wav2vec2-age-gender
This model is a fine-tuned version of audeering/wav2vec2-large-robust-6-ft-age-gender on the arrow dataset. It achieves the following results on the evaluation set:
- Loss: 2.5537
- Accuracy: 0.4728
- F1 Score: 0.2652
- Mse: 1.3505
- Mae: 0.7527
- Mae^m: 1.2759
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: 0.0003
- train_batch_size: 9
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 18
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Mse | Mae | Mae^m |
|---|---|---|---|---|---|---|---|---|
| 1.6537 | 0.6116 | 100 | 1.5436 | 0.3951 | 0.1176 | 1.8719 | 0.9510 | 1.8697 |
| 1.444 | 1.2232 | 200 | 1.3586 | 0.4142 | 0.2591 | 1.1662 | 0.7629 | 1.0623 |
| 1.4129 | 1.8349 | 300 | 1.4103 | 0.3351 | 0.1886 | 1.1853 | 0.8256 | 1.2259 |
| 1.4089 | 2.4465 | 400 | 1.5204 | 0.3188 | 0.1708 | 1.2425 | 0.8556 | 1.1245 |
| 1.3624 | 3.0581 | 500 | 1.4430 | 0.3924 | 0.1947 | 1.2507 | 0.7984 | 1.1701 |
| 1.2793 | 3.6697 | 600 | 1.4646 | 0.4060 | 0.3666 | 1.3678 | 0.8174 | 0.8894 |
| 1.0964 | 4.2813 | 700 | 1.5239 | 0.4441 | 0.2978 | 1.1090 | 0.7275 | 0.9776 |
| 1.1362 | 4.8930 | 800 | 1.6786 | 0.4496 | 0.3366 | 1.2561 | 0.7548 | 0.8756 |
| 0.9191 | 5.5046 | 900 | 1.5732 | 0.4441 | 0.3106 | 1.0845 | 0.7139 | 0.8963 |
| 0.6949 | 6.1162 | 1000 | 1.8249 | 0.4469 | 0.2538 | 1.1008 | 0.7139 | 1.0736 |
| 0.8272 | 6.7278 | 1100 | 1.8225 | 0.4550 | 0.3115 | 1.0599 | 0.6948 | 1.0455 |
| 0.7242 | 7.3394 | 1200 | 2.1688 | 0.3951 | 0.2593 | 1.0599 | 0.7493 | 1.0673 |
| 0.7385 | 7.9511 | 1300 | 2.3316 | 0.4114 | 0.2894 | 1.2234 | 0.7711 | 1.0547 |
| 0.6214 | 8.5627 | 1400 | 2.2313 | 0.4768 | 0.2632 | 0.9455 | 0.6567 | 1.0143 |
| 0.3996 | 9.1743 | 1500 | 2.3696 | 0.4714 | 0.2979 | 1.0763 | 0.6948 | 1.0522 |
| 0.467 | 9.7859 | 1600 | 2.4082 | 0.4005 | 0.2902 | 1.1144 | 0.7493 | 0.8743 |
| 0.3559 | 10.3976 | 1700 | 2.9747 | 0.4632 | 0.3154 | 1.1281 | 0.7084 | 1.1496 |
| 0.5321 | 11.0092 | 1800 | 2.8344 | 0.4578 | 0.3201 | 0.9973 | 0.6812 | 0.9616 |
| 0.3078 | 11.6208 | 1900 | 3.0384 | 0.4251 | 0.3050 | 1.0981 | 0.7330 | 1.0609 |
| 0.216 | 12.2324 | 2000 | 3.1374 | 0.4578 | 0.3000 | 0.9564 | 0.6676 | 0.9610 |
| 0.3241 | 12.8440 | 2100 | 3.6605 | 0.4142 | 0.2530 | 1.0245 | 0.7193 | 1.1348 |
| 0.2642 | 13.4557 | 2200 | 3.4759 | 0.4278 | 0.3076 | 1.0354 | 0.7139 | 0.9519 |
| 0.1692 | 14.0673 | 2300 | 3.9204 | 0.4223 | 0.2506 | 1.0763 | 0.7275 | 1.1008 |
| 0.1729 | 14.6789 | 2400 | 3.8804 | 0.4578 | 0.3171 | 1.0545 | 0.6948 | 1.0291 |
| 0.2636 | 15.2905 | 2500 | 4.1746 | 0.4523 | 0.3043 | 1.0845 | 0.7084 | 0.9463 |
| 0.1523 | 15.9021 | 2600 | 4.1583 | 0.4169 | 0.2919 | 1.1144 | 0.7384 | 0.9452 |
| 0.101 | 16.5138 | 2700 | 4.2574 | 0.4496 | 0.2650 | 0.9837 | 0.6839 | 1.0895 |
| 0.1593 | 17.1254 | 2800 | 4.3649 | 0.4387 | 0.2716 | 1.1253 | 0.7275 | 1.1297 |
| 0.0707 | 17.7370 | 2900 | 4.6972 | 0.4060 | 0.2930 | 1.1907 | 0.7657 | 1.0690 |
| 0.1647 | 18.3486 | 3000 | 4.9096 | 0.4360 | 0.2576 | 1.0518 | 0.7139 | 1.0402 |
| 0.2 | 18.9602 | 3100 | 4.9426 | 0.4142 | 0.2755 | 1.0409 | 0.7248 | 1.0775 |
| 0.0381 | 19.5719 | 3200 | 4.7156 | 0.4441 | 0.2706 | 1.0136 | 0.6975 | 0.9823 |
| 0.1236 | 20.1835 | 3300 | 5.1736 | 0.4305 | 0.2666 | 1.0136 | 0.7084 | 0.9442 |
| 0.0415 | 20.7951 | 3400 | 5.2558 | 0.4305 | 0.2625 | 1.0681 | 0.7248 | 0.9997 |
| 0.0307 | 21.4067 | 3500 | 5.4287 | 0.3924 | 0.2482 | 1.1635 | 0.7711 | 1.0877 |
| 0.0079 | 22.0183 | 3600 | 5.3503 | 0.4169 | 0.2505 | 1.1035 | 0.7439 | 1.0535 |
| 0.0588 | 22.6300 | 3700 | 5.4146 | 0.4060 | 0.2740 | 1.1172 | 0.7520 | 0.9442 |
| 0.1119 | 23.2416 | 3800 | 5.5098 | 0.4278 | 0.2842 | 1.0899 | 0.7248 | 1.0109 |
| 0.0135 | 23.8532 | 3900 | 6.0306 | 0.4196 | 0.2634 | 1.0381 | 0.7221 | 1.0756 |
| 0.0457 | 24.4648 | 4000 | 5.8127 | 0.4469 | 0.2854 | 1.0327 | 0.7003 | 1.1027 |
| 0.1018 | 25.0765 | 4100 | 5.6878 | 0.4469 | 0.2939 | 1.1117 | 0.7193 | 1.0586 |
| 0.0082 | 25.6881 | 4200 | 5.6874 | 0.4332 | 0.2795 | 1.1090 | 0.7330 | 1.0770 |
| 0.0308 | 26.2997 | 4300 | 5.8580 | 0.4305 | 0.2743 | 1.0899 | 0.7302 | 1.0835 |
| 0.0081 | 26.9113 | 4400 | 6.2890 | 0.4332 | 0.2487 | 1.0599 | 0.7221 | 1.1730 |
| 0.0185 | 27.5229 | 4500 | 6.1682 | 0.4441 | 0.2758 | 1.0463 | 0.7084 | 1.0905 |
| 0.016 | 28.1346 | 4600 | 6.0424 | 0.4414 | 0.2930 | 1.0954 | 0.7248 | 1.1141 |
| 0.0156 | 28.7462 | 4700 | 6.1145 | 0.4414 | 0.2847 | 1.0926 | 0.7221 | 1.1320 |
| 0.0035 | 29.3578 | 4800 | 6.0960 | 0.4441 | 0.2912 | 1.1035 | 0.7221 | 1.1203 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
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