metadata

library_name: transformers
language:
  - ja
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - custom_dataset
metrics:
  - wer
model-index:
  - name: Whisper Small Ja Custom
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Custom Dataset
          type: custom_dataset
          config: ja
          split: train
          args: 'config: ja, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 88.03418803418803

Whisper Small Ja Custom

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

Loss: 0.4312
Wer: 88.0342

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
0.1175	2.7322	1000	0.3352	84.7578
0.0102	5.4645	2000	0.3849	84.3305
0.0019	8.1967	3000	0.4231	87.3219
0.0014	10.9290	4000	0.4312	88.0342

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0