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This model is a fine-tuned version of DrishtiSharma/wav2vec2-large-xls-r-300m-hi-d3 on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - UR dataset. It achieves the following results on the evaluation set:

  • Loss: 1.5443
  • Wer: 0.7030

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: 0.000388
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 750
  • num_epochs: 100.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
10.7052 1.96 100 3.4683 1.0
3.2395 3.92 200 3.1489 1.0
2.9951 5.88 300 2.9823 1.0007
2.3574 7.84 400 1.2614 0.7598
1.7287 9.8 500 1.1817 0.7421
1.6144 11.76 600 1.1315 0.7321
1.5598 13.73 700 1.2322 0.7550
1.5418 15.69 800 1.2721 0.7819
1.4578 17.65 900 1.1710 0.7531
1.4311 19.61 1000 1.2042 0.7491
1.3483 21.57 1100 1.1702 0.7465
1.3078 23.53 1200 1.1963 0.7421
1.2576 25.49 1300 1.1501 0.7280
1.2173 27.45 1400 1.2526 0.7299
1.2217 29.41 1500 1.2479 0.7310
1.1536 31.37 1600 1.2567 0.7432
1.0939 33.33 1700 1.2801 0.7247
1.0745 35.29 1800 1.2340 0.7151
1.0454 37.25 1900 1.2372 0.7151
1.0101 39.22 2000 1.2461 0.7376
0.9833 41.18 2100 1.2553 0.7269
0.9314 43.14 2200 1.2372 0.7015
0.9147 45.1 2300 1.3035 0.7358
0.8758 47.06 2400 1.2598 0.7092
0.8356 49.02 2500 1.2557 0.7144
0.8105 50.98 2600 1.2619 0.7236
0.7947 52.94 2700 1.3994 0.7491
0.7623 54.9 2800 1.2932 0.7133
0.7282 56.86 2900 1.2799 0.7089
0.7108 58.82 3000 1.3615 0.7148
0.6896 60.78 3100 1.3129 0.7041
0.6496 62.75 3200 1.4050 0.6934
0.6075 64.71 3300 1.3571 0.7026
0.6242 66.67 3400 1.3369 0.7063
0.5865 68.63 3500 1.4368 0.7140
0.5721 70.59 3600 1.4224 0.7066
0.5475 72.55 3700 1.4798 0.7118
0.5086 74.51 3800 1.5107 0.7232
0.4958 76.47 3900 1.4849 0.7089
0.5046 78.43 4000 1.4451 0.7114
0.4694 80.39 4100 1.4674 0.7089
0.4386 82.35 4200 1.5245 0.7103
0.4516 84.31 4300 1.5032 0.7103
0.4113 86.27 4400 1.5246 0.7196
0.3972 88.24 4500 1.5318 0.7114
0.4006 90.2 4600 1.5543 0.6982
0.4014 92.16 4700 1.5442 0.7048
0.3672 94.12 4800 1.5542 0.7137
0.3666 96.08 4900 1.5414 0.7018
0.3574 98.04 5000 1.5465 0.7059
0.3428 100.0 5100 1.5443 0.7030

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu102
  • Datasets 1.18.3
  • Tokenizers 0.11.0
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Dataset used to train HarrisDePerceptron/xls-r-300m-ur-cv8-hi

Evaluation results

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