metadata
language:
- ja
license: apache-2.0
base_model: openai/whisper-small
tags:
- testing
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_1
metrics:
- wer
model-index:
- name: Whisper Small JA Test
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 16.1
type: mozilla-foundation/common_voice_16_1
config: ja
split: None
args: 'config: ja, split: test'
metrics:
- name: Wer
type: wer
value: 83.60284605433377
Whisper Small JA Test
This model is a fine-tuned version of openai/whisper-small on the Common Voice 16.1 dataset. It achieves the following results on the evaluation set:
- Loss: 0.5351
- Wer: 83.6028
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- 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 |
|---|---|---|---|---|
| 0.2417 | 1.02 | 1000 | 0.5194 | 85.4463 |
| 0.107 | 2.04 | 2000 | 0.5127 | 83.2471 |
| 0.0515 | 3.05 | 3000 | 0.5249 | 83.8616 |
| 0.0306 | 4.07 | 4000 | 0.5351 | 83.6028 |
Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2