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---
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: 0.908879049172687
---
<!-- 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: 0.0506
- Cer: 1.2584
- Wer: 0.9089
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 1.1428 | 1.56 | 100 | 0.8829 | 14.7984 | 14.5304 |
| 0.3434 | 3.12 | 200 | 0.2469 | 2.0625 | 1.7828 |
| 0.0286 | 4.69 | 300 | 0.0447 | 1.6430 | 1.4099 |
| 0.011 | 6.25 | 400 | 0.0382 | 1.5148 | 1.1070 |
| 0.0067 | 7.81 | 500 | 0.0409 | 1.4915 | 1.0837 |
| 0.0042 | 9.38 | 600 | 0.0383 | 1.2118 | 0.9438 |
| 0.0018 | 10.94 | 700 | 0.0396 | 1.3866 | 1.0371 |
| 0.0007 | 12.5 | 800 | 0.0445 | 1.4682 | 1.0604 |
| 0.0004 | 14.06 | 900 | 0.0386 | 1.2584 | 0.9089 |
| 0.0002 | 15.62 | 1000 | 0.0431 | 1.1769 | 0.8273 |
| 0.0011 | 17.19 | 1100 | 0.0475 | 1.2701 | 0.9205 |
| 0.0019 | 18.75 | 1200 | 0.0453 | 1.4915 | 1.1419 |
| 0.0012 | 20.31 | 1300 | 0.0437 | 1.2701 | 0.9205 |
| 0.0013 | 21.88 | 1400 | 0.0454 | 1.3284 | 0.9205 |
| 0.0003 | 23.44 | 1500 | 0.0436 | 1.3400 | 0.9438 |
| 0.0001 | 25.0 | 1600 | 0.0460 | 1.3284 | 0.9904 |
| 0.0001 | 26.56 | 1700 | 0.0464 | 1.3517 | 1.0137 |
| 0.0001 | 28.12 | 1800 | 0.0464 | 1.3400 | 1.0021 |
| 0.0001 | 29.69 | 1900 | 0.0467 | 1.3167 | 0.9788 |
| 0.0001 | 31.25 | 2000 | 0.0468 | 1.3167 | 0.9788 |
| 0.0001 | 32.81 | 2100 | 0.0470 | 1.3284 | 0.9904 |
| 0.0001 | 34.38 | 2200 | 0.0473 | 1.2934 | 0.9438 |
| 0.0 | 35.94 | 2300 | 0.0475 | 1.3051 | 0.9555 |
| 0.0 | 37.5 | 2400 | 0.0477 | 1.3051 | 0.9555 |
| 0.0 | 39.06 | 2500 | 0.0478 | 1.3051 | 0.9555 |
| 0.0 | 40.62 | 2600 | 0.0480 | 1.2934 | 0.9438 |
| 0.0 | 42.19 | 2700 | 0.0482 | 1.2818 | 0.9322 |
| 0.0 | 43.75 | 2800 | 0.0483 | 1.2818 | 0.9322 |
| 0.0 | 45.31 | 2900 | 0.0485 | 1.2818 | 0.9322 |
| 0.0 | 46.88 | 3000 | 0.0486 | 1.2584 | 0.9089 |
| 0.0 | 48.44 | 3100 | 0.0487 | 1.2584 | 0.9089 |
| 0.0 | 50.0 | 3200 | 0.0489 | 1.2584 | 0.9089 |
| 0.0 | 51.56 | 3300 | 0.0490 | 1.2584 | 0.9089 |
| 0.0 | 53.12 | 3400 | 0.0491 | 1.2584 | 0.9089 |
| 0.0 | 54.69 | 3500 | 0.0492 | 1.2584 | 0.9089 |
| 0.0 | 56.25 | 3600 | 0.0493 | 1.2584 | 0.9089 |
| 0.0 | 57.81 | 3700 | 0.0493 | 1.2584 | 0.9089 |
| 0.0 | 59.38 | 3800 | 0.0495 | 1.2584 | 0.9089 |
| 0.0 | 60.94 | 3900 | 0.0495 | 1.2584 | 0.9089 |
| 0.0 | 62.5 | 4000 | 0.0496 | 1.2584 | 0.9089 |
| 0.0 | 64.06 | 4100 | 0.0499 | 1.2584 | 0.9089 |
| 0.0 | 65.62 | 4200 | 0.0501 | 1.2584 | 0.9089 |
| 0.0 | 67.19 | 4300 | 0.0502 | 1.2584 | 0.9089 |
| 0.0 | 68.75 | 4400 | 0.0504 | 1.2584 | 0.9089 |
| 0.0 | 70.31 | 4500 | 0.0505 | 1.2584 | 0.9089 |
| 0.0 | 71.88 | 4600 | 0.0506 | 1.2584 | 0.9089 |
| 0.0 | 73.44 | 4700 | 0.0506 | 1.2584 | 0.9089 |
| 0.0 | 75.0 | 4800 | 0.0506 | 1.2584 | 0.9089 |
| 0.0 | 76.56 | 4900 | 0.0506 | 1.2584 | 0.9089 |
| 0.0 | 78.12 | 5000 | 0.0506 | 1.2584 | 0.9089 |
### Framework versions
- Transformers 4.39.2
- Pytorch 2.0.1
- Datasets 2.18.0
- Tokenizers 0.15.2
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