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wav2vec2-large-sw-cv-20hr-v1

Automatic Speech Recognition
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wav2vec2
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metadata
license: apache-2.0
base_model: facebook/wav2vec2-large
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: wav2vec2-large-sw-cv-20hr-v1
    results: []

Visualize in Weights & Biases

wav2vec2-large-sw-cv-20hr-v1

This model is a fine-tuned version of facebook/wav2vec2-large on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: inf
  • Model Preparation Time: 0.0059
  • Wer: 0.3464
  • Cer: 0.1302

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: 0.0001
  • train_batch_size: 32
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Model Preparation Time Wer Cer
4.0167 0.9976 210 1.3039 0.0059 0.9301 0.3195
0.7784 2.0 421 0.7306 0.0059 0.5818 0.1788
0.5359 2.9976 631 0.5886 0.0059 0.5048 0.1517
0.427 4.0 842 0.5274 0.0059 0.4460 0.1345
0.3657 4.9976 1052 0.5617 0.0059 0.4620 0.1430
0.3219 6.0 1263 0.5162 0.0059 0.408 0.1240
0.2922 6.9976 1473 0.4861 0.0059 0.4074 0.1256
0.2681 8.0 1684 0.5076 0.0059 0.404 0.1253
0.2459 8.9976 1894 0.5042 0.0059 0.3915 0.1205
0.2332 10.0 2105 0.5051 0.0059 0.3706 0.1120
0.2181 10.9976 2315 0.5370 0.0059 0.3750 0.1149
0.2073 12.0 2526 0.5231 0.0059 0.3860 0.1249
0.1982 12.9976 2736 0.5290 0.0059 0.4045 0.1239
0.1875 14.0 2947 0.5184 0.0059 0.3755 0.1153
0.1782 14.9976 3157 0.5215 0.0059 0.3587 0.1100
0.1684 16.0 3368 0.5395 0.0059 0.371 0.1142
0.1629 16.9976 3578 0.5499 0.0059 0.3608 0.1101
0.1563 18.0 3789 0.5478 0.0059 0.3577 0.1107
0.1516 18.9976 3999 0.5290 0.0059 0.3649 0.1148
0.1431 20.0 4210 0.5765 0.0059 0.3657 0.1167
0.1366 20.9976 4420 0.5604 0.0059 0.3617 0.1137
0.1345 22.0 4631 0.5546 0.0059 0.3604 0.1118
0.1303 22.9976 4841 0.5284 0.0059 0.3511 0.1089
0.122 24.0 5052 0.5668 0.0059 0.3555 0.1111
0.1183 24.9976 5262 0.5874 0.0059 0.3521 0.1088
0.1151 26.0 5473 0.5539 0.0059 0.3379 0.1044
0.1108 26.9976 5683 0.6110 0.0059 0.3375 0.1051
0.1089 28.0 5894 0.5582 0.0059 0.3397 0.1029
0.1064 28.9976 6104 0.5774 0.0059 0.3432 0.1062
0.1026 30.0 6315 0.6042 0.0059 0.3420 0.1062
0.0983 30.9976 6525 0.5793 0.0059 0.3402 0.1046
0.0952 32.0 6736 0.6083 0.0059 0.3423 0.1074
0.0927 32.9976 6946 0.6015 0.0059 0.3363 0.1035
0.0895 34.0 7157 0.5790 0.0059 0.3368 0.1041
0.0889 34.9976 7367 0.5530 0.0059 0.3338 0.1023
0.0865 36.0 7578 0.5598 0.0059 0.3267 0.1009
0.0828 36.9976 7788 0.5699 0.0059 0.3249 0.1001
0.0814 38.0 7999 0.5756 0.0059 0.3237 0.0996
0.0819 38.9976 8209 0.5878 0.0059 0.3363 0.1052
0.077 40.0 8420 0.5852 0.0059 0.3216 0.0984
0.075 40.9976 8630 0.5940 0.0059 0.3295 0.1022
0.0725 42.0 8841 0.5779 0.0059 0.3219 0.0997
0.0701 42.9976 9051 0.5962 0.0059 0.3144 0.0965
0.0693 44.0 9262 0.6192 0.0059 0.317 0.0975
0.0659 44.9976 9472 0.5989 0.0059 0.3126 0.0964
0.0662 46.0 9683 0.6069 0.0059 0.3112 0.0975
0.0646 46.9976 9893 0.6309 0.0059 0.3164 0.0986
0.0626 48.0 10104 0.6266 0.0059 0.3199 0.1007
0.062 48.9976 10314 0.6403 0.0059 0.3116 0.0963
0.0591 50.0 10525 0.6140 0.0059 0.3133 0.0965
0.0568 50.9976 10735 0.5947 0.0059 0.3078 0.0950
0.0538 52.0 10946 0.6202 0.0059 0.3029 0.0939
0.0544 52.9976 11156 0.6215 0.0059 0.312 0.0966
0.0526 54.0 11367 0.6637 0.0059 0.3093 0.0959
0.05 54.9976 11577 0.6513 0.0059 0.3079 0.0955
0.0518 56.0 11788 0.6611 0.0059 0.3070 0.0948
0.0493 56.9976 11998 0.6415 0.0059 0.3041 0.0941
0.0482 58.0 12209 0.6386 0.0059 0.3042 0.0939
0.0461 58.9976 12419 0.6664 0.0059 0.316 0.0995
0.0445 60.0 12630 0.6472 0.0059 0.3057 0.0963
0.0449 60.9976 12840 0.6510 0.0059 0.3103 0.0972
0.0437 62.0 13051 0.6696 0.0059 0.3166 0.1005

Framework versions

  • Transformers 4.43.1
  • Pytorch 2.2.0+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1