File size: 37,398 Bytes
906c43a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 | {
"models": [
{
"model": "tiny_pretrained",
"base_model": "openai/whisper-tiny.en",
"examples": 1452,
"wer": 0.06087132862405786,
"cer": 0.02635361763296598,
"average_real_time_factor": 0.016960419383418485,
"average_latency_seconds": 0.11814498409143644,
"peak_gpu_memory_bytes": 151478784,
"average_keyword_error_rate": null
},
{
"model": "tiny_lora",
"base_model": "openai/whisper-tiny.en",
"examples": 1452,
"wer": 0.16764119376753303,
"cer": 0.06636950545986432,
"average_real_time_factor": 0.035790438942107,
"average_latency_seconds": 0.26246351163890297,
"peak_gpu_memory_bytes": 167764992,
"average_keyword_error_rate": null
},
{
"model": "small_pretrained",
"base_model": "openai/whisper-small.en",
"examples": 1452,
"wer": 0.03457599621455369,
"cer": 0.014425138072781379,
"average_real_time_factor": 0.03348686676111526,
"average_latency_seconds": 0.23233147196923243,
"peak_gpu_memory_bytes": 625136128,
"average_keyword_error_rate": null
},
{
"model": "small_lora",
"base_model": "openai/whisper-small.en",
"examples": 1452,
"wer": 0.06256126001284348,
"cer": 0.021669230575240207,
"average_real_time_factor": 0.08788874327648112,
"average_latency_seconds": 0.6192394232105374,
"peak_gpu_memory_bytes": 692038656,
"average_keyword_error_rate": null
},
{
"model": "medium_pretrained",
"base_model": "openai/whisper-medium.en",
"examples": 1452,
"wer": 0.04677730084158583,
"cer": 0.025010717978463166,
"average_real_time_factor": 0.05965827283184351,
"average_latency_seconds": 0.4051003978650144,
"peak_gpu_memory_bytes": 1854183424,
"average_keyword_error_rate": null
},
{
"model": "medium_lora",
"base_model": "openai/whisper-medium.en",
"examples": 1452,
"wer": 0.04231588197519181,
"cer": 0.013687488966786876,
"average_real_time_factor": 0.16856216840888133,
"average_latency_seconds": 1.187795076928749,
"peak_gpu_memory_bytes": 1915784192,
"average_keyword_error_rate": null
}
],
"quality_champion": {
"model": "small_pretrained",
"base_model": "openai/whisper-small.en",
"examples": 1452,
"wer": 0.03457599621455369,
"cer": 0.014425138072781379,
"average_real_time_factor": 0.03348686676111526,
"average_latency_seconds": 0.23233147196923243,
"peak_gpu_memory_bytes": 625136128,
"average_keyword_error_rate": null
},
"browser_champion": {
"model": "tiny_pretrained",
"base_model": "openai/whisper-tiny.en",
"examples": 1452,
"wer": 0.06087132862405786,
"cer": 0.02635361763296598,
"average_real_time_factor": 0.016960419383418485,
"average_latency_seconds": 0.11814498409143644,
"peak_gpu_memory_bytes": 151478784,
"average_keyword_error_rate": null
},
"robustness": [
{
"field": "noise_level",
"value": "clean",
"examples": 300,
"wer": 0.06020535158680772,
"cer": 0.02713122536467474
},
{
"field": "noise_level",
"value": "clipped",
"examples": 300,
"wer": 0.058027380211574366,
"cer": 0.02534741203210864
},
{
"field": "noise_level",
"value": "heavy",
"examples": 300,
"wer": 0.21141879278158057,
"cer": 0.11551629887504675
},
{
"field": "noise_level",
"value": "low_volume",
"examples": 300,
"wer": 0.057560672059738645,
"cer": 0.02459936127974221
},
{
"field": "noise_level",
"value": "medium",
"examples": 300,
"wer": 0.11154324828873678,
"cer": 0.05314037460079984
},
{
"field": "noise_level",
"value": "mild",
"examples": 300,
"wer": 0.06829495955196017,
"cer": 0.029404148804557356
}
],
"onnx_parity": {
"status": "COMPLETED",
"examples": 50,
"pytorch": {
"wer": 0.06120218579234973,
"cer": 0.02532414910858995,
"average_latency_seconds": 0.13398373600095512,
"model_dir": "models/merged/tiny_browser_rtx5090"
},
"quantized_onnx": {
"wer": 0.05901639344262295,
"cer": 0.025121555915721232,
"average_latency_seconds": 3.169425382001791,
"model_dir": "models/onnx/tiny_browser_rtx5090",
"ort_parity_model_dir": "models\\onnx\\tiny_browser_rtx5090"
},
"relative_wer_regression": -0.035714285714285685,
"normalized_prediction_match_rate": 0.98,
"rows": [
{
"audio_path": "data\\processed\\audio\\test\\6930-75918-0000.wav",
"reference": "CONCORD RETURNED TO ITS PLACE AMIDST THE TENTS",
"pytorch_prediction": "Concord returned to its place amidst the tents.",
"onnx_prediction": "Concord returned to its place amidst the tents.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.42229060002136976,
"onnx_latency_seconds": 2.5508497999981046
},
{
"audio_path": "data\\processed\\audio\\test\\6930-75918-0001.wav",
"reference": "THE ENGLISH FORWARDED TO THE FRENCH BASKETS OF FLOWERS OF WHICH THEY HAD MADE A PLENTIFUL PROVISION TO GREET THE ARRIVAL OF THE YOUNG PRINCESS THE FRENCH IN RETURN INVITED THE ENGLISH TO A SUPPER WHICH WAS TO BE GIVEN THE NEXT DAY",
"pytorch_prediction": "The English voted to the French baskets of flowers of which they had made a plentiful provision to greet the arrival of the young princess. The French in return invited the English to a supper, which was to be given the next day.",
"onnx_prediction": "The English voted to the French baskets of flowers of which they had made a plentiful provision to greet the arrival of the young princess. The French in return invited the English to a supper, which was to be given the next day.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.16849699994781986,
"onnx_latency_seconds": 8.927575000037905
},
{
"audio_path": "data\\processed\\audio\\test\\6930-75918-0002.wav",
"reference": "CONGRATULATIONS WERE POURED IN UPON THE PRINCESS EVERYWHERE DURING HER JOURNEY",
"pytorch_prediction": "Congratulations, we're poured in upon the Princess everywhere during her journey.",
"onnx_prediction": "Congratulations, we're poured in upon the princess everywhere during her journey.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.05657219997374341,
"onnx_latency_seconds": 3.467369400023017
},
{
"audio_path": "data\\processed\\audio\\test\\6930-75918-0003.wav",
"reference": "FROM THE RESPECT PAID HER ON ALL SIDES SHE SEEMED LIKE A QUEEN AND FROM THE ADORATION WITH WHICH SHE WAS TREATED BY TWO OR THREE SHE APPEARED AN OBJECT OF WORSHIP THE QUEEN MOTHER GAVE THE FRENCH THE MOST AFFECTIONATE RECEPTION FRANCE WAS HER NATIVE COUNTRY AND SHE HAD SUFFERED TOO MUCH UNHAPPINESS IN ENGLAND FOR ENGLAND TO HAVE MADE HER FORGET FRANCE",
"pytorch_prediction": "From the respect paid her on all sides, she seemed like a queen, and from the adoration with which she was treated by two or three, she appeared in an object of worship. The queen mother gave the French the most affectionate reception. France was her native country, and she had suffered too much unhappiness in England for England to have made her forget France.",
"onnx_prediction": "From the respect paid her on all sides, she seemed like a queen, and from the adoration with which she was treated by two or three, she appeared in an object of worship. The queen mother gave the French the most affectionate reception. France was her native country, and she had suffered too much unhappiness in England for England to have made her forget France.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.31891640002140775,
"onnx_latency_seconds": 11.333077699993737
},
{
"audio_path": "data\\processed\\audio\\test\\6930-75918-0004.wav",
"reference": "SHE TAUGHT HER DAUGHTER THEN BY HER OWN AFFECTION FOR IT THAT LOVE FOR A COUNTRY WHERE THEY HAD BOTH BEEN HOSPITABLY RECEIVED AND WHERE A BRILLIANT FUTURE OPENED BEFORE THEM",
"pytorch_prediction": "She taught her daughter, then, by her own affection for it, that love for a country where they had both been hospitably received and where a brilliant future opened for them.",
"onnx_prediction": "She taught her daughter, then, by her own affection for it, that love for a country where they had both been hospitably received and where a brilliant future opened for them.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.13249340001493692,
"onnx_latency_seconds": 5.973673400003463
},
{
"audio_path": "data\\processed\\audio\\test\\6930-75918-0005.wav",
"reference": "THE COUNT HAD THROWN HIMSELF BACK ON HIS SEAT LEANING HIS SHOULDERS AGAINST THE PARTITION OF THE TENT AND REMAINED THUS HIS FACE BURIED IN HIS HANDS WITH HEAVING CHEST AND RESTLESS LIMBS",
"pytorch_prediction": "The Count had thrown himself back on his seat, leaning his shoulders against the partition of the tent and remained thus. His face buried in his hands, with heaving chest and restless limbs.",
"onnx_prediction": "The Count had thrown himself back on his seat, leaning his shoulders against the partition of the tent and remained thus. His face buried in his hands, with heaving chest and restless limbs.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.15020109998295084,
"onnx_latency_seconds": 6.375870499992743
},
{
"audio_path": "data\\processed\\audio\\test\\6930-75918-0006.wav",
"reference": "THIS HAS INDEED BEEN A HARASSING DAY CONTINUED THE YOUNG MAN HIS EYES FIXED UPON HIS FRIEND",
"pytorch_prediction": "This has indeed been a harassing day continued the young man his eyes fixed upon his friend.",
"onnx_prediction": "This has indeed been a harassing day continued the young man his eyes fixed upon his friend.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.0792918999795802,
"onnx_latency_seconds": 3.896210399980191
},
{
"audio_path": "data\\processed\\audio\\test\\6930-75918-0007.wav",
"reference": "YOU WILL BE FRANK WITH ME I ALWAYS AM",
"pytorch_prediction": "You will be frank with me. I always am.",
"onnx_prediction": "You will be frank with me. I always am.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.061813100008293986,
"onnx_latency_seconds": 2.7161696999683045
},
{
"audio_path": "data\\processed\\audio\\test\\6930-75918-0008.wav",
"reference": "CAN YOU IMAGINE WHY BUCKINGHAM HAS BEEN SO VIOLENT I SUSPECT",
"pytorch_prediction": "Can you imagine why Buckingham has been so violent? I suspect.",
"onnx_prediction": "Can you imagine why Buckingham has been so violent? I suspect.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.05822260002605617,
"onnx_latency_seconds": 2.8017613000120036
},
{
"audio_path": "data\\processed\\audio\\test\\6930-75918-0009.wav",
"reference": "IT IS YOU WHO ARE MISTAKEN RAOUL I HAVE READ HIS DISTRESS IN HIS EYES IN HIS EVERY GESTURE AND ACTION THE WHOLE DAY",
"pytorch_prediction": "It is you who are mistaken, Raul. I have read his distress in his eyes, in his every gesture and action the whole day.",
"onnx_prediction": "It is you who are mistaken, Raul. I have read his distress in his eyes, in his every gesture and action the whole day.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.12850869999965653,
"onnx_latency_seconds": 4.756190599990077
},
{
"audio_path": "data\\processed\\audio\\test\\6930-75918-0010.wav",
"reference": "I CAN PERCEIVE LOVE CLEARLY ENOUGH",
"pytorch_prediction": "I can perceive love clearly enough.",
"onnx_prediction": "I can perceive love clearly enough.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.03721670003142208,
"onnx_latency_seconds": 1.8510372000164352
},
{
"audio_path": "data\\processed\\audio\\test\\6930-75918-0011.wav",
"reference": "I AM CONVINCED OF WHAT I SAY SAID THE COUNT",
"pytorch_prediction": "I am convinced of what I say,\" said the count.",
"onnx_prediction": "I am convinced of what I say,\" said the count.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.05933060002280399,
"onnx_latency_seconds": 2.6531129999784753
},
{
"audio_path": "data\\processed\\audio\\test\\6930-75918-0012.wav",
"reference": "IT IS ANNOYANCE THEN",
"pytorch_prediction": "It is annoyance then.",
"onnx_prediction": "It is annoyance then.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.03238529997179285,
"onnx_latency_seconds": 1.775708000001032
},
{
"audio_path": "data\\processed\\audio\\test\\6930-75918-0013.wav",
"reference": "IN THOSE VERY TERMS I EVEN ADDED MORE",
"pytorch_prediction": "In those very terms, I even added more.",
"onnx_prediction": "In those very terms, I even added more.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.05361050000647083,
"onnx_latency_seconds": 2.608431799977552
},
{
"audio_path": "data\\processed\\audio\\test\\6930-75918-0014.wav",
"reference": "BUT CONTINUED RAOUL NOT INTERRUPTED BY THIS MOVEMENT OF HIS FRIEND HEAVEN BE PRAISED THE FRENCH WHO ARE PRONOUNCED TO BE THOUGHTLESS AND INDISCREET RECKLESS EVEN ARE CAPABLE OF BRINGING A CALM AND SOUND JUDGMENT TO BEAR ON MATTERS OF SUCH HIGH IMPORTANCE",
"pytorch_prediction": "But, continued Raul, not interrupted by this movement of his friend, heaven be praised the French, who are pronounced to be thoughtless and in discreet, reckless even, are capable of bringing a calm and sound judgment to bear on matters of such high importance.",
"onnx_prediction": "But, continued Raul, not interrupted by this movement of his friend, heaven be praised the French, who are pronounced to be thoughtless and indiscreet reckless even, are capable of bringing a calm and sound judgment to bear on matters of such high importance.",
"predictions_match_after_normalization": false,
"pytorch_latency_seconds": 0.18599650001851842,
"onnx_latency_seconds": 6.87660290003987
},
{
"audio_path": "data\\processed\\audio\\test\\6930-75918-0015.wav",
"reference": "THUS IT IS THAT THE HONOR OF THREE IS SAVED OUR COUNTRY'S OUR MASTER'S AND OUR OWN",
"pytorch_prediction": "Thus, it is that the honor of three is saved, our country, our masters and our own.",
"onnx_prediction": "Thus, it is that the honor of three is saved, our country, our masters and our own.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.08556590002262965,
"onnx_latency_seconds": 4.295730300014839
},
{
"audio_path": "data\\processed\\audio\\test\\6930-75918-0016.wav",
"reference": "YES I NEED REPOSE MANY THINGS HAVE AGITATED ME TO DAY BOTH IN MIND AND BODY WHEN YOU RETURN TO MORROW I SHALL NO LONGER BE THE SAME MAN",
"pytorch_prediction": "Yes, I need repose. Many things have agitated me today, both in mind and body. When you return tomorrow, I shall no longer be the same man.",
"onnx_prediction": "Yes, I need repose. Many things have agitated me today, both in mind and body. When you return tomorrow, I shall no longer be the same man.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.14891390001866966,
"onnx_latency_seconds": 5.123385499988217
},
{
"audio_path": "data\\processed\\audio\\test\\6930-75918-0017.wav",
"reference": "BUT IN THIS FRIENDLY PRESSURE RAOUL COULD DETECT THE NERVOUS AGITATION OF A GREAT INTERNAL CONFLICT",
"pytorch_prediction": "but in this friendly pressure, Raoul could detect the nervous agitation of a great internal conflict.",
"onnx_prediction": "but in this friendly pressure, Raoul could detect the nervous agitation of a great internal conflict.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.07525960000930354,
"onnx_latency_seconds": 4.102620700025
},
{
"audio_path": "data\\processed\\audio\\test\\6930-75918-0018.wav",
"reference": "THE NIGHT WAS CLEAR STARLIT AND SPLENDID THE TEMPEST HAD PASSED AWAY AND THE SWEET INFLUENCES OF THE EVENING HAD RESTORED LIFE PEACE AND SECURITY EVERYWHERE",
"pytorch_prediction": "The night was clear, starlet and splendid. The tempest had passed away and the sweet influences of the evening had restored life, peace and security everywhere.",
"onnx_prediction": "The night was clear, starlet and splendid. The tempest had passed away and the sweet influences of the evening had restored life, peace and security everywhere.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.11731409997446463,
"onnx_latency_seconds": 6.81112700002268
},
{
"audio_path": "data\\processed\\audio\\test\\6930-75918-0019.wav",
"reference": "UPON THE LARGE SQUARE IN FRONT OF THE HOTEL THE SHADOWS OF THE TENTS INTERSECTED BY THE GOLDEN MOONBEAMS FORMED AS IT WERE A HUGE MOSAIC OF JET AND YELLOW FLAGSTONES",
"pytorch_prediction": "Upon the large square in front of the hotel, the shadows of the tents intersected by the golden moon beams formed as it were a huge mosaic of jet and yellow flagstones.",
"onnx_prediction": "Upon the large square in front of the hotel, the shadows of the tents intersected by the golden moon beams formed as it were a huge mosaic of jet and yellow flagstones.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.1426975000067614,
"onnx_latency_seconds": 6.006402900035027
},
{
"audio_path": "data\\processed\\audio\\test\\6930-75918-0020.wav",
"reference": "BRAGELONNE WATCHED FOR SOME TIME THE CONDUCT OF THE TWO LOVERS LISTENED TO THE LOUD AND UNCIVIL SLUMBERS OF MANICAMP WHO SNORED AS IMPERIOUSLY AS THOUGH HE WAS WEARING HIS BLUE AND GOLD INSTEAD OF HIS VIOLET SUIT",
"pytorch_prediction": "Braggleon watched for some time the conduct of the two lovers listen to the loud and unsilvel slumbers of Manicamp who snored as impreously as though he was wearing his blue and gold instead of his violet suit.",
"onnx_prediction": "Braggleon watched for some time the conduct of the two lovers listen to the loud and unsilvel slumbers of Manicamp who snored as impreously as though he was wearing his blue and gold instead of his violet suit.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.17875020002247766,
"onnx_latency_seconds": 9.509694499953184
},
{
"audio_path": "data\\processed\\audio\\test\\6930-76324-0000.wav",
"reference": "GOLIATH MAKES ANOTHER DISCOVERY",
"pytorch_prediction": "Goliath makes another discovery.",
"onnx_prediction": "Goliath makes another discovery.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.033184200001414865,
"onnx_latency_seconds": 1.7889484000043012
},
{
"audio_path": "data\\processed\\audio\\test\\6930-76324-0001.wav",
"reference": "THEY WERE CERTAINLY NO NEARER THE SOLUTION OF THEIR PROBLEM",
"pytorch_prediction": "There were certainly no near the solution of their problem.",
"onnx_prediction": "There were certainly no near the solution of their problem.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.049372099980246276,
"onnx_latency_seconds": 2.2901792000047863
},
{
"audio_path": "data\\processed\\audio\\test\\6930-76324-0002.wav",
"reference": "THE POOR LITTLE THINGS CRIED CYNTHIA THINK OF THEM HAVING BEEN TURNED TO THE WALL ALL THESE YEARS",
"pytorch_prediction": "The poor little things, Kritesintia, think of them having been turned to the wall all these years.",
"onnx_prediction": "The poor little things, Kritesintia, think of them having been turned to the wall all these years.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.09983459999784827,
"onnx_latency_seconds": 4.911305600020569
},
{
"audio_path": "data\\processed\\audio\\test\\6930-76324-0003.wav",
"reference": "NOW WHAT WAS THE SENSE OF IT TWO INNOCENT BABIES LIKE THAT",
"pytorch_prediction": "Now what is the sense of it? Two innocent babies like that.",
"onnx_prediction": "Now what is the sense of it? Two innocent babies like that.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.05935559998033568,
"onnx_latency_seconds": 2.628697899985127
},
{
"audio_path": "data\\processed\\audio\\test\\6930-76324-0004.wav",
"reference": "BUT JOYCE HAD NOT BEEN LISTENING ALL AT ONCE SHE PUT DOWN HER CANDLE ON THE TABLE AND FACED HER COMPANION",
"pytorch_prediction": "But Joyce had not been listening. All at once, she put down her candle on the table and faced her companion.",
"onnx_prediction": "But Joyce had not been listening. All at once, she put down her candle on the table and faced her companion.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.10082220000913367,
"onnx_latency_seconds": 3.724064100009855
},
{
"audio_path": "data\\processed\\audio\\test\\6930-76324-0005.wav",
"reference": "THE TWIN BROTHER DID SOMETHING SHE DIDN'T LIKE AND SHE TURNED HIS PICTURE TO THE WALL",
"pytorch_prediction": "The twin brother did something she didn't like and she turned his picture to the wall.",
"onnx_prediction": "The twin brother did something she didn't like and she turned his picture to the wall.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.15020430000731722,
"onnx_latency_seconds": 1.6063471999950707
},
{
"audio_path": "data\\processed\\audio\\test\\6930-76324-0006.wav",
"reference": "HERS HAPPENED TO BE IN THE SAME FRAME TOO BUT SHE EVIDENTLY DIDN'T CARE ABOUT THAT",
"pytorch_prediction": "hers happened to be on the same frame too, but she evidently didn't care about it.",
"onnx_prediction": "hers happened to be on the same frame too, but she evidently didn't care about it.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.1917818000074476,
"onnx_latency_seconds": 1.6017565000220202
},
{
"audio_path": "data\\processed\\audio\\test\\6930-76324-0007.wav",
"reference": "NOW WHAT HAVE YOU TO SAY CYNTHIA SPRAGUE",
"pytorch_prediction": "Now what have you to say, Cynthia Sprague?",
"onnx_prediction": "Now what have you to say, Cynthia Sprague?",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.10486649995436892,
"onnx_latency_seconds": 1.3496473000268452
},
{
"audio_path": "data\\processed\\audio\\test\\6930-76324-0008.wav",
"reference": "I THOUGHT WE WERE STUMPED AGAIN WHEN I FIRST SAW THAT PICTURE BUT IT'S BEEN OF SOME USE AFTER ALL",
"pytorch_prediction": "I thought we were stumped again when I first saw that picture, but it's been of some news after all.",
"onnx_prediction": "I thought we were stumped again when I first saw that picture, but it's been of some news after all.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.19789260003017262,
"onnx_latency_seconds": 2.068150200007949
},
{
"audio_path": "data\\processed\\audio\\test\\6930-76324-0009.wav",
"reference": "DO YOU SUPPOSE THE MINIATURE WAS A COPY OF THE SAME THING",
"pytorch_prediction": "Do you suppose the miniature was a copy of the same thing?",
"onnx_prediction": "Do you suppose the miniature was a copy of the same thing?",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.11732419999316335,
"onnx_latency_seconds": 1.3579797000274993
},
{
"audio_path": "data\\processed\\audio\\test\\6930-76324-0010.wav",
"reference": "WHAT IN THE WORLD IS THAT QUERIED JOYCE",
"pytorch_prediction": "What in the world is it? Query choice.",
"onnx_prediction": "What in the world is it? Query choice.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.09169319999637082,
"onnx_latency_seconds": 1.2775405999855138
},
{
"audio_path": "data\\processed\\audio\\test\\6930-76324-0011.wav",
"reference": "THEY WORRY ME TERRIBLY AND BESIDES I'D LIKE TO SEE WHAT THIS LOVELY FURNITURE LOOKS LIKE WITHOUT SUCH QUANTITIES OF DUST ALL OVER IT GOOD SCHEME CYN",
"pytorch_prediction": "They were imme terribly ambisides. I'd like to see what this lovely furniture looks like without such quantities of dust all over it. Good scheme, sin!",
"onnx_prediction": "They were imme terribly ambisides. I'd like to see what this lovely furniture looks like without such quantities of dust all over it. Good scheme, sin!",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.23321839998243377,
"onnx_latency_seconds": 2.435911100008525
},
{
"audio_path": "data\\processed\\audio\\test\\6930-76324-0012.wav",
"reference": "WE'LL COME IN HERE THIS AFTERNOON WITH OLD CLOTHES ON AND HAVE A REGULAR HOUSE CLEANING",
"pytorch_prediction": "We'll come in here this afternoon with old clothes on and have a regular house cleaning.",
"onnx_prediction": "We'll come in here this afternoon with old clothes on and have a regular house cleaning.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.15974020003341138,
"onnx_latency_seconds": 1.5219050999730825
},
{
"audio_path": "data\\processed\\audio\\test\\6930-76324-0013.wav",
"reference": "IT CAN'T HURT ANYTHING I'M SURE FOR WE WON'T DISTURB THINGS AT ALL",
"pytorch_prediction": "They can't hurt anything. I'm sure for we won't disturb things at all.",
"onnx_prediction": "They can't hurt anything. I'm sure for we won't disturb things at all.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.1666239999467507,
"onnx_latency_seconds": 1.621632700029295
},
{
"audio_path": "data\\processed\\audio\\test\\6930-76324-0014.wav",
"reference": "THIS THOUGHT HOWEVER DID NOT ENTER THE HEADS OF THE ENTHUSIASTIC PAIR",
"pytorch_prediction": "This thought, however, did not enter the heads of the enthusiastic pair.",
"onnx_prediction": "This thought, however, did not enter the heads of the enthusiastic pair.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.1246205999632366,
"onnx_latency_seconds": 1.589230400044471
},
{
"audio_path": "data\\processed\\audio\\test\\6930-76324-0015.wav",
"reference": "SMUGGLING THE HOUSE CLEANING PARAPHERNALIA INTO THE CELLAR WINDOW UNOBSERVED THAT AFTERNOON PROVED NO EASY TASK FOR CYNTHIA HAD ADDED A WHISK BROOM AND DUST PAN TO THE OUTFIT",
"pytorch_prediction": "Smuggling the house cleaning paraphernalia into the cellar window unobserved that afternoon proved no easy task for Cynthia had added a whisk broom and dustpan to the outfit.",
"onnx_prediction": "Smuggling the house cleaning paraphernalia into the cellar window unobserved that afternoon proved no easy task for Cynthia had added a whisk broom and dustpan to the outfit.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.26573570002801716,
"onnx_latency_seconds": 2.2878347000223584
},
{
"audio_path": "data\\processed\\audio\\test\\6930-76324-0016.wav",
"reference": "THE LURE PROVED TOO MUCH FOR HIM AND HE CAME SPORTING AFTER IT AS FRISKILY AS A YOUNG KITTEN MUCH TO CYNTHIA'S DELIGHT WHEN SHE CAUGHT SIGHT OF HIM",
"pytorch_prediction": "The lure proved too much for him and he came sporting after it as fiscally as a young kitten, much to Cynthia's delight when she caught sight of him.",
"onnx_prediction": "The lure proved too much for him and he came sporting after it as fiscally as a young kitten, much to Cynthia's delight when she caught sight of him.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.13798559998394921,
"onnx_latency_seconds": 2.023125200008508
},
{
"audio_path": "data\\processed\\audio\\test\\6930-76324-0017.wav",
"reference": "OH LET HIM COME ALONG SHE URGED I DO LOVE TO SEE HIM ABOUT THAT OLD HOUSE",
"pytorch_prediction": "Oh, let him come along, she urged. I do love to see him about that old house.",
"onnx_prediction": "Oh, let him come along, she urged. I do love to see him about that old house.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.08599089999916032,
"onnx_latency_seconds": 1.6357255000039004
},
{
"audio_path": "data\\processed\\audio\\test\\6930-76324-0018.wav",
"reference": "HE MAKES IT SORT OF COZIER",
"pytorch_prediction": "He makes it sort of cozier.",
"onnx_prediction": "He makes it sort of cozier.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.06121240003267303,
"onnx_latency_seconds": 1.2148417999851517
},
{
"audio_path": "data\\processed\\audio\\test\\6930-76324-0019.wav",
"reference": "NOW LET'S DUST THE FURNITURE AND PICTURES",
"pytorch_prediction": "Now let's dust the furniture and pictures.",
"onnx_prediction": "Now let's dust the furniture and pictures.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.06321479997131974,
"onnx_latency_seconds": 1.219760999956634
},
{
"audio_path": "data\\processed\\audio\\test\\6930-76324-0020.wav",
"reference": "YET LITTLE AS IT WAS IT HAD ALREADY MADE A VAST DIFFERENCE IN THE ASPECT OF THE ROOM",
"pytorch_prediction": "Yet, little as it was, it had already made a vast difference in the aspect of the room.",
"onnx_prediction": "Yet, little as it was, it had already made a vast difference in the aspect of the room.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.17234260000986978,
"onnx_latency_seconds": 1.6179904999444261
},
{
"audio_path": "data\\processed\\audio\\test\\6930-76324-0021.wav",
"reference": "SURFACE DUST AT LEAST HAD BEEN REMOVED AND THE FINE OLD FURNITURE GAVE A HINT OF ITS REAL ELEGANCE AND POLISH",
"pytorch_prediction": "Surface dust, at least, had been removed, and the fine old furniture gave a hint of its real elegance and polish.",
"onnx_prediction": "Surface dust at least had been removed and the fine old furniture gave a hint of its real elegance and polish.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.2011717000277713,
"onnx_latency_seconds": 1.7421887000091374
},
{
"audio_path": "data\\processed\\audio\\test\\6930-76324-0022.wav",
"reference": "THEN SHE SUDDENLY REMARKED",
"pytorch_prediction": "Then she suddenly remarked.",
"onnx_prediction": "Then she suddenly remarked.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.0598817000281997,
"onnx_latency_seconds": 1.1761806000140496
},
{
"audio_path": "data\\processed\\audio\\test\\6930-76324-0023.wav",
"reference": "AND MY POCKET MONEY IS GETTING LOW AGAIN AND YOU HAVEN'T ANY LEFT AS USUAL",
"pytorch_prediction": "and my pocket money is getting low again and you have it any left as usual.",
"onnx_prediction": "and my pocket money is getting low again and you have it any left as usual.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.15345599997090176,
"onnx_latency_seconds": 1.4938246000092477
},
{
"audio_path": "data\\processed\\audio\\test\\6930-76324-0024.wav",
"reference": "THEY SAY ILLUMINATION BY CANDLE LIGHT IS THE PRETTIEST IN THE WORLD",
"pytorch_prediction": "They say illumination by candlelight is the prettiest in the world.",
"onnx_prediction": "They say illumination by candlelight is the prettiest in the world.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.12209950003307313,
"onnx_latency_seconds": 1.4616969999624416
},
{
"audio_path": "data\\processed\\audio\\test\\6930-76324-0025.wav",
"reference": "WHY IT'S GOLIATH AS USUAL THEY BOTH CRIED PEERING IN",
"pytorch_prediction": "Why it's Goliath as usual they both cried peering in",
"onnx_prediction": "Why it's Goliath as usual they both cried peering in",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.150613899983,
"onnx_latency_seconds": 1.4360467999940738
},
{
"audio_path": "data\\processed\\audio\\test\\6930-76324-0026.wav",
"reference": "ISN'T HE THE GREATEST FOR GETTING INTO ODD CORNERS",
"pytorch_prediction": "Isn't he the greatest for getting into odd corners?",
"onnx_prediction": "Isn't he the greatest for getting into odd corners?",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.13219780003419146,
"onnx_latency_seconds": 1.320899099984672
},
{
"audio_path": "data\\processed\\audio\\test\\6930-76324-0027.wav",
"reference": "FORGETTING ALL THEIR WEARINESS THEY SEIZED THEIR CANDLES AND SCURRIED THROUGH THE HOUSE FINDING AN OCCASIONAL PAPER TUCKED AWAY IN SOME ODD CORNER",
"pytorch_prediction": "For getting all their weariness they seize their candles and scurry through the house, finding on occasional paper to tuck away in some odd corner.",
"onnx_prediction": "For getting all their weariness they seize their candles and scurry through the house, finding on occasional paper to tuck away in some odd corner.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.28501849999884143,
"onnx_latency_seconds": 1.8572615000302903
},
{
"audio_path": "data\\processed\\audio\\test\\6930-76324-0028.wav",
"reference": "WELL I'M CONVINCED THAT THE BOARDED UP HOUSE MYSTERY HAPPENED NOT EARLIER THAN APRIL SIXTEENTH EIGHTEEN SIXTY ONE AND PROBABLY NOT MUCH LATER",
"pytorch_prediction": "Well, I'm convinced that the boarded-up house mystery happened not earlier than April 16, 1861, and probably not much later.",
"onnx_prediction": "Well, I'm convinced that the boarded-up house mystery happened not earlier than April 16, 1861, and probably not much later.",
"predictions_match_after_normalization": true,
"pytorch_latency_seconds": 0.23388339998200536,
"onnx_latency_seconds": 1.7979944999678992
}
]
}
} |