---
base_model: openai/whisper-tiny
language:
- ja
library_name: transformers
license: apache-2.0
metrics:
- wer
tags:
- hf-asr-leaderboard
- generated_from_trainer
model-index:
- name: Whisper Tiny Japanese Combine 4k - Chee Li
  results: []
---

<!-- 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 Tiny Japanese Combine 4k - Chee Li

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co./openai/whisper-tiny) on the Meta JSON Japanese Dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8869
- Wer: 396.6874

## 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: Use 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: 8000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer      |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 2.441         | 4.1322  | 1000 | 2.4726          | 406.5217 |
| 1.8098        | 8.2645  | 2000 | 2.0185          | 462.4224 |
| 1.2666        | 12.3967 | 3000 | 1.5918          | 404.3478 |
| 0.8324        | 16.5289 | 4000 | 1.2738          | 460.8696 |
| 0.5744        | 20.6612 | 5000 | 1.0687          | 607.0393 |
| 0.3308        | 24.7934 | 6000 | 0.9561          | 532.7122 |
| 0.242         | 28.9256 | 7000 | 0.9024          | 461.0766 |
| 0.1651        | 33.0579 | 8000 | 0.8869          | 396.6874 |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.20.1