wav2vec2-xlsr-korean-speech-emotion-recognition2_data_rebalance

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: 0.0124
• Accuracy: 0.9976

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: 0.0002
• 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
1.1219	0.04	200	1.4543	0.5792
0.7585	0.08	400	0.6959	0.7301
0.5959	0.13	600	0.7356	0.7671
0.4048	0.17	800	0.2661	0.9128
0.3101	0.21	1000	0.2470	0.9291
0.2805	0.25	1200	0.2115	0.9394
0.2885	0.29	1400	0.3597	0.9152
0.1703	0.33	1600	0.3973	0.9142
0.1818	0.38	1800	0.2164	0.9543
0.1727	0.42	2000	0.1133	0.9730
0.1038	0.46	2200	0.0826	0.9851
0.1343	0.5	2400	0.0823	0.9820
0.1412	0.54	2600	0.0762	0.9830
0.1321	0.58	2800	0.0786	0.9806
0.0738	0.63	3000	0.1036	0.9810
0.0998	0.67	3200	0.1984	0.9640
0.1135	0.71	3400	0.0775	0.9841
0.0552	0.75	3600	0.0923	0.9827
0.0633	0.79	3800	0.0518	0.9900
0.0769	0.83	4000	0.0599	0.9875
0.1026	0.88	4200	0.0800	0.9841
0.0641	0.92	4400	0.2396	0.9606
0.1068	0.96	4600	0.0653	0.9875
0.0802	1.0	4800	0.0844	0.9855
0.0483	1.04	5000	0.0984	0.9834
0.0392	1.09	5200	0.1092	0.9813
0.0408	1.13	5400	0.0719	0.9900
0.0388	1.17	5600	0.0494	0.9903
0.0253	1.21	5800	0.1486	0.9751
0.0448	1.25	6000	0.1370	0.9782
0.0415	1.29	6200	0.0508	0.9907
0.0552	1.34	6400	0.0332	0.9941
0.065	1.38	6600	0.0479	0.9900
0.0391	1.42	6800	0.0470	0.9910
0.0339	1.46	7000	0.0550	0.9886
0.0525	1.5	7200	0.0389	0.9920
0.0393	1.54	7400	0.0543	0.9910
0.0488	1.59	7600	0.0205	0.9965
0.0253	1.63	7800	0.0240	0.9948
0.0438	1.67	8000	0.0308	0.9952
0.0291	1.71	8200	0.0160	0.9969
0.0235	1.75	8400	0.0124	0.9969
0.0061	1.8	8600	0.0191	0.9962
0.022	1.84	8800	0.0178	0.9958
0.0176	1.88	9000	0.0135	0.9965
0.0168	1.92	9200	0.0161	0.9969
0.0068	1.96	9400	0.0124	0.9976

Framework versions

• Transformers 4.22.0.dev0
• Pytorch 1.12.1+cu113
• Datasets 2.4.1.dev0
• Tokenizers 0.12.1