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wav2vec2-xls-r-300m-CV-Fleurs-lg-50hrs-v6

Automatic Speech Recognition
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Safetensors
wav2vec2
Generated from Trainer
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metadata
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: wav2vec2-xls-r-300m-CV-Fleurs-lg-50hrs-v6
    results: []

wav2vec2-xls-r-300m-CV-Fleurs-lg-50hrs-v6

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5412
  • Wer: 0.3807
  • Cer: 0.0816

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: 4
  • eval_batch_size: 2
  • seed: 42
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
1.538 1.0 6320 0.7903 0.8894 0.2387
0.8457 2.0 12640 0.6261 0.8238 0.2064
0.6755 3.0 18960 0.4933 0.6752 0.1561
0.5729 4.0 25280 0.4407 0.5819 0.1323
0.5019 5.0 31600 0.4108 0.5538 0.1249
0.4479 6.0 37920 0.3908 0.5208 0.1158
0.4048 7.0 44240 0.3859 0.4932 0.1092
0.3658 8.0 50560 0.3573 0.4953 0.1105
0.3385 9.0 56880 0.3619 0.4860 0.1114
0.3135 10.0 63200 0.3545 0.4545 0.0995
0.2915 11.0 69520 0.3496 0.4523 0.0983
0.2721 12.0 75840 0.3473 0.4450 0.0982
0.2542 13.0 82160 0.3510 0.4327 0.0946
0.2379 14.0 88480 0.3679 0.4626 0.1020
0.2256 15.0 94800 0.3609 0.4410 0.0961
0.2114 16.0 101120 0.3616 0.4352 0.0947
0.2003 17.0 107440 0.3667 0.4333 0.0943
0.1897 18.0 113760 0.3805 0.4265 0.0926
0.1802 19.0 120080 0.3965 0.4197 0.0917
0.1704 20.0 126400 0.3803 0.4157 0.0903
0.1641 21.0 132720 0.3857 0.4298 0.0922
0.1559 22.0 139040 0.4016 0.4254 0.0925
0.1499 23.0 145360 0.4059 0.4133 0.0884
0.1434 24.0 151680 0.4082 0.4325 0.0915
0.138 25.0 158000 0.4483 0.4037 0.0888
0.1319 26.0 164320 0.4477 0.4197 0.0908
0.1294 27.0 170640 0.4377 0.4051 0.0880
0.1244 28.0 176960 0.4568 0.4252 0.0913
0.1206 29.0 183280 0.4550 0.4035 0.0872
0.1166 30.0 189600 0.4668 0.4074 0.0871
0.1118 31.0 195920 0.4386 0.3967 0.0867
0.1103 32.0 202240 0.4678 0.3915 0.0857
0.1077 33.0 208560 0.4852 0.3912 0.0852
0.1032 34.0 214880 0.4849 0.4003 0.0860
0.0993 35.0 221200 0.4955 0.3939 0.0841
0.0966 36.0 227520 0.4865 0.4001 0.0854
0.0945 37.0 233840 0.4700 0.3886 0.0843
0.0925 38.0 240160 0.4969 0.3801 0.0828
0.0898 39.0 246480 0.5060 0.3903 0.0837
0.0887 40.0 252800 0.5066 0.3910 0.0852
0.087 41.0 259120 0.4886 0.3827 0.0823
0.085 42.0 265440 0.5144 0.3899 0.0844
0.0825 43.0 271760 0.5123 0.3875 0.0838
0.0808 44.0 278080 0.5245 0.3820 0.0824
0.0791 45.0 284400 0.5231 0.3844 0.0822
0.0773 46.0 290720 0.5196 0.3867 0.0825
0.075 47.0 297040 0.5462 0.3839 0.0826
0.0737 48.0 303360 0.5412 0.3807 0.0816

Framework versions

  • Transformers 4.46.1
  • Pytorch 2.1.0+cu118
  • Datasets 3.1.0
  • Tokenizers 0.20.1