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metadata
language:
  - fi
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: Whisper Large v3 Fine-Tuned Finnish - CommonVoice13
    results: []

Whisper Large v3 Fine-Tuned Finnish - CommonVoice13

This model is a fine-tuned version of openai/whisper-large-v3 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3318
  • Wer: 25.9893

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: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • 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: 50
  • training_steps: 800
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
1.3591 0.42 50 0.3653 32.0541
0.4623 0.84 100 0.4383 35.4316
0.3229 1.26 150 0.4386 35.3672
0.2538 1.68 200 0.4324 34.4929
0.1972 2.11 250 0.4287 34.4561
0.1194 2.53 300 0.4235 33.6094
0.1132 2.95 350 0.3826 30.1767
0.0669 3.37 400 0.4073 33.0941
0.0614 3.79 450 0.3869 29.5233
0.0435 4.21 500 0.3942 30.1859
0.032 4.63 550 0.3839 27.9404
0.0184 5.05 600 0.3571 25.7500
0.0094 5.47 650 0.3477 25.8605
0.0055 5.89 700 0.3371 26.6243
0.0026 6.32 750 0.3329 25.4463
0.0015 6.74 800 0.3318 25.9893

Framework versions

  • Transformers 4.37.0.dev0
  • Pytorch 2.0.1
  • Datasets 2.16.1
  • Tokenizers 0.15.0