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metadata
language:
  - da
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
metrics:
  - wer
model-index:
  - name: Whisper Small da - Common Voice+FLEURS
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 11.0, FLEURS
          type: mozilla-foundation/common_voice_11_0
          config: da
          split: test
          args: da
        metrics:
          - name: Wer
            type: wer
            value: 138.8532351394003

Whisper Small da - Common Voice+FLEURS

This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0, FLEURS dataset. It achieves the following results on the evaluation set:

  • Loss: 0.9242
  • Wer: 138.8532

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-07
  • train_batch_size: 64
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 5000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
1.22 15.0 1000 1.2238 197.5434
1.004 30.0 2000 1.0386 197.9221
0.9104 45.01 3000 0.9704 156.6544
0.8455 60.01 4000 0.9352 142.3619
0.8237 75.01 5000 0.9242 138.8532

Framework versions

  • Transformers 4.26.0.dev0
  • Pytorch 1.13.1+cu117
  • Datasets 2.7.1.dev0
  • Tokenizers 0.13.2