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This model was trained from scratch on the librispeech_asr dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7177
  • Wer: 0.1283

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: 3e-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: 1000
  • num_epochs: 25.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
6.1228 1.68 1500 6.0490 1.1433
5.4173 3.36 3000 5.3453 1.4878
4.1635 5.04 4500 4.4185 0.9644
2.1246 6.73 6000 3.2089 0.5026
1.88 8.41 7500 1.9886 0.3438
1.2606 10.09 9000 1.4472 0.2487
0.7492 11.77 10500 1.1716 0.1949
0.8868 13.45 12000 1.0146 0.1702
0.5078 15.13 13500 0.8821 0.1548
0.4515 16.82 15000 0.8181 0.1417
0.3902 18.5 16500 0.7765 0.1364
0.3575 20.18 18000 0.7367 0.1333
0.2903 21.86 19500 0.7211 0.1301
0.2698 23.54 21000 0.7177 0.1283

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu113
  • Datasets 1.18.3
  • Tokenizers 0.11.0
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Dataset used to train sanchit-gandhi/wav2vec2-2-rnd-no-adapter-regularisation

Evaluation results

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