metadata

license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: wav2vec2-xlsr-korean-speech-emotion-recognition2_data_rebalance
    results: []

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