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metadata
library_name: transformers
language:
  - en
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
  - wft
  - whisper
  - automatic-speech-recognition
  - audio
  - speech
  - generated_from_trainer
datasets:
  - ntnu-smil/sandi2025-ds
metrics:
  - wer
model-index:
  - name: whisper-large-v3-turbo-sandi-train-1-ex-transcript-32-r128
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: ntnu-smil/sandi2025-ds
          type: ntnu-smil/sandi2025-ds
        metrics:
          - type: wer
            value: 29.317168607075274
            name: Wer

whisper-large-v3-turbo-sandi-train-1-ex-transcript-32-r128

This model is a fine-tuned version of openai/whisper-large-v3-turbo on the ntnu-smil/sandi2025-ds dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8115
  • Wer: 29.3172
  • Cer: 21.2742
  • Decode Runtime: 200.5385
  • Wer Runtime: 0.1825
  • Cer Runtime: 0.3207

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: 7e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.98) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • training_steps: 732

Training results

Training Loss Epoch Step Validation Loss Wer Cer Decode Runtime Wer Runtime Cer Runtime
0.9816 0.1667 122 0.8325 52.6136 40.1293 213.3993 0.1959 0.3519
1.12 1.1667 244 0.8067 41.8140 31.3145 206.7493 0.1829 0.3292
0.5435 2.1667 366 0.7957 28.9535 21.0430 196.2685 0.1781 0.3176
0.3301 3.1667 488 0.7980 28.7450 20.8476 197.7640 0.1800 0.3185
0.8564 4.1667 610 0.8075 30.0818 21.8933 200.7786 0.1811 0.3254
0.8796 5.1667 732 0.8115 29.3172 21.2742 200.5385 0.1825 0.3207

Framework versions

  • PEFT 0.15.2
  • Transformers 4.48.2
  • Pytorch 2.4.1+cu124
  • Datasets 3.6.0
  • Tokenizers 0.21.1