---
library_name: peft
language:
- multilingual
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Turbo Multilingual (ko, ja, zh, en)
  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 Turbo Multilingual (ko, ja, zh, en)

This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the custom_multilingual dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3860
- Wer: 15.9354

## 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: 0.0005
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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: 200
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.3244        | 1.0971  | 500  | 0.3557          | 19.6171 |
| 0.2701        | 2.1942  | 1000 | 0.3490          | 19.5934 |
| 0.224         | 3.2913  | 1500 | 0.3503          | 17.5891 |
| 0.2132        | 4.3884  | 2000 | 0.3518          | 16.9210 |
| 0.1865        | 5.4855  | 2500 | 0.3571          | 16.2908 |
| 0.1661        | 6.5826  | 3000 | 0.3652          | 16.0491 |
| 0.1467        | 7.6796  | 3500 | 0.3692          | 16.4471 |
| 0.136         | 8.7767  | 4000 | 0.3762          | 15.9496 |
| 0.1229        | 9.8738  | 4500 | 0.3816          | 15.8453 |
| 0.1146        | 10.9709 | 5000 | 0.3860          | 15.9354 |


### Framework versions

- PEFT 0.15.2.dev0
- Transformers 4.46.3
- Pytorch 2.3.1+cu121
- Datasets 3.0.0
- Tokenizers 0.20.3