badrex / xlsr-am-adap-ru

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Total runs: 14
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Model's Last Updated: June 07 2024
automatic-speech-recognition

Introduction of xlsr-am-adap-ru

Model Details of xlsr-am-adap-ru

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xlsr-am-adap-ru

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: 1.7523
  • Wer: 0.7058
  • Cer: 0.2819
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
4.7311 3.0303 100 4.5737 1.0 1.0
4.1724 6.0606 200 4.1384 1.0 0.9880
4.0604 9.0909 300 4.0653 1.0 0.9863
1.8268 12.1212 400 2.0440 0.9610 0.5231
0.9795 15.1515 500 1.6829 0.8855 0.4220
0.8934 18.1818 600 1.4791 0.8375 0.3794
0.4444 21.2121 700 1.4552 0.8174 0.3440
0.3345 24.2424 800 1.3845 0.7862 0.3075
0.3854 27.2727 900 1.5174 0.7846 0.3254
0.2771 30.3030 1000 1.4851 0.7743 0.3161
0.2124 33.3333 1100 1.5143 0.7411 0.3079
0.2229 36.3636 1200 1.5672 0.7468 0.3098
0.2108 39.3939 1300 1.6140 0.7595 0.3094
0.2062 42.4242 1400 1.5804 0.7485 0.3111
0.1375 45.4545 1500 1.6814 0.7489 0.3040
0.1544 48.4848 1600 1.6285 0.7464 0.3030
0.1588 51.5152 1700 1.7064 0.7349 0.3010
0.1559 54.5455 1800 1.7128 0.7485 0.3087
0.1186 57.5758 1900 1.7004 0.7349 0.3004
0.1054 60.6061 2000 1.7703 0.7296 0.2981
0.1306 63.6364 2100 1.7435 0.7304 0.2921
0.1053 66.6667 2200 1.7505 0.7300 0.2951
0.1228 69.6970 2300 1.7204 0.7181 0.2865
0.1359 72.7273 2400 1.6765 0.7173 0.2889
0.0904 75.7576 2500 1.6989 0.7173 0.2831
0.1258 78.7879 2600 1.7484 0.7128 0.2844
0.097 81.8182 2700 1.7627 0.7046 0.2835
0.0814 84.8485 2800 1.7628 0.7165 0.2888
0.0834 87.8788 2900 1.7520 0.7136 0.2886
0.0658 90.9091 3000 1.7474 0.7091 0.2901
0.0977 93.9394 3100 1.7767 0.7066 0.2812
0.0644 96.9697 3200 1.7563 0.7050 0.2835
0.0572 100.0 3300 1.7523 0.7058 0.2819
Framework versions
  • Transformers 4.42.0.dev0
  • Pytorch 2.3.1+cu121
  • Datasets 2.19.2
  • Tokenizers 0.19.1

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