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danieleV9H/
wav2vec2-base-ft-cv3-v3

Name: wav2vec2-base-ft-cv3-v3
Availability: InStock

automatic-speech-recognition

generated_from_trainer

common_voice

apache-2.0

endpoints_compatible

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wav2vec2-base-ft-cv3-v3

This model is a fine-tuned version of facebook/wav2vec2-base on the "mozilla-foundation/common_voice_3_0 english" dataset: "train" and "validation" splits are used for training while "test" split is used for validation. It achieves the following results on the evaluation set:

Loss: 0.5787
Wer: 0.2470

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
num_epochs: 12
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
3.5935	0.1	500	3.0085	1.0
1.6296	0.21	1000	1.0879	0.5895
0.7154	0.31	1500	0.8224	0.4839
0.6387	0.42	2000	0.7290	0.4302
0.5322	0.52	2500	0.6864	0.4044
0.497	0.63	3000	0.6294	0.3746
0.4659	0.73	3500	0.6388	0.3745
0.4452	0.84	4000	0.6122	0.3570
0.4356	0.94	4500	0.5770	0.3443
0.3976	1.05	5000	0.6145	0.3296
0.3767	1.15	5500	0.6099	0.3325
0.3704	1.25	6000	0.5998	0.3263
0.3541	1.36	6500	0.6070	0.3250
0.3592	1.46	7000	0.6076	0.3352
0.3508	1.57	7500	0.5712	0.3239
0.3437	1.67	8000	0.5729	0.3202
0.352	1.78	8500	0.5465	0.3100
0.34	1.88	9000	0.5418	0.3059
0.4086	1.99	9500	0.5189	0.3053
0.2968	2.09	10000	0.5373	0.3076
0.2968	2.2	10500	0.5602	0.3061
0.2956	2.3	11000	0.5651	0.3051
0.2863	2.41	11500	0.5476	0.2982
0.2852	2.51	12000	0.5579	0.2954
0.292	2.61	12500	0.5451	0.2953
0.2877	2.72	13000	0.5468	0.2905
0.285	2.82	13500	0.5283	0.2908
0.2872	2.93	14000	0.5240	0.2867
0.3286	3.03	14500	0.5078	0.2846
0.2526	3.14	15000	0.5373	0.2836
0.2494	3.24	15500	0.5566	0.2861
0.2534	3.35	16000	0.5378	0.2859
0.2435	3.45	16500	0.5225	0.2813
0.3144	3.56	17000	0.5203	0.2808
0.2501	3.66	17500	0.5176	0.2785
0.2469	3.76	18000	0.5022	0.2795
0.242	3.87	18500	0.5228	0.2757
0.242	3.97	19000	0.5024	0.2788
0.2205	4.08	19500	0.5318	0.2729
0.2149	4.18	20000	0.5492	0.2763
0.2186	4.29	20500	0.5599	0.2769
0.2191	4.39	21000	0.5493	0.2695
0.218	4.5	21500	0.5385	0.2709
0.2046	4.6	22000	0.5326	0.2718
0.2064	4.71	22500	0.5591	0.2725
0.2066	4.81	23000	0.5283	0.2700
0.2102	4.92	23500	0.5456	0.2713
0.3345	5.02	24000	0.5474	0.2698
0.1891	5.12	24500	0.5466	0.2672
0.1954	5.23	25000	0.5691	0.2731
0.1971	5.33	25500	0.5595	0.2741
0.1995	5.44	26000	0.5609	0.2716
0.1911	5.54	26500	0.5513	0.2684
0.1954	5.65	27000	0.5282	0.2683
0.193	5.75	27500	0.5460	0.2644
0.1974	5.86	28000	0.5415	0.2650
0.1947	5.96	28500	0.5227	0.2656
0.1836	6.07	29000	0.5361	0.2743
0.1741	6.17	29500	0.5637	0.2649
0.1776	6.27	30000	0.5705	0.2680
0.1747	6.38	30500	0.5587	0.2667
0.1761	6.48	31000	0.5480	0.2683
0.1715	6.59	31500	0.5547	0.2627
0.2424	6.69	32000	0.5254	0.2610
0.1756	6.8	32500	0.5301	0.2633
0.1761	6.9	33000	0.5267	0.2658
0.1751	7.01	33500	0.5611	0.2677
0.1653	7.11	34000	0.5617	0.2663
0.1591	7.22	34500	0.5435	0.2642
0.1559	7.32	35000	0.5608	0.2611
0.1604	7.43	35500	0.5477	0.2611
0.162	7.53	36000	0.5257	0.2559
0.1579	7.63	36500	0.5398	0.2570
0.162	7.74	37000	0.5566	0.2605
0.2351	7.84	37500	0.5371	0.2564
0.1566	7.95	38000	0.5507	0.2565
0.1515	8.05	38500	0.5640	0.2544
0.1459	8.16	39000	0.5739	0.2523
0.1463	8.26	39500	0.5596	0.2522
0.1466	8.37	40000	0.5522	0.2537
0.2372	8.47	40500	0.5567	0.2520
0.1488	8.58	41000	0.5546	0.2506
0.1492	8.68	41500	0.5533	0.2518
0.1454	8.78	42000	0.5488	0.2508
0.148	8.89	42500	0.5635	0.2526
0.1424	8.99	43000	0.5513	0.2509
0.1356	9.1	43500	0.5534	0.2527
0.1346	9.2	44000	0.5735	0.2497
0.1346	9.31	44500	0.5710	0.2489
0.1401	9.41	45000	0.5561	0.2491
0.2212	9.52	45500	0.5564	0.2482
0.1369	9.62	46000	0.5658	0.2484
0.1323	9.73	46500	0.5582	0.2495
0.1369	9.83	47000	0.5560	0.2503
0.1368	9.94	47500	0.5552	0.2489
0.1333	10.04	48000	0.5953	0.2491
0.1305	10.14	48500	0.5818	0.2520
0.1316	10.25	49000	0.5773	0.2506
0.1334	10.35	49500	0.5882	0.2485
0.1351	10.46	50000	0.5750	0.2483
0.1337	10.56	50500	0.5910	0.2486
0.2241	10.67	51000	0.5732	0.2491
0.1327	10.77	51500	0.5839	0.2493
0.1364	10.88	52000	0.5724	0.2464
0.1305	10.98	52500	0.5758	0.2468
0.128	11.09	53000	0.5811	0.2482
0.1267	11.19	53500	0.5903	0.2483
0.1262	11.29	54000	0.5792	0.2483
0.1291	11.4	54500	0.5735	0.2497
0.1228	11.5	55000	0.5920	0.2494
0.1249	11.61	55500	0.5907	0.2488
0.1266	11.71	56000	0.5786	0.2486
0.1235	11.82	56500	0.5790	0.2473
0.1243	11.92	57000	0.5787	0.2470

Framework versions

Transformers 4.19.2
Pytorch 1.11.0+cu113
Datasets 2.2.0
Tokenizers 0.12.1

danieleV9H/wav2vec2-base-ft-cv3-v3

wav2vec2-base-ft-cv3-v3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

danieleV9H/
wav2vec2-base-ft-cv3-v3