whisper-small / README.md
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metadata
library_name: transformers
language:
  - fa
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
metrics:
  - wer
model-index:
  - name: Whisper small- Mohammad Khosravi
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 11.0
          type: mozilla-foundation/common_voice_11_0
          config: fa
          split: None
          args: 'config: fa, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 105.3763440860215

Whisper small- Mohammad Khosravi

This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:

  • Loss: 2.0832
  • Wer: 105.3763

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 OptimizerNames.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: 5
  • training_steps: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
No log 1.4286 10 1.9791 109.6774
No log 2.8571 20 1.6973 107.5269
1.023 4.2857 30 1.6941 109.6774
1.023 5.7143 40 1.7788 107.5269
0.1444 7.1429 50 1.8726 104.3011
0.1444 8.5714 60 1.9535 103.2258
0.1444 10.0 70 1.9987 104.3011
0.0166 11.4286 80 2.0563 102.1505
0.0166 12.8571 90 2.0768 105.3763
0.0062 14.2857 100 2.0832 105.3763

Framework versions

  • Transformers 4.48.0
  • Pytorch 2.5.1+cu121
  • Datasets 3.2.0
  • Tokenizers 0.21.0