wav2vec2-5Class-Validation-Mic

This model is a fine-tuned version of anderloh/Hugginhface-master-wav2vec-pretreined-5-class-train-test on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.5967
• Accuracy: 0.4057

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: 128
• eval_batch_size: 128
• seed: 0
• gradient_accumulation_steps: 4
• total_train_batch_size: 512
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 150.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.92	3	1.6032	0.3203
No log	1.85	6	1.6029	0.3203
No log	2.77	9	1.6024	0.3203
No log	4.0	13	1.6015	0.3025
No log	4.92	16	1.6005	0.3025
No log	5.85	19	1.5994	0.2811
No log	6.77	22	1.5981	0.2705
No log	8.0	26	1.5959	0.2562
No log	8.92	29	1.5941	0.2384
No log	9.85	32	1.5923	0.2206
No log	10.77	35	1.5902	0.2384
No log	12.0	39	1.5872	0.2384
No log	12.92	42	1.5848	0.2491
No log	13.85	45	1.5822	0.2633
No log	14.77	48	1.5797	0.2633
No log	16.0	52	1.5768	0.2384
No log	16.92	55	1.5747	0.2278
No log	17.85	58	1.5729	0.2278
No log	18.77	61	1.5713	0.2313
No log	20.0	65	1.5694	0.2313
No log	20.92	68	1.5681	0.2313
No log	21.85	71	1.5670	0.2313
No log	22.77	74	1.5666	0.2313
No log	24.0	78	1.5666	0.2313
No log	24.92	81	1.5672	0.2313
No log	25.85	84	1.5685	0.2313
No log	26.77	87	1.5707	0.2313
No log	28.0	91	1.5751	0.2313
No log	28.92	94	1.5796	0.2313
No log	29.85	97	1.5857	0.2313
1.5332	30.77	100	1.5937	0.2313
1.5332	32.0	104	1.6070	0.2313
1.5332	32.92	107	1.6198	0.2313
1.5332	33.85	110	1.6357	0.2313
1.5332	34.77	113	1.6535	0.2313
1.5332	36.0	117	1.6803	0.2313
1.5332	36.92	120	1.7035	0.2313
1.5332	37.85	123	1.7277	0.2313
1.5332	38.77	126	1.7509	0.2313
1.5332	40.0	130	1.7757	0.2313
1.5332	40.92	133	1.7878	0.2313
1.5332	41.85	136	1.7966	0.2313
1.5332	42.77	139	1.8039	0.2313
1.5332	44.0	143	1.8047	0.2349
1.5332	44.92	146	1.8001	0.2491
1.5332	45.85	149	1.7924	0.2456
1.5332	46.77	152	1.7863	0.2562
1.5332	48.0	156	1.7770	0.2633
1.5332	48.92	159	1.7693	0.2705
1.5332	49.85	162	1.7656	0.2776
1.5332	50.77	165	1.7619	0.2918
1.5332	52.0	169	1.7609	0.3025
1.5332	52.92	172	1.7629	0.3060
1.5332	53.85	175	1.7646	0.3096
1.5332	54.77	178	1.7646	0.3132
1.5332	56.0	182	1.7650	0.3132
1.5332	56.92	185	1.7623	0.3238
1.5332	57.85	188	1.7614	0.3310
1.5332	58.77	191	1.7595	0.3345
1.5332	60.0	195	1.7589	0.3345
1.5332	60.92	198	1.7556	0.3381
1.2887	61.85	201	1.7556	0.3381
1.2887	62.77	204	1.7508	0.3416
1.2887	64.0	208	1.7468	0.3452
1.2887	64.92	211	1.7416	0.3452
1.2887	65.85	214	1.7356	0.3452
1.2887	66.77	217	1.7274	0.3559
1.2887	68.0	221	1.7196	0.3594
1.2887	68.92	224	1.7133	0.3630
1.2887	69.85	227	1.7103	0.3630
1.2887	70.77	230	1.7120	0.3630
1.2887	72.0	234	1.7099	0.3665
1.2887	72.92	237	1.7038	0.3701
1.2887	73.85	240	1.6975	0.3737
1.2887	74.77	243	1.6929	0.3772
1.2887	76.0	247	1.6884	0.3808
1.2887	76.92	250	1.6822	0.3879
1.2887	77.85	253	1.6749	0.3879
1.2887	78.77	256	1.6709	0.3915
1.2887	80.0	260	1.6645	0.3915
1.2887	80.92	263	1.6606	0.3915
1.2887	81.85	266	1.6586	0.3915
1.2887	82.77	269	1.6515	0.3915
1.2887	84.0	273	1.6471	0.3950
1.2887	84.92	276	1.6459	0.3950
1.2887	85.85	279	1.6428	0.3950
1.2887	86.77	282	1.6446	0.3950
1.2887	88.0	286	1.6454	0.3950
1.2887	88.92	289	1.6433	0.3950
1.2887	89.85	292	1.6395	0.3950
1.2887	90.77	295	1.6372	0.3950
1.2887	92.0	299	1.6350	0.3950
1.1159	92.92	302	1.6332	0.3986
1.1159	93.85	305	1.6306	0.3986
1.1159	94.77	308	1.6296	0.3986
1.1159	96.0	312	1.6273	0.3986
1.1159	96.92	315	1.6257	0.3986
1.1159	97.85	318	1.6229	0.4021
1.1159	98.77	321	1.6211	0.4021
1.1159	100.0	325	1.6199	0.4021
1.1159	100.92	328	1.6203	0.4021
1.1159	101.85	331	1.6201	0.4021
1.1159	102.77	334	1.6200	0.3986
1.1159	104.0	338	1.6153	0.4021
1.1159	104.92	341	1.6125	0.4057
1.1159	105.85	344	1.6099	0.4057
1.1159	106.77	347	1.6073	0.4057
1.1159	108.0	351	1.6028	0.4057
1.1159	108.92	354	1.6007	0.4057
1.1159	109.85	357	1.6002	0.4057
1.1159	110.77	360	1.6003	0.4057
1.1159	112.0	364	1.6025	0.4057
1.1159	112.92	367	1.6049	0.4021
1.1159	113.85	370	1.6071	0.4021
1.1159	114.77	373	1.6078	0.4021
1.1159	116.0	377	1.6086	0.4021
1.1159	116.92	380	1.6080	0.4021
1.1159	117.85	383	1.6063	0.4021
1.1159	118.77	386	1.6059	0.4021
1.1159	120.0	390	1.6057	0.4021
1.1159	120.92	393	1.6052	0.4021
1.1159	121.85	396	1.6048	0.4021
1.1159	122.77	399	1.6036	0.4021
1.0195	124.0	403	1.6036	0.4021
1.0195	124.92	406	1.6032	0.4021
1.0195	125.85	409	1.6019	0.4021
1.0195	126.77	412	1.6004	0.4021
1.0195	128.0	416	1.5979	0.4021
1.0195	128.92	419	1.5969	0.4021
1.0195	129.85	422	1.5966	0.4021
1.0195	130.77	425	1.5965	0.4021
1.0195	132.0	429	1.5959	0.4057
1.0195	132.92	432	1.5960	0.4057
1.0195	133.85	435	1.5960	0.4057
1.0195	134.77	438	1.5962	0.4057
1.0195	136.0	442	1.5966	0.4057
1.0195	136.92	445	1.5967	0.4057
1.0195	137.85	448	1.5967	0.4057
1.0195	138.46	450	1.5967	0.4057

Framework versions

• Transformers 4.39.0.dev0
• Pytorch 2.2.1+cu121
• Datasets 2.17.1
• Tokenizers 0.15.2