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metadata
library_name: transformers
language:
  - my
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
  - generated_from_trainer
datasets:
  - chuuhtetnaing/myanmar-speech-dataset-openslr-80
metrics:
  - wer
model-index:
  - name: Whisper Large V3 Turbo Burmese Finetune
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Myanmar Speech Dataset (OpenSLR-80)
          type: chuuhtetnaing/myanmar-speech-dataset-openslr-80
          args: 'config: my, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 47.10596616206589

Whisper Large V3 Turbo Burmese Finetune

This model is a fine-tuned version of openai/whisper-large-v3-turbo on the Myanmar Speech Dataset (OpenSLR-80) dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1727
  • Wer: 47.1060
  • Cer: 15.6324

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: 3e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 20
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
0.8922 1.0 143 0.4413 95.9484 48.4730
0.2576 2.0 286 0.1971 83.8379 26.9627
0.1481 3.0 429 0.1505 66.4292 22.9769
0.0996 4.0 572 0.1315 62.0214 20.5786
0.0697 5.0 715 0.1344 60.8638 20.5786
0.0507 6.0 858 0.1249 57.3464 19.3075
0.038 7.0 1001 0.1273 55.2538 18.4391
0.0279 8.0 1144 0.1257 54.4524 18.4908
0.02 9.0 1287 0.1374 53.3838 17.9559
0.0147 10.0 1430 0.1422 53.3393 17.9847
0.0101 11.0 1573 0.1530 53.8736 17.9674
0.0066 12.0 1716 0.1512 50.8905 16.8344
0.0043 13.0 1859 0.1526 49.5993 16.2708
0.0026 14.0 2002 0.1594 49.9110 16.4261
0.0017 15.0 2145 0.1612 49.0205 16.2248
0.0008 16.0 2288 0.1646 48.7088 15.9027
0.0003 17.0 2431 0.1676 47.8629 15.9429
0.0001 18.0 2574 0.1707 47.5512 15.6209
0.0001 19.0 2717 0.1721 47.3731 15.6439
0.0 20.0 2860 0.1727 47.1060 15.6324

Framework versions

  • Transformers 4.46.2
  • Pytorch 2.5.1+cu124
  • Datasets 3.1.0
  • Tokenizers 0.20.3