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speech-seq2seq/wav2vec2-2-gpt2-no-adapter

sourceHugging Faceupdated 5y agoView on Hugging Face
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Model Card

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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: 1.1277
  • Wer: 1.0334

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: 0.0003
  • trainbatchsize: 8
  • evalbatchsize: 8
  • seed: 42
  • gradientaccumulationsteps: 2
  • totaltrainbatch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lrschedulertype: linear
  • lrschedulerwarmup_steps: 500
  • num_epochs: 3.0
  • mixedprecisiontraining: Native AMP

Training results

Training LossEpochStepValidation LossWer
4.70150.285005.33131.9454
4.72390.5610005.13161.9288
4.66860.8415004.88121.9646
4.01381.1220004.82741.8905
3.63141.425003.89131.7298
1.95111.6830002.34861.3674
1.2121.9635001.62231.1877
0.80922.2440001.39491.1049
0.4972.5245001.25441.0749
0.44012.850001.12771.0334

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu113
  • Datasets 1.18.3
  • Tokenizers 0.11.0