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xlsr-a-nomimose

Automatic Speech Recognition
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Safetensors
wav2vec2
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metadata
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: xlsr-a-nomimose
    results: []

xlsr-a-nomimose

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4623
  • Wer: 0.3473

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: 0.0004
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • 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_steps: 132
  • num_epochs: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
4.7536 1.8387 200 2.6165 1.0
1.8115 3.6728 400 0.6163 0.6689
0.469 5.5069 600 0.5160 0.5442
0.2745 7.3410 800 0.2917 0.4108
0.1694 9.1751 1000 0.3568 0.4624
0.1782 11.0092 1200 0.3223 0.3901
0.1406 12.8479 1400 0.2959 0.3923
0.1046 14.6820 1600 0.3131 0.3658
0.109 16.5161 1800 0.3343 0.3739
0.1135 18.3502 2000 0.3368 0.3577
0.0875 20.1843 2200 0.4722 0.3695
0.0956 22.0184 2400 0.3427 0.3614
0.0749 23.8571 2600 0.3377 0.3695
0.0727 25.6912 2800 0.3489 0.3599
0.0655 27.5253 3000 0.3348 0.3665
0.0603 29.3594 3200 0.3636 0.3606
0.0537 31.1935 3400 0.3923 0.3555
0.0567 33.0276 3600 0.3476 0.3555
0.0461 34.8664 3800 0.3589 0.3628
0.0459 36.7005 4000 0.4104 0.3584
0.0467 38.5346 4200 0.3686 0.3555
0.0419 40.3687 4400 0.3889 0.3555
0.0381 42.2028 4600 0.4013 0.3540
0.0403 44.0369 4800 0.4077 0.3555
0.0371 45.8756 5000 0.4502 0.3577
0.0354 47.7097 5200 0.4884 0.3739
0.0372 49.5438 5400 0.4227 0.3614
0.0344 51.3779 5600 0.3949 0.3532
0.0288 53.2120 5800 0.4088 0.3532
0.0314 55.0461 6000 0.4194 0.3496
0.0276 56.8848 6200 0.4184 0.3532
0.0232 58.7189 6400 0.4184 0.3496
0.0267 60.5530 6600 0.4028 0.3496
0.0271 62.3871 6800 0.3804 0.3488
0.0205 64.2212 7000 0.4735 0.3466
0.0243 66.0553 7200 0.4108 0.3473
0.0198 67.8940 7400 0.4157 0.3488
0.0193 69.7281 7600 0.4226 0.3547
0.0222 71.5622 7800 0.4147 0.3451
0.0176 73.3963 8000 0.4553 0.3488
0.0209 75.2304 8200 0.4135 0.3525
0.0222 77.0645 8400 0.4300 0.3481
0.0169 78.9032 8600 0.4139 0.3481
0.0174 80.7373 8800 0.4510 0.3473
0.019 82.5714 9000 0.4664 0.3488
0.0145 84.4055 9200 0.4768 0.3459
0.0146 86.2396 9400 0.4678 0.3466
0.0184 88.0737 9600 0.4906 0.3488
0.0141 89.9124 9800 0.4676 0.3481
0.0128 91.7465 10000 0.4612 0.3473
0.0134 93.5806 10200 0.4649 0.3459
0.0134 95.4147 10400 0.4606 0.3481
0.0126 97.2488 10600 0.4646 0.3488
0.014 99.0829 10800 0.4623 0.3473

Framework versions

  • Transformers 4.47.0.dev0
  • Pytorch 2.4.0
  • Datasets 3.0.1
  • Tokenizers 0.20.0