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wav2vec2-xls-r-1b-ro

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - RO dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1113
  • Wer: 0.4770
  • Cer: 0.0306

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: 3e-05
  • train_batch_size: 8
  • 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: 2000
  • num_epochs: 50.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
0.7844 1.67 1500 0.3412 0.8600 0.0940
0.7272 3.34 3000 0.1926 0.6409 0.0527
0.6924 5.02 4500 0.1413 0.5722 0.0401
0.6327 6.69 6000 0.1252 0.5366 0.0371
0.6363 8.36 7500 0.1235 0.5741 0.0389
0.6238 10.03 9000 0.1180 0.5542 0.0362
0.6018 11.71 10500 0.1192 0.5694 0.0369
0.583 13.38 12000 0.1216 0.5772 0.0385
0.5643 15.05 13500 0.1195 0.5419 0.0371
0.5399 16.72 15000 0.1240 0.5224 0.0370
0.5529 18.39 16500 0.1174 0.5555 0.0367
0.5246 20.07 18000 0.1097 0.5047 0.0339
0.4936 21.74 19500 0.1225 0.5189 0.0382
0.4629 23.41 21000 0.1142 0.5047 0.0344
0.4463 25.08 22500 0.1168 0.4887 0.0339
0.4671 26.76 24000 0.1119 0.5073 0.0338
0.4359 28.43 25500 0.1206 0.5479 0.0363
0.4225 30.1 27000 0.1122 0.5170 0.0345
0.4038 31.77 28500 0.1159 0.5032 0.0343
0.4271 33.44 30000 0.1116 0.5126 0.0339
0.3867 35.12 31500 0.1101 0.4937 0.0327
0.3674 36.79 33000 0.1142 0.4940 0.0330
0.3607 38.46 34500 0.1106 0.5145 0.0327
0.3651 40.13 36000 0.1172 0.4921 0.0317
0.3268 41.81 37500 0.1093 0.4830 0.0310
0.3345 43.48 39000 0.1131 0.4760 0.0314
0.3236 45.15 40500 0.1132 0.4864 0.0317
0.312 46.82 42000 0.1124 0.4861 0.0315
0.3106 48.49 43500 0.1116 0.4745 0.0306

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu102
  • Datasets 1.18.2.dev0
  • Tokenizers 0.11.0
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Dataset used to train ubamba98/wav2vec2-xls-r-1b-ro

Evaluation results