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---
language:
- ko
license: apache-2.0
base_model: openai/whisper-base
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
model-index:
- name: oceanstar-bridze
results: []
metrics:
- cer
---
<!-- 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. -->
# oceanstar-bridze
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the bridzeDataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1880
- Cer: 7.3894
## 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: 8
- eval_batch_size: 8
- 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
### Training results
| Training Loss | Epoch | Step | Cer | Validation Loss |
|:-------------:|:-----:|:----:|:-------:|:---------------:|
| 0.3652 | 0.06 | 500 | 11.3504 | 0.3574 |
| 0.2788 | 0.13 | 1000 | 9.1325 | 0.2645 |
| 0.2213 | 0.1 | 1500 | 9.3132 | 0.2388 |
| 0.2257 | 0.13 | 2000 | 8.6295 | 0.2194 |
| 0.1941 | 0.16 | 2500 | 7.5109 | 0.2068 |
| 0.1395 | 0.19 | 3000 | 7.3247 | 0.1969 |
| 0.1787 | 0.23 | 3500 | 7.5517 | 0.1905 |
| 0.1639 | 0.26 | 4000 | 7.3894 | 0.1880 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 1.10.1
- Datasets 2.14.2
- Tokenizers 0.13.3 |