wav2vec2-classifier-aug

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

Loss: 0.5202
Accuracy: 0.8679
Precision: 0.8908
Recall: 0.8679
F1: 0.8667
Binary: 0.9067

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: 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_steps: 500
num_epochs: 30
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Binary
No log	0.19	50	4.4234	0.0162	0.0017	0.0162	0.0031	0.1558
No log	0.38	100	4.2882	0.0350	0.0028	0.0350	0.0047	0.3075
No log	0.58	150	3.9749	0.0404	0.0024	0.0404	0.0043	0.3170
No log	0.77	200	3.7072	0.0458	0.0070	0.0458	0.0109	0.3296
No log	0.96	250	3.4794	0.0836	0.0233	0.0836	0.0217	0.3580
4.1218	1.15	300	3.2647	0.1321	0.0526	0.1321	0.0640	0.3930
4.1218	1.34	350	3.0118	0.2318	0.1558	0.2318	0.1503	0.4623
4.1218	1.53	400	2.7772	0.2642	0.1570	0.2642	0.1752	0.4849
4.1218	1.73	450	2.5522	0.3585	0.3222	0.3585	0.2848	0.5520
4.1218	1.92	500	2.3428	0.3342	0.2725	0.3342	0.2563	0.5372
3.1065	2.11	550	2.0580	0.4124	0.3332	0.4124	0.3326	0.5887
3.1065	2.3	600	1.8454	0.4771	0.4322	0.4771	0.4131	0.6323
3.1065	2.49	650	1.6830	0.5310	0.4926	0.5310	0.4771	0.6733
3.1065	2.68	700	1.5545	0.5580	0.5326	0.5580	0.5096	0.6898
3.1065	2.88	750	1.3593	0.6253	0.5975	0.6253	0.5812	0.7388
2.2273	3.07	800	1.2047	0.6927	0.6715	0.6927	0.6535	0.7849
2.2273	3.26	850	1.1223	0.6765	0.6662	0.6765	0.6461	0.7728
2.2273	3.45	900	1.0296	0.7062	0.7121	0.7062	0.6756	0.7943
2.2273	3.64	950	1.0001	0.7251	0.7388	0.7251	0.7074	0.8081
2.2273	3.84	1000	0.9879	0.7466	0.7650	0.7466	0.7265	0.8229
1.734	4.03	1050	0.9078	0.7466	0.7590	0.7466	0.7323	0.8237
1.734	4.22	1100	0.8344	0.7898	0.8284	0.7898	0.7794	0.8550
1.734	4.41	1150	0.8199	0.7925	0.8029	0.7925	0.7749	0.8558
1.734	4.6	1200	0.7227	0.7951	0.8309	0.7951	0.7892	0.8566
1.734	4.79	1250	0.7666	0.7871	0.8246	0.7871	0.7768	0.8520
1.734	4.99	1300	0.7529	0.7871	0.7989	0.7871	0.7768	0.8531
1.4492	5.18	1350	0.7035	0.8032	0.8287	0.8032	0.7986	0.8633
1.4492	5.37	1400	0.6597	0.8194	0.8522	0.8194	0.8141	0.8739
1.4492	5.56	1450	0.6592	0.8113	0.8472	0.8113	0.8108	0.8690
1.4492	5.75	1500	0.6535	0.8248	0.8547	0.8248	0.8203	0.8784
1.4492	5.94	1550	0.6343	0.8167	0.8568	0.8167	0.8116	0.8701
1.2533	6.14	1600	0.5640	0.8356	0.8589	0.8356	0.8329	0.8860
1.2533	6.33	1650	0.5465	0.8383	0.8669	0.8383	0.8341	0.8889
1.2533	6.52	1700	0.5594	0.8248	0.8549	0.8248	0.8204	0.8776
1.2533	6.71	1750	0.5765	0.8464	0.8776	0.8464	0.8463	0.8935
1.2533	6.9	1800	0.5169	0.8571	0.8758	0.8571	0.8543	0.9000
1.138	7.09	1850	0.5206	0.8410	0.8676	0.8410	0.8421	0.8887
1.138	7.29	1900	0.5258	0.8544	0.8779	0.8544	0.8537	0.8992
1.138	7.48	1950	0.5855	0.8383	0.8693	0.8383	0.8384	0.8879
1.138	7.67	2000	0.5209	0.8491	0.8800	0.8491	0.8493	0.8943
1.138	7.86	2050	0.5150	0.8410	0.8710	0.8410	0.8411	0.8889
1.0249	8.05	2100	0.4937	0.8571	0.8840	0.8571	0.8568	0.9022
1.0249	8.25	2150	0.5344	0.8518	0.8790	0.8518	0.8492	0.8995
1.0249	8.44	2200	0.5322	0.8437	0.8751	0.8437	0.8428	0.8927
1.0249	8.63	2250	0.5533	0.8248	0.8561	0.8248	0.8233	0.8774
1.0249	8.82	2300	0.5242	0.8491	0.8797	0.8491	0.8469	0.8943
0.9523	9.01	2350	0.4938	0.8679	0.8911	0.8679	0.8669	0.9075
0.9523	9.2	2400	0.5037	0.8625	0.8888	0.8625	0.8627	0.9038
0.9523	9.4	2450	0.4973	0.8571	0.8794	0.8571	0.8565	0.9000
0.9523	9.59	2500	0.5343	0.8383	0.8705	0.8383	0.8384	0.8868
0.9523	9.78	2550	0.5493	0.8491	0.8746	0.8491	0.8472	0.8943
0.9523	9.97	2600	0.5226	0.8544	0.8783	0.8544	0.8537	0.8981
0.8792	10.16	2650	0.4883	0.8625	0.8857	0.8625	0.8598	0.9038
0.8792	10.35	2700	0.5178	0.8518	0.8784	0.8518	0.8503	0.8962
0.8792	10.55	2750	0.6273	0.8383	0.8756	0.8383	0.8363	0.8879
0.8792	10.74	2800	0.5229	0.8571	0.8855	0.8571	0.8576	0.9000
0.8792	10.93	2850	0.4617	0.8706	0.8924	0.8706	0.8686	0.9094
0.8251	11.12	2900	0.5764	0.8625	0.8874	0.8625	0.8626	0.9038
0.8251	11.31	2950	0.5111	0.8706	0.8960	0.8706	0.8689	0.9094
0.8251	11.51	3000	0.6013	0.8437	0.8603	0.8437	0.8410	0.8906
0.8251	11.7	3050	0.5968	0.8437	0.8682	0.8437	0.8405	0.8916
0.8251	11.89	3100	0.5467	0.8544	0.8806	0.8544	0.8542	0.8981
0.7578	12.08	3150	0.6015	0.8544	0.8774	0.8544	0.8523	0.8981
0.7578	12.27	3200	0.4897	0.8679	0.8814	0.8679	0.8632	0.9067
0.7578	12.46	3250	0.5395	0.8491	0.8765	0.8491	0.8460	0.8935
0.7578	12.66	3300	0.5873	0.8491	0.8767	0.8491	0.8489	0.8935
0.7578	12.85	3350	0.5386	0.8491	0.8735	0.8491	0.8498	0.8935
0.7295	13.04	3400	0.5826	0.8652	0.8949	0.8652	0.8663	0.9057
0.7295	13.23	3450	0.5358	0.8571	0.8859	0.8571	0.8562	0.9003
0.7295	13.42	3500	0.4802	0.8841	0.9017	0.8841	0.8838	0.9173
0.7295	13.61	3550	0.5709	0.8410	0.8692	0.8410	0.8404	0.8879
0.7295	13.81	3600	0.5420	0.8544	0.8738	0.8544	0.8535	0.8992
0.7295	14.0	3650	0.5384	0.8652	0.8817	0.8652	0.8635	0.9049
0.6874	14.19	3700	0.4911	0.8598	0.8753	0.8598	0.8593	0.9019
0.6874	14.38	3750	0.5172	0.8598	0.8826	0.8598	0.8588	0.9011
0.6874	14.57	3800	0.5024	0.8598	0.8814	0.8598	0.8592	0.9019
0.6874	14.77	3850	0.5202	0.8679	0.8908	0.8679	0.8667	0.9067

Framework versions

Transformers 4.38.2
Pytorch 2.3.0
Datasets 2.19.1
Tokenizers 0.15.1

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Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001

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_steps: 500

num_epochs: 30

mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Binary
No log	0.19	50	4.4234	0.0162	0.0017	0.0162	0.0031	0.1558
No log	0.38	100	4.2882	0.0350	0.0028	0.0350	0.0047	0.3075
No log	0.58	150	3.9749	0.0404	0.0024	0.0404	0.0043	0.3170
No log	0.77	200	3.7072	0.0458	0.0070	0.0458	0.0109	0.3296
No log	0.96	250	3.4794	0.0836	0.0233	0.0836	0.0217	0.3580
4.1218	1.15	300	3.2647	0.1321	0.0526	0.1321	0.0640	0.3930
4.1218	1.34	350	3.0118	0.2318	0.1558	0.2318	0.1503	0.4623
4.1218	1.53	400	2.7772	0.2642	0.1570	0.2642	0.1752	0.4849
4.1218	1.73	450	2.5522	0.3585	0.3222	0.3585	0.2848	0.5520
4.1218	1.92	500	2.3428	0.3342	0.2725	0.3342	0.2563	0.5372
3.1065	2.11	550	2.0580	0.4124	0.3332	0.4124	0.3326	0.5887
3.1065	2.3	600	1.8454	0.4771	0.4322	0.4771	0.4131	0.6323
3.1065	2.49	650	1.6830	0.5310	0.4926	0.5310	0.4771	0.6733
3.1065	2.68	700	1.5545	0.5580	0.5326	0.5580	0.5096	0.6898
3.1065	2.88	750	1.3593	0.6253	0.5975	0.6253	0.5812	0.7388
2.2273	3.07	800	1.2047	0.6927	0.6715	0.6927	0.6535	0.7849
2.2273	3.26	850	1.1223	0.6765	0.6662	0.6765	0.6461	0.7728
2.2273	3.45	900	1.0296	0.7062	0.7121	0.7062	0.6756	0.7943
2.2273	3.64	950	1.0001	0.7251	0.7388	0.7251	0.7074	0.8081
2.2273	3.84	1000	0.9879	0.7466	0.7650	0.7466	0.7265	0.8229
1.734	4.03	1050	0.9078	0.7466	0.7590	0.7466	0.7323	0.8237
1.734	4.22	1100	0.8344	0.7898	0.8284	0.7898	0.7794	0.8550
1.734	4.41	1150	0.8199	0.7925	0.8029	0.7925	0.7749	0.8558
1.734	4.6	1200	0.7227	0.7951	0.8309	0.7951	0.7892	0.8566
1.734	4.79	1250	0.7666	0.7871	0.8246	0.7871	0.7768	0.8520
1.734	4.99	1300	0.7529	0.7871	0.7989	0.7871	0.7768	0.8531
1.4492	5.18	1350	0.7035	0.8032	0.8287	0.8032	0.7986	0.8633
1.4492	5.37	1400	0.6597	0.8194	0.8522	0.8194	0.8141	0.8739
1.4492	5.56	1450	0.6592	0.8113	0.8472	0.8113	0.8108	0.8690
1.4492	5.75	1500	0.6535	0.8248	0.8547	0.8248	0.8203	0.8784
1.4492	5.94	1550	0.6343	0.8167	0.8568	0.8167	0.8116	0.8701
1.2533	6.14	1600	0.5640	0.8356	0.8589	0.8356	0.8329	0.8860
1.2533	6.33	1650	0.5465	0.8383	0.8669	0.8383	0.8341	0.8889
1.2533	6.52	1700	0.5594	0.8248	0.8549	0.8248	0.8204	0.8776
1.2533	6.71	1750	0.5765	0.8464	0.8776	0.8464	0.8463	0.8935
1.2533	6.9	1800	0.5169	0.8571	0.8758	0.8571	0.8543	0.9000
1.138	7.09	1850	0.5206	0.8410	0.8676	0.8410	0.8421	0.8887
1.138	7.29	1900	0.5258	0.8544	0.8779	0.8544	0.8537	0.8992
1.138	7.48	1950	0.5855	0.8383	0.8693	0.8383	0.8384	0.8879
1.138	7.67	2000	0.5209	0.8491	0.8800	0.8491	0.8493	0.8943
1.138	7.86	2050	0.5150	0.8410	0.8710	0.8410	0.8411	0.8889
1.0249	8.05	2100	0.4937	0.8571	0.8840	0.8571	0.8568	0.9022
1.0249	8.25	2150	0.5344	0.8518	0.8790	0.8518	0.8492	0.8995
1.0249	8.44	2200	0.5322	0.8437	0.8751	0.8437	0.8428	0.8927
1.0249	8.63	2250	0.5533	0.8248	0.8561	0.8248	0.8233	0.8774
1.0249	8.82	2300	0.5242	0.8491	0.8797	0.8491	0.8469	0.8943
0.9523	9.01	2350	0.4938	0.8679	0.8911	0.8679	0.8669	0.9075
0.9523	9.2	2400	0.5037	0.8625	0.8888	0.8625	0.8627	0.9038
0.9523	9.4	2450	0.4973	0.8571	0.8794	0.8571	0.8565	0.9000
0.9523	9.59	2500	0.5343	0.8383	0.8705	0.8383	0.8384	0.8868
0.9523	9.78	2550	0.5493	0.8491	0.8746	0.8491	0.8472	0.8943
0.9523	9.97	2600	0.5226	0.8544	0.8783	0.8544	0.8537	0.8981
0.8792	10.16	2650	0.4883	0.8625	0.8857	0.8625	0.8598	0.9038
0.8792	10.35	2700	0.5178	0.8518	0.8784	0.8518	0.8503	0.8962
0.8792	10.55	2750	0.6273	0.8383	0.8756	0.8383	0.8363	0.8879
0.8792	10.74	2800	0.5229	0.8571	0.8855	0.8571	0.8576	0.9000
0.8792	10.93	2850	0.4617	0.8706	0.8924	0.8706	0.8686	0.9094
0.8251	11.12	2900	0.5764	0.8625	0.8874	0.8625	0.8626	0.9038
0.8251	11.31	2950	0.5111	0.8706	0.8960	0.8706	0.8689	0.9094
0.8251	11.51	3000	0.6013	0.8437	0.8603	0.8437	0.8410	0.8906
0.8251	11.7	3050	0.5968	0.8437	0.8682	0.8437	0.8405	0.8916
0.8251	11.89	3100	0.5467	0.8544	0.8806	0.8544	0.8542	0.8981
0.7578	12.08	3150	0.6015	0.8544	0.8774	0.8544	0.8523	0.8981
0.7578	12.27	3200	0.4897	0.8679	0.8814	0.8679	0.8632	0.9067
0.7578	12.46	3250	0.5395	0.8491	0.8765	0.8491	0.8460	0.8935
0.7578	12.66	3300	0.5873	0.8491	0.8767	0.8491	0.8489	0.8935
0.7578	12.85	3350	0.5386	0.8491	0.8735	0.8491	0.8498	0.8935
0.7295	13.04	3400	0.5826	0.8652	0.8949	0.8652	0.8663	0.9057
0.7295	13.23	3450	0.5358	0.8571	0.8859	0.8571	0.8562	0.9003
0.7295	13.42	3500	0.4802	0.8841	0.9017	0.8841	0.8838	0.9173
0.7295	13.61	3550	0.5709	0.8410	0.8692	0.8410	0.8404	0.8879
0.7295	13.81	3600	0.5420	0.8544	0.8738	0.8544	0.8535	0.8992
0.7295	14.0	3650	0.5384	0.8652	0.8817	0.8652	0.8635	0.9049
0.6874	14.19	3700	0.4911	0.8598	0.8753	0.8598	0.8593	0.9019
0.6874	14.38	3750	0.5172	0.8598	0.8826	0.8598	0.8588	0.9011
0.6874	14.57	3800	0.5024	0.8598	0.8814	0.8598	0.8592	0.9019
0.6874	14.77	3850	0.5202	0.8679	0.8908	0.8679	0.8667	0.9067

Framework versions

Transformers 4.38.2

Pytorch 2.3.0

Datasets 2.19.1

Tokenizers 0.15.1

fydhfzh/wav2vec2-classifier-aug

wav2vec2-classifier-aug

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

wav2vec2-classifier-aug

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions