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xlsr-a-no

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: 0.4542
  • Wer: 0.3208

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
4.2929 2.8633 200 2.3482 1.0
0.9068 5.7194 400 0.2885 0.5495
0.1691 8.5755 600 0.2612 0.3732
0.0828 11.4317 800 0.2968 0.3595
0.0473 14.2878 1000 0.3520 0.3515
0.0428 17.1439 1200 0.3341 0.3595
0.0358 20.0 1400 0.3301 0.3345
0.0267 22.8633 1600 0.3461 0.3242
0.0247 25.7194 1800 0.3533 0.3333
0.0219 28.5755 2000 0.3746 0.3242
0.0245 31.4317 2200 0.3684 0.3254
0.0151 34.2878 2400 0.3780 0.3197
0.0119 37.1439 2600 0.3322 0.3220
0.0151 40.0 2800 0.3688 0.3231
0.0131 42.8633 3000 0.3895 0.3231
0.0118 45.7194 3200 0.4120 0.3242
0.0079 48.5755 3400 0.4206 0.3231
0.0062 51.4317 3600 0.4137 0.3242
0.0059 54.2878 3800 0.3937 0.3220
0.0085 57.1439 4000 0.4004 0.3208
0.0068 60.0 4200 0.4266 0.3242
0.0065 62.8633 4400 0.3980 0.3208
0.0067 65.7194 4600 0.3957 0.3208
0.0051 68.5755 4800 0.4251 0.3220
0.0035 71.4317 5000 0.4428 0.3220
0.0047 74.2878 5200 0.4380 0.3231
0.0034 77.1439 5400 0.4388 0.3220
0.0033 80.0 5600 0.4544 0.3197
0.002 82.8633 5800 0.4368 0.3197
0.0042 85.7194 6000 0.4402 0.3197
0.0028 88.5755 6200 0.4482 0.3220
0.002 91.4317 6400 0.4547 0.3208
0.0012 94.2878 6600 0.4565 0.3208
0.0017 97.1439 6800 0.4542 0.3208

Framework versions

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