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w2v-bert-2.0-CV_Fleurs-lg-50hrs-v5

Automatic Speech Recognition
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wav2vec2-bert
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metadata
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: w2v-bert-2.0-CV_Fleurs-lg-50hrs-v5
    results: []

w2v-bert-2.0-CV_Fleurs-lg-50hrs-v5

This model is a fine-tuned version of facebook/w2v-bert-2.0 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3755
  • Wer: 0.3108
  • Cer: 0.0651

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: 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
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 80
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
0.8769 1.0 6320 0.3126 0.3631 0.0763
0.2352 2.0 12640 0.2544 0.3542 0.0705
0.2161 3.0 18960 0.2639 0.3415 0.0695
0.2088 4.0 25280 0.2666 0.3524 0.0745
0.2063 5.0 31600 0.2863 0.3655 0.0789
0.2043 6.0 37920 0.2792 0.3409 0.0700
0.2036 7.0 44240 0.2787 0.3519 0.0736
0.2051 8.0 50560 0.2774 0.3550 0.0746
0.1967 9.0 56880 0.2710 0.3457 0.0728
0.1754 10.0 63200 0.2714 0.3425 0.0721
0.157 11.0 69520 0.2800 0.3490 0.0727
0.1411 12.0 75840 0.2571 0.3165 0.0671
0.1305 13.0 82160 0.2768 0.3486 0.0726
0.1164 14.0 88480 0.2963 0.3330 0.0718
0.1067 15.0 94800 0.2663 0.3131 0.0670
0.0954 16.0 101120 0.2660 0.3254 0.0667
0.0849 17.0 107440 0.2751 0.3103 0.0659
0.0769 18.0 113760 0.2721 0.3290 0.0695
0.0675 19.0 120080 0.2986 0.3148 0.0670
0.0606 20.0 126400 0.2850 0.3122 0.0653
0.0536 21.0 132720 0.2987 0.3260 0.0687
0.0478 22.0 139040 0.3226 0.3191 0.0654
0.0429 23.0 145360 0.2981 0.3373 0.0678
0.038 24.0 151680 0.3210 0.3172 0.0656
0.0343 25.0 158000 0.3454 0.3056 0.0635
0.0311 26.0 164320 0.3092 0.3153 0.0655
0.0283 27.0 170640 0.3285 0.3165 0.0647
0.0265 28.0 176960 0.3413 0.3125 0.0650
0.024 29.0 183280 0.3894 0.3062 0.0636
0.0223 30.0 189600 0.3681 0.3084 0.0645
0.0205 31.0 195920 0.3552 0.3134 0.0655
0.0188 32.0 202240 0.3656 0.3105 0.0661
0.018 33.0 208560 0.3640 0.3148 0.0659
0.0163 34.0 214880 0.3805 0.3099 0.0649
0.0153 35.0 221200 0.3755 0.3108 0.0651

Framework versions

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