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metadata
language:
  - sv-SE
license: cc0-1.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
datasets:
  - common_voice
model-index:
  - name: ''
    results: []

This model is a fine-tuned version of marinone94/xls-r-300m-sv-robust on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SV-SE dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1497
  • Wer: 0.1261

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: 0.00025
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 2000
  • num_epochs: 50.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
3.3533 1.1 100 3.2807 1.0
3.1709 2.2 200 3.1325 1.0
3.0573 3.3 300 3.0615 1.0
3.0314 4.39 400 3.0990 1.0
3.0129 5.49 500 3.0400 1.0
2.9964 6.59 600 2.9990 1.0
2.9602 7.69 700 2.9620 1.0
2.8756 8.79 800 2.7302 1.0
2.2931 9.89 900 1.5058 0.9776
1.8427 10.98 1000 0.9155 0.7832
1.4286 12.09 1100 0.4075 0.3796
1.2229 13.19 1200 0.2893 0.2652
1.1106 14.28 1300 0.2469 0.2254
1.0663 15.38 1400 0.2219 0.1973
1.0667 16.48 1500 0.2129 0.1894
1.0193 17.58 1600 0.1991 0.1789
0.9816 18.68 1700 0.1940 0.1801
0.9814 19.78 1800 0.1860 0.1667
0.9787 20.87 1900 0.1888 0.1642
0.9699 21.97 2000 0.1875 0.1704
0.9616 23.08 2100 0.1802 0.1617
0.9378 24.17 2200 0.1793 0.1577
0.888 25.27 2300 0.1764 0.1545
0.8942 26.37 2400 0.1674 0.1492
0.8701 27.47 2500 0.1739 0.1512
0.8555 28.57 2600 0.1690 0.1446
0.8513 29.67 2700 0.1649 0.1477
0.8659 30.77 2800 0.1637 0.1422
0.8419 31.86 2900 0.1614 0.1397
0.8491 32.96 3000 0.1595 0.1401
0.8395 34.07 3100 0.1607 0.1376
0.83 35.16 3200 0.1538 0.1379
0.7835 36.26 3300 0.1602 0.1408
0.7703 37.36 3400 0.1601 0.1369
0.7474 38.46 3500 0.1514 0.1342
0.7719 39.56 3600 0.1593 0.1353
0.7638 40.66 3700 0.1536 0.1338
0.771 41.75 3800 0.1531 0.1317
0.7594 42.85 3900 0.1498 0.1288
0.7383 43.95 4000 0.1527 0.1300
0.7565 45.05 4100 0.1482 0.1289
0.7697 46.15 4200 0.1495 0.1272
0.7194 47.25 4300 0.1493 0.1269
0.7479 48.35 4400 0.1490 0.1276
0.7132 49.45 4500 0.1501 0.1265

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu102
  • Datasets 1.18.3
  • Tokenizers 0.11.0