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NbAiLab/wav2vec2-xlsr-300M-NPSC-OH

🤗 Hugging Face sourceautomatic-speech-recognitionapache-2.0109 GBother✓ 17 checksumsupdated today
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wav2vec2-xlsr-300M-NPSC-OH

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the NBAILAB/NPSC - 16K_MP3 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1692
  • Wer: 0.1663

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: 7.5e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 13
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 2000
  • num_epochs: 15.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
3.1638 0.66 500 3.0686 1.0
2.9311 1.31 1000 2.9208 1.0
2.4175 1.97 1500 1.5009 0.9049
1.4442 2.63 2000 0.4426 0.3783
1.2624 3.28 2500 0.3193 0.2998
1.1889 3.94 3000 0.2867 0.2630
1.1315 4.6 3500 0.2566 0.2444
1.0864 5.26 4000 0.2368 0.2294
1.093 5.91 4500 0.2240 0.2151
1.0368 6.57 5000 0.2117 0.2056
1.0178 7.23 5500 0.2020 0.1954
1.0035 7.88 6000 0.2005 0.1924
0.9759 8.54 6500 0.1971 0.1863
0.9795 9.2 7000 0.1892 0.1812
0.9601 9.85 7500 0.1863 0.1795
0.9673 10.51 8000 0.1809 0.1761
0.9233 11.17 8500 0.1818 0.1755
0.9382 11.83 9000 0.1767 0.1741
0.9242 12.48 9500 0.1743 0.1703
0.9703 13.14 10000 0.1711 0.1711
0.9139 13.8 10500 0.1718 0.1672
0.9073 14.45 11000 0.1700 0.1665

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.1+cu102
  • Datasets 1.18.2.dev0
  • Tokenizers 0.11.0