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wav2vec2-base-timit-demo-google-colab

This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5436
  • Wer: 0.3401

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: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1000
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
3.5276 1.0 500 1.9983 1.0066
0.8606 2.01 1000 0.5323 0.5220
0.4339 3.01 1500 0.4697 0.4512
0.3026 4.02 2000 0.4342 0.4266
0.2297 5.02 2500 0.5001 0.4135
0.1939 6.02 3000 0.4350 0.3897
0.1613 7.03 3500 0.4740 0.3883
0.1452 8.03 4000 0.4289 0.3825
0.1362 9.04 4500 0.4721 0.3927
0.1146 10.04 5000 0.4707 0.3730
0.1061 11.04 5500 0.4470 0.3701
0.0947 12.05 6000 0.4694 0.3722
0.0852 13.05 6500 0.5222 0.3733
0.0741 14.06 7000 0.4881 0.3657
0.069 15.06 7500 0.4957 0.3677
0.0679 16.06 8000 0.5241 0.3634
0.0618 17.07 8500 0.5091 0.3564
0.0576 18.07 9000 0.5055 0.3557
0.0493 19.08 9500 0.5013 0.3515
0.0469 20.08 10000 0.5506 0.3530
0.044 21.08 10500 0.5564 0.3528
0.0368 22.09 11000 0.5213 0.3509
0.0355 23.09 11500 0.5707 0.3495
0.0357 24.1 12000 0.5558 0.3483
0.0285 25.1 12500 0.5613 0.3455
0.0285 26.1 13000 0.5533 0.3480
0.0266 27.11 13500 0.5526 0.3462
0.0249 28.11 14000 0.5488 0.3429
0.0237 29.12 14500 0.5436 0.3401

Framework versions

  • Transformers 4.17.0
  • Pytorch 1.11.0+cu115
  • Datasets 1.18.3
  • Tokenizers 0.12.1
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