---
language:
- ko
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- slplab/asd_apac
metrics:
- wer
pipeline_tag: automatic-speech-recognition
duplicated_from: slplab/whisper-large_v2-asd_v1
base_model: openai/whisper-large-v2
model-index:
- name: whisper-large_v2-asd_v1
  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_v2-asd_v1

This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on [slplab/asd_apac](https://huggingface.co/slplab/asd_apac) dataset.
It achieves the following results on the validation set:
- Loss: 0.8553
- Wer: 78.4722

## 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: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1000

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0617        | 10.53  | 100  | 0.6858          | 81.9444 |
| 0.004         | 21.05  | 200  | 0.7322          | 79.8611 |
| 0.0005        | 31.58  | 300  | 0.7923          | 80.5556 |
| 0.0003        | 42.11  | 400  | 0.8131          | 79.1667 |
| 0.0002        | 52.63  | 500  | 0.8263          | 76.7361 |
| 0.0002        | 63.16  | 600  | 0.8365          | 77.4306 |
| 0.0002        | 73.68  | 700  | 0.8451          | 78.4722 |
| 0.0002        | 84.21  | 800  | 0.8503          | 78.4722 |
| 0.0002        | 94.74  | 900  | 0.8541          | 78.4722 |
| 0.0002        | 105.26 | 1000 | 0.8553          | 78.4722 |

### Test results

- Loss: 0.6359
- Wer: 36.6876

### Framework versions

- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3