badrex / xlsr-am-adap-ar

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Total runs: 11
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Model's Last Updated: June 07 2024
automatic-speech-recognition

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Model Details of xlsr-am-adap-ar

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xlsr-am-adap-ar

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:

  • Loss: 1.6979
  • Wer: 0.6968
  • Cer: 0.2762
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
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • 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: 500
  • num_epochs: 100
  • mixed_precision_training: Native AMP
Training results
Training Loss Epoch Step Validation Loss Wer Cer
4.7315 3.0303 100 4.5550 1.0 1.0
4.1365 6.0606 200 4.1643 1.0 0.9883
3.2632 9.0909 300 3.2750 0.9996 0.8189
1.3193 12.1212 400 1.7260 0.9146 0.4165
0.7873 15.1515 500 1.5051 0.8658 0.3773
0.7762 18.1818 600 1.4205 0.8014 0.3242
0.4153 21.2121 700 1.4724 0.8006 0.3200
0.3188 24.2424 800 1.4552 0.7682 0.3034
0.3318 27.2727 900 1.4800 0.7690 0.3010
0.2333 30.3030 1000 1.5290 0.7452 0.2903
0.2029 33.3333 1100 1.5773 0.7661 0.3207
0.1871 36.3636 1200 1.5868 0.7628 0.3004
0.2034 39.3939 1300 1.6034 0.7403 0.2931
0.1979 42.4242 1400 1.5973 0.7542 0.3052
0.1806 45.4545 1500 1.6037 0.7230 0.2878
0.1041 48.4848 1600 1.6310 0.7300 0.2869
0.1451 51.5152 1700 1.6029 0.7275 0.2913
0.132 54.5455 1800 1.7031 0.7444 0.2977
0.1037 57.5758 1900 1.6885 0.7214 0.2822
0.1224 60.6061 2000 1.7125 0.7210 0.2853
0.0921 63.6364 2100 1.7166 0.7333 0.2865
0.1183 66.6667 2200 1.7051 0.7329 0.2800
0.1212 69.6970 2300 1.7752 0.7255 0.2861
0.1153 72.7273 2400 1.7066 0.7279 0.2793
0.0902 75.7576 2500 1.7348 0.7251 0.2838
0.1237 78.7879 2600 1.6664 0.7128 0.2751
0.1001 81.8182 2700 1.7235 0.7103 0.2845
0.0831 84.8485 2800 1.7273 0.7046 0.2847
0.0729 87.8788 2900 1.7377 0.7029 0.2819
0.0755 90.9091 3000 1.7035 0.7107 0.2826
0.0998 93.9394 3100 1.7066 0.6963 0.2793
0.0586 96.9697 3200 1.7023 0.6914 0.2767
0.0669 100.0 3300 1.6979 0.6968 0.2762
Framework versions
  • Transformers 4.42.0.dev0
  • Pytorch 2.3.1+cu121
  • Datasets 2.19.2
  • Tokenizers 0.19.1

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