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wav2vec2-xlsr-1B-NPSC-NN

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metadata
license: apache-2.0
tags:
  - generated_from_trainer
  - automatic-speech-recognition
  - NbAiLab/NPSC
  - robust-speech-event
  - false
  - nn-NO
  - hf-asr-leaderboard
datasets:
  - NbAiLab/NPSC
language:
  - nn-NO
model-index:
  - name: wav2vec2-xlsr-1B-NPSC-NN
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: NPSC
          type: NbAiLab/NPSC
          args: 16K_mp3_nynorsk
        metrics:
          - name: Test (Nynorsk) WER
            type: wer
            value: 0.13347099680871036
          - name: Test (Nynorsk) CER
            type: cer
            value: 0.04537322093454329

wav2vec2-xlsr-1B-NPSC-NN

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the NBAILAB/NPSC - 16K_MP3 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4562
  • Wer: 0.1531

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: 6e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • 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
1.6894 1.08 500 1.2423 0.8619
0.7543 2.15 1000 0.5956 0.3817
0.5481 3.23 1500 0.5043 0.3246
0.4661 4.3 2000 0.4813 0.2793
0.3901 5.38 2500 0.4371 0.2592
0.3512 6.45 3000 0.4216 0.2458
0.3016 7.53 3500 0.3814 0.2257
0.278 8.6 4000 0.4151 0.2145
0.2435 9.68 4500 0.4816 0.2130
0.2122 10.75 5000 0.4489 0.2137
0.1949 11.83 5500 0.3978 0.2063
0.1929 12.9 6000 0.3823 0.2026
0.1757 13.98 6500 0.3409 0.1965
0.1771 15.05 7000 0.3844 0.1936
0.1452 16.13 7500 0.3749 0.1900
0.1341 17.2 8000 0.4407 0.2026
0.13 18.28 8500 0.4253 0.1883
0.1183 19.35 9000 0.4311 0.1880
0.118 20.43 9500 0.4431 0.1882
0.1123 21.51 10000 0.4753 0.1820
0.1037 22.58 10500 0.4087 0.1834
0.1066 23.66 11000 0.4151 0.1845
0.0977 24.73 11500 0.4367 0.1783
0.0968 25.81 12000 0.4237 0.1756
0.0835 26.88 12500 0.4729 0.1781
0.0919 27.96 13000 0.4153 0.1701
0.0677 29.03 13500 0.4317 0.1693
0.0726 30.11 14000 0.4380 0.1736
0.066 31.18 14500 0.4384 0.1681
0.0713 32.26 15000 0.4215 0.1629
0.0605 33.33 15500 0.4574 0.1714
0.0632 34.41 16000 0.4343 0.1642
0.0567 35.48 16500 0.4231 0.1601
0.0556 36.56 17000 0.4404 0.1667
0.0426 37.63 17500 0.4459 0.1625
0.0445 38.71 18000 0.4484 0.1629
0.0463 39.78 18500 0.4508 0.1596
0.0448 40.86 19000 0.4395 0.1605
0.0434 41.94 19500 0.4490 0.1607
0.0347 43.01 20000 0.4772 0.1582
0.0332 44.09 20500 0.4729 0.1582
0.037 45.16 21000 0.4559 0.1573
0.0328 46.24 21500 0.4664 0.1560
0.0366 47.31 22000 0.4543 0.1543
0.0377 48.39 22500 0.4507 0.1560
0.0331 49.46 23000 0.4567 0.1533

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.1+cu102
  • Datasets 1.18.2.dev0
  • Tokenizers 0.11.0