metadata

library_name: transformers
language:
  - en
license: apache-2.0
base_model: openai/whisper-medium
tags:
  - generated_from_trainer
datasets:
  - Alsman68/CapstoneDataset3
metrics:
  - wer
model-index:
  - name: capstone-whisper-final-training-data
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Simulated Aviation Audio For Capstone, Final Training Data
          type: Alsman68/CapstoneDataset3
          args: 'config: en, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 0.05652911249293386

capstone-whisper-final-training-data

This model is a fine-tuned version of openai/whisper-medium on the Simulated Aviation Audio For Capstone, Final Training Data dataset. It achieves the following results on the evaluation set:

Loss: 0.0000
Wer: 0.0565

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: 16
eval_batch_size: 8
seed: 42
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: 4000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0001	52.6316	1000	0.0001	0.0565
0.0	105.2632	2000	0.0000	0.0565
0.0	157.8947	3000	0.0000	0.0565
0.0	210.5263	4000	0.0000	0.0565

Framework versions

Transformers 4.50.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1