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wav2vec2-large-xls-r-1b-frisian-cv-8-10h

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the common_voice_8_0 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1207
  • Wer: 0.0961

And on the test set:

  • Wer: 0.0883

Model description

This model has been developed for my Master's thesis in "Voice Technology" at Rijksuniversiteit Groningen - Campus Fryslân. It corresponds to experiment 3 where I use as training set 10 hours of Frisian speech randomly selected from all validated data except the test and evaluation sets.

Intended uses & limitations

The intended use is for recognizing Frisian speech.

Limitations include no LM rescoring and using version 8.0 of Common Voice instead of 13.0.

Training and evaluation data

The evaluation split used is the one available in the Common Voice 8.0 Frisian subset. The train split is 10 hours of Frisian randomly selected from validated data except for the recordings from test and evaluation splits.

Training procedure

The script used for training this model can be found in this GitHub repository: link.

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 32
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 50
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
5.6342 1.32 300 2.9760 1.0
2.2716 2.63 600 0.6877 0.6024
1.1303 3.95 900 0.3522 0.3450
0.9038 5.26 1200 0.2714 0.2603
0.846 6.58 1500 0.2143 0.2036
0.8044 7.89 1800 0.1829 0.1788
0.7069 9.21 2100 0.1751 0.1667
0.6995 10.53 2400 0.1741 0.1727
0.7115 11.84 2700 0.1591 0.1486
0.677 13.16 3000 0.1636 0.1459
0.6032 14.47 3300 0.1535 0.1439
0.6218 15.79 3600 0.1427 0.1406
0.6519 17.11 3900 0.1498 0.1488
0.5739 18.42 4200 0.1438 0.1319
0.567 19.74 4500 0.1379 0.1322
0.4982 21.05 4800 0.1315 0.1237
0.5825 22.37 5100 0.1349 0.1252
0.5085 23.68 5400 0.1297 0.1233
0.4946 25.0 5700 0.1343 0.1127
0.5677 26.32 6000 0.1323 0.1228
0.4858 27.63 6300 0.1292 0.1098
0.4709 28.95 6600 0.1267 0.1204
0.3241 30.26 6900 0.1315 0.1274
0.2796 31.58 7200 0.1315 0.1202
0.3171 32.89 7500 0.1315 0.1200
0.2591 34.21 7800 0.1322 0.1106
0.2716 35.53 8100 0.1233 0.1030
0.2446 36.84 8400 0.1273 0.1087
0.2377 38.16 8700 0.1243 0.1101
0.2183 39.47 9000 0.1230 0.1116
0.2059 40.79 9300 0.1240 0.1001
0.1916 42.11 9600 0.1223 0.1003
0.196 43.42 9900 0.1246 0.0965
0.1969 44.74 10200 0.1222 0.1038
0.1951 46.05 10500 0.1208 0.1003
0.1809 47.37 10800 0.1213 0.1003
0.1793 48.68 11100 0.1202 0.0959
0.1837 50.0 11400 0.1207 0.0961

Framework versions

  • Transformers 4.28.1
  • Pytorch 2.0.0+cu117
  • Datasets 2.11.0
  • Tokenizers 0.13.3
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Evaluation results