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wav2vec2-xls-r-300m-vivos

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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-xls-r-300m-vivos
    results: []

wav2vec2-xls-r-300m-vivos

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: 0.5156
  • Wer: 0.3337

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: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1000
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
8.3411 0.66 500 3.5728 1.0
3.418 1.31 1000 3.1432 1.0
1.6726 1.97 1500 0.7995 0.7146
0.8244 2.62 2000 0.5569 0.5370
0.6392 3.28 2500 0.4773 0.4744
0.5537 3.94 3000 0.4592 0.4631
0.4956 4.59 3500 0.4649 0.4536
0.4539 5.25 4000 0.4345 0.4175
0.4144 5.91 4500 0.4291 0.4204
0.3899 6.56 5000 0.4325 0.4105
0.3748 7.22 5500 0.4151 0.3954
0.3543 7.87 6000 0.4320 0.4070
0.3335 8.53 6500 0.4061 0.3776
0.3266 9.19 7000 0.4307 0.3899
0.3107 9.84 7500 0.4404 0.3866
0.2886 10.5 8000 0.4528 0.3825
0.2897 11.15 8500 0.4027 0.3731
0.2757 11.81 9000 0.4423 0.3837
0.2582 12.47 9500 0.4412 0.3717
0.2598 13.12 10000 0.4410 0.3609
0.2421 13.78 10500 0.4398 0.3651
0.2414 14.44 11000 0.4488 0.3585
0.2259 15.09 11500 0.4528 0.3572
0.2269 15.75 12000 0.4613 0.3590
0.2109 16.4 12500 0.4492 0.3610
0.2097 17.06 13000 0.4468 0.3522
0.1992 17.72 13500 0.4520 0.3531
0.1949 18.37 14000 0.4782 0.3525
0.1924 19.03 14500 0.4643 0.3459
0.1906 19.69 15000 0.4839 0.3519
0.1837 20.34 15500 0.4891 0.3427
0.1744 21.0 16000 0.4905 0.3481
0.1705 21.65 16500 0.4758 0.3445
0.1697 22.31 17000 0.4765 0.3441
0.1657 22.97 17500 0.5059 0.3447
0.1582 23.62 18000 0.4941 0.3446
0.159 24.28 18500 0.4977 0.3469
0.1562 24.93 19000 0.4966 0.3415
0.1516 25.59 19500 0.5130 0.3403
0.144 26.25 20000 0.5049 0.3390
0.1429 26.9 20500 0.5130 0.3355
0.1378 27.56 21000 0.5140 0.3371
0.1436 28.22 21500 0.5172 0.3351
0.1363 28.87 22000 0.5215 0.3361
0.1332 29.53 22500 0.5156 0.3337

Framework versions

  • Transformers 4.39.3
  • Pytorch 2.1.2
  • Datasets 2.18.0
  • Tokenizers 0.15.2