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xtreme_s_xlsr_300m_voxpopuli_en

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
English
wav2vec2
voxpopuli
google/xtreme_s
Generated from Trainer
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xtreme_s_xlsr_300m_voxpopuli_en / README.md
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metadata
language:
  - en
license: apache-2.0
tags:
  - voxpopuli
  - google/xtreme_s
  - generated_from_trainer
datasets:
  - google/xtreme_s
model-index:
  - name: xtreme_s_xlsr_300m_voxpopuli_en
    results: []

xtreme_s_xlsr_300m_voxpopuli_en

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the GOOGLE/XTREME_S - VOXPOPULI.EN dataset. It achieves the following results on the evaluation set:

  • Cer: 0.0966
  • Loss: 0.3127
  • Wer: 0.1549
  • Predict Samples: 1842

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: 0.0003
  • train_batch_size: 8
  • eval_batch_size: 1
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 8
  • total_train_batch_size: 64
  • total_eval_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 2000
  • num_epochs: 10.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
1.4221 0.19 500 1.3325 0.8224 0.3432
0.8429 0.38 1000 0.7087 0.5028 0.2023
0.7377 0.57 1500 0.4900 0.2778 0.1339
0.5641 0.77 2000 0.4460 0.2540 0.1284
0.5787 0.96 2500 0.4242 0.2148 0.1167
0.3465 1.15 3000 0.4210 0.2087 0.1154
0.2787 1.34 3500 0.3954 0.2090 0.1155
0.2775 1.53 4000 0.3938 0.1992 0.1133
0.262 1.72 4500 0.3748 0.2104 0.1151
0.3138 1.92 5000 0.3825 0.1993 0.1134
0.4331 2.11 5500 0.3648 0.1935 0.1104
0.3802 2.3 6000 0.3966 0.1910 0.1109
0.3928 2.49 6500 0.3995 0.1898 0.1100
0.3441 2.68 7000 0.3764 0.1887 0.1103
0.3673 2.87 7500 0.3800 0.1843 0.1086
0.3422 3.07 8000 0.3932 0.1830 0.1092
0.2933 3.26 8500 0.3672 0.1915 0.1104
0.1785 3.45 9000 0.3820 0.1796 0.1072
0.321 3.64 9500 0.3533 0.1994 0.1126
0.1673 3.83 10000 0.3683 0.1856 0.1084
0.1757 4.02 10500 0.3365 0.1925 0.1102
0.1881 4.22 11000 0.3528 0.1775 0.1066
0.3106 4.41 11500 0.3909 0.1754 0.1063
0.25 4.6 12000 0.3734 0.1723 0.1052
0.2005 4.79 12500 0.3358 0.1900 0.1092
0.2982 4.98 13000 0.3513 0.1766 0.1060
0.1552 5.17 13500 0.3720 0.1729 0.1059
0.1645 5.37 14000 0.3569 0.1713 0.1044
0.2065 5.56 14500 0.3639 0.1720 0.1048
0.1898 5.75 15000 0.3660 0.1726 0.1050
0.1397 5.94 15500 0.3731 0.1670 0.1033
0.2056 6.13 16000 0.3782 0.1650 0.1030
0.1859 6.32 16500 0.3903 0.1667 0.1033
0.1374 6.52 17000 0.3721 0.1736 0.1048
0.2482 6.71 17500 0.3899 0.1643 0.1023
0.159 6.9 18000 0.3847 0.1687 0.1032
0.1487 7.09 18500 0.3817 0.1671 0.1030
0.1942 7.28 19000 0.4120 0.1616 0.1018
0.1517 7.47 19500 0.3856 0.1635 0.1020
0.0946 7.67 20000 0.3838 0.1621 0.1016
0.1455 7.86 20500 0.3749 0.1652 0.1020
0.1303 8.05 21000 0.4074 0.1615 0.1011
0.1207 8.24 21500 0.4121 0.1606 0.1008
0.0727 8.43 22000 0.3948 0.1607 0.1009
0.1123 8.62 22500 0.4025 0.1603 0.1009
0.1606 8.82 23000 0.3963 0.1580 0.1004
0.1458 9.01 23500 0.3991 0.1574 0.1002
0.2286 9.2 24000 0.4149 0.1596 0.1009
0.1284 9.39 24500 0.4251 0.1572 0.1002
0.1141 9.58 25000 0.4264 0.1579 0.1002
0.1823 9.77 25500 0.4230 0.1562 0.0999
0.2514 9.97 26000 0.4242 0.1564 0.0999

Framework versions

  • Transformers 4.18.0.dev0
  • Pytorch 1.10.1+cu111
  • Datasets 1.18.4.dev0
  • Tokenizers 0.11.6