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wav2vec2-xlsr-53-ft-btb-ccv-cy

Automatic Speech Recognition
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wav2vec2
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wav2vec2-xlsr-53-ft-btb-ccv-cy / README.md
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metadata
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: wav2vec2-xlsr-53-ft-btb-ccv-cy
    results: []

wav2vec2-xlsr-53-ft-btb-ccv-cy

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4061
  • Wer: 0.3193

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: 0.0003
  • 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: 500
  • training_steps: 10000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
No log 0.0194 100 3.5565 1.0
No log 0.0387 200 3.0302 1.0
No log 0.0581 300 2.9461 1.0
No log 0.0774 400 1.8143 0.9407
3.9521 0.0968 500 1.4196 0.8693
3.9521 0.1161 600 1.1669 0.8055
3.9521 0.1355 700 1.0757 0.7596
3.9521 0.1549 800 0.9945 0.7224
3.9521 0.1742 900 0.9381 0.6871
1.0266 0.1936 1000 0.8978 0.6615
1.0266 0.2129 1100 0.8771 0.6450
1.0266 0.2323 1200 0.8516 0.6404
1.0266 0.2516 1300 0.8274 0.6138
1.0266 0.2710 1400 0.7993 0.5970
0.8454 0.2904 1500 0.7769 0.5888
0.8454 0.3097 1600 0.7664 0.5998
0.8454 0.3291 1700 0.7401 0.5592
0.8454 0.3484 1800 0.7465 0.5650
0.8454 0.3678 1900 0.7253 0.5791
0.7537 0.3871 2000 0.7039 0.5344
0.7537 0.4065 2100 0.6932 0.5168
0.7537 0.4259 2200 0.6969 0.5364
0.7537 0.4452 2300 0.6781 0.5174
0.7537 0.4646 2400 0.6761 0.5050
0.681 0.4839 2500 0.6721 0.5287
0.681 0.5033 2600 0.6598 0.5195
0.681 0.5226 2700 0.6555 0.4976
0.681 0.5420 2800 0.6535 0.4994
0.681 0.5614 2900 0.6259 0.4819
0.6737 0.5807 3000 0.6299 0.4802
0.6737 0.6001 3100 0.6379 0.4893
0.6737 0.6194 3200 0.6226 0.4806
0.6737 0.6388 3300 0.6089 0.4627
0.6737 0.6581 3400 0.6029 0.4735
0.6419 0.6775 3500 0.5871 0.4592
0.6419 0.6969 3600 0.6001 0.4611
0.6419 0.7162 3700 0.5849 0.4473
0.6419 0.7356 3800 0.5924 0.4638
0.6419 0.7549 3900 0.5768 0.4585
0.6183 0.7743 4000 0.5673 0.4453
0.6183 0.7937 4100 0.5575 0.4452
0.6183 0.8130 4200 0.5632 0.4475
0.6183 0.8324 4300 0.5499 0.4401
0.6183 0.8517 4400 0.5663 0.4310
0.5877 0.8711 4500 0.5585 0.4317
0.5877 0.8904 4600 0.5464 0.4200
0.5877 0.9098 4700 0.5381 0.4192
0.5877 0.9292 4800 0.5454 0.4202
0.5877 0.9485 4900 0.5238 0.4124
0.5621 0.9679 5000 0.5304 0.4135
0.5621 0.9872 5100 0.5163 0.4061
0.5621 1.0066 5200 0.5160 0.3993
0.5621 1.0259 5300 0.5089 0.3899
0.5621 1.0453 5400 0.5111 0.3986
0.4882 1.0647 5500 0.5010 0.3857
0.4882 1.0840 5600 0.4941 0.3859
0.4882 1.1034 5700 0.4940 0.3813
0.4882 1.1227 5800 0.4914 0.3782
0.4882 1.1421 5900 0.4875 0.3745
0.4569 1.1614 6000 0.4842 0.3807
0.4569 1.1808 6100 0.4861 0.3737
0.4569 1.2002 6200 0.4814 0.3761
0.4569 1.2195 6300 0.4781 0.3741
0.4569 1.2389 6400 0.4771 0.3682
0.4416 1.2582 6500 0.4710 0.3734
0.4416 1.2776 6600 0.4721 0.3660
0.4416 1.2969 6700 0.4679 0.3639
0.4416 1.3163 6800 0.4623 0.3665
0.4416 1.3357 6900 0.4611 0.3602
0.4324 1.3550 7000 0.4689 0.3609
0.4324 1.3744 7100 0.4573 0.3603
0.4324 1.3937 7200 0.4575 0.3546
0.4324 1.4131 7300 0.4556 0.3584
0.4324 1.4324 7400 0.4496 0.3507
0.4255 1.4518 7500 0.4461 0.3467
0.4255 1.4712 7600 0.4434 0.3462
0.4255 1.4905 7700 0.4436 0.3516
0.4255 1.5099 7800 0.4406 0.3458
0.4255 1.5292 7900 0.4387 0.3439
0.4094 1.5486 8000 0.4325 0.3410
0.4094 1.5679 8100 0.4360 0.3419
0.4094 1.5873 8200 0.4286 0.3377
0.4094 1.6067 8300 0.4301 0.3336
0.4094 1.6260 8400 0.4297 0.3323
0.4018 1.6454 8500 0.4270 0.3339
0.4018 1.6647 8600 0.4267 0.3320
0.4018 1.6841 8700 0.4224 0.3328
0.4018 1.7034 8800 0.4207 0.3298
0.4018 1.7228 8900 0.4197 0.3298
0.3899 1.7422 9000 0.4184 0.3258
0.3899 1.7615 9100 0.4165 0.3262
0.3899 1.7809 9200 0.4118 0.3229
0.3899 1.8002 9300 0.4134 0.3232
0.3899 1.8196 9400 0.4127 0.3209
0.3665 1.8389 9500 0.4108 0.3211
0.3665 1.8583 9600 0.4090 0.3199
0.3665 1.8777 9700 0.4076 0.3209
0.3665 1.8970 9800 0.4065 0.3198
0.3665 1.9164 9900 0.4062 0.3192
0.3698 1.9357 10000 0.4061 0.3193

Framework versions

  • Transformers 4.40.2
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1