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haseong8012
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whisper-tiny_child-10k-sr48k

Automatic Speech Recognition
Transformers
PyTorch
Korean
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results
Inference Endpoints
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whisper-tiny_child-10k-sr48k / README.md
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metadata
language:
  - ko
license: apache-2.0
base_model: openai/whisper-tiny
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
datasets:
  - haseong8012/korean-child-command-voice_train-0-10000
metrics:
  - wer
model-index:
  - name: >-
      openai/whisper-tiny-Ko-haseong8012/korean-child-command-voice_train-0-10000
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: haseong8012/korean-child-command-voice_train-0-10000
          type: haseong8012/korean-child-command-voice_train-0-10000
          args: 'config: ko, split: train, validation, test'
        metrics:
          - name: Wer
            type: wer
            value: 27.187571395933286

openai/whisper-tiny-Ko-haseong8012/korean-child-command-voice_train-0-10000

This model is a fine-tuned version of openai/whisper-tiny on the haseong8012/korean-child-command-voice_train-0-10000 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1263
  • Wer: 27.1876
  • Cer: 19.8556

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-05
  • train_batch_size: 32
  • eval_batch_size: 16
  • 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

Training results

Training Loss Epoch Step Validation Loss Wer Cer
1.1915 2.0 500 0.3624 78.8440 158.9770
0.0459 4.0 1000 0.2271 108.7274 129.0652
0.0111 6.0 1500 0.1616 44.8481 48.1227
0.0035 8.0 2000 0.1742 46.9043 44.5694
0.0014 10.0 2500 0.1635 32.8536 33.4027
0.0003 12.0 3000 0.1325 25.9995 21.7572
0.0001 14.0 3500 0.1300 27.1190 25.5743
0.0001 16.0 4000 0.1285 27.3475 24.4084
0.0001 18.0 4500 0.1266 27.1190 19.7030
0.0001 20.0 5000 0.1263 27.1876 19.8556

Framework versions

  • Transformers 4.33.2
  • Pytorch 1.12.1
  • Datasets 2.14.5
  • Tokenizers 0.13.3