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Roooy
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whisper-tiny-ko-common

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
Korean
whisper
Generated from Trainer
Eval Results (legacy)
Model card Files
xet
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How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("automatic-speech-recognition", model="Roooy/whisper-tiny-ko-common")
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq

processor = AutoProcessor.from_pretrained("Roooy/whisper-tiny-ko-common")
model = AutoModelForSpeechSeq2Seq.from_pretrained("Roooy/whisper-tiny-ko-common")
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Whisper Tiny Ko - Roooy

This model is a fine-tuned version of openai/whisper-tiny on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:

  • Loss: 1.3774
  • Cer: 30.0833
  • Wer: 61.8411
  • Cer Wer Avg: 45.9622

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: 16
  • 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: 10000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Cer Wer Cer Wer Avg
0.0005 22.2222 1000 1.0691 27.3648 58.8235 43.0942
0.0002 44.4444 2000 1.1396 30.6350 62.9488 46.7919
0.0001 66.6667 3000 1.1884 31.1967 63.5982 47.3974
0.0001 88.8889 4000 1.2300 31.4776 64.2093 47.8435
0.0 111.1111 5000 1.2656 31.7284 64.7441 48.2362
0.0 133.3333 6000 1.2993 32.1396 65.0497 48.5946
0.0 155.5556 7000 1.3272 32.3804 64.9351 48.6577
0.0 177.7778 8000 1.3518 29.9829 61.8029 45.8929
0.0 200.0 9000 1.3693 30.1836 61.8793 46.0314
0.0 222.2222 10000 1.3774 30.0833 61.8411 45.9622

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.3.0+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1
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