---
library_name: ctranslate2
license: apache-2.0
base_model: openai/whisper-small
tags:
- audio
- automatic-speech-recognition
- ctranslate2
- faster-whisper
- generated_from_trainer
- whisper
metrics:
- cer
- wer
model-index:
- name: whisper-small-jp
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: mozilla-foundation/common_voice_17_0 (ja)
      type: mozilla-foundation/common_voice_17_0
      config: ja
      split: test
      args:
        language: ja
    metrics:
    - name: CER
      type: cer
      value: 0.23043221252477486
---

> **This repository contains the CTranslate2 export of the fine-tuned model.**
>
> • Base Transformers model: [drepic/whisper-small-jp](https://huggingface.co/drepic/whisper-small-jp)  
> • Use with `faster-whisper`:
>
> ```python
> from faster_whisper import WhisperModel
> model = WhisperModel("drepic/whisper-small-jp-ct2", device="cuda", compute_type="float16")
> ```

# OTHER FINETUNES
- Want better accuracy? Try [drepic/whisper-medium-jp-ct2](https://huggingface.co/drepic/whisper-medium-jp-ct2)

<!-- 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-small-jp

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on a Japanese youtube based dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6168
- Wer: 0.2600
- Cer: 0.2600

## Model description

Better suited for transcribing japanese youtube content. 

## 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: 5e-06
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 16
- total_eval_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 300
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 0.6589        | 1.0   | 7154  | 0.6615          | 0.2735 | 0.2735 |
| 0.6273        | 2.0   | 14308 | 0.6457          | 0.2699 | 0.2699 |
| 0.6251        | 3.0   | 21462 | 0.6359          | 0.2660 | 0.2660 |
| 0.6427        | 4.0   | 28616 | 0.6283          | 0.2642 | 0.2642 |
| 0.6389        | 5.0   | 35770 | 0.6243          | 0.2631 | 0.2631 |
| 0.6078        | 6.0   | 42924 | 0.6242          | 0.2615 | 0.2615 |
| 0.5788        | 7.0   | 50078 | 0.6195          | 0.2603 | 0.2603 |
| 0.5801        | 8.0   | 57232 | 0.6180          | 0.2596 | 0.2596 |
| 0.5866        | 9.0   | 64386 | 0.6145          | 0.2598 | 0.2598 |
| 0.6052        | 10.0  | 71540 | 0.6168          | 0.2600 | 0.2600 |


### Framework versions

- Transformers 4.56.1
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.0