metadata
library_name: transformers
language:
- en
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- Japanese_english
metrics:
- wer
model-index:
- name: Whisper tiny Japanese
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Japanese English
type: Japanese_english
args: 'config: default, split: test'
metrics:
- name: Wer
type: wer
value: 21.50689223057644
Whisper tiny Japanese
This model is a fine-tuned version of openai/whisper-tiny on the Japanese English dataset. It achieves the following results on the evaluation set:
- Loss: 0.4624
- Wer: 21.5069
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 |
|---|---|---|---|---|
| 0.446 | 0.2488 | 200 | 0.6420 | 30.8741 |
| 0.3568 | 0.4975 | 400 | 0.5394 | 26.0182 |
| 0.2883 | 0.7463 | 600 | 0.5164 | 23.2926 |
| 0.41 | 0.9950 | 800 | 0.4806 | 23.1673 |
| 0.137 | 1.2438 | 1000 | 0.4815 | 22.1648 |
| 0.167 | 1.4925 | 1200 | 0.4720 | 21.6949 |
| 0.0589 | 1.7413 | 1400 | 0.4677 | 21.8672 |
| 0.1126 | 1.9900 | 1600 | 0.4565 | 21.9142 |
| 0.0299 | 2.2388 | 1800 | 0.4622 | 21.5382 |
| 0.0899 | 2.4876 | 2000 | 0.4624 | 21.5069 |
Framework versions
- Transformers 4.50.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0