metadata

library_name: transformers
license: apache-2.0
base_model: openai/whisper-large-v2
tags:
  - generated_from_trainer
datasets:
  - cymen-arfor/25awr
metrics:
  - wer
model-index:
  - name: whisper-large-v2-ft-ca-25awr
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: cymen-arfor/25awr default
          type: cymen-arfor/25awr
          args: default
        metrics:
          - name: Wer
            type: wer
            value: 0.40249424956871765

whisper-large-v2-ft-ca-25awr

This model is a fine-tuned version of openai/whisper-large-v2 on the cymen-arfor/25awr default dataset. It achieves the following results on the evaluation set:

Loss: 0.8440
Wer: 0.4025

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: 16
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Use 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
0.4138	1.6488	1000	0.5216	0.5082
0.1535	3.2976	2000	0.5362	0.4263
0.084	4.9464	3000	0.5920	0.4038
0.0185	6.5952	4000	0.7443	0.4076
0.0038	8.2440	5000	0.8440	0.4025

Framework versions

Transformers 4.46.2
Pytorch 2.5.1+cu124
Datasets 3.1.0
Tokenizers 0.20.3