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tonyalves/
output

Name: output
Rating: 2.5 (9 reviews)

transformers

pytorch

wav2vec2

automatic-speech-recognition

mozilla-foundation/common_voice_8_0

generated_from_trainer

common_voice

apache-2.0

endpoints_compatible

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output

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - PT dataset. It achieves the following results on the evaluation set:

Loss: 0.1505
Wer: 0.1352

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: 7.5e-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_steps: 2000
num_epochs: 50.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
4.1367	0.64	500	3.8825	1.0
2.9677	1.29	1000	2.9498	1.0
1.5884	1.93	1500	0.6722	0.6493
1.2292	2.57	2000	0.3635	0.3202
1.1314	3.22	2500	0.2970	0.2680
1.0879	3.86	3000	0.2671	0.2486
1.0344	4.5	3500	0.2625	0.2239
1.0109	5.15	4000	0.2520	0.2230
0.9966	5.79	4500	0.2280	0.2105
0.9815	6.43	5000	0.2254	0.2179
0.9744	7.08	5500	0.2301	0.2137
0.9487	7.72	6000	0.2224	0.2051
0.9431	8.37	6500	0.2105	0.1992
0.9365	9.01	7000	0.2114	0.2019
0.9268	9.65	7500	0.2097	0.1988
0.9292	10.3	8000	0.2120	0.1986
0.929	10.94	8500	0.2048	0.1998
0.9017	11.58	9000	0.2035	0.1999
0.8898	12.23	9500	0.1961	0.1908
0.8799	12.87	10000	0.1945	0.1817
0.869	13.51	10500	0.1929	0.1844
0.8572	14.16	11000	0.1941	0.1888
0.8691	14.8	11500	0.1912	0.1804
0.8645	15.44	12000	0.1950	0.1851
0.8468	16.09	12500	0.1879	0.1770
0.8405	16.73	13000	0.1881	0.1759
0.8647	17.37	13500	0.1861	0.1740
0.8477	18.02	14000	0.1782	0.1702
0.811	18.66	14500	0.1915	0.1757
0.8165	19.3	15000	0.1820	0.1724
0.8166	19.95	15500	0.1798	0.1697
0.8167	20.59	16000	0.1805	0.1752
0.7908	21.24	16500	0.1761	0.1699
0.7925	21.88	17000	0.1740	0.1709
0.7803	22.52	17500	0.1815	0.1727
0.7839	23.17	18000	0.1737	0.1694
0.7815	23.81	18500	0.1732	0.1630
0.767	24.45	19000	0.1724	0.1648
0.7672	25.1	19500	0.1706	0.1596
0.7691	25.74	20000	0.1718	0.1618
0.7547	26.38	20500	0.1694	0.1565
0.7498	27.03	21000	0.1706	0.1582
0.7459	27.67	21500	0.1663	0.1586
0.7374	28.31	22000	0.1651	0.1567
0.7499	28.96	22500	0.1668	0.1549
0.7471	29.6	23000	0.1667	0.1553
0.7369	30.24	23500	0.1659	0.1556
0.7389	30.89	24000	0.1668	0.1538
0.7197	31.53	24500	0.1687	0.1561
0.71	32.17	25000	0.1666	0.1516
0.7199	32.82	25500	0.1640	0.1523
0.7194	33.46	26000	0.1659	0.1528
0.6923	34.11	26500	0.1662	0.1507
0.7054	34.75	27000	0.1641	0.1486
0.6955	35.39	27500	0.1634	0.1497
0.7084	36.04	28000	0.1618	0.1478
0.6917	36.68	28500	0.1589	0.1471
0.687	37.32	29000	0.1589	0.1450
0.6914	37.97	29500	0.1588	0.1465
0.6646	38.61	30000	0.1602	0.1468
0.6667	39.25	30500	0.1588	0.1444
0.6754	39.9	31000	0.1587	0.1455
0.6632	40.54	31500	0.1586	0.1461
0.6619	41.18	32000	0.1571	0.1441
0.6561	41.83	32500	0.1564	0.1420
0.6492	42.47	33000	0.1539	0.1437
0.6649	43.11	33500	0.1512	0.1406
0.6511	43.76	34000	0.1539	0.1384
0.6551	44.4	34500	0.1520	0.1384
0.6452	45.05	35000	0.1510	0.1368
0.6155	45.69	35500	0.1522	0.1375
0.628	46.33	36000	0.1522	0.1366
0.6389	46.97	36500	0.1513	0.1377
0.6265	47.62	37000	0.1512	0.1369
0.6197	48.26	37500	0.1511	0.1362
0.621	48.91	38000	0.1510	0.1357
0.6259	49.55	38500	0.1506	0.1353

Framework versions

Transformers 4.17.0
Pytorch 1.9.1+cu102
Datasets 2.0.0
Tokenizers 0.11.6

tonyalves/output

output

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

tonyalves/
output