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---
library_name: transformers
language:
- ko
license: apache-2.0
base_model: openai/whisper-medium
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- whsNect/__g__d___
metrics:
- wer
model-index:
- name: __g__d____model
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: whsNect/__g__d___
type: whsNect/__g__d___
args: 'config: ko, split: valid'
metrics:
- type: wer
value: 8.460209304600138
name: Wer
---
<!-- 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. -->
# __g__d____model
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the whsNect/__g__d___ dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0502
- Wer: 8.4602
## 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: 5e-06
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use adamw_bnb_8bit 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: 15000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:-----:|:---------------:|:-------:|
| 0.0361 | 1.6722 | 500 | 0.0385 | 9.2003 |
| 0.0099 | 3.3445 | 1000 | 0.0313 | 5.2457 |
| 0.006 | 5.0167 | 1500 | 0.0335 | 6.3769 |
| 0.003 | 6.6890 | 2000 | 0.0348 | 4.8773 |
| 0.0021 | 8.3612 | 2500 | 0.0351 | 17.5822 |
| 0.0013 | 10.0334 | 3000 | 0.0369 | 5.0892 |
| 0.0016 | 11.7057 | 3500 | 0.0371 | 10.6837 |
| 0.0011 | 13.3779 | 4000 | 0.0367 | 5.8716 |
| 0.0014 | 15.0502 | 4500 | 0.0385 | 46.1350 |
| 0.0008 | 16.7224 | 5000 | 0.0408 | 10.2338 |
| 0.0006 | 18.3946 | 5500 | 0.0400 | 9.9077 |
| 0.0007 | 20.0669 | 6000 | 0.0410 | 11.2053 |
| 0.0003 | 21.7391 | 6500 | 0.0414 | 22.9192 |
| 0.0002 | 23.4114 | 7000 | 0.0415 | 17.6768 |
| 0.0009 | 25.0836 | 7500 | 0.0420 | 22.1074 |
| 0.0005 | 26.7559 | 8000 | 0.0440 | 14.8828 |
| 0.0005 | 28.4281 | 8500 | 0.0417 | 10.4065 |
| 0.0001 | 30.1003 | 9000 | 0.0441 | 20.4545 |
| 0.0001 | 31.7726 | 9500 | 0.0453 | 9.3176 |
| 0.0001 | 33.4448 | 10000 | 0.0460 | 11.3553 |
| 0.0001 | 35.1171 | 10500 | 0.0466 | 10.9999 |
| 0.0001 | 36.7893 | 11000 | 0.0471 | 11.0749 |
| 0.0001 | 38.4615 | 11500 | 0.0479 | 12.3887 |
| 0.0 | 40.1338 | 12000 | 0.0483 | 10.3413 |
| 0.0 | 41.8060 | 12500 | 0.0487 | 8.3363 |
| 0.0001 | 43.4783 | 13000 | 0.0491 | 8.6852 |
| 0.0 | 45.1505 | 13500 | 0.0495 | 7.7462 |
| 0.0 | 46.8227 | 14000 | 0.0499 | 8.1472 |
| 0.0 | 48.4950 | 14500 | 0.0501 | 7.9516 |
| 0.0 | 50.1672 | 15000 | 0.0502 | 8.4602 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.2.2+cu121
- Datasets 3.4.1
- Tokenizers 0.21.1
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