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whisper-small-ha-v10

Automatic Speech Recognition
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metadata
library_name: transformers
language:
  - ha
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - eldad-akhaumere/common_voice_16_0_
metrics:
  - wer
model-index:
  - name: Whisper Small Ha v10 - Eldad Akhaumere
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 16.0
          type: eldad-akhaumere/common_voice_16_0_
          config: ha
          split: None
          args: 'config: ha, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 79.57463115539375

Whisper Small Ha v10 - Eldad Akhaumere

This model is a fine-tuned version of openai/whisper-small on the Common Voice 16.0 dataset. It achieves the following results on the evaluation set:

  • Loss: 2.3326
  • Wer Ortho: 81.6211
  • Wer: 79.5746

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 OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: constant_with_warmup
  • lr_scheduler_warmup_steps: 50
  • num_epochs: 90.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Ortho Wer
0.0914 3.1847 500 1.7962 85.2344 83.1194
0.0298 6.3694 1000 1.9290 82.9492 80.9734
0.022 9.5541 1500 2.0141 84.1797 82.4104
0.021 12.7389 2000 2.1154 80.8984 78.8848
0.0141 15.9236 2500 2.1146 83.8086 81.9506
0.0101 19.1083 3000 2.2107 79.2383 77.5628
0.0072 22.2930 3500 2.2648 82.5391 80.9925
0.0084 25.4777 4000 2.3229 81.3477 79.0190
0.0116 28.6624 4500 2.3326 81.6211 79.5746

Framework versions

  • Transformers 4.48.3
  • Pytorch 2.5.1+cu124
  • Datasets 3.3.2
  • Tokenizers 0.21.0