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wav2vec2-xls-r-1b-npsc-bokmaal

Automatic Speech Recognition
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
  - automatic-speech-recognition
  - NbAiLab/NPSC
  - robust-speech-event
  - false
  - nb-NO
  - hf-asr-leaderboard
datasets:
  - NbAiLab/NPSC
language:
  - nb-NO
model-index:
  - name: wav2vec2-xls-r-1b-npsc-bokmaal
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: NPSC
          type: NbAiLab/NPSC
          args: 16K_mp3_bokmaal
        metrics:
          - name: Test (Bokmål) WER
            type: wer
            value: 0.07901700231893541
          - name: Test (Bokmål) CER
            type: cer
            value: 0.029734583252347752

wav2vec2-xls-r-1b-npsc

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the NbAiLab/NPSC (16K_mp3_bokmaal) dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1598
  • WER: 0.0966

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: 0.0001
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 2
  • 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: 15.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.8361 0.32 500 0.6304 0.4970
0.5703 0.64 1000 0.3195 0.2775
0.5451 0.97 1500 0.2700 0.2246
0.47 1.29 2000 0.2564 0.2329
0.4063 1.61 2500 0.2459 0.2099
0.374 1.93 3000 0.2175 0.1894
0.3297 2.26 3500 0.2036 0.1755
0.3145 2.58 4000 0.1957 0.1757
0.3989 2.9 4500 0.1923 0.1723
0.271 3.22 5000 0.1889 0.1649
0.2758 3.55 5500 0.1768 0.1588
0.2683 3.87 6000 0.1720 0.1534
0.2341 4.19 6500 0.1689 0.1471
0.2316 4.51 7000 0.1706 0.1405
0.2383 4.84 7500 0.1637 0.1426
0.2148 5.16 8000 0.1584 0.1347
0.2085 5.48 8500 0.1601 0.1387
0.2944 5.8 9000 0.1566 0.1294
0.1944 6.13 9500 0.1494 0.1271
0.1853 6.45 10000 0.1561 0.1247
0.235 6.77 10500 0.1461 0.1215
0.2286 7.09 11000 0.1447 0.1167
0.1781 7.41 11500 0.1502 0.1199
0.1714 7.74 12000 0.1425 0.1179
0.1725 8.06 12500 0.1427 0.1173
0.143 8.38 13000 0.1448 0.1142
0.154 8.7 13500 0.1392 0.1104
0.1447 9.03 14000 0.1404 0.1094
0.1471 9.35 14500 0.1404 0.1088
0.1479 9.67 15000 0.1414 0.1133
0.1607 9.99 15500 0.1458 0.1171
0.166 10.32 16000 0.1652 0.1264
0.188 10.64 16500 0.1713 0.1322
0.1461 10.96 17000 0.1423 0.1111
0.1289 11.28 17500 0.1388 0.1097
0.1273 11.61 18000 0.1438 0.1074
0.1317 11.93 18500 0.1312 0.1066
0.1448 12.25 19000 0.1446 0.1042
0.1424 12.57 19500 0.1386 0.1015
0.1392 12.89 20000 0.1379 0.1005
0.1408 13.22 20500 0.1408 0.0992
0.1239 13.54 21000 0.1338 0.0968
0.1244 13.86 21500 0.1335 0.0957
0.1254 14.18 22000 0.1382 0.0950
0.1597 14.51 22500 0.1544 0.0970
0.1566 14.83 23000 0.1589 0.0963

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu113
  • Datasets 1.18.3.dev0
  • Tokenizers 0.11.0