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metadata
language:
  - ar
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
datasets:
  - common_voice
model-index:
  - name: ''
    results: []

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - AR dataset. It achieves the following results on the evaluation set:

  • Loss: 1.1373
  • Wer: 0.8607

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: 6.5e-05
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • 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: 30.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
2.2416 0.84 500 1.2867 0.8875
2.3089 1.67 1000 1.8336 0.9548
2.3614 2.51 1500 1.5937 0.9469
2.5234 3.35 2000 1.9765 0.9867
2.5373 4.19 2500 1.9062 0.9916
2.5703 5.03 3000 1.9772 0.9915
2.4656 5.86 3500 1.8083 0.9829
2.4339 6.7 4000 1.7548 0.9752
2.344 7.54 4500 1.6146 0.9638
2.2677 8.38 5000 1.5105 0.9499
2.2074 9.21 5500 1.4191 0.9357
2.3768 10.05 6000 1.6663 0.9665
2.3804 10.89 6500 1.6571 0.9720
2.3237 11.72 7000 1.6049 0.9637
2.317 12.56 7500 1.5875 0.9655
2.2988 13.4 8000 1.5357 0.9603
2.2906 14.24 8500 1.5637 0.9592
2.2848 15.08 9000 1.5326 0.9537
2.2381 15.91 9500 1.5631 0.9508
2.2072 16.75 10000 1.4565 0.9395
2.197 17.59 10500 1.4304 0.9406
2.198 18.43 11000 1.4230 0.9382
2.1668 19.26 11500 1.3998 0.9315
2.1498 20.1 12000 1.3920 0.9258
2.1244 20.94 12500 1.3584 0.9153
2.0953 21.78 13000 1.3274 0.9054
2.0762 22.61 13500 1.2933 0.9073
2.0587 23.45 14000 1.2516 0.8944
2.0363 24.29 14500 1.2214 0.8902
2.0302 25.13 15000 1.2087 0.8871
2.0071 25.96 15500 1.1953 0.8786
1.9882 26.8 16000 1.1738 0.8712
1.9772 27.64 16500 1.1647 0.8672
1.9585 28.48 17000 1.1459 0.8635
1.944 29.31 17500 1.1414 0.8616

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu102
  • Datasets 1.18.2.dev0
  • Tokenizers 0.11.0