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wav2vec2-xls-r-300m-gn-cv8

Automatic Speech Recognition
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wav2vec2
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robust-speech-event
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - common_voice
model-index:
  - name: wav2vec2-xls-r-300m-gn-cv8
    results: []

wav2vec2-xls-r-300m-gn-cv8

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

  • Loss: 1.0820
  • Wer: 0.7212

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: 0.0001
  • train_batch_size: 1
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 100
  • training_steps: 5000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
12.7853 2.76 100 4.7861 1.0
3.4153 5.55 200 3.5519 1.0
3.2923 8.33 300 3.3052 1.0
3.2119 11.11 400 3.1202 1.0
2.5099 13.87 500 1.6023 0.9872
1.3373 16.66 600 1.1878 0.9182
0.913 19.44 700 1.0049 0.8875
0.7013 22.22 800 0.9810 0.8542
0.5439 24.98 900 0.9463 0.8568
0.4581 27.76 1000 0.9771 0.8261
0.392 30.55 1100 0.9489 0.8389
0.3555 33.33 1200 0.8846 0.8107
0.3219 36.11 1300 0.8567 0.7980
0.2794 38.87 1400 0.8851 0.7775
0.2649 41.66 1500 0.9642 0.7954
0.2407 44.44 1600 0.9540 0.8133
0.2184 47.22 1700 0.8820 0.7494
0.2181 49.98 1800 0.9349 0.8031
0.1863 52.76 1900 0.9557 0.7494
0.1728 55.55 2000 1.0587 0.7519
0.1848 58.33 2100 1.0072 0.8056
0.1602 61.11 2200 0.9321 0.7980
0.1479 63.87 2300 0.9669 0.8005
0.1464 66.66 2400 0.9914 0.7545
0.1442 69.44 2500 1.0479 0.8184
0.1385 72.22 2600 1.0065 0.7647
0.1201 74.98 2700 0.9956 0.7801
0.1264 77.76 2800 1.0153 0.7801
0.1143 80.55 2900 0.9973 0.7826
0.1145 83.33 3000 0.9762 0.7698
0.1264 86.11 3100 0.9494 0.7391
0.1093 88.87 3200 1.0091 0.7801
0.0988 91.66 3300 1.0605 0.7621
0.103 94.44 3400 0.9910 0.7340
0.0972 97.22 3500 1.0412 0.7519
0.0974 99.98 3600 1.0361 0.7621
0.0836 102.76 3700 0.9969 0.7673
0.0795 105.55 3800 1.0198 0.7545
0.0839 108.33 3900 1.0269 0.7698
0.0856 111.11 4000 0.9913 0.7442
0.0721 113.87 4100 1.0239 0.7621
0.0711 116.66 4200 1.0360 0.7468
0.0771 119.44 4300 1.0799 0.7289
0.0624 122.22 4400 1.1323 0.7238
0.0748 124.98 4500 1.0868 0.7366
0.0644 127.76 4600 1.0658 0.7289
0.0667 130.55 4700 1.0731 0.7212
0.0624 133.33 4800 1.0794 0.7289
0.0714 136.11 4900 1.0832 0.7238
0.0627 138.87 5000 1.0820 0.7212

Framework versions

  • Transformers 4.15.0
  • Pytorch 1.10.0+cu111
  • Datasets 1.18.1
  • Tokenizers 0.10.3