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wav2vec2-base-timit-demo-colab

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metadata
license: apache-2.0
tags:
  - generated_from_trainer
model-index:
  - name: wav2vec2-base-timit-demo-colab
    results: []

wav2vec2-base-timit-demo-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: 0.6259
  • Wer: 0.3544

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: 4
  • 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
3.6744 0.5 500 2.9473 1.0
1.4535 1.01 1000 0.7774 0.6254
0.7376 1.51 1500 0.6923 0.5712
0.5848 2.01 2000 0.5445 0.5023
0.4492 2.51 2500 0.5148 0.4958
0.4006 3.02 3000 0.5283 0.4781
0.3319 3.52 3500 0.5196 0.4628
0.3424 4.02 4000 0.5285 0.4551
0.2772 4.52 4500 0.5060 0.4532
0.2724 5.03 5000 0.5216 0.4422
0.2375 5.53 5500 0.5376 0.4443
0.2279 6.03 6000 0.6051 0.4308
0.2091 6.53 6500 0.5084 0.4423
0.2029 7.04 7000 0.5083 0.4242
0.1784 7.54 7500 0.6123 0.4297
0.1774 8.04 8000 0.5749 0.4339
0.1542 8.54 8500 0.5110 0.4033
0.1638 9.05 9000 0.6324 0.4318
0.1493 9.55 9500 0.6100 0.4152
0.1591 10.05 10000 0.5508 0.4022
0.1304 10.55 10500 0.5090 0.4054
0.1234 11.06 11000 0.6282 0.4093
0.1218 11.56 11500 0.5817 0.3941
0.121 12.06 12000 0.5741 0.3999
0.1073 12.56 12500 0.5818 0.4149
0.104 13.07 13000 0.6492 0.3953
0.0934 13.57 13500 0.5393 0.4083
0.0961 14.07 14000 0.5510 0.3919
0.0965 14.57 14500 0.5896 0.3992
0.0921 15.08 15000 0.5554 0.3947
0.0751 15.58 15500 0.6312 0.3934
0.0805 16.08 16000 0.6732 0.3948
0.0742 16.58 16500 0.5990 0.3884
0.0708 17.09 17000 0.6186 0.3869
0.0679 17.59 17500 0.5837 0.3848
0.072 18.09 18000 0.5831 0.3775
0.0597 18.59 18500 0.6562 0.3843
0.0612 19.1 19000 0.6298 0.3756
0.0514 19.6 19500 0.6746 0.3720
0.061 20.1 20000 0.6236 0.3788
0.054 20.6 20500 0.6012 0.3718
0.0521 21.11 21000 0.6053 0.3778
0.0494 21.61 21500 0.6154 0.3772
0.0468 22.11 22000 0.6052 0.3747
0.0413 22.61 22500 0.5877 0.3716
0.0424 23.12 23000 0.5786 0.3658
0.0403 23.62 23500 0.5828 0.3658
0.0391 24.12 24000 0.5913 0.3685
0.0312 24.62 24500 0.5850 0.3625
0.0316 25.13 25000 0.6029 0.3611
0.0282 25.63 25500 0.6312 0.3624
0.0328 26.13 26000 0.6312 0.3621
0.0258 26.63 26500 0.5891 0.3581
0.0256 27.14 27000 0.6259 0.3546
0.0255 27.64 27500 0.6315 0.3587
0.0249 28.14 28000 0.6547 0.3579
0.025 28.64 28500 0.6237 0.3565
0.0228 29.15 29000 0.6187 0.3559
0.0209 29.65 29500 0.6259 0.3544

Framework versions

  • Transformers 4.11.3
  • Pytorch 1.10.0+cu102
  • Datasets 1.18.3
  • Tokenizers 0.10.3