speech-emotion-recognition-with-facebook-wav2vec2-large-xlsr-53

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the RAVDESS, SAVEE, TESS, and URDU dataset. It achieves the following results on the evaluation set:

• Loss: 0.4989
• Accuracy: 0.9168
• Precision: 0.9209
• Recall: 0.9168
• F1: 0.9166

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: 5e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 5
• total_train_batch_size: 10
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 25
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.9343	0.9995	394	1.9277	0.2505	0.1425	0.2505	0.1691
1.7944	1.9990	788	1.6446	0.4574	0.5759	0.4574	0.4213
1.4601	2.9985	1182	1.3242	0.5953	0.6183	0.5953	0.5709
1.0551	3.9980	1576	1.0764	0.6623	0.6659	0.6623	0.6447
0.8934	5.0	1971	0.9209	0.7059	0.7172	0.7059	0.6825
1.1156	5.9995	2365	0.8292	0.7465	0.7635	0.7465	0.7442
0.6307	6.9990	2759	0.6439	0.8043	0.8090	0.8043	0.8020
0.774	7.9985	3153	0.6666	0.7921	0.8117	0.7921	0.7916
0.5537	8.9980	3547	0.5111	0.8245	0.8268	0.8245	0.8205
0.3762	10.0	3942	0.5506	0.8306	0.8390	0.8306	0.8296
0.716	10.9995	4336	0.5499	0.8276	0.8465	0.8276	0.8268
0.5372	11.9990	4730	0.5463	0.8377	0.8606	0.8377	0.8404
0.3746	12.9985	5124	0.4758	0.8611	0.8714	0.8611	0.8597
0.4317	13.9980	5518	0.4438	0.8742	0.8843	0.8742	0.8756
0.2104	15.0	5913	0.4426	0.8803	0.8864	0.8803	0.8806
0.3193	15.9995	6307	0.4741	0.8671	0.8751	0.8671	0.8683
0.3445	16.9990	6701	0.3850	0.9037	0.9047	0.9037	0.9038
0.2777	17.9985	7095	0.4802	0.8834	0.8923	0.8834	0.8836
0.4406	18.9980	7489	0.4053	0.9047	0.9096	0.9047	0.9043
0.1707	20.0	7884	0.4434	0.9067	0.9129	0.9067	0.9069
0.2138	20.9995	8278	0.5051	0.9037	0.9155	0.9037	0.9053
0.1812	21.9990	8672	0.4238	0.8955	0.9007	0.8955	0.8953
0.3639	22.9985	9066	0.4021	0.9138	0.9182	0.9138	0.9143
0.3193	23.9980	9460	0.4989	0.9168	0.9209	0.9168	0.9166
0.2067	24.9873	9850	0.4959	0.8976	0.9032	0.8976	0.8975

Framework versions

• Transformers 4.44.2
• Pytorch 2.4.1+cu121
• Datasets 3.0.0
• Tokenizers 0.19.1