metadata

language:
  - ko
license: apache-2.0
tags:
  - hf-asr-leaderboard
  - whisper-event
  - generated_from_trainer
datasets:
  - kresnik/zeroth_korean
metrics:
  - wer
model-index:
  - name: Whisper Medium Korean
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Zeroth Korean
          type: kresnik/zeroth_korean
          config: clean
          split: test
          args: 'split: test'
        metrics:
          - name: Wer
            type: wer
            value: 3.6440295136274656

Whisper Medium Korean

This model is a fine-tuned version of openai/whisper-medium on the Zeroth Korean dataset. It achieves the following results on the evaluation set:

Loss: 0.0727
Wer: 3.6440
Cer: 1.4840

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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
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	Wer	Cer
0.0873	0.72	1000	0.1086	7.7549	2.5597
0.0258	1.44	2000	0.0805	4.5475	1.7588
0.0091	2.16	3000	0.0719	3.7946	1.5664
0.0086	2.88	4000	0.0704	3.5537	1.5232
0.0019	3.59	5000	0.0727	3.6440	1.4840

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.13.0a0+d0d6b1f
Datasets 2.7.1
Tokenizers 0.13.2