Wav2Vec2_Fine_tuned_on_CremaD_Speech_Emotion_Recognition

Yassmen/Wav2Vec2_Fine_tuned_on_CremaD_Speech_Emotion_Recognition

Wav2Vec2_Fine_tuned_on_CremaD_Speech_Emotion_Recognition is a machine learning model from Yassmen. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Library: transformers
License: apache-2.0
Model type: wav2vec2
Access: Public
Created: Apr 14, 2024
Updated: Apr 18, 2024
SHA: 65130cff

Base models

jonatasgrosman/wav2vec2-large-xlsr-53-english

Model card

From the Hugging Face model README

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

Library: transformers
Type: wav2vec2
License: apache-2.0
Created: Apr 14, 2024
Updated: Apr 18, 2024