whisper-large-sme / README.md
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Librarian Bot: Add base_model information to model (#1)
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metadata
language:
  - se
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - audiofolder
metrics:
  - wer
base_model: openai/whisper-large-v2
model-index:
  - name: Whisper Large Northern Sámi
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: audiofolder
          type: audiofolder
          config: default
          split: test
          args: default
        metrics:
          - type: wer
            value: 24.914285714285715
            name: Wer

Whisper Large Northern Sámi

This model is a fine-tuned version of openai/whisper-large-v2 on the audiofolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5559
  • Wer: 24.9143

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: 1e-05
  • train_batch_size: 12
  • eval_batch_size: 6
  • seed: 42
  • distributed_type: multi-GPU
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 60000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.4665 58.0 1000 0.8572 54.5143
0.3041 117.0 2000 0.6711 44.1143
0.2671 176.0 3000 0.5794 39.7714
0.1761 235.0 4000 0.5357 35.0857
0.2089 294.0 5000 0.5094 33.6
0.1456 352.0 6000 0.4959 33.0286
0.1514 411.0 7000 0.4864 32.5714
0.1203 470.0 8000 0.4625 31.4286
0.0879 529.0 9000 0.4916 45.4857
0.0825 588.0 10000 0.4962 30.6286
0.0753 647.0 11000 0.4723 31.2
0.0812 705.0 12000 0.4574 28.6857
0.062 764.0 13000 0.4628 28.8000
0.0604 823.0 14000 0.4668 28.0000
0.0666 882.0 15000 0.4697 28.6857
0.0405 941.0 16000 0.4908 54.6286
0.0349 999.0 17000 0.4728 28.4571
0.0409 1058.0 18000 0.4884 28.4571
0.0292 1117.0 19000 0.4576 27.3143
0.0247 1176.0 20000 0.4734 28.9143
0.0229 1235.0 21000 0.4899 29.9429
0.0271 1294.0 22000 0.4790 28.1143
0.0271 1352.0 23000 0.5012 30.1714
0.0184 1411.0 24000 0.5008 27.3143
0.0211 1470.0 25000 0.5118 27.6571
0.0183 1529.0 26000 0.5398 30.0571
0.0164 1588.0 27000 0.5006 27.3143
0.0169 1647.0 28000 0.5059 27.0857
0.0147 1705.0 29000 0.5325 27.7714
0.0104 1764.0 30000 0.4818 26.1714
0.0128 1823.0 31000 0.5259 28.3429
0.0145 1882.0 32000 0.5299 26.2857
0.0075 1941.0 33000 0.5082 27.4286
0.0087 1999.0 34000 0.5144 26.6286
0.005 2058.0 35000 0.5590 27.0857
0.0099 2117.0 36000 0.5546 28.9143
0.007 2176.0 37000 0.5364 26.8571
0.0045 2235.0 38000 0.5574 27.2000
0.0064 2294.0 39000 0.5051 25.7143
0.0079 2352.0 40000 0.5247 25.9429
0.0083 2411.0 41000 0.5514 25.6
0.0101 2470.0 42000 0.5710 25.6
0.0062 2529.0 43000 0.5830 28.0000
0.0046 2588.0 44000 0.5828 26.8571
0.0053 2647.0 45000 0.5621 27.4286
0.0047 2705.0 46000 0.5673 25.9429
0.0045 2764.0 47000 0.5220 25.6
0.0065 2823.0 48000 0.5704 27.7714
0.0039 2882.0 49000 0.5741 27.7714
0.0027 2941.0 50000 0.5762 26.0571
0.0019 2999.0 51000 0.5559 24.9143
0.0015 3058.0 52000 0.5777 28.5714
0.0026 3117.0 53000 0.5589 25.2571
0.0032 3176.0 54000 0.6061 26.9714
0.0025 3235.0 55000 0.5776 25.1429
0.0046 3294.0 56000 0.5753 27.3143
0.0015 3352.0 57000 0.5736 27.2000
0.003 3411.0 58000 0.5933 25.6
0.002 3470.0 59000 0.6036 25.6
0.0007 58.0 60000 0.5975 25.2571

Framework versions

  • Transformers 4.26.0.dev0
  • Pytorch 1.13.0+cu117
  • Datasets 2.7.1.dev0
  • Tokenizers 0.11.0