
+▁public 639
+▁room 640
+▁sub 641
+▁order 642
+▁important 643
+ient 644
+▁el 645
+▁children 646
+▁second 647
+▁bri 648
+▁business 649
+▁hope 650
+▁move 651
+fa 652
+▁however 653
+▁follow 654
+▁able 655
+▁word 656
+▁yet 657
+▁fla 658
+▁stand 659
+ize 660
+▁je 661
+▁service 662
+▁nothing 663
+▁report 664
+▁called 665
+▁grow 666
+▁continue 667
+▁issue 668
+▁since 669
+▁book 670
+▁lu 671
+▁qui 672
+▁develop 673
+▁gen 674
+▁certain 675
+light 676
+▁cor 677
+▁small 678
+▁took 679
+▁question 680
+▁whole 681
+▁problem 682
+▁side 683
+▁child 684
+▁full 685
+▁best 686
+▁mm 687
+▁probably 688
+fi 689
+▁qua 690
+▁sur 691
+▁market 692
+▁left 693
+▁everything 694
+▁during 695
+▁understand 696
+ook 697
+wa 698
+▁cent 699
+▁water 700
+▁quite 701
+▁leave 702
+▁himself 703
+ip 704
+▁near 705
+▁saw 706
+▁together 707
+▁large 708
+▁having 709
+▁already 710
+▁invest 711
+▁pretty 712
+▁direct 713
+▁hour 714
+▁fact 715
+way 716
+▁run 717
+▁bra 718
+▁clear 719
+▁fra 720
+▁area 721
+▁union 722
+▁enough 723
+▁consider 724
+▁lead 725
+▁remain 726
+▁president 727
+▁system 728
+▁def 729
+▁stuff 730
+▁food 731
+▁job 732
+▁heard 733
+▁err 734
+▁mind 735
+▁rest 736
+▁speak 737
+▁asked 738
+ator 739
+▁half 740
+▁father 741
+com 742
+▁less 743
+▁arm 744
+▁human 745
+ency 746
+▁matter 747
+▁group 748
+▁girl 749
+▁current 750
+▁main 751
+ttle 752
+▁later 753
+▁learn 754
+▁strong 755
+▁sign 756
+▁check 757
+▁light 758
+▁else 759
+▁true 760
+▁term 761
+qui 762
+▁minute 763
+▁spec 764
+▁return 765
+▁answer 766
+▁reason 767
+▁count 768
+▁shall 769
+▁communi 770
+▁travel 771
+▁wait 772
+▁provide 773
+▁low 774
+▁mother 775
+▁expect 776
+▁cause 777
+▁line 778
+▁general 779
+lf 780
+▁getting 781
+▁parliament 782
+▁bank 783
+▁company 784
+▁stop 785
+cause 786
+▁power 787
+▁gi 788
+▁europe 789
+▁moment 790
+▁among 791
+▁walk 792
+▁allow 793
+▁idea 794
+▁office 795
+▁town 796
+▁cannot 797
+▁countries 798
+▁become 799
+▁appear 800
+▁present 801
+▁bring 802
+▁least 803
+▁almost 804
+▁kids 805
+▁remember 806
+▁include 807
+▁short 808
+▁sometimes 809
+▁game 810
+▁level 811
+▁exactly 812
+▁particular 813
+▁social 814
+▁land 815
+▁woman 816
+▁north 817
+▁nice 818
+▁concern 819
+▁sort 820
+▁effect 821
+▁national 822
+▁several 823
+▁safe 824
+▁until 825
+▁further 826
+▁cost 827
+▁wonder 828
+▁whether 829
+▁either 830
+▁future 831
+▁pra 832
+▁council 833
+▁knew 834
+▁common 835
+▁south 836
+▁making 837
+▁morning 838
+▁process 839
+▁situation 840
+▁white 841
+▁result 842
+▁suppose 843
+▁employ 844
+▁political 845
+▁program 846
+▁along 847
+▁women 848
+▁ski 849
+▁court 850
+▁please 851
+▁shi 852
+▁possible 853
+▁protect 854
+▁experience 855
+▁definitely 856
+▁require 857
+▁account 858
+▁myself 859
+▁black 860
+▁example 861
+▁america 862
+▁thirty 863
+▁student 864
+▁view 865
+▁product 866
+▁wife 867
+▁health 868
+▁major 869
+▁difficult 870
+▁death 871
+▁visit 872
+▁across 873
+▁receive 874
+▁voice 875
+▁citizen 876
+▁regard 877
+▁author 878
+▁treat 879
+▁especially 880
+▁local 881
+▁taking 882
+▁information 883
+▁seemed 884
+▁success 885
+ability 886
+▁break 887
+▁whatever 888
+▁security 889
+▁address 890
+▁felt 891
+▁fifty 892
+▁million 893
+▁third 894
+▁usually 895
+▁gonna 896
+▁brother 897
+▁began 898
+▁period 899
+▁east 900
+▁economic 901
+▁increase 902
+▁financial 903
+▁respect 904
+▁enjoy 905
+▁christ 906
+▁education 907
+▁brought 908
+▁organ 909
+▁parents 910
+▁policy 911
+▁round 912
+▁became 913
+▁region 914
+▁lady 915
+▁discuss 916
+▁single 917
+▁early 918
+▁couple 919
+▁type 920
+▁itself 921
+▁serve 922
+▁measure 923
+▁husband 924
+ified 925
+▁music 926
+▁ground 927
+▁companies 928
+▁street 929
+▁behind 930
+▁value 931
+▁therefore 932
+▁police 933
+▁complete 934
+▁john 935
+▁daughter 936
+▁affect 937
+▁perhaps 938
+▁international 939
+▁themselves 940
+▁improve 941
+▁condition 942
+▁hotel 943
+▁deliver 944
+▁sense 945
+▁relation 946
+▁sorry 947
+▁credit 948
+▁effort 949
+▁instead 950
+▁york 951
+▁united 952
+▁partner 953
+▁spoke 954
+▁strange 955
+▁everybody 956
+▁horse 957
+▁depend 958
+▁subject 959
+▁project 960
+▁approach 961
+▁involve 962
+▁listen 963
+▁draw 964
+▁computer 965
+▁married 966
+▁record 967
+▁happy 968
+▁sudden 969
+▁represent 970
+▁somebody 971
+▁correct 972
+▁serious 973
+▁decision 974
+▁society 975
+▁including 976
+▁college 977
+▁english 978
+▁attack 979
+▁perform 980
+▁cross 981
+▁accept 982
+▁control 983
+▁flow 984
+▁although 985
+▁drink 986
+▁front 987
+▁wrong 988
+▁twi 989
+▁according 990
+▁slow 991
+▁peace 992
+▁amount 993
+▁object 994
+▁movie 995
+▁benefit 996
+▁yup 997
+▁challenge 998
+▁private 999
+▁church 1000
+▁wood 1001
+▁field 1002
+▁above 1003
+▁ensure 1004
+▁immediate 1005
+▁figure 1006
+▁foreign 1007
+▁available 1008
+▁insurance 1009
+▁proposal 1010
+▁doubt 1011
+▁strength 1012
+▁difference 1013
+▁stood 1014
+▁implement 1015
+▁economy 1016
+▁detail 1017
+▁umhum 1018
+▁restaurant 1019
+▁collect 1020
+▁global 1021
+▁broke 1022
+q 1023
+<blk> 1024