wav2vec2-base-960h-finetuned-ks

This model is a fine-tuned version of facebook/wav2vec2-base-960h on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.9308
• Accuracy: 0.7752
• F1: 0.7749

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: 3e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• 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

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
1.3739	0.99	35	1.3646	0.3654	0.2858
1.3444	2.0	71	1.3366	0.3833	0.3172
1.3193	2.99	106	1.2654	0.4324	0.3350
1.2447	4.0	142	1.2093	0.4649	0.3611
1.2087	4.99	177	1.2030	0.4582	0.3714
1.1539	6.0	213	1.1419	0.4920	0.4317
1.0795	6.99	248	1.1794	0.4721	0.4207
1.0525	8.0	284	1.0922	0.5020	0.4684
1.0615	8.99	319	1.0459	0.5471	0.5158
0.9381	10.0	355	1.0080	0.5656	0.5464
0.8945	10.99	390	1.1166	0.5378	0.5108
0.8497	12.0	426	1.0068	0.5855	0.5772
0.7729	12.99	461	1.1214	0.5517	0.5406
0.6984	14.0	497	1.0416	0.5889	0.5729
0.6856	14.99	532	1.0135	0.6180	0.6185
0.6095	16.0	568	1.0088	0.6320	0.6299
0.5899	16.99	603	0.9208	0.6585	0.6612
0.5922	18.0	639	0.8657	0.6757	0.6749
0.537	18.99	674	0.8910	0.6850	0.6892
0.4767	20.0	710	1.0544	0.6525	0.6499
0.4864	20.99	745	0.8024	0.7255	0.7232
0.3546	22.0	781	0.8628	0.7168	0.7205
0.3567	22.99	816	0.8921	0.7168	0.7177
0.381	24.0	852	0.9130	0.7069	0.7081
0.3031	24.99	887	1.0026	0.7023	0.7039
0.412	26.0	923	0.8413	0.7420	0.7430
0.3175	26.99	958	0.8705	0.7294	0.7335
0.2581	28.0	994	0.8628	0.7387	0.7431
0.328	28.99	1029	0.9022	0.7414	0.7417
0.263	30.0	1065	0.9787	0.7248	0.7251
0.249	30.99	1100	0.8658	0.7454	0.7481
0.2242	32.0	1136	0.9386	0.7354	0.7380
0.2848	32.99	1171	0.8553	0.7633	0.7639
0.2457	34.0	1207	0.8789	0.7692	0.7674
0.1557	34.99	1242	0.8542	0.7553	0.7594
0.169	36.0	1278	0.9132	0.7573	0.7600
0.171	36.99	1313	0.9550	0.7467	0.7481
0.2209	38.0	1349	0.9843	0.7407	0.7408
0.1674	38.99	1384	0.9523	0.7460	0.7468
0.1998	40.0	1420	0.8683	0.7686	0.7697
0.1101	40.99	1455	1.0123	0.7354	0.7370
0.1466	42.0	1491	0.9332	0.7633	0.7651
0.1376	42.99	1526	0.9193	0.7739	0.7743
0.0939	44.0	1562	0.9234	0.7626	0.7634
0.1333	44.99	1597	0.9308	0.7752	0.7749
0.1183	46.0	1633	0.9375	0.7706	0.7712
0.1031	46.99	1668	0.9298	0.7739	0.7750
0.1154	48.0	1704	0.9373	0.7739	0.7745
0.1317	48.99	1739	0.9611	0.7646	0.7654
0.1132	49.3	1750	0.9606	0.7626	0.7635

Framework versions

• Transformers 4.36.2
• Pytorch 2.1.1+cu121
• Datasets 2.16.1
• Tokenizers 0.15.0