rasr_sample / README.md
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metadata
language:
  - sv-SE
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_7_0
  - generated_from_trainer
datasets:
  - common_voice
model-index:
  - name: rasr_sample
    results: []

rasr_sample

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - SV-SE dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3147
  • Wer: 0.2676

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: 7.5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 2000
  • num_epochs: 50.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
3.3332 1.45 500 3.3031 1.0
2.9272 2.91 1000 2.9353 0.9970
2.0736 4.36 1500 1.1565 0.8714
1.7339 5.81 2000 0.7156 0.6688
1.5989 7.27 2500 0.5791 0.5519
1.4916 8.72 3000 0.5038 0.5169
1.4562 10.17 3500 0.4861 0.4805
1.3893 11.63 4000 0.4584 0.4761
1.3797 13.08 4500 0.4298 0.4686
1.3508 14.53 5000 0.4138 0.3744
1.3165 15.99 5500 0.4015 0.3578
1.281 17.44 6000 0.3883 0.3472
1.2682 18.89 6500 0.3904 0.3434
1.2477 20.35 7000 0.3726 0.3321
1.2364 21.8 7500 0.3685 0.3281
1.2041 23.26 8000 0.3597 0.3194
1.1901 24.71 8500 0.3542 0.3203
1.1903 26.16 9000 0.3500 0.3138
1.1677 27.61 9500 0.3458 0.3067
1.1718 29.07 10000 0.3595 0.3112
1.1562 30.52 10500 0.3433 0.3022
1.1392 31.97 11000 0.3440 0.2936
1.1258 33.43 11500 0.3396 0.2950
1.1067 34.88 12000 0.3379 0.2939
1.0953 36.34 12500 0.3370 0.2868
1.0835 37.79 13000 0.3317 0.2860
1.0772 39.24 13500 0.3302 0.2854
1.0853 40.7 14000 0.3265 0.2783
1.0689 42.15 14500 0.3306 0.2770
1.0394 43.6 15000 0.3233 0.2757
1.0581 45.06 15500 0.3199 0.2713
1.0362 46.51 16000 0.3154 0.2683
1.0406 47.96 16500 0.3176 0.2688
1.0082 49.42 17000 0.3149 0.2679

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu102
  • Datasets 1.18.2.dev0
  • Tokenizers 0.11.0