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wav2vec2-xlsr-korean-speech-emotion-recognition
This model is a fine-tuned version of jungjongho/wav2vec2-large-xlsr-korean-demo-colab_epoch15 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6651
- Accuracy: 0.7667
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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
1.7098 | 0.02 | 20 | 1.6849 | 0.1986 |
1.6093 | 0.05 | 40 | 1.6102 | 0.2237 |
1.5673 | 0.07 | 60 | 1.5126 | 0.3696 |
1.554 | 0.1 | 80 | 1.4260 | 0.3624 |
1.3676 | 0.12 | 100 | 1.2504 | 0.4761 |
1.3763 | 0.14 | 120 | 1.3997 | 0.4091 |
1.2727 | 0.17 | 140 | 1.3728 | 0.4211 |
1.4632 | 0.19 | 160 | 1.2427 | 0.4653 |
1.2167 | 0.22 | 180 | 1.1379 | 0.5371 |
1.1789 | 0.24 | 200 | 1.1165 | 0.5514 |
1.1639 | 0.26 | 220 | 1.0029 | 0.5813 |
1.1664 | 0.29 | 240 | 1.1682 | 0.4844 |
1.1223 | 0.31 | 260 | 1.0946 | 0.5526 |
1.2748 | 0.33 | 280 | 1.0023 | 0.5837 |
1.1422 | 0.36 | 300 | 1.0873 | 0.5215 |
0.9999 | 0.38 | 320 | 1.0378 | 0.5742 |
1.0444 | 0.41 | 340 | 1.0049 | 0.5921 |
1.0099 | 0.43 | 360 | 0.9367 | 0.6160 |
0.9251 | 0.45 | 380 | 1.0681 | 0.5694 |
0.8257 | 0.48 | 400 | 1.0516 | 0.5813 |
1.3777 | 0.5 | 420 | 1.0250 | 0.6280 |
0.9415 | 0.53 | 440 | 0.9033 | 0.6316 |
0.9927 | 0.55 | 460 | 0.9360 | 0.6184 |
1.0245 | 0.57 | 480 | 0.9133 | 0.6232 |
1.145 | 0.6 | 500 | 0.8876 | 0.6459 |
0.916 | 0.62 | 520 | 0.9421 | 0.6256 |
0.9833 | 0.65 | 540 | 0.8567 | 0.6400 |
0.7927 | 0.67 | 560 | 0.9217 | 0.6579 |
1.0793 | 0.69 | 580 | 0.8519 | 0.6663 |
0.9642 | 0.72 | 600 | 0.8192 | 0.6758 |
0.9433 | 0.74 | 620 | 0.8412 | 0.6627 |
0.5469 | 0.77 | 640 | 0.8177 | 0.6794 |
0.9734 | 0.79 | 660 | 0.8130 | 0.6794 |
0.7071 | 0.81 | 680 | 0.9579 | 0.6579 |
0.9347 | 0.84 | 700 | 0.7870 | 0.6878 |
0.8024 | 0.86 | 720 | 0.9395 | 0.6411 |
0.9428 | 0.88 | 740 | 0.8817 | 0.6663 |
0.8024 | 0.91 | 760 | 0.7953 | 0.6974 |
0.7028 | 0.93 | 780 | 0.8279 | 0.6890 |
0.8357 | 0.96 | 800 | 0.8291 | 0.6950 |
0.8304 | 0.98 | 820 | 0.7778 | 0.6914 |
0.8157 | 1.0 | 840 | 0.7726 | 0.7081 |
0.5431 | 1.03 | 860 | 0.7628 | 0.7117 |
0.6816 | 1.05 | 880 | 0.7928 | 0.7057 |
0.7498 | 1.08 | 900 | 0.7732 | 0.7069 |
0.6785 | 1.1 | 920 | 0.8298 | 0.7033 |
0.5951 | 1.12 | 940 | 0.8020 | 0.7225 |
0.4782 | 1.15 | 960 | 0.7541 | 0.7297 |
0.6825 | 1.17 | 980 | 0.9647 | 0.6687 |
0.5793 | 1.2 | 1000 | 0.9230 | 0.7057 |
0.4872 | 1.22 | 1020 | 0.8361 | 0.7141 |
0.5422 | 1.24 | 1040 | 0.8629 | 0.7225 |
0.5671 | 1.27 | 1060 | 0.8376 | 0.7261 |
0.6285 | 1.29 | 1080 | 0.7478 | 0.7524 |
0.6647 | 1.32 | 1100 | 0.8001 | 0.7261 |
0.7846 | 1.34 | 1120 | 0.7344 | 0.7428 |
0.6362 | 1.36 | 1140 | 0.6980 | 0.75 |
0.4498 | 1.39 | 1160 | 0.7186 | 0.7536 |
0.5002 | 1.41 | 1180 | 0.7003 | 0.7656 |
0.5366 | 1.44 | 1200 | 0.7670 | 0.7404 |
0.6324 | 1.46 | 1220 | 0.7095 | 0.7536 |
0.6089 | 1.48 | 1240 | 0.7791 | 0.7428 |
0.5368 | 1.51 | 1260 | 0.6889 | 0.7679 |
0.6087 | 1.53 | 1280 | 0.7078 | 0.7620 |
0.5514 | 1.55 | 1300 | 0.7207 | 0.7644 |
0.6472 | 1.58 | 1320 | 0.6864 | 0.7691 |
0.5265 | 1.6 | 1340 | 0.7005 | 0.7679 |
0.5339 | 1.63 | 1360 | 0.6983 | 0.7667 |
0.5886 | 1.65 | 1380 | 0.7075 | 0.7739 |
0.4888 | 1.67 | 1400 | 0.6967 | 0.7715 |
0.5152 | 1.7 | 1420 | 0.6864 | 0.7679 |
0.3835 | 1.72 | 1440 | 0.7277 | 0.7608 |
0.5798 | 1.75 | 1460 | 0.7023 | 0.7644 |
0.4918 | 1.77 | 1480 | 0.7007 | 0.7667 |
0.6233 | 1.79 | 1500 | 0.7429 | 0.7596 |
0.5679 | 1.82 | 1520 | 0.6910 | 0.7488 |
0.6393 | 1.84 | 1540 | 0.7151 | 0.7512 |
0.6167 | 1.87 | 1560 | 0.6984 | 0.7512 |
0.4379 | 1.89 | 1580 | 0.6685 | 0.7656 |
0.4348 | 1.91 | 1600 | 0.6662 | 0.7703 |
0.5685 | 1.94 | 1620 | 0.6648 | 0.7691 |
0.4126 | 1.96 | 1640 | 0.6656 | 0.7667 |
0.4846 | 1.99 | 1660 | 0.6651 | 0.7667 |
Framework versions
- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.4.1.dev0
- Tokenizers 0.12.1