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wav2vec2-turkish-tr-voice

This model is a fine-tuned version of facebook/wav2vec2-large on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0239

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: 0.0001
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 2
  • total_train_batch_size: 8
  • total_eval_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1000
  • num_epochs: 50
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss
2.479 0.0765 1000 0.2672
0.3629 0.1531 2000 0.1266
0.2485 0.2296 3000 0.1020
0.2082 0.3062 4000 0.0916
0.1913 0.3827 5000 0.0737
0.1757 0.4593 6000 0.0741
0.1638 0.5358 7000 0.0686
0.1567 0.6124 8000 0.0636
0.153 0.6889 9000 0.0608
0.1488 0.7655 10000 0.0583
0.1374 0.8420 11000 0.0536
0.1357 0.9186 12000 0.0511
0.1393 0.9951 13000 0.0525
0.1314 1.0716 14000 0.0491
0.1194 1.1482 15000 0.0498
0.1277 1.2247 16000 0.0484
0.1173 1.3013 17000 0.0443
0.1214 1.3778 18000 0.0443
0.1136 1.4544 19000 0.0453
0.1107 1.5309 20000 0.0444
0.1155 1.6075 21000 0.0419
0.1102 1.6840 22000 0.0406
0.107 1.7606 23000 0.0414
0.104 1.8371 24000 0.0397
0.1095 1.9137 25000 0.0405
0.1004 1.9902 26000 0.0370
0.1038 2.0667 27000 0.0394
0.097 2.1433 28000 0.0422
0.0983 2.2198 29000 0.0369
0.0953 2.2964 30000 0.0405
0.0958 2.3729 31000 0.0362
0.0934 2.4495 32000 0.0376
0.0939 2.5260 33000 0.0348
0.0909 2.6026 34000 0.0356
0.0927 2.6791 35000 0.0332
0.0915 2.7557 36000 0.0324
0.0933 2.8322 37000 0.0354
0.0905 2.9088 38000 0.0328
0.0905 2.9853 39000 0.0351
0.0869 3.0618 40000 0.0303
0.0869 3.1384 41000 0.0351
0.0877 3.2149 42000 0.0339
0.0859 3.2915 43000 0.0342
0.0922 3.3680 44000 0.0331
0.0848 3.4446 45000 0.0350
0.0836 3.5211 46000 0.0307
0.0887 3.5977 47000 0.0317
0.088 3.6742 48000 0.0298
0.083 3.7508 49000 0.0325
0.0824 3.8273 50000 0.0318
0.0811 3.9039 51000 0.0315
0.0797 3.9804 52000 0.0289
0.0794 4.0570 53000 0.0330
0.0844 4.1335 54000 0.0325
0.0784 4.2100 55000 0.0330
0.0765 4.2866 56000 0.0295
0.0793 4.3631 57000 0.0293
0.0775 4.4397 58000 0.0302
0.076 4.5162 59000 0.0283
0.074 4.5928 60000 0.0277
0.0766 4.6693 61000 0.0296
0.074 4.7459 62000 0.0274
0.0744 4.8224 63000 0.0276
0.0784 4.8990 64000 0.0295
0.0763 4.9755 65000 0.0277
0.0722 5.0521 66000 0.0290
0.0713 5.1286 67000 0.0277
0.0708 5.2051 68000 0.0308
0.0714 5.2817 69000 0.0300
0.0742 5.3582 70000 0.0273
0.0717 5.4348 71000 0.0261
0.0699 5.5113 72000 0.0277
0.0695 5.5879 73000 0.0275
0.0686 5.6644 74000 0.0267
0.0702 5.7410 75000 0.0272
0.0672 5.8175 76000 0.0269
0.0724 5.8941 77000 0.0274
0.0696 5.9706 78000 0.0246
0.07 6.0472 79000 0.0325
0.0903 6.1237 80000 0.0276
0.0693 6.2002 81000 0.0250
0.0714 6.2768 82000 0.0255
0.0655 6.3533 83000 0.0258
0.0652 6.4299 84000 0.0270
0.0709 6.5064 85000 0.0253
0.0666 6.5830 86000 0.0253
0.0678 6.6595 87000 0.0257
0.0692 6.7361 88000 0.0236
0.0657 6.8126 89000 0.0287
0.0657 6.8892 90000 0.0240
0.0646 6.9657 91000 0.0245
0.0616 7.0423 92000 0.0254
0.0653 7.1188 93000 0.0291
0.0653 7.1953 94000 0.0253
0.0617 7.2719 95000 0.0239
0.0616 7.3484 96000 0.0245
0.0661 7.4250 97000 0.0237
0.0629 7.5015 98000 0.0239

Framework versions

  • Transformers 4.44.0
  • Pytorch 2.4.0+cu121
  • Datasets 2.18.0
  • Tokenizers 0.19.1
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