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---
library_name: transformers
language:
- ko
license: apache-2.0
base_model: openai/whisper-small
tags:
- whisper-event
- generated_from_trainer
datasets:
- GGarri/241113_newdata
metrics:
- wer
model-index:
- name: Whisper Small ko
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: customdata
type: GGarri/241113_newdata
metrics:
- name: Wer
type: wer
value: 0.8156606851549755
---
<!-- 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 Small ko
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the customdata dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0498
- Cer: 1.1070
- Wer: 0.8157
## 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: 32
- eval_batch_size: 8
- seed: 42
- 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: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer | Wer |
|:-------------:|:-------:|:----:|:---------------:|:-------:|:-------:|
| 1.1429 | 1.5625 | 100 | 0.8829 | 14.7984 | 14.5304 |
| 0.3401 | 3.125 | 200 | 0.2637 | 2.0625 | 1.7828 |
| 0.0413 | 4.6875 | 300 | 0.0599 | 1.5498 | 1.3167 |
| 0.0163 | 6.25 | 400 | 0.0462 | 1.2818 | 0.9904 |
| 0.0127 | 7.8125 | 500 | 0.0517 | 1.5265 | 1.1885 |
| 0.0065 | 9.375 | 600 | 0.0402 | 1.5031 | 1.0487 |
| 0.0028 | 10.9375 | 700 | 0.0396 | 1.7012 | 1.3167 |
| 0.001 | 12.5 | 800 | 0.0406 | 1.5148 | 1.1186 |
| 0.0004 | 14.0625 | 900 | 0.0405 | 1.4216 | 1.0371 |
| 0.0005 | 15.625 | 1000 | 0.0424 | 1.5847 | 1.1885 |
| 0.0001 | 17.1875 | 1100 | 0.0425 | 1.2701 | 0.9788 |
| 0.0001 | 18.75 | 1200 | 0.0429 | 1.3051 | 1.0137 |
| 0.0001 | 20.3125 | 1300 | 0.0432 | 1.2701 | 0.9788 |
| 0.0001 | 21.875 | 1400 | 0.0436 | 1.2818 | 0.9904 |
| 0.0001 | 23.4375 | 1500 | 0.0439 | 1.2934 | 1.0021 |
| 0.0001 | 25.0 | 1600 | 0.0441 | 1.2934 | 1.0021 |
| 0.0001 | 26.5625 | 1700 | 0.0443 | 1.2934 | 1.0021 |
| 0.0001 | 28.125 | 1800 | 0.0446 | 1.2934 | 1.0021 |
| 0.0001 | 29.6875 | 1900 | 0.0448 | 1.2818 | 0.9904 |
| 0.0001 | 31.25 | 2000 | 0.0449 | 1.2002 | 0.9089 |
| 0.0001 | 32.8125 | 2100 | 0.0454 | 1.2002 | 0.9089 |
| 0.0001 | 34.375 | 2200 | 0.0458 | 1.2002 | 0.9089 |
| 0.0 | 35.9375 | 2300 | 0.0461 | 1.2002 | 0.9089 |
| 0.0 | 37.5 | 2400 | 0.0463 | 1.1769 | 0.8856 |
| 0.0 | 39.0625 | 2500 | 0.0465 | 1.1769 | 0.8856 |
| 0.0 | 40.625 | 2600 | 0.0467 | 1.1536 | 0.8623 |
| 0.0 | 42.1875 | 2700 | 0.0469 | 1.1303 | 0.8390 |
| 0.0 | 43.75 | 2800 | 0.0471 | 1.1536 | 0.8623 |
| 0.0 | 45.3125 | 2900 | 0.0473 | 1.1536 | 0.8623 |
| 0.0 | 46.875 | 3000 | 0.0474 | 1.1536 | 0.8623 |
| 0.0 | 48.4375 | 3100 | 0.0476 | 1.1536 | 0.8623 |
| 0.0 | 50.0 | 3200 | 0.0477 | 1.1303 | 0.8390 |
| 0.0 | 51.5625 | 3300 | 0.0478 | 1.1419 | 0.8506 |
| 0.0 | 53.125 | 3400 | 0.0479 | 1.1186 | 0.8273 |
| 0.0 | 54.6875 | 3500 | 0.0481 | 1.1186 | 0.8273 |
| 0.0 | 56.25 | 3600 | 0.0482 | 1.1186 | 0.8273 |
| 0.0 | 57.8125 | 3700 | 0.0483 | 1.1186 | 0.8273 |
| 0.0 | 59.375 | 3800 | 0.0484 | 1.1070 | 0.8157 |
| 0.0 | 60.9375 | 3900 | 0.0485 | 1.1070 | 0.8157 |
| 0.0 | 62.5 | 4000 | 0.0487 | 1.1070 | 0.8157 |
| 0.0 | 64.0625 | 4100 | 0.0490 | 1.1070 | 0.8157 |
| 0.0 | 65.625 | 4200 | 0.0492 | 1.1070 | 0.8157 |
| 0.0 | 67.1875 | 4300 | 0.0494 | 1.1070 | 0.8157 |
| 0.0 | 68.75 | 4400 | 0.0495 | 1.1070 | 0.8157 |
| 0.0 | 70.3125 | 4500 | 0.0496 | 1.1070 | 0.8157 |
| 0.0 | 71.875 | 4600 | 0.0497 | 1.1070 | 0.8157 |
| 0.0 | 73.4375 | 4700 | 0.0497 | 1.1070 | 0.8157 |
| 0.0 | 75.0 | 4800 | 0.0497 | 1.1070 | 0.8157 |
| 0.0 | 76.5625 | 4900 | 0.0498 | 1.1070 | 0.8157 |
| 0.0 | 78.125 | 5000 | 0.0498 | 1.1070 | 0.8157 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.4.0
- Datasets 2.18.0
- Tokenizers 0.20.3
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