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whisper-large-v3-turbo

Automatic Speech Recognition
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metadata
library_name: transformers
language:
  - fa
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_17_0
metrics:
  - wer
model-index:
  - name: Whisper-large-v3-turbo-fa - Sadegh Karimi
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 17.0
          type: mozilla-foundation/common_voice_17_0
          config: fa
          split: test
          args: 'config: hi, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 9.627528266117483

Whisper-large-v3-turbo-fa - Sadegh Karimi

This model is a fine-tuned version of openai/whisper-large-v3-turbo on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0839
  • Wer: 9.6275

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: 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_steps: 500
  • training_steps: 10000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.1789 0.0217 500 0.2427 26.4099
0.2077 0.0435 1000 0.2296 27.1873
0.1928 0.0652 1500 0.2320 27.5951
0.1801 0.0869 2000 0.2026 24.0409
0.1865 0.1086 2500 0.1925 22.3742
0.1535 0.1304 3000 0.1872 22.9511
0.1463 0.1521 3500 0.1786 21.5436
0.0935 0.1738 4000 0.1749 20.5330
0.1052 0.1956 4500 0.1597 19.0314
0.091 0.2173 5000 0.1553 20.2125
0.0743 0.2390 5500 0.1474 16.9160
0.096 0.2607 6000 0.1352 15.9027
0.111 0.2825 6500 0.1259 14.9071
0.089 0.3042 7000 0.1179 14.1146
0.0813 0.3259 7500 0.1101 12.8653
0.072 0.3477 8000 0.1012 11.8138
0.0715 0.3694 8500 0.0948 10.9791
0.0683 0.3911 9000 0.0903 10.2563
0.0634 0.4128 9500 0.0861 9.6616
0.0739 0.4346 10000 0.0839 9.6275

Framework versions

  • Transformers 4.47.1
  • Pytorch 2.1.0+cu118
  • Datasets 3.2.0
  • Tokenizers 0.21.0