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---
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 |