wav2vec2-base-finetuned-organ

This model is a fine-tuned version of facebook/wav2vec2-base on the GTZAN dataset. It achieves the following results on the evaluation set:

• Loss: 1.0117
• Accuracy: 0.8182

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: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 50
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.0965	1.0	6	1.0843	0.4545
1.0989	2.0	12	1.0883	0.3636
1.0931	3.0	18	1.0914	0.5455
1.0702	4.0	24	1.0578	0.4545
0.9822	5.0	30	0.9994	0.7273
0.9139	6.0	36	0.9735	0.5455
0.8008	7.0	42	0.7004	0.9091
0.6798	8.0	48	0.7404	0.8182
0.5969	9.0	54	0.7192	0.7273
0.4976	10.0	60	0.4668	0.9091
0.436	11.0	66	0.7406	0.7273
0.5859	12.0	72	0.6139	0.7273
0.3788	13.0	78	0.6551	0.7273
0.3176	14.0	84	0.4746	0.9091
0.2892	15.0	90	0.8285	0.7273
0.2452	16.0	96	0.8523	0.7273
0.1464	17.0	102	0.9791	0.7273
0.4589	18.0	108	1.2469	0.6364
0.1641	19.0	114	1.1607	0.6364
0.1765	20.0	120	0.7318	0.8182
0.1553	21.0	126	1.1178	0.6364
0.2048	22.0	132	1.2835	0.6364
0.2477	23.0	138	0.7558	0.8182
0.2042	24.0	144	0.8053	0.8182
0.2242	25.0	150	1.1131	0.7273
0.2063	26.0	156	1.1455	0.7273
0.1148	27.0	162	1.1386	0.7273
0.0948	28.0	168	1.0196	0.7273
0.2296	29.0	174	1.2216	0.7273
0.1771	30.0	180	1.2645	0.7273
0.0749	31.0	186	1.3599	0.6364
0.0973	32.0	192	1.2880	0.7273
0.0231	33.0	198	0.9015	0.8182
0.1185	34.0	204	0.9180	0.8182
0.1645	35.0	210	1.3635	0.7273
0.0163	36.0	216	1.3961	0.7273
0.0743	37.0	222	1.3699	0.7273
0.0211	38.0	228	0.8085	0.8182
0.0713	39.0	234	0.8418	0.8182
0.0122	40.0	240	0.7659	0.8182
0.0116	41.0	246	0.9891	0.8182
0.0117	42.0	252	1.4963	0.7273
0.0738	43.0	258	1.4932	0.7273
0.0718	44.0	264	1.4665	0.7273
0.1334	45.0	270	0.9666	0.8182
0.0662	46.0	276	0.9798	0.8182
0.0973	47.0	282	0.9954	0.8182
0.0105	48.0	288	1.0073	0.8182
0.0092	49.0	294	1.0107	0.8182
0.0089	50.0	300	1.0117	0.8182

Framework versions

• Transformers 4.42.3
• Pytorch 2.3.1+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1