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whisper-tiny-nob

Automatic Speech Recognition
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metadata
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - NbAiLab/NCC_S
metrics:
  - wer
base_model: openai/whisper-tiny
model-index:
  - name: Whisper Tiny Norwegian Bokmål
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: NbAiLab/NCC_S
          type: NbAiLab/NCC_S
          config: 'no'
          split: validation
          args: 'no'
        metrics:
          - type: wer
            value: 24.878197320341048
            name: Wer

Whisper Tiny Norwegian Bokmål

This model is a fine-tuned version of openai/whisper-tiny on the NbAiLab/NCC_S dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5100
  • Wer: 24.8782

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: 3e-06
  • train_batch_size: 256
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: constant_with_warmup
  • lr_scheduler_warmup_steps: 1000
  • training_steps: 100000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
1.8819 0.01 1000 1.1869 61.9671
1.6425 0.02 2000 0.9991 53.6541
1.548 0.03 3000 0.9147 50.2132
1.4636 0.04 4000 0.8605 47.0767
1.4113 0.05 5000 0.8253 45.7369
1.3484 0.01 6000 0.7946 43.4531
1.3127 0.02 7000 0.7740 42.2655
1.2994 0.03 8000 0.7551 40.8952
1.265 0.04 9000 0.7378 39.8599
1.2458 0.05 10000 0.7257 39.8904
1.2257 0.06 11000 0.7114 39.7990
1.2126 0.07 12000 0.6972 37.8806
1.1971 0.08 13000 0.6871 37.3021
1.1786 1.01 14000 0.6786 37.4239
1.1486 1.02 15000 0.6703 36.9976
1.1505 1.03 16000 0.6647 36.3581
1.1238 1.04 17000 0.6559 36.3886
1.1184 1.05 18000 0.6509 36.5104
1.115 1.06 19000 0.6452 35.9927
1.1013 1.07 20000 0.6382 34.5006
1.0969 1.08 21000 0.6331 34.3484
1.0784 2.0 22000 0.6304 34.2875
1.0774 2.01 23000 0.6249 34.1048
1.0719 2.02 24000 0.6194 33.8307
1.0638 2.03 25000 0.6158 32.9781
1.0592 2.04 26000 0.6105 32.6431
1.0493 2.05 27000 0.6041 32.7345
1.047 2.06 28000 0.6040 32.7649
1.0323 2.07 29000 0.5984 31.6078
1.0189 3.0 30000 0.5957 31.3033
1.0078 3.01 31000 0.5924 31.4251
1.0146 3.02 32000 0.5940 31.3033
1.0128 3.03 33000 0.5892 31.0292
1.0025 3.04 34000 0.5873 31.1815
0.999 3.05 35000 0.5838 30.6334
1.0045 3.06 36000 0.5799 30.4202
1.0005 3.07 37000 0.5770 30.1766
1.0017 3.08 38000 0.5733 29.6590
0.9878 4.01 39000 0.5745 30.2680
0.9854 4.02 40000 0.5720 30.0548
0.9624 4.03 41000 0.5703 29.5981
0.9639 4.04 42000 0.5681 29.5067
0.9569 4.05 43000 0.5679 29.6285
0.9682 4.06 44000 0.5643 29.5676
0.9539 4.07 45000 0.5601 29.5676
0.946 4.08 46000 0.5562 29.7199
0.9429 5.01 47000 0.5592 29.2935
0.9462 5.02 48000 0.5540 29.0804
0.9312 5.03 49000 0.5535 29.2935
0.9462 5.04 50000 0.5536 28.6845
0.922 5.05 51000 0.5539 28.7150
0.9253 5.06 52000 0.5510 28.8368
0.9065 0.01 53000 0.5493 28.5932
0.9096 0.02 54000 0.5490 28.5018
0.9329 0.03 55000 0.5483 28.2887
0.9181 0.04 56000 0.5471 27.9842
0.914 0.05 57000 0.5457 28.4105
0.9149 0.06 58000 0.5449 27.5883
0.9092 0.07 59000 0.5405 27.8319
0.9101 0.08 60000 0.5402 27.3447
0.9046 1.01 61000 0.5374 27.5579
0.8917 1.02 62000 0.5390 27.7406
0.8993 1.03 63000 0.5386 27.4056
0.8875 1.04 64000 0.5361 26.8575
0.8892 1.05 65000 0.5358 27.3447
0.8929 1.06 66000 0.5346 26.7357
0.8703 0.01 67000 0.5332 26.8270
0.8709 0.02 68000 0.5336 26.7052
0.8917 0.03 69000 0.5329 27.0706
0.8867 0.04 70000 0.5323 26.3398
0.8778 0.05 71000 0.5315 27.2838
0.8757 0.06 72000 0.5317 26.2485
0.8726 0.07 73000 0.5269 26.6443
0.8792 0.08 74000 0.5268 26.1571
0.8706 1.01 75000 0.5247 26.1571
0.8585 1.02 76000 0.5265 26.3703
0.8659 1.03 77000 0.5262 26.7357
0.8551 1.04 78000 0.5249 26.0658
0.8572 1.05 79000 0.5249 26.2789
0.8612 1.06 80000 0.5235 25.7613
0.8598 1.07 81000 0.5208 25.7004
0.8686 1.08 82000 0.5214 25.7004
0.8503 2.0 83000 0.5214 25.7004
0.8545 2.01 84000 0.5215 28.2278
0.8594 2.02 85000 0.5186 25.6699
0.86 2.03 86000 0.5196 25.5786
0.8514 2.04 87000 0.5203 25.1827
0.8505 2.05 88000 0.5164 28.0146
0.8512 2.06 89000 0.5174 25.0914
0.8495 2.07 90000 0.5141 25.5481
0.8381 3.0 91000 0.5130 24.9695
0.8253 3.01 92000 0.5147 25.5786
0.8387 3.02 93000 0.5168 24.9086
0.8425 3.03 94000 0.5135 25.2436
0.8339 3.04 95000 0.5162 25.6699
0.8402 3.05 96000 0.5147 25.7308
0.8396 3.06 97000 0.5143 25.6699
0.8432 3.07 98000 0.5100 24.8782
0.844 3.08 99000 0.5100 25.0609
0.8333 4.01 100000 0.5128 24.9695

Framework versions

  • Transformers 4.26.0.dev0
  • Pytorch 1.13.0+cu117
  • Datasets 2.7.1.dev0
  • Tokenizers 0.13.2