whisper-turbo-ar / README.md
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metadata
library_name: transformers
language:
  - ps
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 Small PS - Hanif Rahman
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 17.0
          type: mozilla-foundation/common_voice_17_0
          config: ps_af
          split: test+validation
          args: 'config: ps, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 40.057062876830315

Whisper Small PS - Hanif Rahman

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: 0.5707
  • Wer Ortho: 40.7188
  • Wer: 40.0571

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine_with_restarts
  • lr_scheduler_warmup_steps: 200
  • training_steps: 1000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Ortho Wer
0.9719 0.2268 100 0.8098 59.2165 59.0924
0.8427 0.4535 200 0.7384 55.1748 54.5596
0.7493 0.6803 300 0.6743 48.8614 48.3473
0.684 0.9070 400 0.6384 46.1094 45.5534
0.4819 1.1338 500 0.6348 44.3341 43.7123
0.4777 1.3605 600 0.6026 43.6758 42.9264
0.4433 1.5873 700 0.5789 41.7386 40.9991
0.446 1.8141 800 0.5647 40.2709 39.5995
0.3166 2.0408 900 0.5681 40.4490 39.7771
0.3187 2.2676 1000 0.5707 40.7188 40.0571

Framework versions

  • Transformers 4.45.1
  • Pytorch 2.4.1+cu121
  • Datasets 3.0.1
  • Tokenizers 0.20.0