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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: 0.6977
  • Wer: 0.1231

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
6.1467 1.68 1500 6.0558 1.3243
5.4388 3.36 3000 5.4711 1.5604
3.3434 5.04 4500 3.4808 0.7461
1.5259 6.73 6000 2.1931 0.3430
1.4285 8.41 7500 1.5883 0.2784
1.0687 10.09 9000 1.2481 0.2069
0.6425 11.77 10500 1.0507 0.1758
0.7147 13.45 12000 0.9397 0.1584
0.5083 15.13 13500 0.8452 0.1453
0.4287 16.82 15000 0.7915 0.1388
0.3499 18.5 16500 0.7477 0.1315
0.3733 20.18 18000 0.7307 0.1287
0.2609 21.86 19500 0.7061 0.1263
0.2602 23.54 21000 0.6977 0.1231

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-regularisation

Evaluation results

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