Wav2Vec2_Fine_tuned_on_CremaD_Speech_Emotion_Recognition

This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-english.

The dataset used to fine-tune the original pre-trained model is the CremaD dataset. This dataset provides 7442 samples of recordings from actors performing on 6 different emotions in English, which are:

emotions = ['angry', 'disgust', 'fearful', 'happy', 'neutral', 'sad']

It achieves the following results on the evaluation set:

Loss: 0.6258
Accuracy: 0.7890

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: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.7923	0.01	10	1.8102	0.2554
1.7712	0.03	20	1.7128	0.2560
1.6854	0.04	30	1.7213	0.2823
1.6129	0.05	40	1.5384	0.3851
1.5121	0.07	50	1.5442	0.3810
1.532	0.08	60	1.4817	0.4234
1.3681	0.09	70	1.6103	0.3474
1.6408	0.11	80	1.5118	0.3495
1.4527	0.12	90	1.3684	0.4671
1.3219	0.13	100	1.3871	0.4698
1.5121	0.15	110	1.4060	0.4328
1.4013	0.16	120	1.5057	0.4180
1.3605	0.17	130	1.3576	0.4348
1.3813	0.19	140	1.3194	0.4933
1.2232	0.2	150	1.2804	0.5114
1.3133	0.22	160	1.2345	0.5356
1.2686	0.23	170	1.2445	0.5161
1.2539	0.24	180	1.1071	0.5766
1.1747	0.26	190	1.2424	0.5060
1.1644	0.27	200	1.3082	0.4892
1.2624	0.28	210	1.3811	0.5155
1.2036	0.3	220	1.2410	0.5349
1.2191	0.31	230	1.0329	0.5988
1.1212	0.32	240	1.1005	0.5806
1.1243	0.34	250	1.2593	0.5262
1.1951	0.35	260	1.0575	0.5981
1.0971	0.36	270	1.1753	0.5565
1.0209	0.38	280	1.0568	0.5840
1.1628	0.39	290	1.1174	0.5793
1.1894	0.4	300	1.0343	0.6183
1.0605	0.42	310	1.1357	0.5578
1.0701	0.43	320	1.0726	0.6042
0.9606	0.44	330	1.2933	0.5222
0.9128	0.46	340	1.1310	0.5827
1.1218	0.47	350	1.1245	0.6102
0.9566	0.48	360	1.0386	0.6116
1.1211	0.5	370	0.9842	0.6324
1.2184	0.51	380	0.9250	0.6593
1.1452	0.52	390	0.9282	0.6573
0.7752	0.54	400	1.0523	0.6102
1.0063	0.55	410	0.9372	0.6364
1.1807	0.56	420	1.0236	0.6176
1.0624	0.58	430	0.9036	0.6606
1.1832	0.59	440	0.9229	0.6458
1.0186	0.6	450	0.8801	0.6707
0.8184	0.62	460	0.9526	0.6398
0.8863	0.63	470	0.8996	0.6761
0.9068	0.65	480	0.8378	0.7030
0.8077	0.66	490	0.9574	0.6694
0.9824	0.67	500	1.0673	0.6499
0.8002	0.69	510	0.8819	0.6922
0.9411	0.7	520	0.8553	0.6815
1.0061	0.71	530	0.9180	0.6673
0.7496	0.73	540	0.9676	0.6505
0.8208	0.74	550	0.9990	0.6519
0.9846	0.75	560	0.8613	0.6962
0.9968	0.77	570	0.8798	0.6949
0.9485	0.78	580	0.9894	0.6223
0.9165	0.79	590	0.9384	0.6465
0.9393	0.81	600	0.7944	0.7137
0.9086	0.82	610	0.8543	0.6767
0.9175	0.83	620	0.8039	0.6996
0.8692	0.85	630	0.8488	0.6949
0.759	0.86	640	0.8890	0.6895
1.0115	0.87	650	1.0963	0.6210
0.766	0.89	660	0.9505	0.6277
1.2062	0.9	670	0.8218	0.6962
0.8678	0.91	680	0.7918	0.7056
0.9055	0.93	690	0.7626	0.7204
0.7303	0.94	700	0.8733	0.6714
0.9239	0.95	710	0.8488	0.6962
0.8024	0.97	720	0.7996	0.7083
0.7927	0.98	730	0.8690	0.6821
0.8371	0.99	740	0.9029	0.6727
0.8419	1.01	750	0.7640	0.7211
0.5163	1.02	760	0.8040	0.7292
0.4603	1.03	770	0.7946	0.7211
0.7675	1.05	780	0.9796	0.6774
0.9771	1.06	790	0.7548	0.7433
0.6141	1.08	800	0.7334	0.7386
0.71	1.09	810	0.7037	0.7547
0.6074	1.1	820	0.8142	0.7137
1.0638	1.12	830	0.8786	0.7036
0.7303	1.13	840	0.7548	0.7292
0.5361	1.14	850	0.7000	0.7513
0.6014	1.16	860	0.8950	0.6902
0.5635	1.17	870	0.7070	0.75
0.5585	1.18	880	0.7612	0.7473
0.8462	1.2	890	1.0107	0.6761
0.6256	1.21	900	0.7899	0.7272
0.7361	1.22	910	0.7397	0.7312
0.5147	1.24	920	0.8835	0.7003
0.5843	1.25	930	0.8751	0.7016
0.5077	1.26	940	0.7542	0.7278
0.6421	1.28	950	0.8593	0.7090
0.7138	1.29	960	0.7012	0.7601
0.5414	1.3	970	0.7669	0.7372
0.662	1.32	980	0.7620	0.7272
0.6002	1.33	990	0.6881	0.7628
0.8094	1.34	1000	0.7783	0.7433
0.6081	1.36	1010	0.7272	0.75
0.5943	1.37	1020	0.7667	0.7440
0.6295	1.38	1030	0.7453	0.7399
0.6415	1.4	1040	0.7053	0.7560
0.4686	1.41	1050	0.8764	0.7171
0.5586	1.42	1060	0.7406	0.75
0.4292	1.44	1070	0.7160	0.7708
0.6343	1.45	1080	0.8051	0.7298
0.6209	1.47	1090	0.9153	0.7198
0.834	1.48	1100	0.7113	0.7614
0.5106	1.49	1110	0.7978	0.7352
0.6587	1.51	1120	0.7805	0.7440
0.5694	1.52	1130	0.7192	0.7587
0.6949	1.53	1140	0.7119	0.7614
0.4578	1.55	1150	0.7249	0.7594
0.6219	1.56	1160	0.7289	0.7554
0.6857	1.57	1170	0.6933	0.7587
0.631	1.59	1180	0.6719	0.7749
0.6944	1.6	1190	0.7028	0.7587
0.5063	1.61	1200	0.6815	0.7587
0.6884	1.63	1210	0.7068	0.7534
0.797	1.64	1220	0.7583	0.7426
0.5841	1.65	1230	0.7034	0.7446
0.7062	1.67	1240	0.7050	0.7513
0.7438	1.68	1250	0.6894	0.7560
0.6627	1.69	1260	0.6438	0.7769
0.4233	1.71	1270	0.6523	0.7695
0.5555	1.72	1280	0.6859	0.7634
0.7625	1.73	1290	0.7076	0.7513
0.6136	1.75	1300	0.6515	0.7769
0.5207	1.76	1310	0.6463	0.7708
0.5175	1.77	1320	0.6442	0.7762
0.6413	1.79	1330	0.6515	0.7742
0.7482	1.8	1340	0.6608	0.7735
0.5284	1.81	1350	0.6717	0.7681
0.7004	1.83	1360	0.6800	0.7628
0.7958	1.84	1370	0.6577	0.7769
0.3887	1.85	1380	0.6428	0.7829
0.4225	1.87	1390	0.6465	0.7809
0.7193	1.88	1400	0.6590	0.7776
0.5101	1.9	1410	0.6519	0.7789
0.7712	1.91	1420	0.6510	0.7789
0.3919	1.92	1430	0.6566	0.7809
0.4867	1.94	1440	0.6531	0.7755
0.5402	1.95	1450	0.6441	0.7789
0.7002	1.96	1460	0.6344	0.7809
0.5943	1.98	1470	0.6278	0.7870
0.5144	1.99	1480	0.6258	0.7890

Framework versions

Transformers 4.40.0.dev0
Pytorch 2.2.1+cu121
Datasets 2.18.1.dev0
Tokenizers 0.15.2

Yassmen
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Wav2Vec2_Fine_tuned_on_CremaD_Speech_Emotion_Recognition

Wav2Vec2_Fine_tuned_on_CremaD_Speech_Emotion_Recognition

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for Yassmen/Wav2Vec2_Fine_tuned_on_CremaD_Speech_Emotion_Recognition

Evaluation results