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FT-frisian-1h

Automatic Speech Recognition
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metadata
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_6_1
metrics:
  - wer
model-index:
  - name: Whisper Small Frisian 1h
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 6.1
          type: mozilla-foundation/common_voice_6_1
          args: 'config: frisian, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 47.79183746212796

Whisper Small Frisian 1h

This model is a fine-tuned version of openai/whisper-small on the Common Voice 6.1 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.9900
  • Wer: 47.7918

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-06
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 50
  • training_steps: 2000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
2.4073 1.1236 100 2.2555 82.9549
1.5143 2.2472 200 1.6651 73.4557
1.1865 3.3708 300 1.4237 65.1256
0.9368 4.4944 400 1.2874 59.4832
0.8009 5.6180 500 1.1957 56.5461
0.6722 6.7416 600 1.1345 54.6890
0.5726 7.8652 700 1.0894 53.1919
0.5068 8.9888 800 1.0575 51.7769
0.4239 10.1124 900 1.0351 50.8002
0.3799 11.2360 1000 1.0197 49.9198
0.295 12.3596 1100 1.0110 49.3673
0.2852 13.4831 1200 1.0022 48.7507
0.2478 14.6067 1300 0.9965 48.3800
0.2267 15.7303 1400 0.9931 48.1911
0.1986 16.8539 1500 0.9916 48.1412
0.1922 17.9775 1600 0.9907 47.9558
0.1724 19.1011 1700 0.9905 47.8703
0.1709 20.2247 1800 0.9900 47.9059
0.1749 21.3483 1900 0.9900 47.7598
0.145 22.4719 2000 0.9900 47.7918

Framework versions

  • Transformers 4.40.1
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.0
  • Tokenizers 0.19.1