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---
language:
- ko
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
metrics:
- wer
base_model: openai/whisper-large-v2
model-index:
- name: whisper_finetune
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_finetune
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the aihub_100000 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1966
- Cer: 5.9236
- Wer: 23.0770
## 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-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Cer | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:------:|:---------------:|:-------:|
| 0.1866 | 0.16 | 1000 | 6.0386 | 0.1963 | 23.2684 |
| 0.1788 | 0.32 | 2000 | 6.0483 | 0.1979 | 23.2267 |
| 0.1541 | 0.48 | 3000 | 6.0116 | 0.1929 | 23.5519 |
| 0.1692 | 0.64 | 4000 | 0.1966 | 5.9236 | 23.0770 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.16.1
- Tokenizers 0.15.1
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