---
library_name: peft
language:
- ja
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
model-index:
- name: Whisper Large v3
  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 Large v3

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0346

## 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: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.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: 50
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0923        | 0.2151 | 20   | 0.0633          |
| 0.045         | 0.4301 | 40   | 0.0610          |
| 0.0664        | 0.6452 | 60   | 0.0720          |
| 0.0622        | 0.8602 | 80   | 0.0552          |
| 0.0589        | 1.0753 | 100  | 0.0781          |
| 0.0382        | 1.2903 | 120  | 0.0480          |
| 0.03          | 1.5054 | 140  | 0.0445          |
| 0.022         | 1.7204 | 160  | 0.0404          |
| 0.0201        | 1.9355 | 180  | 0.0387          |
| 0.0131        | 2.1505 | 200  | 0.0412          |
| 0.0111        | 2.3656 | 220  | 0.0366          |
| 0.0111        | 2.5806 | 240  | 0.0357          |
| 0.0142        | 2.7957 | 260  | 0.0346          |


### Framework versions

- PEFT 0.15.1
- Transformers 4.51.1
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1