README.md

metadata

language:
  - ko
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
  - generated_from_trainer
datasets:
  - gglabs/stt-test2-1223
metrics:
  - wer
model-index:
  - name: Whisper Small ko
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: custom
          type: gglabs/stt-test2-1223
          args: 'config: ko, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 52.71739130434783

Whisper Small ko

This model is a fine-tuned version of openai/whisper-large-v3-turbo on the custom dataset. It achieves the following results on the evaluation set:

• Loss: 1.4327
• Wer: 52.7174

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: 2
• 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: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
1.0243	0.2	10	1.4327	52.7174

Framework versions

• Transformers 4.42.3
• Pytorch 2.3.1+cu121
• Datasets 3.0.0
• Tokenizers 0.19.1