metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_16_0
metrics:
- wer
base_model: openai/whisper-large-v3
model-index:
- name: whisper-large-v3-ja
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: common_voice_16_0
type: common_voice_16_0
config: ja
split: validation
args: ja
metrics:
- type: wer
value: 14.696501005043272
name: Wer
whisper-large-v3-ja
This model is a fine-tuned version of openai/whisper-large-v3 on the common_voice_16_0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4210
- Wer: 14.6965
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: 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 |
|---|---|---|---|---|
| 0.1542 | 1.69 | 500 | 0.2712 | 15.6149 |
| 0.0351 | 3.39 | 1000 | 0.3074 | 16.1866 |
| 0.0081 | 5.08 | 1500 | 0.3475 | 15.3802 |
| 0.0049 | 6.78 | 2000 | 0.3427 | 15.1804 |
| 0.001 | 8.47 | 2500 | 0.3851 | 14.7302 |
| 0.0004 | 10.17 | 3000 | 0.4109 | 14.7254 |
| 0.0003 | 11.86 | 3500 | 0.4168 | 14.6953 |
| 0.0003 | 13.56 | 4000 | 0.4210 | 14.6965 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2