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Automatic Speech Recognition
Transformers
PyTorch
Portuguese
wav2vec2
mozilla-foundation/common_voice_8_0
Generated from Trainer
Inference Endpoints
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metadata
language:
  - pt
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
datasets:
  - common_voice
model-index:
  - name: output
    results: []

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