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metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-medium
tags:
  - generated_from_trainer
datasets:
  - bigcgen
metrics:
  - wer
model-index:
  - name: whisper-medium-bigcgen-combined-5hrs-62
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: bigcgen
          type: bigcgen
        metrics:
          - name: Wer
            type: wer
            value: 0.5565156468939748

whisper-medium-bigcgen-combined-5hrs-62

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

  • Loss: 0.7209
  • Wer: 0.5565

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: 2
  • eval_batch_size: 2
  • seed: 62
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 8
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • 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
1.0959 0.6079 200 0.9655 0.6610
0.6562 1.2158 400 0.8052 0.5699
0.6063 1.8237 600 0.7209 0.5565
0.402 2.4316 800 0.7347 0.5566
0.3066 3.0395 1000 0.7320 0.5467
0.2262 3.6474 1200 0.7329 0.5896
0.117 4.2553 1400 0.7819 0.5211

Framework versions

  • Transformers 4.53.0.dev0
  • Pytorch 2.6.0+cu124
  • Datasets 3.6.0
  • Tokenizers 0.21.0