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wav2vec2-large-xls-r-300m-lg-cv-130hr-v2

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wav2vec2
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metadata
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: wav2vec2-large-xls-r-300m-lg-cv-130hr-v2
    results: []

Visualize in Weights & Biases

wav2vec2-large-xls-r-300m-lg-cv-130hr-v2

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4316
  • Wer: 0.2045
  • Cer: 0.0457

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: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 50
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
0.7427 1.0 5194 0.2769 0.3444 0.0749
0.2071 2.0 10388 0.2579 0.2897 0.0640
0.1752 3.0 15582 0.2357 0.2781 0.0608
0.1555 4.0 20776 0.2445 0.2626 0.0581
0.1437 5.0 25970 0.2393 0.2610 0.0584
0.1341 6.0 31164 0.2422 0.2572 0.0578
0.1244 7.0 36358 0.2358 0.2523 0.0562
0.1148 8.0 41552 0.2342 0.2532 0.0556
0.1077 9.0 46746 0.2475 0.2462 0.0551
0.0983 10.0 51940 0.2505 0.2439 0.0549
0.0895 11.0 57134 0.2556 0.2482 0.0556
0.0812 12.0 62328 0.2633 0.2492 0.0551
0.0727 13.0 67522 0.2726 0.2448 0.0546
0.0676 14.0 72716 0.2694 0.2464 0.0539
0.0611 15.0 77910 0.2984 0.2423 0.0533
0.0557 16.0 83104 0.2989 0.2418 0.0534
0.052 17.0 88298 0.3213 0.2402 0.0534
0.0478 18.0 93492 0.3279 0.2390 0.0532
0.0451 19.0 98686 0.3247 0.2352 0.0522
0.0422 20.0 103880 0.3452 0.2344 0.0521
0.0393 21.0 109074 0.3420 0.2384 0.0529
0.0378 22.0 114268 0.3429 0.2301 0.0509
0.036 23.0 119462 0.3442 0.2356 0.0520
0.0337 24.0 124656 0.3563 0.2276 0.0502
0.0319 25.0 129850 0.3480 0.2262 0.0499
0.0299 26.0 135044 0.3634 0.2233 0.0491
0.0281 27.0 140238 0.3593 0.2265 0.0499
0.0269 28.0 145432 0.3640 0.2242 0.0491
0.0249 29.0 150626 0.3713 0.2225 0.0491
0.0246 30.0 155820 0.3849 0.2193 0.0489
0.0228 31.0 161014 0.3869 0.2199 0.0482
0.0215 32.0 166208 0.3933 0.2182 0.0483
0.0205 33.0 171402 0.3920 0.2158 0.0471
0.0191 34.0 176596 0.3992 0.2166 0.0479
0.0183 35.0 181790 0.3969 0.2127 0.0467
0.0176 36.0 186984 0.3998 0.2138 0.0472
0.0168 37.0 192178 0.4068 0.2107 0.0464
0.016 38.0 197372 0.4216 0.2113 0.0477
0.0154 39.0 202566 0.4102 0.2102 0.0469
0.0149 40.0 207760 0.4267 0.2087 0.0465
0.0142 41.0 212954 0.4248 0.2097 0.0469
0.0136 42.0 218148 0.4254 0.2074 0.0467
0.0133 43.0 223342 0.4304 0.2074 0.0465
0.0131 44.0 228536 0.4314 0.2063 0.0462
0.0126 45.0 233730 0.4267 0.2047 0.0458
0.0122 46.0 238924 0.4291 0.2049 0.0457
0.012 47.0 244118 0.4305 0.2048 0.0458
0.0122 48.0 249312 0.4321 0.2046 0.0457
0.0121 49.0 254506 0.4316 0.2045 0.0457
0.0122 50.0 259700 0.4318 0.2045 0.0457

Framework versions

  • Transformers 4.42.3
  • Pytorch 2.2.0+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1