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wav2vec2-bert-CV16-en-cv-2

ylacombe / wav2vec2-bert-CV16-en-cv-2

Run on huggingface.co
huggingface.co
Total runs: 6
24-hour runs: 0
7-day runs: 0
30-day runs: 0
Model's Last Updated: February 01 2024
automatic-speech-recognition

Introduction of wav2vec2-bert-CV16-en-cv-2

Model Details of wav2vec2-bert-CV16-en-cv-2

wav2vec2-bert-CV16-en-cv-2

This model is a fine-tuned version of facebook/w2v-bert-2.0 on the MOZILLA-FOUNDATION/COMMON_VOICE_16_0 - EN dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4363
  • Wer: 0.1304
  • Cer: 0.0527
Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure
Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 3e-05
  • train_batch_size: 12
  • eval_batch_size: 12
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 3
  • gradient_accumulation_steps: 3
  • total_train_batch_size: 108
  • total_eval_batch_size: 36
  • optimizer: Adam with betas=(0.9,0.96) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 10000
  • num_epochs: 50.0
  • mixed_precision_training: Native AMP
Training results
Training Loss Epoch Step Validation Loss Wer Cer
0.1884 0.49 5000 0.3214 0.1872 0.0699
0.1862 0.98 10000 0.2829 0.1759 0.0674
0.1493 1.47 15000 0.2955 0.1680 0.0658
0.1346 1.96 20000 0.2727 0.1649 0.0647
0.1273 2.45 25000 0.2677 0.1608 0.0631
0.1241 2.94 30000 0.2613 0.1594 0.0631
0.1059 3.43 35000 0.2737 0.1600 0.0636
0.1201 3.92 40000 0.2633 0.1553 0.0618
0.0961 4.4 45000 0.2666 0.1549 0.0617
0.0971 4.89 50000 0.2647 0.1536 0.0616
0.088 5.38 55000 0.2504 0.1500 0.0597
0.0931 5.87 60000 0.2494 0.1500 0.0599
0.0906 6.36 65000 0.2604 0.1507 0.0602
0.0788 6.85 70000 0.2629 0.1506 0.0603
0.0692 7.34 75000 0.2500 0.1484 0.0590
0.0896 7.83 80000 0.2525 0.1468 0.0586
0.0794 8.32 85000 0.2494 0.1458 0.0583
0.0908 8.81 90000 0.2539 0.1475 0.0587
0.0646 9.3 95000 0.2539 0.1457 0.0578
0.0877 9.79 100000 0.2503 0.1453 0.0583
0.0657 10.28 105000 0.2589 0.1457 0.0586
0.0715 10.77 110000 0.2638 0.1462 0.0586
0.0704 11.26 115000 0.2562 0.1419 0.0571
0.0582 11.75 120000 0.2663 0.1425 0.0577
0.0583 12.23 125000 0.2615 0.1408 0.0573
0.0776 12.72 130000 0.2593 0.1434 0.0575
0.0624 13.21 135000 0.2603 0.1438 0.0577
0.0619 13.7 140000 0.2512 0.1391 0.0564
0.0642 14.19 145000 0.2539 0.1386 0.0558
0.0593 14.68 150000 0.2609 0.1406 0.0567
0.0596 15.17 155000 0.2567 0.1390 0.0562
0.0671 15.66 160000 0.2618 0.1404 0.0574
0.0537 16.15 165000 0.2668 0.1391 0.0565
0.0543 16.64 170000 0.2583 0.1379 0.0560
0.056 17.13 175000 0.2612 0.1395 0.0564
0.0605 17.62 180000 0.2654 0.1375 0.0557
0.0536 18.11 185000 0.2703 0.1356 0.0549
0.0469 18.6 190000 0.2571 0.1348 0.0548
0.0548 19.09 195000 0.2621 0.1359 0.0551
0.0507 19.58 200000 0.2628 0.1348 0.0549
0.0513 20.06 205000 0.2722 0.1367 0.0558
0.0423 20.55 210000 0.2752 0.1349 0.0546
0.049 21.04 215000 0.2777 0.1358 0.0552
0.0434 21.53 220000 0.2671 0.1336 0.0549
0.0443 22.02 225000 0.2815 0.1333 0.0544
0.0533 22.51 230000 0.2674 0.1334 0.0542
0.0458 23.0 235000 0.2746 0.1320 0.0541
0.0527 23.49 240000 0.2750 0.1351 0.0546
0.0458 23.98 245000 0.2748 0.1322 0.0539
0.0434 24.47 250000 0.2774 0.1317 0.0538
0.0434 24.96 255000 0.2756 0.1322 0.0534
0.041 25.45 260000 0.2786 0.1337 0.0542
0.0408 25.94 265000 0.2785 0.1320 0.0534
0.0486 26.43 270000 0.2882 0.1325 0.0536
0.0469 26.92 275000 0.2796 0.1315 0.0532
0.041 27.41 280000 0.2786 0.1319 0.0536
0.0333 27.89 285000 0.2893 0.1316 0.0532
0.0391 28.38 290000 0.2893 0.1318 0.0537
0.0427 28.87 295000 0.3006 0.1326 0.0535
0.0469 29.36 300000 0.2846 0.1308 0.0530
0.0317 29.85 305000 0.3140 0.1311 0.0534
0.0373 30.34 310000 0.2951 0.1314 0.0533
0.0367 30.83 315000 0.2976 0.1309 0.0532
0.0385 31.32 320000 0.3068 0.1311 0.0534
0.0372 31.81 325000 0.3085 0.1295 0.0527
0.0342 32.3 330000 0.3150 0.1290 0.0527
0.035 32.79 335000 0.3133 0.1299 0.0530
0.0331 33.28 340000 0.3201 0.1303 0.0530
0.0334 33.77 345000 0.3310 0.1294 0.0527
0.0353 34.26 350000 0.3105 0.1287 0.0523
0.0367 34.75 355000 0.3180 0.1302 0.0530
0.0397 35.24 360000 0.3322 0.1297 0.0528
0.0384 35.72 365000 0.3290 0.1303 0.0530
0.0349 36.21 370000 0.3358 0.1298 0.0529
0.0352 36.7 375000 0.3351 0.1347 0.0546
0.0333 37.19 380000 0.3420 0.1296 0.0528
0.0282 37.68 385000 0.3426 0.1317 0.0534
0.0247 38.17 390000 0.3606 0.1318 0.0531
0.0312 38.66 395000 0.3509 0.1494 0.0601
0.0288 39.15 400000 0.3516 0.1325 0.0536
0.0281 39.64 405000 0.3489 0.1303 0.0528
0.0208 40.13 410000 0.3661 0.1430 0.0576
0.0276 40.62 415000 0.3620 0.1300 0.0524
0.0253 41.11 420000 0.3786 0.1328 0.0538
0.025 41.6 425000 0.3782 0.1321 0.0535
0.02 42.09 430000 0.3721 0.1297 0.0523
0.0192 42.58 435000 0.4099 0.1308 0.0528
0.0197 43.07 440000 0.3970 0.1292 0.0525
0.0177 43.55 445000 0.3946 0.1306 0.0531
0.0185 44.04 450000 0.4060 0.1293 0.0526
0.0176 44.53 455000 0.3968 0.1303 0.0529
0.0126 45.02 460000 0.3994 0.1304 0.0529
0.0142 45.51 465000 0.3975 0.1300 0.0527
0.0122 46.0 470000 0.4055 0.1287 0.0523
0.0115 46.49 475000 0.4211 0.1303 0.0526
0.0102 46.98 480000 0.4148 0.1298 0.0525
0.0119 47.47 485000 0.4238 0.1301 0.0527
0.0098 47.96 490000 0.4293 0.1299 0.0526
0.0125 48.45 495000 0.4375 0.1300 0.0526
0.0095 48.94 500000 0.4268 0.1303 0.0527
0.0055 49.43 505000 0.4286 0.1305 0.0527
0.0089 49.92 510000 0.4371 0.1304 0.0528
Framework versions
  • Transformers 4.37.0.dev0
  • Pytorch 2.1.0+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.0

Runs of ylacombe wav2vec2-bert-CV16-en-cv-2 on huggingface.co

6
Total runs
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24-hour runs
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3-day runs
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7-day runs
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