wav2vec2-large-xlsr-53-finetuned-ks

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.4923
• Accuracy: 0.7871
• F1: 0.7863

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: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• 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
1.379	1.0	141	1.3767	0.2991	0.1377
1.3611	2.0	283	1.3600	0.2991	0.1377
1.3393	3.0	424	1.3515	0.2991	0.1377
1.2932	4.0	566	1.3306	0.3607	0.3098
1.2356	5.0	707	1.2202	0.4397	0.3926
1.2222	6.0	849	1.3719	0.3601	0.2778
1.036	7.0	990	1.2779	0.4290	0.3781
1.0348	8.0	1132	1.2845	0.4257	0.3824
0.9044	9.0	1273	1.2239	0.4927	0.4646
0.8557	10.0	1415	1.6261	0.3926	0.3253
0.804	11.0	1556	1.0748	0.5703	0.5558
0.6517	12.0	1698	1.2891	0.5471	0.5294
0.6063	13.0	1839	0.9921	0.6552	0.6514
0.5008	14.0	1981	1.4346	0.5391	0.5162
0.5425	15.0	2122	1.3406	0.5802	0.5573
0.3806	16.0	2264	1.2260	0.6353	0.6291
0.4022	17.0	2405	1.7530	0.5444	0.5197
0.3001	18.0	2547	1.3619	0.6247	0.6132
0.1921	19.0	2688	1.3687	0.6505	0.6443
0.2704	20.0	2830	1.2533	0.6810	0.6745
0.3145	21.0	2971	1.6079	0.6233	0.6133
0.2045	22.0	3113	1.1432	0.7215	0.7198
0.2444	23.0	3254	1.4012	0.6936	0.6861
0.2223	24.0	3396	1.5944	0.6585	0.6533
0.2415	25.0	3537	1.1057	0.7454	0.7420
0.2233	26.0	3679	1.4083	0.7036	0.6997
0.119	27.0	3820	1.3240	0.7341	0.7323
0.1125	28.0	3962	1.8332	0.6658	0.6590
0.1577	29.0	4103	1.8048	0.6764	0.6714
0.1169	30.0	4245	1.3329	0.7573	0.7563
0.1348	31.0	4386	2.0588	0.6485	0.6359
0.1203	32.0	4528	1.6487	0.7082	0.7012
0.1262	33.0	4669	1.5428	0.7261	0.7236
0.0679	34.0	4811	1.5458	0.7374	0.7357
0.0741	35.0	4952	1.4596	0.7546	0.7508
0.0913	36.0	5094	1.3710	0.7699	0.7702
0.2104	37.0	5235	1.6693	0.7367	0.7344
0.0856	38.0	5377	1.6339	0.75	0.7483
0.0931	39.0	5518	1.6512	0.7580	0.7571
0.0613	40.0	5660	1.6046	0.7646	0.7638
0.0713	41.0	5801	1.4553	0.7785	0.7779
0.025	42.0	5943	1.5725	0.7639	0.7625
0.0811	43.0	6084	1.7562	0.75	0.7474
0.0315	44.0	6226	1.4923	0.7871	0.7863
0.1026	45.0	6367	1.6013	0.7712	0.7706
0.0489	46.0	6509	1.7439	0.7533	0.7502
0.0248	47.0	6650	1.6019	0.7745	0.7730
0.0269	48.0	6792	1.6128	0.7679	0.7659
0.0114	49.0	6933	1.5737	0.7798	0.7788
0.0609	49.82	7050	1.6570	0.7712	0.7692

Framework versions

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