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Bagus/
wav2vec2_swbd_emodb

Name: wav2vec2_swbd_emodb
Availability: InStock

transformers

pytorch

wav2vec2

generated_from_trainer

facebook/wav2vec2-large-robust-ft-swbd-300h

finetune

apache-2.0

endpoints_compatible

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finetuned

This model is a fine-tuned version of facebook/wav2vec2-large-robust-ft-swbd-300h on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.0281
Uar: 0.7318
Acc: 0.7721

For the test set:

UAR: 0.74
ACC: 0.794

Model description

This model is to predict four emotion categories given and audio file. Labels are anger', 'happiness', 'sadness', 'neutral'. This wav2vec2-based model is known cannot detect 'happiness'.

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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Uar	Acc
No log	0.15	1	1.3899	0.25	0.1985
No log	0.31	2	1.3850	0.25	0.1985
No log	0.46	3	1.3815	0.25	0.1985
No log	0.62	4	1.3772	0.25	0.1985
No log	0.77	5	1.3714	0.25	0.4044
No log	0.92	6	1.3656	0.25	0.4044
1.4878	1.08	7	1.3610	0.25	0.4044
1.4878	1.23	8	1.3583	0.25	0.4044
1.4878	1.38	9	1.3549	0.25	0.4044
1.4878	1.54	10	1.3518	0.25	0.4044
1.4878	1.69	11	1.3491	0.25	0.4044
1.4878	1.85	12	1.3458	0.25	0.4044
1.4878	2.0	13	1.3425	0.25	0.4044
1.2316	2.15	14	1.3401	0.25	0.4044
1.2316	2.31	15	1.3380	0.25	0.4044
1.2316	2.46	16	1.3354	0.25	0.4044
1.2316	2.62	17	1.3326	0.25	0.4044
1.2316	2.77	18	1.3292	0.2778	0.4265
1.2316	2.92	19	1.3250	0.2963	0.4412
1.3835	3.08	20	1.3212	0.3519	0.4853
1.3835	3.23	21	1.3158	0.4029	0.5221
1.3835	3.38	22	1.3096	0.5047	0.6029
1.3835	3.54	23	1.3019	0.5695	0.6544
1.3835	3.69	24	1.2944	0.6485	0.7059
1.3835	3.85	25	1.2856	0.6534	0.6985
1.3835	4.0	26	1.2773	0.6768	0.7059
1.1038	4.15	27	1.2688	0.6540	0.6691
1.1038	4.31	28	1.2554	0.6404	0.6471
1.1038	4.46	29	1.2404	0.6359	0.6397
1.1038	4.62	30	1.2222	0.6586	0.6765
1.1038	4.77	31	1.2057	0.6631	0.6838
1.1038	4.92	32	1.1874	0.6769	0.6985
1.075	5.08	33	1.1624	0.6953	0.7206
1.075	5.23	34	1.1427	0.7182	0.75
1.075	5.38	35	1.1270	0.7182	0.75
1.075	5.54	36	1.1085	0.7227	0.7574
1.075	5.69	37	1.0982	0.7227	0.7574
1.075	5.85	38	1.0943	0.7227	0.7574
1.075	6.0	39	1.0930	0.7136	0.7426
0.7211	6.15	40	1.0903	0.7091	0.7353
0.7211	6.31	41	1.0858	0.7091	0.7353
0.7211	6.46	42	1.0816	0.7045	0.7279
0.7211	6.62	43	1.0734	0.7091	0.7353
0.7211	6.77	44	1.0617	0.7136	0.7426
0.7211	6.92	45	1.0536	0.7136	0.7426
0.6595	7.08	46	1.0450	0.7318	0.7721
0.6595	7.23	47	1.0370	0.7364	0.7794
0.6595	7.38	48	1.0323	0.7364	0.7794
0.6595	7.54	49	1.0301	0.7364	0.7794
0.6595	7.69	50	1.0307	0.7364	0.7794
0.6595	7.85	51	1.0302	0.7318	0.7721
0.6595	8.0	52	1.0307	0.7318	0.7721
0.5067	8.15	53	1.0317	0.7318	0.7721
0.5067	8.31	54	1.0324	0.7318	0.7721
0.5067	8.46	55	1.0324	0.7318	0.7721
0.5067	8.62	56	1.0326	0.7273	0.7647
0.5067	8.77	57	1.0315	0.7318	0.7721
0.5067	8.92	58	1.0297	0.7318	0.7721
0.5617	9.08	59	1.0287	0.7318	0.7721
0.5617	9.23	60	1.0281	0.7318	0.7721

Framework versions

Transformers 4.32.0
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.13.3

Bagus/wav2vec2_swbd_emodb