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whisper-base-th-1

Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Thai
whisper
whisper-event
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metadata
language:
  - th
license: apache-2.0
base_model: openai/whisper-base
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_16_0
metrics:
  - wer
model-index:
  - name: Whisper Base Thai
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: mozilla-foundation/common_voice_16_0 th
          type: mozilla-foundation/common_voice_16_0
          config: th
          split: test
          args: th
        metrics:
          - name: Wer
            type: wer
            value: 44.56011784657663

Whisper Base Thai

This model is a fine-tuned version of openai/whisper-base on the mozilla-foundation/common_voice_16_0 th dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4390
  • Wer: 44.5601

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-07
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 64
  • 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 Wer
0.8395 1.02 500 0.7280 60.6811
0.7819 2.03 1000 0.6414 56.1244
0.6456 4.01 1500 0.5940 53.4778
0.6091 5.03 2000 0.5633 52.0691
0.5465 7.01 2500 0.5383 50.4822
0.5406 8.02 3000 0.5200 49.5537
0.4863 10.01 3500 0.5047 48.9992
0.4691 11.02 4000 0.4919 47.9767
0.5183 13.0 4500 0.4823 47.6833
0.5025 14.02 5000 0.4730 46.7202
0.5426 15.03 5500 0.4661 46.3501
0.4713 17.01 6000 0.4594 45.9985
0.4274 18.03 6500 0.4546 45.6061
0.4248 20.01 7000 0.4500 45.3598
0.4404 21.03 7500 0.4467 45.1097
0.4144 23.01 8000 0.4438 44.8411
0.4004 24.02 8500 0.4416 44.6938
0.4165 26.0 9000 0.4403 44.6443
0.4218 27.02 9500 0.4393 44.5750
0.453 28.03 10000 0.4390 44.5601

Framework versions

  • Transformers 4.37.0.dev0
  • Pytorch 2.1.2+cu121
  • Datasets 2.16.2.dev0
  • Tokenizers 0.15.0