README.md

metadata

library_name: transformers
language:
  - en
license: apache-2.0
base_model: openai/whisper-tiny
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
datasets:
  - British_english
metrics:
  - wer
model-index:
  - name: Whisper tiny Japanese
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: British English
          type: British_english
          args: 'config: default, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 13.939980638915781

Whisper tiny Japanese

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

• Loss: 0.2952
• Wer: 13.9400

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: 1
• 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: 2000

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.325	0.9901	1000	0.3010	15.1431
0.1716	1.9802	2000	0.2952	13.9400

Framework versions

• Transformers 4.50.0.dev0
• Pytorch 2.5.1+cu124
• Datasets 3.3.2
• Tokenizers 0.21.0