---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# whisper-large-v3-ja

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/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