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---
language:
- ko
license: apache-2.0
base_model: INo0121/whisper-base-ko-callvoice
tags:
- generated_from_trainer
datasets:
- kresnik/zeroth_korean
metrics:
- wer
model-index:
- name: tmtms/whisper_checkpoints
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Zeroth-Korean
type: kresnik/zeroth_korean
args: 'config: ko, split: test'
metrics:
- name: Wer
type: wer
value: 10.856798674898359
---
<!-- 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. -->
# tmtms/whisper_checkpoints
This model is a fine-tuned version of [INo0121/whisper-base-ko-callvoice](https://huggingface.co/INo0121/whisper-base-ko-callvoice) on the Zeroth-Korean dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1501
- Wer: 10.8568
## 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: 24
- 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1644 | 1.08 | 1000 | 0.2571 | 22.2406 |
| 0.0822 | 2.16 | 2000 | 0.1818 | 14.2448 |
| 0.0528 | 3.23 | 3000 | 0.1575 | 11.1128 |
| 0.0383 | 4.31 | 4000 | 0.1501 | 10.8568 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.15.2
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